

September 2020

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Health Services Research & Development Service

Washington, DC 20420 

Prepared by:

Evidence Synthesis Program (ESP) Center

Portland VA Health Care System

Portland, OR 

Devan Kansagara, MD, MCR, Director

Biological Measures and 

Diagnostic Tools for Gulf War 

Illness: A Systematic Review

Authors: 

Principal Investigator: 

Emily G. Gean, PhD 

Co-Investigators: 

Chelsea K. Ayers, MPH

Kara A. Winchell, MA

Michele Freeman, MPH

Ashlyn M. Press, MPH

Robin Paynter, MLIS 

Devan Kansagara, MD, MCR

Shannon M. Nugent, PhD 

Evidence Synthesis Program

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GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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PREFACE 

The VA Evidence Synthesis Program (ESP) was established in 2007 to provide timely and accurate 

syntheses of targeted health care topics of importance to clinicians, managers, and policymakers as they 

work to improve the health and health care of Veterans. These reports help: 

• Develop clinical policies informed by evidence;

• Implement effective services to improve patient outcomes and to support VA clinical practice 

guidelines and performance measures; and 

• Set the direction for future research to address gaps in clinical knowledge.

The program is comprised of three ESP Centers across the US and a Coordinating Center located in 

Portland, Oregon. Center Directors are VA clinicians and recognized leaders in the field of evidence 

synthesis with close ties to the Agency for Healthcare Research and Quality (AHRQ) Evidence-based 

Practice Center Program and Cochrane Collaboration. The Coordinating Center was created to manage 

program operations, ensure methodological consistency and quality of products, and interface with 

stakeholders. To ensure responsiveness to the needs of decision-makers, the program is governed by a 

Steering Committee comprised of health system leadership and researchers. The program solicits 

nominations for review topics several times a year via the program website. 

Comments on this evidence report are welcome and can be sent to Nicole Floyd, Deputy Director, ESP 

Coordinating Center at Nicole.Floyd@va.gov.

Recommended citation: Gean E, Ayers CK, Winchell KA, Freeman M, Press AM, Paynter R, 

Kansagara D, and Nugent SM. Biological measures and Diagnostic Tools for Gulf War Illness - A 

Systematic Review. Washington, DC: Evidence Synthesis Program, Health Services Research and 

Development Service, Office of Research and Development, Department of Veterans Affairs. VA ESP 

Project #05-225; 2020. Available at: https://www.hsrd.research.va.gov/publications/esp/reports.cfm. 

This report is based on research conducted by the Evidence Synthesis Program (ESP) Center located at the located 

at the Portland VA Health Care System, Portland, OR, funded by the Department of Veterans Affairs, Veterans

Health Administration, Health Services Research and Development. The findings and conclusions in this document 

are those of the author(s) who are responsible for its contents; the findings and conclusions do not necessarily 

represent the views of the Department of Veterans Affairs or the United States government. Therefore, no statement 

in this article should be construed as an official position of the Department of Veterans Affairs. No investigators 

have any affiliations or financial involvement (eg, employment, consultancies, honoraria, stock ownership or 

options, expert testimony, grants or patents received or pending, or royalties) that conflict with material presented 

in the report.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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ACKNOWLEDGMENTS 

This topic was developed in response to a nomination by Karen Block, PhD, Director of Gulf 

War Research in the Veterans Affairs (VA) Office of Research and Development (ORD) Gulf 

War Research Program, for the purpose of informing the planning for a state-of-the-art meeting 

on Gulf War Research and providing guidance for ORD funding priorities in Gulf War research. 

The scope was further developed with input from the topic nominators (ie, Operational Partners), 

the ESP Coordinating Center, the review team, and the Technical Expert Panel (TEP).

In designing the study questions and methodology at the outset of this report, the ESP consulted 

several technical and content experts. Broad expertise and perspectives were sought. Divergent 

and conflicting opinions are common and perceived as healthy scientific discourse that results in 

a thoughtful, relevant systematic review. Therefore, in the end, study questions, design, 

methodologic approaches, and/or conclusions do not necessarily represent the views of 

individual technical and content experts. 

The authors gratefully acknowledge the following individuals for their contributions to this 

project: 

Operational Partners

Operational partners are system-level stakeholders who have requested the report to inform 

decision-making. They recommend TEP participants; assure VA relevance; help develop and 

approve final project scope and timeframe for completion; provide feedback on draft report; and 

provide consultation on strategies for dissemination of the report to field and relevant groups.

Karen Block, PhD

Office of Research and Development (ORD) (10P9) – Gulf War Research Program

Washington, DC

Drew Helmer, MD, MS

Deputy Director, Center for Innovations in Quality, Effectiveness and Safety (IQuESt)

Houston, TX

Technical Expert Panel (TEP)

To ensure robust, scientifically relevant work, the TEP guides topic refinement; provides input 

on key questions and eligibility criteria, advising on substantive issues or possibly overlooked 

areas of research; assures VA relevance; and provides feedback on work in progress. TEP 

members are listed below:

Mark Helfand, MD, MPH, MS

VA Portland Health Care System

Portland, OR

Arash Javanbakht, MD

Wayne State University

Detroit, MI
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Eva Lee, PhD

Georgia Institute of Technology 

Atlanta, GA

Kristy B. Lidie, PhD 

Department of Defense - Congressionally Directed Medical Research Programs (CDMRP)

Fort Detrick, MD

Peer Reviewers

The Coordinating Center sought input from external peer reviewers to review the draft report and 

provide feedback on the objectives, scope, methods used, perception of bias, and omitted 

evidence. Peer reviewers must disclose any relevant financial or non-financial conflicts of 

interest. Because of their unique clinical or content expertise, individuals with potential conflicts 

may be retained. The Coordinating Center and the ESP Center work to balance, manage, or 

mitigate any potential nonfinancial conflicts of interest identified. 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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TABLE OF CONTENTS 

ACKNOWLEDGMENTS ........................................................................................................... II

ABSTRACT ................................................................................................................................... 1

EXECUTIVE SUMMARY .......................................................................................................... 2

Aim ............................................................................................................................................. 2

Methods....................................................................................................................................... 2

Results ......................................................................................................................................... 2

Figure i. Number of studies of GWI biological measures by biological system ........................ 3

Conclusion .................................................................................................................................. 4

Abbreviations Table .................................................................................................................... 5

EVIDENCE REPORT ................................................................................................................ 10

INTRODUCTION....................................................................................................................... 10

METHODS .................................................................................................................................. 12

Topic Development ................................................................................................................... 12

Search Strategy ......................................................................................................................... 13

Study Selection ......................................................................................................................... 13

Data Abstraction ....................................................................................................................... 16

Quality Assessment ................................................................................................................... 16

Data Synthesis ........................................................................................................................... 16

RESULTS .................................................................................................................................... 16

Key Question 1: Which diagnostic tests (or test combinations) are candidates for 

distinguishing individuals diagnosed with GWI from individuals without GWI? ................... 18

Key Question 2: Which biological measures have been examined for their potential 

association with GWI, and which among them have been shown to be associated 

with GWI? ................................................................................................................................. 18

Immune System Biological Measure Studies ....................................................................... 27

Central Nervous System Biological Measure Studies .......................................................... 32

Autonomic Nervous System ................................................................................................. 37

Genetic Biological Measures ................................................................................................ 41

Other Biological Systems ..................................................................................................... 43

Key Question 3: Which ongoing or unpublished research studies examine diagnostic 

tests or biological measures for potential association with GWI? ............................................ 48

DISCUSSION .............................................................................................................................. 56

Limitations ................................................................................................................................ 58

Study Design ......................................................................................................................... 59
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Heterogeneity ........................................................................................................................ 60

Research Gaps/Future Research ............................................................................................... 60

CONCLUSIONS ......................................................................................................................... 61

REFERENCES ............................................................................................................................ 63

TABLES

Table 1. PICOTS by Key Question ........................................................................................... 15

Table 2. Characteristics of Gulf War Illness Biological Measure Studies ............................... 19

Table 3. Results of Gulf War Illness Immune System Biological Measure Studies ................ 29

Table 4. Results of Gulf War Illness Central Nervous System Biological Measure Studies ... 34

Table 5. Results of Gulf War Illness Autonomic Nervous System 

Biological Measure Studies ...................................................................................................... 38

Table 6. Results of Gulf War Illness Genetic Biological Measure Studies .............................. 42

Table 7. Results of Gulf War Illness Other Biological Systems Biological 

Measure Studies ........................................................................................................................ 45

Table 8. Ongoing Studies of Biological Measures for Gulf War Illness .................................. 49

Table 9. Quality Assessment of Studies of Biological Measures for Gulf War Illness ............ 79

Table 10. Studies of Gulf War Illness Biological Measures Using Lower-priority 

Comparator Groups* or No Comparator .................................................................................. 82

Table 11. Gulf War Illness Biological Measure Studies with Insufficient 

Sample Size (N<25) .................................................................................................................. 92

FIGURES

Figure 1. Process framework .................................................................................................... 12

Figure 2. Biomarker development Process ............................................................................... 13

Figure 3. Literature flow chart .................................................................................................. 17

Figure 4. Number of studies of GWI biological measures by biological system ..................... 58

Figure 5. Studies of GWI biological measures with lower priority comparator group,* no 

comparator, or inadequate sample size (N<25) by biological system and promise as a 

biomarker .................................................................................................................................. 58

APPENDIX A. SEARCH STRATEGIES ................................................................................. 73

APPENDIX B. STUDY SELECTION ...................................................................................... 77

APPENDIX C. QUALITY ASSESSMENT .............................................................................. 79

APPENDIX D. SUPPLEMENTAL MATERIAL .................................................................... 82

APPENDIX E. PEER REVIEW COMMENTS/AUTHOR RESPONSES ............................ 93
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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ABSTRACT

Background: Gulf War Illness (GWI) is a chronic multisymptom illness comprised of a wide 

range of systemic symptoms and functional impairments. We conducted a systematic review to 

catalogue studies (both published and unpublished/ongoing) of validated biological tests for 

diagnosing GWI and studies of associations between biological measures and GWI for their 

promise as biomarkers.

Materials and Methods: We searched multiple electronic databases, clinical trial registries, and 

reference lists through February 2020 for all observational studies of diagnostic tests of GWI and 

completed or ongoing studies of associations between biological measures and GWI. We 

abstracted data on study design, demographics, and outcomes. Two reviewers independently 

assessed the risk of bias of included studies using established methods. 

Results: We did not identify any studies validating tests of biomarkers that distinguish cases of 

GWI from non-cases. We included 32 completed and 24 ongoing or unpublished studies of 

associations between GWI and biological measures that included comparator groups that 

provided the most useful information. Studies (n=77) with other comparator groups, no 

comparator group, or with N<25 were included in a table. Considering all studies, most fell 

within the central nervous and immune systems and indicated a significant association of the 

biological measure with GWI case status. Biological measures were heterogeneous across 

studies.

Conclusion: Our review indicates that there are no existing validated biological tests to 

determine GWI case status. Many studies have assessed the potential association between a 

variety of biological measures and GWI, the majority of which pertain to the immune and central 

nervous systems. More importantly, while most studies indicated a significant association 

between biological measures and GWI case status, the biological measures across studies were 

extremely heterogeneous. Due to the great heterogeneity, the focus of the review is to map out 

what has been examined, rather than synthesize information. 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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EXECUTIVE SUMMARY 

AIM 

We conducted a systematic review to catalogue studies (both published and 

unpublished/ongoing) of validated biological tests for diagnosing GWI and studies of 

associations between biological measures and GWI for their promise as biomarkers.

METHODS

We searched electronic databases (Ovid MEDLINE, and Ovid PsycINFO, and Ovid EBM 

Reviews [Cochrane Database of Systematic Reviews and Cochrane Central Register of 

Controlled Trials]) through February 20, 2020 for all observational studies of diagnostic tests of 

GWI and completed or ongoing studies of associations between biological measures and GWI. 

To identify in-progress or unpublished studies, we searched ClinicalTrials.gov and the World 

Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). We 

reviewed the bibliographies of relevant articles, contacted experts, and reviewed lists of funded 

trials from the Department of Defense (DoD) and Veterans Affairs (VA) to identify additional 

studies. 

We included studies of biological measures that have been examined for their promise as 

diagnostic biomarkers for and/or their association with GWI. We included completed and 

ongoing/unpublished studies of Veterans with GWI, identified using any GWI diagnostic 

criteria, in which the comparator population members were Veterans deployed to the Persian 

Gulf theater during the First Gulf War who did not develop GWI, with or without comorbid 

conditions (eg, posttraumatic stress disorder, anxiety). We excluded studies of Veterans with 

GWI compared to other populations (eg, non-deployed Gulf War Veterans, civilians), and those 

with insufficient sample sizes (N<25) but did summarize these studies in the Appendix to the full 

report.

For all included studies, we abstracted data on study design, sample size, population 

characteristics, case definition, comparator(s), participant inclusion and exclusion criteria, details 

of the biological measure of interest, and findings for measures of association as available. Data 

abstraction was confirmed by a second reviewer. Two reviewers independently assessed the risk 

of bias of included studies using established methods. Discordant ratings were resolved by 

consensus or an additional reviewer.

RESULTS

We identified no studies of diagnostic tests for GWI. We identified 56 studies of associations 

between GWI and biological measures (32 completed and 24 ongoing or unpublished). 

Key Question 1: Which diagnostic tests (or test combinations) are candidates for 

distinguishing individuals diagnosed with GWI from individuals without GWI?

We did not identify any studies validating tests of biomarkers that distinguish cases of GWI from 

non-cases, regardless of the diagnostic criteria used in the study. 
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Key Question 2: Which biological measures have been examined for their 

potential association with GWI, and which among them have been shown to be 

associated with GWI?

We identified 32 studies of biological measures that have been examined for their association 

with GWI and grouped them broadly into categories under distinct biological systems: 10 studies 

of immune system biological measures; 10 central nervous system (CNS) studies; 5 studies of 

autonomic nervous system (ANS) biological measures; 1 of genetic biological measures; and 6 

studies of biological measures in other biological systems (See Figure i). We also briefly 

summarized an additional 77 studies that examined biological measures, but do not include an 

ideal control group (n=72 studies) or had a sample size less than 25 (n=5 studies; See Appendix 

D of full report), indicated in the grey areas in Figure i.

Only 1 included study used a comparator population of deployed GWVs with health conditions 

other than GWI; in all other studies health conditions were not reported and participants were 

presumed healthy. 

All studies had additional methodological shortcomings. Biological measures were 

heterogeneous across studies, even those categorized within the same biological system, with the 

exception of 2 studies that had 1 replication study each. 

Key Question 3: Which ongoing or unpublished research studies examine 

diagnostic tests or biological measures for potential association with GWI?

We did not identify any ongoing/unpublished studies examining diagnostic tests for GWI. We 

found 24 ongoing or unpublished studies examining biological measures for their potential 

association with GWI. Similar to the completed studies many are investigating measures of the 

immune and central nervous system (see Figure i). 

Figure i. Number of studies of GWI biological measures by biological system 

Abbreviations: ANS=Autonomic Nervous System; CNS=Central Nervous System; KQ=Key Question

5

2

6

10

9

1

3

10

5

5

5

10

14

20

28

0 5 10 15 20 25 30 35 40 45

ANS

Genetic

Other

Immune

CNS

Published (KQ2) Unpublished (KQ3) Lower priority controls/N<25
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CONCLUSION

In the current review, we sought to evaluate studies validating existing diagnostic tests for GWI, 

and to determine whether biological measurements with promise for further establishment as 

biomarkers either in completed or ongoing/upcoming studies have been demonstrated. The 

establishment of biological measures for GWI would allow for increased accuracy in diagnosis 

and potential mechanisms for treatment.

Our review indicates that there are no existing validated biological tests to determine GWI case 

status. It is not surprising that no such studies were found, because the case definition for GWI is 

still debated. In the absence of a gold standard definition or diagnostic test, the determination of 

biological measures to distinguish a case from a non-case is challenging.

We did identify many studies that have assessed, or are currently assessing, the potential 

association between a variety of biological measures and GWI. Most of the studies could be 

characterized as “biomarker discovery” studies and were largely designed to shed light on the 

potential causes of GWI. Our review indicates that biological measures within the immune and 

central nervous systems have more often been investigated for their potential relationship with 

GWI, consistent with some dominant theories of disease etiology and dysfunction, but the 

literature also suggests other avenues of inquiry in upcoming studies, such as the gut 

microbiome. More importantly, our review revealed that existing studies are insufficient for 

determining promising biomarkers due to the extent of heterogeneity in biological measures 

across studies, inadequate comparator groups, and several other methodological limitations. 

Future studies that employ ideal control groups, reproduce findings of existing studies, and 

otherwise apply rigorous methodological practices and reporting specifically appropriate for 

investigating potential biomarkers would contribute to the establishment of a base of targeted, 

highly reliable studies from which lines of investigation could grow. 

 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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ABBREVIATIONS TABLE

Abbreviation Definition

AA Arachidonic Acid or African American

ACE Angiotensin-Converting Enzyme

ACh Acetylcholine

ADP Adenosine Diphosphate

AHI Apnea-Hypopnea Index

AHRQ Agency for Healthcare Research and Quality

ANA Antinuclear Antibody

ANS Autonomic Nervous System

apoAI Plasma Apolipoprotein AI

ASA Anti-Squalene Antibody

ASL Arterial Spin Labelling

ATP Adenosine Triphosphate

AUC Area Under the Curve

BBB Blood-Brain Barrier

BBRAIN Boston Biorepository and Integrated Network

BDNF Brain-Derived Neurotrophic Factor

BIDMC Beth Israel Deaconess Medical Center

BOLD Blood-Oxygen-Level-Dependent

BP Blood Pressure

BPM Beats-Per-Minute

BRINM Biomedical Research Institute of New Mexico

BuChE Butyrylchoninesterase

CASS Composite Autonomic Severity Score 

CBF Cerebral Blood Flow

CCEP Comprehensive Clinical Evaluation Program

CDC Centers for Disease Control and Prevention

CDMRP Congressionally Directed Medical Research Program

CFS Chronic Fatigue Syndrome

CI Confidence Interval

CMAP Compound Motor Action Potential

CMI Chronic Multisymptom Illness

CNDP1 Carnosine Dipeptidase 1

CNS Central Nervous System

CO2 Carbon Dioxide

CpG Cytidine-Phosphateguanosine

CPTh Cold Pressor Threshold

CRP C-Reactive Protein 

CSF Cerebrospinal Fluid

CSP Cooperative Studies Program

CSRD Clinical Sciences Research and Development Service
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CV Conduction Velocity

D-GWV Deployed Gulf War Veteran

DF Degrees of Freedom

DHA Docosahexaenoic Acid

DKI Diffusion Kurtosis Imaging

DLPFC Dorsolateral Prefrontal Cortex

DNA Deoxyribonucleic Acid

DoD Department of Defense

DTI Diffusion Tensor Imaging

dUTPase Deoxyuridine Triphosphate Diphosphatase

EBV Epstein-Barr Virus

ECG Electrocardiogram

ECL Electrochemiluminescence 

EEG Electroencephalograph

ELISA Enzyme-Linked Immunosorbent Assay

EMG Electromyography

ERP Event Related Potential

ESP Evidence Synthesis Program

FM Fibromyalgia

fMRI Functional Magnetic Resonance Imaging

GI Gastrointestinal

GW Gulf War

GWI Gulf War Illness

GWIC Gulf War Illness Consortium

GWIRP Gulf War Illness Research Program

GWV Gulf War Veteran

HARDI High-Angular Resolution Diffusion Imaging

HCMV Human Cytomegalovirus

HDL High-Density Lipoprotein 

H-FABP Heart-type Fatty Acid Binding Protein 

HHV Human Herpesvirus

HLA Human Leukocyte Antigen

HPA Hypothalamic-Pituitary-Adrenal

HPTh Heat Pain Threshold

HR Heart Rate

HRV Heart Rate Variability

HUT Head-Up Tilt

IBS Irritable Bowel Syndrome

ICA Independent Component Analysis

ICD-9-CM International Classification of Diseases, Ninth Revision, Clinical Modification

IFN Interferon

Ig Immunoglobulin

IL Interleukin
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IoM Institutes of Medicine

IPF Immature Platelet Fraction 

IQuESt Center for Innovations in Quality, Effectiveness and Safety

KQ Key Question

LF Low Frequency

LLOD Lowest Level of Detection

LPC Lysophosphatidylcholines

LPE Lysophosphatidylethanolamine

LPS Lipopolysaccharide

MAP Multi-Analyte Profile

MCD Mean Consecutive Difference

ME Myalgic Encephalitis 

MEG Magnetoencephalograph

MiRNA Micro Ribonucleic Acid 

MMP Matrix Metalloproteinase

MN Minnesota

MPF Macromolecular Proton Fraction

MPV Mean Platelet Volume

MR Magnetic Resonance

MRI Magnetic Resonance Imaging

mRNA Messenger Ribonucleic Acid

MRS Magnetic Resonance Spectroscopy

MRSI Magnetic Resonance Spectroscopic Imaging

MSD Meso Scale Discovery

MSI Multisymptom Illness

mt Mitochondria

mtDNA Mitochondrial Deoxyribonucleic Acid

MUAP Motor Unit Action Potential

MUFA Monosaturated Fatty Acids

MVP Million Veteran Program

NAA N-acetyl Aspartate

NCM Neurocutaneous Melanocytosis

NCT National Clinical Trial

ND No Difference

NIH National Institutes of Health 

NPV Negative Predictive Value

NR Not Reported

NREM Non-Rapid Eye Movement

OR Odds Ratio

ORD Office of Research and Development

OS Oxidative Stress

PBMC Peripheral Mononuclear Cell

PC Phosphatidylcholine
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PCr Creatine Phosphate

PCR Polymerase Chain Reaction

PE Phosphatidylethanolamine

PEF Post Exertional Fatigue

PEM Post Exertional Malaise 

PET Positron Emission Tomography

Pi Inorganic Phosphate

PI Principal Investigator or Phosphatidylinositol

PICOTS Population, Intervention, Comparator, Outcome, Timing, Setting, and Study Design

PL Phospholipid

PON1 Paraoxanase 1

PPV Positive Predictive Value

PRP Platelet-Rich Plasma

PSC Percent Signal Change

PSG Polysomnograph

PTSD Posttraumatic Stress Disorder

PUFA Polyunsaturated Fatty Acids

RCT Randomized Controlled Trial

REM Rapid Eye Movement

RERA Respiratory Effort Related Arousal

RNA Ribonucleic Acid

ROC Receiver Operating Characteristic

SD Standard Deviation

SEM Standard Error of the Mean

SF-36 Short Form-36 Item

SFA Saturated Fatty Acids

SFVAFRE South Florida Veterans Affairs Foundation for Research and Education

SNP Single-Nucleotide Polymorphism

SPRC Standardized Partial Regression Coefficient

TCR T-Cell Receptor

TEP Technical Expert Panel

TNF Tumor Necrosis Factor

TPO Plasma Thrombopoietin 

TRAP Thrombin Receptor Agonist Peptide

UC University of California

UK United Kingdom

US United States

USACHPPM United States Army Center for Health Promotion and Preventive Medicine

USAMRDC United States Army Medical Research and Materiel Command

VA Veterans Affairs

VAMC Veterans Affairs Medical Center

VHA Veterans Health Administration

VLF Very Low Frequency
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VO2 Oxygen Volume

WM White Matter

XMRV Xenotropic Murine Leukemia Virus-Related Virus
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EVIDENCE REPORT

INTRODUCTION

The setting of the 1990-1991 conflict in the Persian Gulf was fraught with potential exposure to 

multiple toxins and stressors, including environmental and chemical exposures (eg, solvents, 

depleted uranium, excessive heat, oil-well fire smoke, pesticides, nerve agents and their 

prophylaxis, and vaccines) and psychological stressors associated with a combat setting (eg, 

separation from family and work, uncertainty about presence of chemical and biological agents, 

and witnessing dead or wounded soldiers and citizens).1 After the conflict, many Gulf War

Veterans began reporting numerous unexplained symptoms. These symptom clusters included 

fatigue, headaches, joint pain, indigestion, insomnia, dizziness, respiratory disorders, and 

memory problems.

Clusters of symptoms were categorized into 6 definitions between 1997 and 2009.2-7 In 2014, the 

Institutes of Medicine (IoM) determined that, together, 2 definitions best captured the 

symptoms—the Centers for Disease Control and Prevention (CDC) and Kansas definitions.8 The 

common symptoms in all studies reviewed by the committee in the selection of the definition 

included reports of fatigue, pain, and neurocognitive symptoms. The CDC definition requires at 

least 1 symptom from 2 of 3 categories (fatigue, mood and cognition, and musculoskeletal) for 6 

months or longer.3 The Kansas approach defines a case as having 1 moderately severe, or 2 or 

more chronic symptoms in at least 3 of 6 domains (including fatigue or sleep; pain; neurologic,

cognitive, or mood; gastrointestinal; respiratory; and skin).7 The proportion of Gulf Wardeployed Veterans who meet case criteria for Gulf War Illness (GWI) is approximately 34% 

(based on the Kansas case definition) to 50% (based on the CDC case definition).8

Yet these definitions remain disputed and outdated, and GWI is still a largely medically 

unexplained chronic multisymptom illness, due to its widely varying symptoms, reliance on selfreported symptoms for diagnosis, and lack of understanding of its pathophysiology and etiology.9

The identification of promising biological markers could help to refine the illness definitions; 

better detect, predict, or distinguish subgroups of GWI; and ultimately lead to the development 

of biologically plausible treatments. Identification and treatment of GWI is a top research 

priority for the VA Office of Research and Development (ORD). While causes of GWI 

symptomology and areas of biological dysfunction are unknown, investigation of biological 

systems underlying GWI symptomology has been partially driven by hypotheses involving 

exposure to acetylcholinesterase-inhibiting toxins – such as nerve agents, Pyridostigmine 

bromide pills, and pesticides – that are thought to contribute to dysfunction in energy metabolism 

(mitochondria), leading to dysfunction in cholinergic systems10-12 and to chronic inflammation 

that damages the central nervous system (CNS).13-15 Most studies to date have included 

biological markers that have focused around the biological systems (eg, central nervous system, 

immune system, and autonomic nervous system, as well as genes that regulate these systems)

that are involved in the aforementioned hypothesized GWI mechanisms.

This systematic review provides an overview of the state of biomarker research in GWI. It is not 

intended to directly guide clinical care but is intended to help future research planning efforts by 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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summarizing what the field has already examined and by identifying any potentially promising 

areas of biomarker research. We examined the literature to identify studies of the use of 

biomarkers as a diagnostic test for GWI, and also to identify studies that evaluated the 

differential expression of biomarkers among participants with and without GWI.
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METHODS 

TOPIC DEVELOPMENT

The key research questions for the review were as follows: 

1. Which diagnostic tests (or test combinations) are candidates for distinguishing 

individuals diagnosed with GWI from individuals without GWI? 

2. Which biological measures have been examined for their potential association with GWI, 

and which among them have been shown to be associated with GWI? 

3. Which ongoing or unpublished research studies examine diagnostic tests or biological 

measures for potential association with GWI? 

Our approach was guided by our process framework, which we developed in consultation with 

our operational partners (Figure 1). The protocol was posted to a publicly accessible website 

prior to commencing the review (PROSPERO registration ID: CRD42020169099).

Figure 1. Process framework

Abbreviations: GWI=Gulf War Illness

Cases: Veterans and 

civilian contractors present 

in the Persian Gulf during 

1990-1991 with diagnosis 

of GWI

Comparator populations: 

Gulf War-deployed 

Veterans with or without 

medical conditions other 

than GWI

1

2/3

Diagnostic tests to 

distinguish GWI 

from non-GWI

Measures of 

diagnostic 

accuracy

Biological 

measures and 

other diagnostic 

tools examined for 

association with 

GWI case status

Measures of 

association 

between GWI 

and biological 

measures
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Figure 2. Biomarker development Process

 

Note. Reproduced from Fall et al, 201416

The biomarker development process (Figure 2; reproduced from Fall et al, 201416) provides a 

framework from which to conceptualize the biomarkers included in our report. Key Question 1, 

regarding the validity of diagnostic tests, fits in the Verification and Validation stage of the 

diagram. Key Questions 2 and 3, regarding associations of biological measures with GWI case 

status, fits into the stages prior to the Verification stage. 

SEARCH STRATEGY

We conducted a primary review of the literature by systematically searching, reviewing, and 

analyzing the scientific evidence as it pertains to the research questions. To identify relevant 

articles, we searched Ovid MEDLINE, and Ovid PsycINFO, and Ovid EBM Reviews (Cochrane 

Database of Systematic Reviews and Cochrane Central Register of Controlled Trials) through 

February 20, 2020. Search strategies were developed in consultation with a research librarian, 

and peer reviewed by a second research librarian using the instrument for Peer Review of Search 

Strategies (PRESS).

17 The search strategy included terms to identify Veterans from the Gulf War

era (eg, Desert Shield, Desert Storm, Kuwait War, Operation GRANBY) combined with past and 

present terms to identify GWI (eg, Chronic Multisymptom Illness, Chronic Fatigue, Gulf War

Syndrome). We limited our search to English-language publications but did not limit by 

publication status or study design. To identify in-progress or unpublished studies, we searched

ClinicalTrials.gov and the World Health Organization (WHO) International Clinical Trials 

Registry Platform (ICTRP). We reviewed the bibliographies of relevant articles, contacted

experts, and reviewed lists of funded trials from the Department of Defense (DoD) and Veterans

Affairs (VA) to identify additional studies.

STUDY SELECTION

Criteria for population, interventions, comparators, outcomes, timing, setting, and study design 

(PICOTS; Table 1) were developed in collaboration with our operational partners and Technical 

Expert Panel. Two reviewers independently assessed studies for inclusion based on pre-specified 

criteria. All discordant results were resolved through consensus or consultation with a third 

reviewer. Articles meeting eligibility criteria were included for data abstraction. 

Number of Samples

Number of Analytes
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We defined biomarkers broadly: we included any biological measurement across a broad variety 

of biological functions or systems. This could include blood tests or even imaging studies. We 

distinguish biological measurements from other assessments such as symptom questionnaires 

which were excluded. We only included studies of biomarkers in humans.

For evidence on diagnostic test accuracy (Key Question [KQ] 1), we included all studies that 

compared a test’s classification of GWI diagnosis with any reference standard’s classification

(eg, diagnosis of GWI according to Kansas or CDC/Fukuda 1998 criteria). While we recognize 

that there are limitations to these reference standards, they are the currently recommended and 

most current, widely used definitions, and a standard case definition was required to evaluate 

diagnostic accuracy.

For evidence on biological measures (KQ2), we included all studies that compared the 

prevalence or quantity of a biological measure in Veterans clinically diagnosed with GWI with 

Veterans without GWI (we included studies that used any case definition for KQ2). For 

emerging research (KQ3), we included studies with any of the above designs that are 

unpublished or in progress. Again, we included cases identified using any GWI diagnostic 

criteria. 

We included studies in which the comparator populations were deployed Gulf War Veterans 

(GWVs) who were either healthy or had health conditions other than GWI. These groups were 

prioritized as they would best control for the effects of combat and exposure present in, and 

associated with, the conflict area. Also, these studies would be further along the biomarker 

development pathway and would therefore provide results more applicable to situations in which 

there is diagnostic uncertainty (ie, the clinical use of biomarkers would most likely be in 

participants who had been deployed and therefore had the potential to have GWI).

We were particularly interested in the group of GWVs with other health conditions, as 

differences in biological measures between this group and the GWI group could potentially help 

distinguish GWI cases from cases of other health conditions. In other words, it is for the Veteran 

with symptoms and a history of deployment that the clinical application of a biomarker would be 

most relevant and, therefore, studies which enrolled GWVs with symptoms (some of whom 

would have GWI as the cause of symptoms and others who would have symptoms secondary to 

another illness) are of particular interest.

We also acknowledge that other controls groups, including healthy non-Veterans or nondeployed GWVs, can help provide information about potentially pertinent biomarkers. We

therefore identified a group of lower-priority studies, excluded from the main review, that are 

summarized in a supplementary table (Appendix D; Table 10). These were studies whose 

comparator groups consisted of non-deployed Gulf War-era Veterans, Veterans who were 

deployed elsewhere (other than Persian Gulf) during the Gulf War, civilians with other health 

conditions/conditions with similar symptomology to GWI (eg, chronic fatigue syndrome, 

neurodegenerative disorders, or musculoskeletal problems) and healthy civilian controls. We also 

considered studies with 25 or fewer participants as lower priority because these were likely to be 

too small18 to determine with any confidence whether or not a given measure was associated 

with GWI.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Table 1. PICOTS by Key Question

Key 

Question 

KQ1. Which diagnostic tests (or test 

combinations) are candidates for 

distinguishing individuals diagnosed 

with GWI from individuals without GWI?

KQ2. Which biological measures have been 

examined for their potential association with 

GWI, and which among them have been shown 

to be associated with GWI? 

KQ3. Which ongoing or unpublished 

research studies examine diagnostic tests 

or biological measures for potential 

association with GWI? 

Population Case definition: Veterans and civilian contractors who were deployed to the Persian Gulf region between Aug 2, 1990 and Nov 1991, and 

diagnosed with GWI (ie, according to either CDC/Fukuda 1998 or Kansas criteria, or other criteria). Include studies of international Veteran

populations if they use any definition of GWI (limited to English-language publications).*

Comparator populations: Gulf War-deployed Veterans with or without medical conditions other than GWI

Exclude: children and birth outcomes of Gulf War Veterans

Intervention Measures of any of the following categories of biological functions/systems that are potential loci of dysfunction:

• Genes

• Immune activation/inflammation

• Neurodegeneration

• Autonomic nervous system

• Endocrine system

• Energy metabolism

• General brain activity (central nervous system)

• Other

Exclude: Assessments that do not include biological measurements (eg, questionnaires)

Comparators Compares a test’s classification of GWI 

diagnosis with a reference standard’s 

classification (eg, diagnosis of GWI 

according to Kansas or CDC/Fukuda 1998 

criteria). 

Compares Veterans clinically diagnosed with GWI vs any comparator group (see comparator 

populations above)

Outcomes Measures of diagnostic accuracy:

• Sensitivity and specificity

• Positive and negative predicative values 

(PPV, NPV) 

• Likelihood ratio

• The area under the ROC curve (AUC)

Measure of association between biological 

measurement and GWI

Study objectives, status, outcome measures, 

and available findings.

Timing No limits

Settings No limits

Study 

Design

Non-experimental, cross-sectional study; 

systematic reviews of diagnostic accuracy 

studies.

Cross-sectional, cohort, case series, and casecontrol studies that compare the results of a 

diagnostic tool, or the prevalence or quantity of a 

biological measure; or systematic review of such 

studies.

Cross-sectional study or other comparative 

observational study design.

*We recognize other countries may use different case definitions

Abbreviations: AUC=Area Under the Curve; CDC=Centers for Disease Control and Prevention; GWI=Gulf War Illness; KQ=Key Question; NPV=Negative Predictive Value; 

PPV=Positive Predictive Value; ROC=Receiver Operating Characteristic 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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DATA ABSTRACTION

Data from studies meeting inclusion criteria were abstracted by 1 Evidence Synthesis Program 

(ESP) reviewer and were confirmed by at least 1 additional reviewer. From each study, we 

abstracted the following where available: study design, sample size, population characteristics, 

case definition, comparator(s), participant inclusion and exclusion criteria, details of the 

biological measure of interest, and findings for measures of association (Table 2).

If a study had multiple comparator groups, we abstracted only data that pertained to our 

comparators of interest: the GWI group and control participants who were deployed to the Gulf 

War theater in 1990-1991.

QUALITY ASSESSMENT

Two reviewers independently assessed the risk of bias of each study. To assess the risk of bias

we used an adapted version of the Newcastle-Ottawa tool for quality assessment19 along with 

adapted elements of the risk of bias tool for cross-sectional studies using biological measure data 

(BIOCROSS).

20 Had we identified any studies of the validation of diagnostic tests, we would 

have used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool for those 

studies. Disagreements were resolved by consensus or a third reviewer. 

DATA SYNTHESIS

We qualitatively synthesized the evidence and compiled evidence tables of study characteristics 

and findings for each key question (Table 2). We also briefly summarized information about 

excluded studies in Appendix D.

We did not perform a formal certainty of evidence assessment due to the heterogeneity among 

studies and because the intent of the review was largely to identify areas of promise for further 

investigation of biological measures rather than to assess the body of evidence for clinical 

recommendations.

RESULTS

We reviewed a total of 2,102 titles and abstracts and identified 270 articles for review at the fulltext level. We included a total of 56 studies for KQs 1-3 (Figure 3). However, we also 

summarized a larger group of lower-priority studies that met our inclusion criteria but did not 

include a priority comparator group (72 studies), and/or had total sample sizes of less than 25 (5 

studies) – these studies are listed in Appendix D.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Figure 3. Literature flow chart

270 potentially relevant 

articles for full text review

98 citations identified from reference lists of 

relevant articles and reviews, key experts, 

and other sources

2,200 citations compiled for 

review of titles and abstracts

56 included studies

2,102 unique citations identified from electronic search on Feb 21, 2020. 

Yield from each database prior to deduplication: 

1,736 PubMed/Ovid MEDLINE

608 PsycINFO

53 WHO International Clinical Trials Registry Platform

 117 Ovid EBM Reviews, Cochrane Central Register of Controlled Trials

4210 Ovid EBM Reviews, Cochrane Database of Systematic Reviews

36 Clinicaltrials.gov

18 Department of Defense database

1,930 titles and abstracts 

excluded for lack of relevance

KQ 1:

No studies

KQ 2:

32 published studies

KQ 3: 

24 unpublished studies 

Abbreviations: GWI=Gulf War Illness; GWV=Gulf War Veteran; KQ=Key Question

137 excluded publications:

 1 Used for background or discussion

53 Does not meet population criteria

64 No data on GWI biological measures

15 Excluded study design or publication type

 4 Duplicate or preliminary publication of another 

study

77 Excluded studies summarized in Appendix D:

 72 Comparator were not deployed GWVs 

 (lower priority controls)

5 Sample size <25
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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KEY QUESTION 1: Which diagnostic tests (or test combinations) are 

candidates for distinguishing individuals diagnosed with GWI from 

individuals without GWI?

We did not identify any studies that evaluated diagnostic tests (or test combinations) for their 

ability to distinguish individuals diagnosed with GWI (with CDC or Kansas criteria, or any other 

criteria) from individuals without GWI (Key Question 1). 

In order to identify a biomarker for use as a diagnostic test for GWI, studies would first need to 

assess the diagnostic accuracy of a given biomarker in identifying people with GWI. In a 

diagnostic accuracy study, all participants would be subjected to a “gold standard” test in order 

to distinguish those with the disease from those without the disease. Ideally, there would be 

diagnostic uncertainty among all participants in a diagnostic accuracy study (ie, as opposed to a 

study which enrolled participants with severe symptoms and participants with no symptoms). 

The biomarker in question would be assessed in all participants without knowledge of the results 

of the “gold standard” test, and then measures of diagnostic accuracy such as sensitivity and 

specificity could be assessed.

KEY QUESTION 2: Which biological measures have been examined 

for their potential association with GWI, and which among them have 

been shown to be associated with GWI?

We identified 32 studies of biological measures that have been examined for their association 

with GWI and grouped them broadly into categories under distinct biological systems: 10 studies 

of immune system biological measures; 10 central nervous system (CNS) studies; 5 studies of 

autonomic nervous system (ANS) biological measures; 1 of genetic biological measures; and 6

studies of biological measures in other biological systems (See Figure 4). All cases of GWI in all 

included studies were in participants who were Gulf War Veterans (ie, no studies including 

civilian contractors present in the Persian Gulf during the same time period were identified). 

Only 1 study21 included deployed GWVs with health conditions other than GWI; in all other 

studies, health conditions were not reported and participants were presumed healthy. Eleven

studies used the CDC case definition to identify participants with GWI and 2 used the Kansas 

definition. Ten studies used the Haley criteria, alone or in combination with CDC criteria. The 

Haley criteria categorizes cases of GWI into 6 syndromes based on symptom groups, 3 of which 

are referenced in the included studies: impaired cognition (Syndrome 1); confusion-ataxia 

(Syndrome 2); and arthro-myo-neuropathy (Syndrome 3).4 Table 2 details the characteristics of 

these studies. The detailed study findings are reported according to biological system below.

Further, we identified 77 studies that used a lower-priority comparator group (that is, a 

comparator group other than deployed GWVs without GWI, with or without other health 

conditions), no comparator group, or an inadequate sample size (N<25). While not included in 

this main report, a summary of these studies is provided in Appendix D and Figure 5. Figure 5 

indicates that most studies examined biological measures within the central nervous and immune 

systems, and that the majority of studies in all biological systems showed a statistically 

significant association of the biological measure with GWI case status.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Table 2. Characteristics of Gulf War Illness Biological Measure Studies

Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

IMMUNE SYSTEM

Asa, 200023

Cross-sectional

N=50

Squalene antibody 

status

Serum samples.

Anti-squalene Antibody Assay which 

measures the binding of serum 

immunoglobulin (IgG) to squalene immobilized 

on nitrocellulose

GWI: 38

Comparator: 12 healthy

Case def: CDC

Source: Service in the US or UK 

military during Persian Gulf during 

1990-1991

Age: NR

Female: NR

Race: NR

Butterick, 201928

Cross-sectional

N=80

IFN-γ, IL-6, IL-8, IL10, and TNF-α and Creactive proteinMeso Scale Discovery (MSD)

plate-based electrochemiluminescence

(ECL) assay platform to quantify plasma 

concentrations of interferon gamma (IFN-γ), 

IL-1β, interleukin 2 (IL-2), interleukin 4 (IL-4), 

IL-6, interleukin 8 (IL-8), interleukin 10 (IL-10), 

interleukin 12 p70 (IL-12 p70), interleukin 13 

(IL-13), and tumor necrosis factor alpha (TNFα).

GWI: 53

Comparator: 27 healthy

Case def: CDC 

Source: Parent study “Biological 

measures of Gulf War Veterans’ 

Illnesses: Tissue Factor, Chronic 

Coagulopathy, and Inflammation”

Age: NR

Female: NR

Race: NR

Emmerich, 

201724

Cross-sectional

N=33

Several phospholipid 

species in the plasma. 

PC, LPC, PE, LPE 

and PI

lipid extracts from plasma were resuspended 

in isopropanol and separation was achieved 

using hydrophilic interaction chromatography

GWI: 22

Comparator: 11

Case def: Kansas

Source: GWI biorepository from 

Boston and Miami areas

Age: 48.4 (6.3) vs 48.5 

(7.7)

Female: 23 vs 18 

Race: Caucasian 54 vs 

45; Hispanic 9 vs 18;

Other 5 vs 9

Georgopoulos, 

201625

Cross-sectional

N=82

HLA alleles DNA isolation, 3 ml of whole blood. Highresolution HLA Sequence-based Typing 

conducted on purified DNA.

GWI: 66

Comparator: 16 healthy

Case def: Kansas or CDC

Source: VA medical records

Age: 50.6 (7.9) vs 50.6 

(7.9)

Female: 3 vs 6

Race: NR

James, 201627

Cross-sectional 

N=81

Human leukocyte 

antigen, brain 

synchronicity 

High-resolution HLA Sequence-based Typing 

conducted on purified DNA and MRI

GWI: 65

Comparator: 16 healthy

Case def: CDC

Source: Same sample (-1) studied in a 

previous study25

Age: 50.8 (7.9) vs 54.9 

(10.2) years

Female: 3 vs 6

Race: NR

Johnson, 201326

Cross-sectional

Platelet count, 

immature platelet

CRP and TPO assayed by Multi-Analyte 

Profiling. Platelet aggregation was measured 

GWI: 43

Comparator: 21 Healthy

Age: 49.9 (8.55) vs 50.4

(11.0)
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

N=64 fraction (IPF), plasma 

thrombopoietin (TPO), 

C-reactive protein 

(CRP), platelet 

aggregation and ATP 

secretion 

by preparing citrated blood at 160g for 25 min, 

completed within 4 h of venipuncture. 

Aggregation was evaluated at the platelet 

count of the PRP without dilution or 

concentration. Agonists (0.5 mmol/l 

arachidonic acid, 5mmol/l epinephrine, 

5mmol/l ADP, 0.5mmol/lU46619, 1.0mmol/l 

U46619, 1mg/ml collagen, 10mmol/thrombin 

receptor agonist peptide 6 (TRAP 6), and 50 

mmol/l TRAP 6; final concentrations) were 

added to 450ml PRP, preincubated at 37°C, 

and stirred in a Chronolog LumiAggregometer. ATP release was measured by 

Chronolog Lumi-Aggregometer.

Case def: CDC

Source: Veterans identified through 

the resources of the US Department of 

Veterans Affairs as persons who 

served in Kuwaiti Theater of 

Operations 1990-1991

Female: 7 vs 0

Race: NR

Johnson, 201629

Cross-sectional

N=85

Plasma lymphocytes, 

monocytes and 

neutrophils

Proteomic analysis performed

by quantitative multiplexed immunoassays 

using a Multi-Analyte Profile (MAP) platform

GWI: 57

Comparator: 28 Healthy

Case def: CDC

Source: VA Minnesota Gulf War

Registry, invited via letter

Age: Median – 46 vs 48

Female: 5 vs 4

Race: White 93 vs 93; 

Black 4 vs 7; Hispanic 4 vs 

0

Lo, 200022

Cross-sectional,

population-based

N=2,951

Mycoplasma 

fermentans antibodies

Pre- and post-deployment serum was 

obtained from the DoD Serum Repository 

GWI: 718

Comparator: 2,233 healthy

Case def: Per medical examination 

Source: Pt selection and serum 

specimen coding were performed by 

USACHPPM

Age >35 years: 136 (18.9) 

vs 425 (19.0)

Female: 15.9 vs 15.7

Race: Black/AA 230 (32.0) 

vs 494 (22.1); White 410 

(57.1) vs 1,552 (69.5); 

Other 78 (10.9) vs 187 

(8.4)

Phillips, 200930

Case-control

N=175

Squalene antibody 

status

ELISA for antibodies to squalene in human 

serum

GWI: 29 

Comparator: 146 healthy

Case def: Unusual fatigue with 3 of 38 

additional symptoms

Source: 3 epidemiologic team visits 

(late 1994 - early 1995) to each of 2 

Navy Seabee Centers

Age: 25.9 vs NR

Female: 0 vs NR

Race: White 84 vs NR; 

Black/AA 5 vs NR; 

Other/unknown 12 vs NR

Skowera, 200431

Cross-sectional

N=120

Th1/Th2 balance by 

measuring intracellular 

production of IFN-γ, 

Blood samples were obtained; Flow cytometry 

was used to measure intracellular cytokine 

production by CD4 T lymphocytes.

GWI: 40

Comparator: 80 healthy

Age: NR

Female: NR
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

IL-2 (Th1), IL-4 (Th2),

and IL-10 by CD4 T 

cells

Case def: Score ≤72.2 on SF-36 

Physical Functioning subscale

Source: Random sample of Veterans

of the Gulf conflict (1990-91) 

Race: NR

CENTRAL NERVOUS SYSTEM

Calley, 201032

Cross-sectional 

N=38

Percent signal change brain activation during semantic memory task: 

indicate if 2 features (written words) elicit the 

retrieval of a memory of a specific object 

(retrieval), or not (non-retrieval)

GWI: 26 (syndromes 1-3: 9;10;7)

Comparator: 12 Healthy

Case def: Haley criteria

Source: Construction Battalion in US 

Naval Reserve

Age: NR

Female: 0

Race: NR

Cooper, 201633

Cross-sectional 

N=67

Regional brain 

activation

brain activation during a face-name episodic 

memory task 

GWI: 57; syndrome 1,2,3=19, 20, 18

Comparator: 10 Healthy

Case def: Haley criteria

Source: Sampled from nationally 

representative sample of Gulf War-era 

Veterans (N=8020)

GWI (syndromes 1, 2, 3) 

vs comparators:

Age: 49.16 (8.84) vs 49.95

(8.39) vs 51.44 (8.15) vs 

45.60 (7.41) 

Female: 32, 30, 17 vs 10

Race/eth: Caucasian 68, 

80, 72 vs 60; Black/AA 11, 

15, 17 vs 30; Hispanic 5, 

5, 6 vs 10; Other 16, 0, 6 

vs 0

Gopinath, 201234

Cross-sectional 

N=54

Brain activation brain activation to innocuous and noxious heat 

stimuli

GWI (syndromes 1, 2, 3): 11; 17; 12

Comparator: 14 Healthy

Case def: Haley criteria

Source: 24th Reserve Naval Mobile 

Construction Battalion

Age: 51 (6); 63 (7); 57 (7) 

vs 61 (7)

Female: 0%

Race: NR

Liu, 201135

Cross-sectional 

N=47

Cerebral blood flow MRI with an inhibitory cholinergic challenge,

physostigmine infusion

GWI: syndromes 1,2, 3; 11;12;10 

Comparator: 14 Healthy

Case Def: Fukuda/Haley

Source: Members of the U.S. Naval 

construction battalion

GWI (syndromes 1, 2, 3) 

vs comparators:

Age: 51.4 (6.1), 60.9 (6.0), 

57.3 (6.7) vs 60.1 (6.3) 

Female: 0

Race: NR

Odegard, 201336

Cross-sectional 

N=47

Brain activation brain activation during face-name associative 

memory paradigm

GWI: 33

Comparator: 14 Healthy

GWI (syndromes 1, 2, 3) 

vs comparators: 

Age: 51.30, 61.67, 57.27 

vs 60.36
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Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

Case def: Haley criteria

Source: Construction battalion of the 

U.S. Naval Reserve

Female: NR

Race: NR

Tillman, 201037

Cross-sectional 

N=48

Event-related potential EEG recordings during a go/no-go task 

(response inhibition)

GWI: 25

Comparator: 23 Healthy

Case def: Presented with major 

cognitive complaints

Source: Construction battalion of the 

U.S. Naval Reserve during 1991 

Persian Gulf War

Age: 58.4 vs 58.8 

Female: 0

Race: NR

Tillman, 201238

Cross-sectional 

N=28

Event-related potential EEG recordings during an auditory task: 

threatening sounds as distractor stimuli, 1000 

hz square wave tone as nontarget stimulus, 

250-hz square wave tone as the target 

stimulus

GWI: 20

Comparator: 8 Healthy

Case def: Haley criteria

Source: Construction battalion of the 

U.S. Naval Reserve during the 1991 

Persian Gulf War

GWI (syndromes 1, 2, 3) 

vs comparators:

Age: 53.17 (5.38), 63.75 

(7.05), 53.833 (6.85) vs 

61.6 (7.58)

Female: NR

Race: NR

Tillman, 201339

Cross-sectional

N=30

Event-related 

potentials 

EEG recordings during visual task: threatening 

distractor pics, target stimuli (animals), 

nonthreatening nontarget stimuli

GWI: 22

Comparator: 8 Healthy

Case def: Haley criteria

Source: Construction battalion in U.S. 

Naval Reserve during the 1991 

Persian Gulf War

Age: 57.2 vs 61.6 

Female: 0

Race: NR

Tillman, 201940

Cross-sectional 

N=62

Event-related potential EEG recordings during an auditory task: 

threatening distractor sounds, target tone 

stimulus

GWI: 40

Comparator: 12 Healthy

Case def: Haley criteria

Source: Nationally representative 

dataset of the Gulf War Veteran

population from 4 military branches

GWI (syndromes 1, 2, 3) 

vs comparators:

Age: 49.2 (10.4), 52.0 

(7.3), 49.7 (7.8) vs 48.4 

(7.9)

Female: 25 vs 14

Race: Caucasian: 87 vs 

NR; Other: 13 vs NR

Weiner, 201141

Cross-sectional

N=178

N-acetylaspartate, 

creatine-and cholinecontaining metabolites

Spectroscopy (MRI, MRS, MRSI) GWI: 81

Comparator: 97 healthy

Case def: CDC and Haley criteria

Source: N. California VA GWI clinics, 

fliers at VA hospitals/clinics, and direct 

mailing to DoD list of GWVs

Age: 44.6 (8.8) vs 44.6 

(9.9) 

Female: 9 vs 15

Race/eth: White 64 vs 61; 

Black/AA 14 vs 16; 

Hispanic 11 vs 9; Asian 2 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

vs 3; Pacific islander 2 vs 

4; Other 2 vs 4

AUTONOMIC NERVOUS SYSTEM

Blanchard, 

201942

Cross-sectional 

case-control 

cohort study

N=295

24-hr heart-rate 

variability, urinary 

catecholamines and 

cortisol, hypertension, 

insulin sensitivity, 

dyslipidemia, body fat, 

bone mineral density, 

and ultrasensitive 

CRP

Hypothalamic-Pituitary-Adrenal Axis 

measures: overnight dexamethasone 

suppression testing

ANS Measures: ECG via 24-hour digital 

monitors, looking at HRV

GWI: 73

Comparator: 111 healthy

Case def: CMI – CDC definition

Source: NR

Age: 47.1 (8.1) vs 47.5 

(9.0)

Female: 36 vs 17

Race: White 73 vs 83

Davis, 200043

Cross-sectional

N=27

Heart rate, blood 

pressure

3-stage tilt-table testing with isoproterenol GWI: 14

Comparator: 13 healthy

Case def: Chronic Fatigue (ICD-9-CM 

Code 780.7) 

Source: Mostly active duty soldiers 

who went through DoD CCEP (Evans 

Army Community Hospital; 1994-1997)

Age: 32.1 (1.6) vs 38.9 

(1.9)

Female: 14 vs 8

Race: NR

Haley, 201344

Cross-sectional

N=82

Autonomic function

and high-frequency 

HRV

CASS, measuring the severity of autonomic 

dysfunction from 0 (no deficit) to 10 (maximal 

deficit), using sudomotor (range, 0-3), 

cardiovagal (range, 0-3), and adrenergic 

(range, 0-4) measurements. 24-hour Holter 

ECG recordings, performed at home, 

complexes and artifacts. 

GWI: 66 (syndromes 1, 2, 3: 21, 24, 

21)

Comparator: 16 healthy

Case def: CDC and Haley criteria

Source: Population sample of the US 

Military Health Survey

GWI (syndromes 1, 2, 3) 

vs comparators:

Age: 48.2 (8.6), 49.8 (8.0), 

51.0 (7.9) vs 47.8 (7.9)

Female: 33, 29, 19 vs 19

Race: Black/AA 14, 17, 14 

vs 25

Li, 201445

Cross-sectional

N=28

BP, HR, tilt-table, 

nerve conduction, 

sensory testing, 

sudomotor axon reflex 

testing

BP, HR, tilt-table, nerve conduction, sensory 

testing, sudomotor axon reflex testing

GWI: 16

Comparator: 12 healthy

Case def: Post-exertional fatigue: 

patient-reported recurrent postexertional fatigue lasting >24 hours, or 

chronic fatigue >6 months since 

deployment

Source: “Health of US Veterans of 

1991 Gulf War: A Follow-Up Survey in 

10 years” 2005 survey pts in 

Washington DC and nearby states

Age: 48.3 (1.4) vs 48.1 

(2.0) 

Female: 19 vs 8

Race: NR
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

Nagelkirk, 200346

Cross-sectional 

N=38

Cardiorespiratory and 

metabolic responses 

(maximal oxygen 

uptake, HR, exercise 

time, workload 

achieved) to maximal 

exercise test 

Maximal exercise test: BP electronically 

monitored + verified with concurrent mercury 

sphygmomanometry; HR monitored by ECG; 

expired air collected with one-way 

nonrebreathing valve + analyzed with Max-1 

system

GWI: 19

Comparator: 19 healthy

Case def: CDC case def for CFS

Source: Mailed health survey; study 

site East Orange, NJ VAMC

Age: 41.9 (7.8) vs 43.1 

(5.1) 

Female: 16 vs 16

Race: NR

GENETIC

Hotopf, 200347

Cross-sectional, 

population-based

N=190

Paraoxonase (PON1) 

activity and genotype

PON1–55 and -192 genotype determined by 

polymerase chain reaction and restriction 

enzyme digestion using standard published 

protocols. Plasma apolipoprotein AI (apoAI) 

and high-density lipoprotein (HDL) measured 

with Cobas Mira S autoanalyser with reagents. 

GWI: 95

Comparator: 95 healthy

Case def: SF-36 (physical functioning 

subscale)

Source: Randomly selected cohorts of 

UK Armed Forces

Age: 36.9 (7.3) vs 34.3 

(5.4)

Female: 6 vs 5

Race: NR

OTHER BIOLOGICAL SYSTEMS

Amin, 201148

Cross-sectional 

N=29

Sleep parameters;

Sleep-related 

respiratory parameters

2 nights in sleep lab: full night PSG, surface 

electromyographic activity (for movement); 

nasal/oral pressure catheter (nose+mouth 

airflow); piezoelectric belts (for Thoracoabdominal movement); oxyhemoglobin 

saturation monitored at the finger using pulse 

oximeter. Continuous ECG monitored HR and 

rhythm

GWI: 18

Comparator: 11 healthy

Case def: Modified CDC 

Source: Gulf War Registry

Age: 42 (4) vs 41 (6.6) 

Female: 0%

Race: NR

Haines, 201721

Cross-sectional, 

population-based

N=43

Cholinesterase, 

Serum cytokines

Peripheral venous blood draw GWI: 25

Comparator: 4 healthy, 14 PTSD

Case def: Neurologic factor (4 

symptoms): blurred vision, balance 

problems/dizziness, tremors/shaking, 

and speech difficulty

Source: UK military personnel

Age: NR

Female: NR

Race: NR

Roland, 200049

Cross-sectional

N=33

Integrity of auditory 

pathways (inner ear 

through upper brain 

stem and 

vestibulocular reflex)

Audiovestibular testing: Rotary chair for 

Sinusoidal Harmonic Acceleration; 

Electronystagmography for ocular motor, 

positional, and caloric responses;

Electrocochleography for auditory-evoked 

GWI: 23 (Syndrome 1, 2, 3:

5, 13, 5)

Comparator: 10 healthy

Case def: Haley criteria

Source: 24th Reserve Naval Mobile 

Construction Battalion

Age: 46.6 ± 8.6 vs 48.0 ± 

6.2

Female: NR

Race: NR
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Study 

Design

N=total 

participants

Biological 

Measure(s) 

Examined Data Collection

Population 

n GWI vs comparator 

GWI case definition

Population/Sample source

Demographics

GWI vs comparators

Age: Mean years (SD)

Female: %

Race/Ethnicity: %

potentials; dynamic platform posturography for 

balance.

Sharief, 200250

Cross-sectional,

population-based

N=75

Distal motor latency, 

amplitude of 

compound motor 

action potential 

(CMAP), motor nerve 

conduction velocity 

(CV), and F-wave 

latency; functions of 

unmyelinated (C) + 

small myelinated (A-) 

fibers

Nerve conduction studies on dominant limbs + 

symptomatic side if symptoms were unilateral, 

at skin temperatures of 34 °C. Motor 

conductions recordings 

Quantitative sensory and autonomic function

tests: thermal thresholds in dominant hand 

and foot using TSA-2001 machine

Concentric needle and single-fiber EMG: On 

proximal + distal muscles in upper + lower 

limbs (biceps brachii, first dorsal interosseus, 

vastus medialis, and tibialis anterior)

GWI: 49

Comparator: 26 healthy

Case def: >4 neuromuscular 

symptoms: fatigue, joint stiffness, 

muscle weakness, myalgia at rest or 

after exercise, sensory symptoms, and 

autonomic symptoms + SF-36 score 

<72.2

Source: UK servicemember database

Age: NR

Female: NR

Race: NR

Wallace, 199951

Cross-sectional, 

not populationbased

N=78

(HHVs) HHV6, HHV7, 

Epstein-Barr virus 

(EBV), and 

cytomegalovirus

Blood samples: DNA extraction from PBMC 

cells; PCR was performed with primers for 

HHV6, HHV7, EBV, HCMV, TCR-beta 

GWI: 46

Comparator: 32 healthy

Case def: CDC

Source: Veterans from 10 states east 

of the Mississippi River

Age: 35.3 vs 32

Female: 17% vs NR

Race: Caucasian 73% vs 

NR

Zhou, 201852

Cross-sectional

N=91

Heat pain threshold, 

cold pressor pain 

threshold; ischemic 

pain threshold and 

ischemic pain 

tolerance 

Experimental pain procedures (3 random pain 

stimuli)

GWI: 53 GWI+GI symptoms; 38 no GI 

symptoms

Comparator: 47 healthy

Case def: Documented GWI +/- GI 

symptoms

Source: VAMCs in Cincinnati, OH and 

Gainesville, FL

GWI (with, without GI 

symptoms) vs 

comparators:

Age: 49.1 (3.7), 48.2 (1.2) 

vs 46.6 (4.1)

Female: 9, 0 vs 10

Race/eth: White 51, 83 vs 

21; Black/AA 47, 17 vs 45; 

Hispanic 0, 0 vs 0; Asian 

2, 0 vs 0

Abbreviations: AA=African American; ADP=Adenosine Diphosphate; ANS=Autonomic Nervous System; apoAI=Plasma Apolipoprotein AI; ATP=Adenosine Triphosphate; 

BOLD=Blood Oxygen Level Dependent; BP=Blood Pressure; CASS=Composite Autonomic Severity Score; CCEP=Comprehensive Clinical Evaluation Program; CDC=Centers 

for Disease Control and Prevention; CFS=Chronic Fatigue Syndrome; CMAP=Compound Motor Action Potential; CMI=Chronic Multisymptom Illness; CRP=C-Reactive Protein; 

CV=Conduction Velocity; DNA=Deoxyribonucleic Acid; DoD=Department of Defense; EBV=Epstein-Barr Virus; ECG=Electrocardiogram; ECL=Electrochemiluminescence; 

EEG=Electroencephalograph; ELISA=Enzyme-Linked Immunosorbent Assay; EMG=Electromyograph; fMRI=Functional Magnetic Resonance Reasoning; GI=Gastrointestinal; 

GWI=Gulf War Illness; GWV=Gulf War Veteran; HCMV=Human Cytomegalovirus; HDL=High-Density Lipoprotein; HHV=Human Herpesvirus; HLA=Human Leukocyte 

Antigen; HR=Heart Rate; HRV=Heart Rate Variation; ICD-9-CM=International Classification of Diseases, Ninth Revision, Clinical Modification; IFN=Interferon;

IgG=Immunoglobulin; IL=Interleukin; IPF=Immature Platelet Fraction; LPC=Lysophosphatidylcholines; LPE=Lysophosphatidylethanolamine; MAP=Multi-Analyte Profile;

MRI=Magnetic Resonance Imaging; MRS=Magnetic Resonance Spectroscopy; MRSI=Magnetic Resonance Spectroscopic Imaging; MSD=Meso Scale Discovery; NR=Not 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Reported; PBMC=Peripheral Mononuclear Cell; PC=Phosphatidylcholine; PCR=Polymerase Chain Reaction; PE=Phosphatidylethanolamine; PI=Phosphatidylinositol; 

PON1=Paraoxonase; PRP=Platelet-Rich Plasma; PSG=Polysomnogram; PTSD=Posttraumatic Stress Disorder; SD=Standard Deviation; SF-36=Short Form-36 Question 

Questionnaire; TCR=T-Cell Receptor; TNF=Tumor Necrosis Factor; TPO=Plasma Thrombopoietin; TRAP=Thrombin Receptor Agonist Peptide; UK=United Kingdom; 

US=United States; USACHPPM=United States Army Center for Health Promotion and Preventive Medicine; VA=Veterans Affairs; VAMC=Veterans Affairs Medical Center
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Immune System Biological Measure Studies

Table 3 provides results of the 10 studies of immune system biological measures we identified. 

Identified studies focused on a wide range of immune system-related functions including 

squalene antibodies (2 studies23,30), a variety of inflammatory cytokines (5 studies24,26,28,29,31), 

human leukocyte antigen (HLA) alleles (2 studies25,27), and mycoplasma antibodies (1 study22).

Two studies assessed the associations between the presence of squalene antibodies and GWI 

status.

23,30 The first was a case-control design that included 43 Veterans with Chronic 

Multisymptom Illness (CMI) and 536 who did not have CMI, as defined by unusual fatigue 

accompanied by 3 of a list of symptoms in broad categories such as altered mood, GI issues, 

pain, dermatologic issues, and generalized malaise (eg, sore throat and weakness).30 Whole blood 

or sera was collected using standardized approaches and subjects were followed up with 3 years 

later. There was no significant difference between those who were squalene antibody-positive or 

-negative with regard to presence of GWI. Limitations to this study included use of a nonstandard GWI case definition, lack of comparability between cases and control, and limited data 

on sample attrition. In contrast, the second study to examine squalene antibodies did find a 

difference in the presence of squalene antibodies between those with GWI (n=38) and compared 

to those with idiopathic immune disease or healthy controls (n=12), such that 95% of those with 

GWI had squalene antibodies, while 0% of healthy deployed GWVs had squalene antibodies. 

This study was a feasibility study, so no power analyses or inferential statistics were provided.23

Many of the other studies focused on measures of inflammation with peripheral blood 

cytokines26,28,29,31 and phospholipid species.

24 Two studies focused on peripheral blood cytokines 

(Th1/Th2 balance measured by intracellular production of Interferon (IFN)-γ, Interleukin (IL)-2 

(Th1), IL-4 (Th2), and IL-10 by CD4 T cells) among those who were symptomatic Gulf War

Veterans versus non-symptomatic Gulf War Veterans.

28,31 The first found that those who were ill 

had elevated levels of Th1 immune activation as evidenced by elevated levels of IL-10 by CD4 

cells (p<0.001), IL-2 (p=0.01) and IL-4 (p=0.001).31 Analyses corrected for age, gender, 

vaccination status, antidepressant use, Beck Depression Inventory score, and history of atopic 

illness. All other cytokines were normal. This study was limited by not accounting for multiple 

testing bias, the cases and controls not being drawn from the same population, and a nonstandard GWI case definition being used to identify cases (used Short Form [SF]-36 score to 

identify cases). The other study compared those with GWI (n=53) to healthy, deployed GWVs

(n=27) using the CDC case definition. A similar set of cytokines were examined (IL-6, IL-8, IL10, IFN-γ, tumor necrosis factor (TNF)-α, and C-reactive protein (CRP)) and no statistically 

significant between group differences were detected based on GWI status.

28 Limitations include 

sample size and power not being calculated and adjustment for multiple comparisons not being 

conducted. Johnson and colleagues included 43 men with GWI and 21 men without GWI and 

compared CRP, platelet counts, platelet aggregation, and several other measures of platelet 

function and found GWI had elevated platelet counts, spontaneous aggregation, and thrombin 

receptor agonist peptide (TRAP) 6-induced secretion, but no impairment of platelet counts.26

Study limitations include multiple testing not adjusted for and non-response rate not reported. 

Another study examined a similar set of blood inflammatory cytokines measures that also 

included lymphocyte, monocyte, and neutrophil among 57 individuals with GWI compared to 28 

GWVs without GWI. All included inflammatory markers were higher among those with GWI

(see Table 3 for specific results).

29 The combinations of lymphocytes, monocytes, and CPR had a 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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predicted probability of 90% (95% CI=76-90%) for diagnosing GWI when the probability of 

having GWI was above 70%. Limitations of this study included not adjusting for multiple 

comparisons or difference between cases and controls, as well as some of the more ubiquitous 

limitations discussed listed below.

Another study obtained samples of phospholipids, which are inflammatory modulators, from a 

biorepository (11 controls matched for gender, age, and ethnicity, and 22 who met Kansas 

criteria for GWI) to examine several different phospholipids (Phosphatidylcholine [PC]; 

phosphatidylethanolamine [PE]).24 Peripheral lipids were present in GWI (human and animal 

models). The study found that multiple species of phospholipids were elevated in humans with 

GWI, suggesting dysfunction within docosahexaenoic acid and arachidonic acid containing 

phospholipid (PL) species. This study did correct for multiple testing, but it was unclear whether 

the cases and controls were derived from the same population and are comparable. 

Two additional studies examined HLA alleles.25,27 The first included 82 Veterans, 66 with and 16 

without GWI, and found that the number of copies of the 6 HLA alleles was significantly higher 

in the control group and correctly classified the GWI status of 84.1% of participants (13/16 

control and 56/66 GWI).25 Limitations included lack of clarity about selection of controls and 

potential lack of comparability between cases and controls. An additional study examined HLA 

alleles and brain synchronicity in 81 Veterans (65 with GWI and 16 without GWI). Controls had 

higher counts of HLA protective alleles than those with GWI (chi-square test=21.9, 

p=0.000018). There was also an overall strong and significant effect of the HLA-related x neural 

synchrony interaction on symptom severity (SPRC |β|=0.274,0.232, and 0.200 for 

neurocutaneous melanocytosis (NCM), pain, and fatigue, respectively, p<0.001 for all 3

coefficients.

27 Limitations included unrepresentativeness of cases and controls, lack of power 

calculation, lack of comparability between cases and control, and lack of reporting of blinding 

and non-response rate. 

Finally, 1 study examined the presence of antibodies to Mycoplasma (M.) fermentans22 among 

718 Veterans with GWI and 2,233 healthy Gulf War Veterans. There was no difference between 

rates of seroconversion between cases and controls, indicating that there is no association 

between GWI and M. fermentans infection.

Limitations across immune system studies included lack of blinding of outcome assessor and/or 

study team. Additionally, recruitment non-response rate was not reported, giving us little insight 

into issues of response bias. Sample size and power calculations were also often not reported, 

and very few studies discussed issues related to data normality and handling of outliers. 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Table 3. Results of Gulf War Illness Immune System Biological Measure Studies

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

Human leukocyte antigen, 

brain synchronicity (zerolag, pairwise cross 

correlations calculated 

from pre-whitened, 60-

second resting-state MEG

recordings)27

65 vs 16

Human leukocyte antigen protective alleles: Cases had higher 

counts than healthy GWI chi-square test=21.9 (p=0.000018) 

Brain synchronicity: Significant effect of the HLA-related x neural 

synchrony interaction on symptom severity (SPRC 

|β|=0.274,0.232, and 0.200 for NCM, pain, and fatigue, 

respectively, p<0.001 for all t coefficients).

HLA-related effects spared the right anterior temporal lobe and 

were widespread, vs non-HLA-related effects focused on anterior 

temporal lobe, medial prefrontal cortex, posterior parietal, and 

occipital cortex.

HLA antigens are higher in 

those with GWI; HLA 

related x neural synchrony 

is related to symptom 

severity.

Lack of 

representativeness of 

cases and controls, lack

of power calculation, lack 

of comparability between 

cases and control, lack of 

blinding, lack of reporting 

of non-response rate.

Mycoplasma (M.)

fermentans antibodies22 

718 vs 2,233

Positive for M. fermentans-specific antibodies, (%):

Pre-deployment: 34/718 (4.8%) vs 116/2233 (5.2%)

Post-deployment seroconversion from negative to positive: 8/718 

(1.1%) vs 26/2233 (1.2%) 

Seropositive at both pre- and post-deployment: 17 (2.4%) vs 54 

(2.4%); OR=0.97 (95% CI: 0.56 to 1.69)

Positive on pre-deployment and negative and post-deployment: 7 

(2.4%) vs 62 (2.8%); OR=0.85 (95% CI: 0.49 to 1.46)

There was no difference 

between rates of 

seroconversion between 

cases and controls, 

indicating that there is no 

association between GWI 

and M. fermentans 

infection.

Lack of comparability of 

cases and controls, power 

calculation, non-response 

rate not reported, lack of 

blinding of outcome 

assessors.

Squalene antibody 

status23

38 vs 12

% participants with positive ASA reactivity: 95% of those with 

GWI vs 0% of healthy controls

Squalene antibodies may 

be associated with GWI. 

Lack of comparability

between cases and 

controls; 

unrepresentativeness of

cases and controls.

Cytokines: IFN-γ, IL-6, IL8, IL-10, and TNF-α and 

CRP28

53 vs 27

Plasma cytokine concentrations below the lowest level of 

detection (LLOD): Plasma concentrations for 4 cytokines were 

below the LLOD and were excluded.

Included plasma cytokines: No difference in concentrations of 

IFN-γ, IL-6, IL-8, IL-10, and TNF-α, but trend toward significance in 

IL-6 (p=0.08)

No between group 

difference detected on any 

of the cytokines measured.

Power calculations not 

reported; no adjustments 

made for multiple 

comparisons.

Phospholipid species in 

plasma: PC, LPC, PE, 

LPE and PI24

22 vs 11

Phospholipid levels in GWI vs healthy GWI controls:

No differences in PC, PE, LPE, PI (p>0.05)

LPC was 15% greater for GWI (p=0.020)

Unsaturation of PL classes: no differences in degree of 

unsaturation for PE and PI

Multiple species of 

phospholipids were 

elevated in humans with 

GWI

Comparability of cases 

and controls; and nonresponse rate not 

reported
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

SFA containing PC species: reduced by 22% in GWI compared 

to controls (p=0.024)

LPC, SFA, MUFA, and PUFA containing species: elevated in 

GWI compared to controls by 16%, 15%, and 23% (p<0.05)

LPE, PUFA containing species: increase of 50% in GWI 

compared to controls (p<0.001)

SFA and MUFA containing LPE species: no difference between 

GWI and controls 

Ether lipids in plasma: no differences for ePC, eLPC, ePE 

between GWI and controls

eLPE: increased in GWI by 43% compared to controls (p<0.001)

AA- and DHA-containing phospholipid species: AA species 

within LPC and LPE were increased by 22% in GWI compared to 

controls (p=0.023) and 40% respectively (p=0.005)

Platelet count, IPF, TPO, 

CRP, platelet aggregation 

and ATP secretion26

43 vs 21

Platelet count, mean platelet volume, immature platelet 

fraction, C-reactive protein, and thrombopoietin:

mean platelet count and plasma CRP: 4.05 (4.58) vs 1.54 (1.32); 

p=0.020

Mean MPV, IPF and TPO platelet aggregation: no difference

Spontaneous aggregation: 7.6 (2.2) vs (6.1 (2.5); p=0.017

Aggregation responses of platelets with each agonist: no 

difference

Platelet ATP secretion (stimulated with TRAP 6): 15.46 (4.48) vs 

12.42 (2.84); p=0.011

GWI had elevated platelet 

counts, spontaneous 

aggregation, and TRAP 6-

induced secretion, but no 

impairment of platelet 

counts.

Multiple testing not 

adjusted for, nonresponse rate not 

reported

Plasma lymphocytes, 

monocytes and 

neutrophils29

57 vs 28

Hematological data:

Lymphocyte, monocyte, neutrophil, and platelet counts: higher in 

GWI 

Proteomic analysis:

plasma CRP, leptin, BDNF, and MMP-9: higher in GWI H-FABP 

and MMP-2: lower in blood of GWI 

Diagnostic model:

GWI diagnostic model using 3 biological measures (lymphocytes, 

monocytes, and CRP): c-statistic=0.77 (95% CI: 0.67 to 0.88; 

p=0.05)

Monocyte, and neutrophil 

and found that all were 

higher in those with GWI.

Lack of blinding of 

outcome assessors, lack 

of correction for multiple 

comparisons, no

adjustment for differences 

between cases and 

controls

Squalene antibody 

status30

Squalene antibodies Negative: 13/29 (44.8%) vs 71/146 (48.6%)

Squalene antibodies Positive: 75/146 (51.4%) vs 16/29 (55.2%)

Study did not find an 

association between 

Non-standard GWI case 

definition used, unclear 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

CMI

29 vs 146

No association between squalene antibody status and CMI

(p=0.465).

presence of squalene 

antibodies between those 

with GWI compared to 

health controls.

whether cases and 

controls were 

comparable, lack of 

reporting of biological 

measure outliers

Th1/Th2 balance by 

measuring intracellular 

production of IFN-γ , IL-2 

(Th1), IL-4 (Th2),

and IL-10 by CD4 T 

cells31

40 vs 80

Immune activation in nonstimulated CD4 cells:

Mean levels of nonstimulated, IL-4C and IL-2C cells: higher in GWI 

(for IL-4, P <0.05; for IL-2,P <0.01)

Memory cell cytokine balance: level of IL-10 producing CD4 cells 

higher in GWI (p<0.0001)

Non-stimulated cytokine-positive cells: higher levels of IFN-γ, and 

IL-2 in GWI

Polyclonally activated cytokine-positive CD4 cells: higher levels of 

IL-10+ cells for GWI

IL -4C, -2C and -10 

production by CD4 cells 

was elevated in those with 

GWI. 

Cases and controls not 

selected from the same 

populations, using a nonstandard definition of 

GWI, did not account for 

multiple testing bias

HLA alleles25

66 vs 16

Identified 144 HLA alleles in the sample, of which 6 Class II alleles 

yielded 84.1% correct classification as GWI vs control. All 6 allele 

frequencies were lower in GWI group (p=0.002) and all ORs < 1, 

ln(ω)=−1.792 ± 0.383 (mean ± SEM), t=−4.671, DF=5, p=0.005.

Negative relationship between overall symptom severity and 

number of allele copies (t=−4.148, DF=80, p=0.000083, R2=0.177)

6 HLA alleles correctly 

classified GWI vs control.

These alleles were 

significantly less frequent in 

GWI. The number of allele 

copies were significantly 

associated with symptom 

severity. Authors 

interpretation: reduced HLA 

protection (ie, genetic 

susceptibility) in Veterans

with GWI.

Non-standard case 

definition, sampling not 

population-based, lack of 

adjustment for multiple 

comparisons

Abbreviations: AA=Arachidonic Acid; ASA=Anti-Squalene Antibody; ATP=Adenosine Triphosphate; BDNF=Brain-derived Neurotrophic Factor; CI=Confidence 

Interval; CMI=Chronic Multisymptom Illness; CRP=C-reactive Protein; DF=Degrees of Freedom; DHA=Docosahexaenoic Acid; GWI=Gulf War Illness; HFABP=Heart-type Fatty Acid Binding Protein; HLA=Human Leukocyte Antigen; IFN=Interferon; IL=Interleuken; IPF=Immature Platelet Fraction; LLOD=Lowest Level 

of Detection; LPC=Lysophosphatidylcholines; LPE=Lysophosphatidylethanolamine; MEG=Magnetoencephalograph; MMP=Matrix Metalloproteinase; MPV=Mean 

Platelet Volume; MUFA=Monosaturated Fatty Acids; NCM=Neurocutaneous Melanocytosis; OR=Odds Ratio; PC=Phosphatidylcholine; PE=Phosphatidylethanolamine;

PL=Phospholipid; PI=Phosphatidylinositol; PUFA=Polyunsaturated Fatty Acids; SEM=Standard Error of the Mean; SFA=Saturated Fatty Acid; SPRC=Standardized 

Partial Regression Coefficient; TNF=Tumor Necrosis Factor; TPO=Plasma Thrombopoietin; TRAP=Thrombin Receptor Agonist Profile
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Central Nervous System Biological Measure Studies

Table 4 provides results of the 10 studies of central nervous system biological measures we 

identified. All but 1 of the studies measured brain activation, either with functional magnetic 

resonance imaging (fMRI), MRI, or electroencephalograph (EEG), during the presentation of 

specific stimuli/tasks. While there were 2 studies with an accompanying replication study each, 

the body of evidence was otherwise comprised of studies that employed varied stimuli/tasks.

Seven studies, 5 with cognitive/emotional tasks32,33,37-39 and 2 with physical challenges,

34,35

reported differences in brain activation between GWI and comparator groups, while only 2

studies, which involved cognitive/emotional tasks and were each replication/replicated studies, 

did not identify any group differences in brain activation.

36,40

In 4 of the 9 CNS studies, the participants engaged in a cognitive task: 1 semantic memory 

processing task study,

32 2 face-name association task studies (the second a replication of the first 

with a different sample and larger sample size),

33,36 and 1 response inhibition task study.37 In the 

first (N=47) of the 2 studies employing the face-name association task, no difference in brain 

activation was reported between the GWI and control groups,

36 while in the replication study, 

there was greater brain activation in the putamen of GWI Syndromes 1 and 2, each compared to 

controls (N=67; t=3.63; t=3.45, respectively).

33 Differences in brain activation between 

participants with GWI and controls were also reported in the study involving a semantic memory 

task, including differences in brain activation between GWI and controls in the thalamus and 

caudate head associated with participants correctly indicating a lack of association between 

words, and a difference in the relationships between reaction time to memory task stimuli and 

brain activation in GWI Syndrome 2 versus controls (N=38; p=0.02).

32 In the study involving a 

response inhibition task, brain activation (ERP P3 amplitude) was dampened in the GWI group 

versus controls (N=48; F(1, 46)=6.501, p=0.0142, η2=.095).

37

In another set of 3 studies, brain activation during the presentation of potentially 

threatening/emotionally triggering stimuli was recorded. In 2 of these studies (1 of them a 

replication study drawing from a larger, more representative pool of participants40) the stimuli 

were presented auditorily, and, in a third study, visually.39,40 There were differences in brain 

activation between GWI and control groups in the original study involving auditory stimuli, with 

differences in brain activation (EEG P1 amplitude and latency) between Syndromes 2 and 3 

compared to controls and Syndrome 1 (N=28; F(1, 23)=9.915, p=0.004; (F(1, 23)=22.025, 

p=0.0001)). There were also differences in EEG P3a amplitude between Syndromes 1 and 2 

versus controls and Syndrome 3 (N=28; F(1, 23)=11.172, p=0.003), and in EEG P3b amplitudes 

between GWI and controls (N=28; p=0.003),38 but there were not group differences in the 

replication study (N=62).

40 There were also group differences in brain activation in the study in 

which the visual version of the task was presented, with greater brain activity (P3b amplitude) in 

the control than GWI groups (N=30; p=0.0004).39

In 2 studies, the stimuli presented during the measurement of brain activation were innocuous 

and noxious heat,

34 and physostigmine (to provide an inhibitory cholinergic challenge),35

respectively. In the study involving innocuous and noxious heat, Syndromes 1 and 2 had greater 

brain activation to innocuous heat in ventral anterior cingulate (N=54; p<0.05) and less 

activation to innocuous heat in areas involving heat perception (N=54; p<0.05), but greater 

activation to noxious heat in brain areas involving pain, compared to controls. Syndrome 3 had 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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greater activation to innocuous heat compared to controls only in the dorsolateral prefrontal 

cortex.34 In the study involving a physostigmine challenge, change in cerebral blood flow with 

the challenge was significantly greater in controls than GWI groups (N=47; p=0.014).35 The 

remaining study measured the brain chemical N-acetylaspartate, and reported no difference in 

concentrations in the basal ganglia or pons between GWI and controls.41

The body of evidence was limited in that none of the studies used an optimal control group for 

identifying biological measures that differentiate GWI cases from similar conditions – namely, 

deployed GWVs with health conditions other than GWI. Further, the outcome assessor was often 

not reported to be blinded to group status,

32,36-40 participants in the CNS studies were often

selected from the same battalion,

32,34-36 decreasing the representativeness of the sample, and, 

when multiple comparisons were conducted, it was often not reported if statistical corrections 

were made.32-35,37,39

Also of note was the use of the Haley criteria for case definition in most of these CNS studies. 

Seven32-34,36,38-40 of the 9 studies identified cases with the Haley criteria, and another used both 

the CDC and the Haley criteria.35 The Haley criteria categorizes cases into 6 syndromes based on 

clusters of symptoms, as described above.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Table 4. Results of Gulf War Illness Central Nervous System Biological Measure Studies

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

Percent BOLD signal 

change (PSC)32

26 (syndromes 1, 2, 3: 

9, 10, 7) vs 12

Percent (BOLD) signal change (PSC): Thalamus PSC 

increase while correctly indicating no association between 2 

given words in Syndrome 2 GWI vs control. 

Caudate head PSC decrease while correctly indicating no 

association between 2 given words in GWI Syndrome 1 vs 

control. 

Positive association between reaction time and PSC in GWI 

Syndrome 2 which differed from that of controls (p=0.02). 

Thalamus PSC associated with an increase in reaction time in 

GWI Syndrome 2, trend toward statistical difference from all 

other groups (p<0.08)

Differences in a semantic 

memory processing task were 

associated with differences in 

BOLD PSC in the thalamus and 

caudate in GWI vs controls. The 

relationship between reaction 

time to task stimuli and brain 

activation also differed between 

GWI and controls.

Haley criteria case 

definition, nonrepresentative sample, 

small sample size, lack 

of blinding of outcome 

assessors

Regional brain 

activation33

57 (syndromes 1, 2, 3: 

19, 20, 18) vs 10

Regional brain activation (fMRI BOLD activity): Left insula

(putamen) activation greater in control vs Syndromes 1 and 2 

(t=3.63; t=3.45, respectively). No difference in activation in GWI 

Syndrome 3 vs control. 

Greater brain activation in areas 

of the brain associated with 

processes including sensation, 

perception, and emotion during a 

face-name associative memory 

task in GWI Syndromes 1 and 2 

vs controls.

Haley criteria case 

definition, small sample 

size

Brain activation36

33 vs 14

Brain activation (fMRI BOLD activity): No group differences. There were no demonstrated 

differences in brain activation, as 

measured with fMRI, between 

GWI and controls during a facename associative memory 

paradigm.

Haley criteria case 

definition, small sample 

size, lack of outcome 

assessor blinding

Event-related potential37

25 vs 23 

EEG N2 amplitude during behavioral inhibition task: No 

interaction between group and condition (F(1, 46)=2.062, 

p=0.1578). Trend toward a main effect of group (F(1, 46)=3.373, 

p=0.0727, p=0.0727)

EEG P3 amplitude during behavioral inhibition task:

Interaction between group and condition (F(1, 46)=6.569, 

p=0.0137, η2=.017) on P3 amplitude. P3 amplitude showed an 

effect of group (F(1, 46)=6.501, p=0.0142, η2=.095) amplitude, 

with the control group greater than GWI.

EEG P3 amplitude in GWI during 

a task requiring behavioral 

inhibition was dampened 

compared to in controls.

Non-standard case 

definition, small sample 

size, no blinding of 

outcome assessor
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

35

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

Event-related potential38

20 vs 8

EEG P1: Main effect of illness group on P1 amplitude (F(3, 

23)=3.509, MSerror=2.021, p=0.031), with only 1 of the contrasts 

significant, Syndromes 2 and 3 compared to controls and 

Syndrome 1 (F(1, 23)=9.915, p=0.004, p=0.004). 

Main effect of syndrome group on P1 latency (F(3, 23)=7.416, 

MSerror=115.830, p=0.001), with only 1 of the contrasts 

significant, Syndromes 2 and 3 had longer latencies vs controls 

and Syndrome 1 (F(1, 23)=22.025, p=0.0001). 

EEG P3: No interaction between distractor stimulus type and 

group, p>0.273.

Effect of syndrome group on P3a amplitude (F(3, 23)=4.700,

MSerror=1.188, p=0.011) with only 1 contrast significant, with 

controls and Syndrome 3 greater than Syndromes 1 and 2 (F(1, 

23)=11.172, p=0.003). 

There was no omnibus effect of GW Illness syndrome group on

P3a latency (F(3, 23)=1.775, MSerror=441.372, p=0.18). 

Effect of group on P3b amplitudes (p=0.006), with greater 

amplitude of the control group was greater than that of GWI 

p=0.003). 

No effect of group on P3b latency (p=0.148).

EEG P1 and P3a responses to 

auditory stimuli that were 

previously demonstrated to elicit 

hyperarousal responses in GWI 

differed between GWI and 

control groups.

Haley criteria case 

definition, small sample 

size, lack of blinding of 

outcome assessor

Event-related potential40

40 vs 22

EEG P1 amplitude: No difference between deployed and 

nondeployed controls in P1 amplitude (p=0.550) or latency 

(p=0.555); data were collapsed into 1 control group. Interaction 

between group and condition on P1 amplitude (p=0.014), but no 

significant contrasts including control group comparisons. 

EEG P1 latency: There was neither an effect of GWI syndrome 

group on P1 latency (F(3, 58)=0.748, MSE=275.714, p=0.528) 

nor an interaction between group and condition (F(9, 

174)=0.955, MSerror=75.297, p=0.479).

No differences between GWI 

and control groups in EEG

responses during a task 

involving the presentation of 

auditory stimuli previously 

demonstrated to elicit 

hyperarousal responses in GWI. 

Haley criteria case 

definition, small sample 

size, no reported blinding 

of outcome assessors

Event-related 

potentials39

22 vs 8

EEG P3 amplitude: No effect of group on P3a amplitude (F(3, 

26)=.339, p=0.7973). Significant effect of group on P3b 

amplitude (F(3, 26)=5.282, p=0.0056, η2=0.3787), with control 

group amplitude higher than ill groups (p=0.0004). 

EEG P3 latencies: Latencies of P3a and P3b did not differ 

significantly by group (p>0.27).

EEG P3b amplitude during a 

task involving the presentation of 

visual trauma-related stimuli was 

greater in control groups than in 

GWI.

Haley criteria case 

definition on, no reported 

blinding of outcome 

assessors, small sample 

size
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

Brain activation34

40 vs 14

Brain activation to innocuous heat: Syndromes 1 and 2 GWI 

greater (p<0.05) activation to innocuous heat than controls in 

ventral anterior cingulate and less activation than controls in 

areas involving heat perception (p<0.05). Syndrome 3 greater 

activation than controls in left DLPFC, but no other areas. 

Brain activation to noxious heat: Syndromes 1-2 hyperactivation (p<0.05) compared to controls in regions involving 

pain. Syndromes 1-3 GWI significant (p<0.0001) activation to 

noxious heat in similar areas as controls. Syndrome 1 higher 

noxious heat activation than controls in left amygdala,

arahippocampal gyrus, thalamus, and right basal ganglia. 

Syndrome 3 activation was not different from controls (p>0.05).

GWI Syndromes 1 and 2 had 

greater brain activation to 

innocuous heat in the ventral 

anterior cingulate than controls, 

and less activation to innocuous 

heat in brain areas involving heat 

perception, but greater activation 

to noxious heat in brain areas 

involving pain. Syndrome 3 had 

greater activation to innocuous 

heat than controls in only the left 

DLPFC, and no different 

activation to noxious heat than 

controls.

Haley criteria case 

definition on, nonrepresentative sample, 

insufficient sample size, 

incomparability of cases 

and controls

Cerebral blood flow35

33 vs 14

Cerebral blood flow with physostigmine challenge: GWI 

(Syndrome groups combined) and controls CBF change

significantly greater in control (p=0.014). 

Change in cerebral blood flow 

with an inhibitory cholinergic 

challenge significantly greater in 

control than GWI groups,

indicating persistent cholinergic 

deficits in GWI.

Non-representative 

sample, small sample 

size

Quantities of Nacetylaspartate in the 

basal ganglia and 

pons41

81 vs 97

No difference in PON1 genotype or paraoxonase activity 

between controls and GWI.

No group differences in N-acetylaspartate concentrations in the 

left basal ganglia and pons.

No differences in concentrations 

of N-acetylaspartate were found 

in the basal ganglia or pons of 

GWI vs controls.

Multiple comparisons 

without correction.

Abbreviations: BOLD=Blood-Oxygen-Level-Dependent; CBF=Cerebral Blood Flow; DLPFC=Dorsolateral Prefrontal Cortex; EEG=Electroencephalogram; GWI=Gulf 

War Illness; fMRI=functional Magnetic Resonance Imaging; PSC=Percent Signal Change
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Autonomic Nervous System

Table 5 provides results of the 5 studies examining various autonomic nervous system biological 

measures, including cardiovascular measures (using an electrocardiogram [ECG] or tilt-table 

test)42-46 and nervous system measures (using nervous conduction/reflex testing).44,45 There were 

biological measures in each of the studies that differed significantly between cases and controls.

Three studies examined primarily cardiovascular biological measures. In 1 study (N=184),

42

measurement of heart rate with an ECG over 24 hours indicated increased randomness of beatto-beat heart rate changes (measured by the short-term fractal scaling exponent [DFA1]) in the 

CMI group versus control group (1.28±0.16 versus 1.35±0.15; p=0.005). In the second study 

focused on cardiovascular measurements, (N=27)43 maximum heart rate variation (p<0.05) and 

maximum heart rate response to tilt (p<0.05) in a tilt test plus isoproterenol was greater in cases 

than controls. A study examining cardiorespiratory and metabolic responses to maximal exercise 

test found no difference in exercise capacity between GWVs with Chronic Fatigue Syndrome 

(CFS) and healthy GWVs controls.

46

Two studies examined both cardiovascular and nervous system biological measures. In a 2013 

(N=97) study,44 24-hour heart rate variability (HRV) using an ECG was measured in 16 healthy 

Veteran controls and 3 other groups stratified by symptom domains (n=66): Syndrome 1 

(impaired cognition, n=21); Syndrome 2 (confusion/ataxia, n=24); and Syndrome 3 (central 

neuropathic pain, n=21). High frequency HRV increased normally at night in control group, but 

not in syndrome groups (p<0.001). The syndrome groups had reduced distal postganglionic 

sudomotor function compared to controls. In the quantitative sensory tests, the confusion/ataxia 

group (Syndrome 2) had increased cooling detection threshold versus controls (p<0.05). A 2014 

cross-sectional study (N=28)45 examined changes in blood pressure and heart rate (using a tilttable test), nerve conduction, sensory testing, and sudomotor axon reflex testing in self-reported 

post-exertional fatigue (PEF, also known as post-exertional malaise [PEM]) versus controls. 

There was no significant difference in blood pressure, but there was in supine (p=0.003) and 

standing (p<0.001) heart rate between groups at baseline. There were no significant differences 

in thermal or vibration threshold testing on hands and feet, or large fiber nerve variables on sural 

sensory and peroneal motor nerves.

Only 242,46 of the 5 studies used a “gold standard” definition for GWI/CMI/CFS (CDC or 

Kansas), with the others using ICD-9 codes,

43 self-report45 for post-exertional fatigue, or another 

validated (Haley) definition/characterization of GWI44 as criteria for inclusion into the study. 

The sample sizes were relatively small: 2 studies had fewer than 30 total participants,43,45 1 had 

fewer than 100,44 and 1 had fewer than 200 Veterans.

42
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Table 5. Results of Gulf War Illness Autonomic Nervous System Biological Measure Studies

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

24-hr HRV, urinary 

catecholamines and cortisol, 

hypertension, insulin 

sensitivity, dyslipidemia, body 

fat, bone mineral density, and 

ultrasensitive CRP42

184 (n=73 with CMI) vs 111

Adjusted Least Squares Mean difference 

(95% CI):

HR: 1.85 (-0.76 to 4.45). p=0.16

SDNN (SD of the N-N interval): -6.64 (-

16.64 to 3.35). p=0.19

SDNN index: -0.07 (-0.16 to 0.02). p=0.14

VLF: -0.15 (-0.32 to 0.03). p=0.10

LF: -0.15 (-0.36 to 0.06). p=0.16

DFA1 (short-term fractal scaling 

exponent): -0.03 (-0.07 to 0.01). p=0.03

HPA measures: Plasma and 24-hour urinary 

cortisol levels following overnight 

dexamethasone suppression testing did not 

differ between groups.

Values for a nonlinear heart-ratevariability parameter (the short-term 

fractal scaling exponent [DFA1]) 

were lower in cases than controls, 

but there were no group differences 

in HPA measures (cortisol levels 

following dexamethasone 

suppression testing).

Sample not representative of 

whole GW population, distribution 

of biological measure data not 

reported, there were significant 

descriptive differences that were 

not adjusted for, enrollment nonresponse rate not reported, 

unclear if outcome assessor was 

blinded.

Heart rate, blood pressure43

14 vs 13

Positive tilt response: not different between 

the 3 groups (p=0.098). 

70-degree head-up tilt: Maximum heart rate 

response to tilt was higher in the fatigued 

GWVs than in each of the control groups 

(p<0.05).

Maximum HRV was highest in the GWVs 

fatigued group (p<0.05).

No group difference in fall in systolic blood 

pressure with tilt.

Maximum heart rate response and 

variation to 70-degree tilt was higher 

in GWVs with fatigue symptoms than 

in controls.

Non-standard definition for GWI, 

sample not representative of 

whole GW population, sample 

size/power calculation not 

provided, distribution of biological 

measure data not reported, there 

were significant descriptive 

differences that were not adjusted 

for, enrollment non-response rate 

not reported.

Nerve conduction, sensory 

testing, sudomotor axon 

reflex testing45

16 vs 12

Cardiovascular measures: 

BP: no differences at supine, active standing, 

or 70 degrees tilt.

HR: greater in GWI group at 5 min supine 

(P<0.001) and 3 min standing (p=0.003) 

Absolute heart rate increment (by HUT): no 

difference

HRV to Valsalva maneuver and HRV deep 

breathing: no difference

Greater supine and standing heart 

rates in cases versus controls, but 

no differences in neurological 

measures (large fiber nerve 

variables, or in thermal or vibration 

thresholds on hands and feet).

Definition for GWI other than 

CDC/Kansas used, sample not 

representative of whole GW 

population, sample size/power 

calculation not provided, there 

were significant descriptive 

differences that were not adjusted 

for, distribution of biological 

measure data not reported,

enrollment non-response rate not 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

39

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

Large and small fiber nerve variables:

No group differences for large fiber nerve 

variables measured on sural sensory

(amplitude: p=0.598, latency: p=0.235) and 

peroneal (amplitude: p=0.178; latency: 

p=0.462) motor nerves. 

No difference between groups in averaged 

sweat volume from the feet. 

Quantitative sensory testing:

No group difference for thermal (hands [left, 

right]: p=0.625, p=0.582; feet: p=0.491, 

p=0.925) and vibration (hands [left, right]: 

p=0.076, p=0.402; feet: p=0.500, p=0.031) 

threshold on hands and feet.

reported, unclear if outcome 

assessor was blinded.

CASS, and high-frequency 

heart rate variability from a 

24-hour ECG44

66 (syndromes 1, 2, 3: 21, 24, 

21) vs 16

CASS: varied across groups (p=0.45) and 

was higher in syndrome 2 than controls 

(p=0.02). Syndrome groups had reduced 

distal postganglionic sudomotor function in 

the foot (p=0.02), ankle, and upper leg, but 

not in the arm, compared to controls. No 

group differences in tear production, 

sympathetic adrenergic function, or pupillary 

measures. 

Quantitative Sensory Tests: Syndrome 2 

increased cooling detection threshold vs 

controls (p<0.05). No group difference on 

heat pain threshold. 

Circadian variation in parasympathetic 

tone: High frequency HRV increased 

normally at night in control group, but not 

syndrome groups. 

High frequency HRV during the day for 

syndrome 1 not different from controls, but 

syndrome 2 significantly lower, and syndrome 

3 significantly higher.

GWI groups had neurological 

differences from controls, with 

reduced distal postganglionic 

sudomotor function in the foot, and 

increased cooling detection 

threshold in syndrome 2, as well as 

cardiovascular differences from 

controls, with abnormal HRV at 

night, and lower and higher high 

frequency HRV during the day for 

syndromes 2 and 3, respectively.

Definition for GWI other than 

CDC/Kansas used, sample 

size/power calculation not 

provided, distribution of biological 

measure data not reported, 

enrollment non-response rate not 

reported. 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

Cardiorespiratory and 

metabolic responses to 

maximal exercise test46

19 v 19

Maximal oxygen uptake: 28.9 (6.7) mL/kg/min 

vs 30.8 (7.1) mL/kg/min; p=0.39

Heart rate: 155.8 (16.1) bpm vs 163.3 (14.9) 

bpm; p=0.17

Exercise time: 9.6 (1.5) minutes vs 10.2 (1.4) 

minutes; p=0.26

Workload achieved: 208 (36.7) W for CFS vs 

224 +/- 42.9 W for controls; p=0.25

Submaximal intensities: ND (p > 0.05)

*W=workload

No difference in exercise capacity 

between CFS and healthy controls.

Small, non-representative sample, 

data modeling not addressed, 

multiple comparisons without 

adjustment, response rate not 

reported.

Abbreviations: BP=Blood Pressure; BPM=Beats Per Minute; CASS=Composite Autonomic Severity Score; CDC=Centers for Disease Control and Prevention; 

CFS=Chronic Fatigue Syndrome; CI=Confidence Interval; CMI=Chronic Multisymptom Illness; CPTh=Cold Pressor Threshold; CRP=C-reactive Protein; 

ECG=Electrocardiogram; GI=Gastrointestinal; GW=Gulf War; GWI=Gulf War Illness; GWV=Gulf War Veterans; HPA=Hypothalamic-Pituitary-Adrenal Axis; 

HPTh=Heat Pain Threshold; HR=Heart Rate; HRV=Heart Rate Variability; HUT=Head-Up Tilt; LF=Low Frequency; ND=No Difference; SD=Standard Deviation; 

VLF=Very Low Frequency
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Genetic Biological Measures

Table 6 provides results of the 1 study examining associations between genetic measures and 

GWI. This study (N=190) of paraoxonase (PON1) genotypes and levels of PON1 activity in 

serum detected slightly lower levels of PON1 activity among Veterans with GWI, though the 

difference was not statistically significant (median difference 23.1; 95% CI=-27.7 to 73.9).47

This study examined genotype variants resulting from amino-acid substitutions at positions 55 

and 192, which are associated with different levels of enzymatic activity on various substrates.

No difference in the distribution of PON1-192 genotypes was observed (p=0.52). The L to M 

substitution of PON1-55 occurred more frequently among healthy GWVs (61.9%) compared 

with Veterans with GWI (41.2%), and the difference was statistically significant (p=0.02).

Statistical correction for multiple comparisons was not performed, and the applicability of this 

study may be further limited by the use of a non-standard case definition for GWI. Nonpopulation-based sampling in this study limits the interpretation of findings. While there was a 

genetic component in some of the other studies represented in other biological system sections,

studies were categorized in the genetic section if the genetic measures were the emphasis of the 

study. For example, a study of HLA alleles is included with the previous section on immune 

system biological measures.

25
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

42

Table 6. Results of Gulf War Illness Genetic Biological Measure Studies

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Results

GWI vs control Summary of Findings Study Limitations

PON1 activity and 

genotype47

95 vs 95/85

PON1 activity, median (range):

145.85 (48.3–423) vs 168.92 (59.3–521); p=0.03

GWI had slightly lower PON1 activity, but not statistically 

significant: Mean Difference=23.1 (95% CI -27.7 to 73.9) 

High-Density Lipoprotein (HDL): lower in GWI; Mean 

Difference=0.14 (95% CI: 0.04 to 0.25) 

PON1 genotype: 

No difference in the distribution of PON1-192 genotype 

(p=0.52).

Difference between ill and well groups in PON1-55 

genotype (p=0.02), with a higher proportion of D-GWVs 

healthy with LM genotype compared with GWI.

No statistically significant 

differences in PON1 activity 

between GWI and GWVs healthy.

Non-standard case definition 

(SF-36) and multiple 

comparisons. 

Abbreviations: CI=Confidence Interval; D-GWV=Deployed Gulf War Veteran; GWI=Gulf War Illness; GWV=Gulf War Veteran; HDL=High Density Lipoprotein;

PON1=Paraoxonase; SF-36=Short Form-36 Question Questionnaire 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Other Biological Systems

We identified 6 studies of other biological measures of GWI not categorized under the preceding 

biological systems. These included: 1 study of sleep parameters,48 1 study examining 

cholinesterase and serum cytokines,21 1 of neurophysiologic markers,50 a study of 

audiovestibular function,49 1 study of pain tolerance, 1 study of human herpes viruses (HHVs),

51

and 1 study of temperature and pain thresholds.52 Table 7 contains these study details.

Three studies found significant differences in biological measures assessed between GWVs with 

GWI and controls. In a study of sleep parameters and sleep-related respiratory parameters in

GWVs with GWI versus healthy GWVs, sleep parameters did not differ, but sleep-disordered 

breathing (measured by sleep respiratory parameters: Apnea-Hypopnea Index [AHI], Respiratory 

Effort Related Arousal [RERA] index, and percent flow-limited breaths) was greater in those 

with GWI: RERA index (p=0.018); AHI (p=0.006); percent flow-limited breaths (p<0.0001).48

There were also significant differences between GWI and controls within all audiovestibular 

testing domains in a study of audiovestibular function in GWVs with the 3 Haley-defined 

syndromes and healthy GWVs controls: greater GWI interocular asymmetry of gain in rotational 

nystagmus; diminished GWI nystagmic velocity after caloric stimulation; greater GWI interaural 

asymmetry of caloric response; greater GWI saccadic velocity; more frequent GWI pathologic 

nystagmus; greater GWI interpeak latency differences between ears; lower GWI response 

strength from right component of platform posturography.49 One study examined only 

differences in nervous system functioning, namely pain tolerance and threshold. A 2018 study 

(N=91)52 examined hot, cold, and ischemic pain tolerances and thresholds in GWVs with 

“documented” (no definition specified) GWI and gastrointestinal (GI) symptoms (n=53), GWVs 

with “documented” GWI and no GI symptoms (n=47), and GWVs without GWI or GI symptoms 

(n=38). Veterans with GWI and GI symptoms showed lower pain thresholds for each stimulus 

(p<0.001) compared with controls, and 20% of Veterans with GWI and GI symptom showed 

hypersensitivity to all 3 stimuli.

In the remaining 4 studies, there were no differences in biological measures assessed between 

GWVs with GWI and controls. Notably, 1 of these studies used our ideal comparator group –

deployed GWVs with a non-GWI health condition (in this case, posttraumatic stress disorder 

[PTSD]). Compared to GWVs with PTSD (but no GWI) and 4 healthy GWVs, the GWVs with 

GWI (“neurologic factor”) did not have significantly different activity in the “organophosphate 

detoxifying” enzymes (Butyrylchoninesterase [BuChE] and PON1), that, according to the 

authors, suggests that organophosphate exposure is not associated with neurological symptoms 

of GWI.

21 In the study of neurophysiological markers, GWVs reporting neuromuscular 

symptoms had no quantitative evidence of neurological disorders and were not significantly 

different from healthy GWVs.

50 Finally, in a study comparing prevalence of HHVs in GWVs 

with CFS versus healthy GWVs, there was no difference in prevalence of HHV7 between 

groups.51

All 6 studies suffered from methodological issues. Notably, they had small sample sizes that did 

not provide sufficient power to detect differences or power calculations were not described, 

insufficient or no description of data modeling such as normality of data and outlier detection, 

nor adjustment for multiple comparisons. Additional issues in some studies included unequal or 

unreported non-response rates and outcome assessor blinding not used or reported. For these 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

44

reasons, and due to the fact that there were no 2 studies addressing the same biological system 

and/or marker, these findings do not provide sufficient evidence for GWI association, or lack 

thereof, with any of these biological measures.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

45

Table 7. Results of Gulf War Illness Other Biological Systems Biological Measure Studies

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Findings

GWI vs control Summary of Findings Study Limitations

Sleep parameters and

sleep-related respiratory 

parameters48

18 v 11

Sleep parameters:

Total sleep time: 333 (95) min vs 329 (75) min; p=0.90 

Sleep efficiency: 73% (24%) vs 76% (16%); p=0.66

Sleep latency: 13 (15) minutes vs 28 (30); p=0.08

REM latency: 137 (69) minutes vs 136 (65) minutes; p=0.97

NREM 1: 29% (17%) vs 22% (14%); p=0.26

NREM 2: 48% (13%) vs 46% (11%); p=0.74

Slow wave sleep: 12% (9%) vs 1%7 (11); p=0.17

REM: 12% (7%) vs 11% (7%); p=0.73

Arousals/hour: 34 (26) vs 10 (6); p=0.006

Total stage shifts: 39 (12) vs 40 (14); p=0.89

Sleep-related respiratory parameters:

RERA index: 16 (12) vs 6 (4); p=0.018

AHI: 18 (25) vs 3 (5); p=0.006

% flow-limited: 96 (5) vs 36 (25); p<0.0001

Significantly greater sleepdisordered breathing in 

Veterans with GWI: RERA 

index (p=0.018); AHI 

(p=0.006); % flow-limited 

(p<0.0001). No difference 

in sleep parameters 

except arousals/hour 

(p=0.006).

Small, non-representative 

sample, data modeling not 

adequately addressed, 

multiple comparisons 

without adjustment, 

response rate not 

reported.

Integrity of auditory 

pathways (inner ear 

through upper brain stem 

and vestibulocular reflex)49

Syndrome 1 vs 2 vs 3 vs 

control: 5 vs 13 vs 5 vs 10

Sinusoidal harmonic acceleration: Greater interocular asymmetry of gain 

in rotational nystagmus in GWI vs controls. 

Asymmetry values of 0.01, 0.02, and 0.04 Hz, differed from controls for 

Syndrome 1 (p=0.015), Syndrome 2 (p=0.002), but not for Syndrome 3 

(p=0.8). In controls, the magnitude of asymmetry decreased monotonically, 

but not in any GWI groups. 

Nystagmic velocity after caloric stimulation: diminished in Syndrome 3 

vs controls for all 4 irrigations (cool right, p=0.02; cool left, p=0.004; warm 

right, p=0.009; warm left, p=0.004). Interaural asymmetry of caloric 

response greater in Syndrome 2 than controls (p=0.07). 

Asymmetry of saccadic velocity: greater in Syndrome 2 than controls 

(p<0.05)

Nystagmus: pathologic nystagmus in 4 ill Veterans, but none of the 

controls (p=0.09)

Inter-side asymmetry of wave I to III interpeak latency on auditory 

brain stem response: wave 1 to 3 interpeak latency differences between 

ears greater in cases than controls (p=0.02) - in Syndromes 1 (p=0.005) 

and 2 (p=0.07), but not 3. Similar number of cases and controls with 

unilateral latencies or amplitudes exceeding normal limits. 

Platform posturography: Syndrome 3 lower response strength from right 

and left forward components of the platform than controls (p=0.10)

No difference between GWI and controls in somatosensory, visual, and 

visual preference ratios

Within all audiovestibular 

testing domains, there 

were differences between 

GWI and controls.

Haley criteria for case 

definition, nonrepresentative sample, 

inclusion/exclusion not 

equally applied across 

groups, small sample 

size, blinding of outcome 

assessor not reported. 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

46

Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Findings

GWI vs control Summary of Findings Study Limitations

Cholinesterase;

Serum cytokines21

25 vs 14 vs 4

Serum enzyme activity: 

PON1: 577 (81) vs 479 (107) vs 518 (248) μmol/mL/min (no differences)

Arylesterase: 111 (3) vs 102 (7) vs 116 (8) μmol/mL/min (no differences)

Serum BuChE activity: 0.63 (0.03) vs 0.64 (0.04) vs 0.65 (0.07) 

μM/mL/min (no differences)

Serum cytokines (Th1, Th2, and proinflammatory): both GWI and PTSD 

groups had serum levels ≥20% higher than highest level expressed by 

controls

“Neurologic factor” in 

GWVs not correlated with 

low activity of the 

enzymes BuChE and 

PON1

Not standard case 

definition, small sample 

size (+ very small control 

group), comparability of 

cases and controls not 

established, non-response 

rate and blinding NR

Neurophysiologic 

assessment: nerve 

conduction studies; 

quantitative sensory and 

autonomic function 

testing; concentric needle 

and single-fiber EMG50

49 vs 26

Nerve conduction studies: No evidence for axonal or demyelinating 

peripheral neuropathy. Comparisons of motor conduction measurements 

from median, ulnar, and common peroneal nerves showed no major 

peripheral nerve abnormalities. No differences. P-values NR throughout.

Quantitative sensory and autonomic assessments: No specific 

abnormalities in GWI group. No differences in vibration perception, warm 

and cool sensory thresholds; thermoregulatory function in face and limbs, or 

cardiovascular reflexes.

Concentric needle EMG: Combined mean polyphasic units in the 4 

muscles examined: 6.1% vs 7.7%

Duration or amplitude of MUAPs: ND

Turns analysis: 240±96 vs 299±82 turns/s (ND, p-value NR); No difference 

in ratio of number of turns/second to mean amplitude

Single-fiber EMG: no difference in MCD or fiber density values

Results for GWI similar to 

controls: Peripheral 

nervous system 

functional; no chronic 

denervation or myopathic

abnormalities; no impulse 

blocking. 

No evidence of peripheral 

neurological disorders

Not standard case 

definition; small sample 

size; unequal nonresponse rate

HHV6, HHV7, EBV, and 

cytomegalovirus51

46 vs 32

Prevalence of:

HHV6 DNA: Detected in 1 control pt.

HHV7 DNA: 22/46 (47.8%) vs 14/32 (43.8%); p=0.82

EBV DNA: Detected in 1 CFS pt.

HCMV DNA: None detected

No difference in HHV7 

infection rates

No description of 

sampling strategy, power 

NR, data modeling NR 

and no adjustment for 

multiple comparisons, 

non-response rate NR

Heat pain threshold, cold 

pressor pain threshold; 

ischemic pain threshold 

and ischemic pain 

tolerance52

100 (GWI+GI symptoms) 

vs 38 healthy

Heat Pain Threshold (HPTh):

GWI+GI had lower HPTh compared with GWI no GI symptoms (p<0.01) 

and controls (p<0.001). No significant differences between controls and 

GWI no GI symptoms. 

Cold Pressor Test (Cold Pressor Threshold [CPTh]): GWI+GI had lower 

CPTh compared with GWI-no GI symptoms (p<0.01) and vet controls 

(p<0.001). No differences between control and GWI-no GI symptoms. 

Ischemic Pain Threshold and Ischemic Tolerance Test: GWI+GI had 

shorter time to ischemic pain threshold (p<0.001) than controls. GWI-no GI 

symptoms also had significantly shorter time to ischemic pain threshold 

(p<0.01) compared to controls. GWI+GI had lower ischemic pain tolerance 

Veterans with GWI and GI 

symptoms showed lower 

pain thresholds to heat, 

cold, and tourniquet tests 

(p<0.001) compared with 

controls, and 20% of the 

GWI+GI vets showed 

hypersensitivity to all 3 

stimuli.

Definition for GWI other 

than CDC/Kansas used, 

sample not representative 

of whole GW population, 

sample size/power 

calculation not provided, 

distribution of biological 

measure data not 

reported, did not adjust for 

multiple comparisons, 

enrollment non-response 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Biological Measure/

Outcome Measure 

n GWI vs n comparator

Detailed Findings

GWI vs control Summary of Findings Study Limitations

compared to controls (p<0.001). GWI-no GI symptoms also had significantly 

shorter time to tolerance compared to controls (p<0.01).

rate not reported, unclear 

if outcome assessor was

blinded.

Abbreviations: AHI=Apnea-Hypopnea Index; BPM=Beats Per Minute; BuChE= Butyrylchoninesterase; CFS=Chronic Fatigue Syndrome; DNA=Deoxyribonucleic Acid; 

EBV=Epstein-Barr Virus; EMG=Electromyography; GWI=Gulf War Illness; GWV=Gulf War Veteran; HCMV=Human Cytomegalovirus; HHV=Human Herpesvirus; 

MCD=Mean Consecutive Difference; MUAP=Motor Unit Action Potential; ND=No Difference; NR=Not Reported; NREM=Non-Rapid Eye Movement; PON1=Paraoxanase; 

PTSD=Posttraumatic Stress Disorder; REM=Rapid Eye Movement; RERA=Respiratory Effort Related Arousal
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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KEY QUESTION 3: Which ongoing or unpublished research studies 

examine diagnostic tests or biological measures for potential 

association with GWI?

Table 8 provides results of the 24 ongoing research studies examining a wide variety of potential 

biological measures for GWI. Five studies are looking at how GWI impacts the central nervous 

system in GWVs.

53-57 Three studies58-60 plan to look at genetic components associated with GWI

(including 1 using data from the Million Veterans Program59). Five studies are examining the 

immune system.61-65 One study will look at autonomic function testing in Veterans with GWI,

66

and 10 studies are examining biological measures in other biological systems.

67-76

Of those studies proposing to examine measurements within other biological systems, 3 include 

measures of mitochondrial dysfunction67-69 and 2 of the gut microbiome.

70,71 The 3 studies that 

include measures of mitochondrial dysfunction propose to examine mitochondrial and 

bioenergetic impairments,

68 peripheral mononuclear cells (PBMCs) to determine mitochondrial 

function,

67 and mitochondria and peroxisome function and lipids specific to inflammation.69

Studies of gut microbiomes will look generally at differences in gut microbiomes between 

GWVs with and without GWI,70 and at microbes of the small intestine.

71

The remaining studies will examine plasma proteomics;

72 plasma metabolomes;73 serum 

analytes;

74 associations between adrenal, immune, inflammatory and coagulation in Veterans

with GWI;

75 and XMRV, a retrovirus.76

For all studies with control group information available, the comparator is healthy GWVs. No 

study uses our ideal comparator of ill GWVs without GWI.

Of note, the VA Cooperative Studies Program (CSP) 585,77 also known as The Gulf War Era 

Cohort and Biorepository, has enrolled 1,275 Veterans. These Veterans have submitted to VA 

researchers blood samples to analyze for research related to health conditions and other related 

factors. While we were unable to identify published studies using this particular biorepository, 

we did identify 1 study using data from VA CSP 2006, which analyzes the genomics of Gulf 

War Illness.59
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Table 8. Ongoing Studies of Biological Measures for Gulf War Illness

Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

CENTRAL NERVOUS SYSTEM

Killiany, R.56

Cross-sectional

W81XWH-19-1-0765

CDMRP GWIRP

Boston University Medical 

Campus

Chronic inflammation 20 GWVs: 10 GWI 

and 10 healthy 

controls

Case definition: Kansas

Participants will be 

recruited from the 

BBRAIN for GWI

Astrocyte and microglial activation using PET

Peskind, E. R.53

Study design: NR 

Project number: 

1I01CX001049-01

NIH

VA Puget Sound Health 

Care System

Started: Jan 1, 2014

Anticipated completion: 

Dec 31, 2017

Brain metabolism, 

neuronal damage; 

and abnormalities in 

central and peripheral 

systems regulating 

pain perception, 

fatigue, and sleep

NR Case definition: NR

Criteria not specified

• Cerebral glucose metabolism in brain regions 

relevant to cognition (eg, medial temporal lobes) 

using fluorodeoxyglucose-PET

• Structural and compositional structural integrity 

using MRI, diffusion MRI and MPF mapping

• Brain regional connectivity among nodes of the 

ventral and dorsal attention networks on blood 

oxygen level dependent functional connectivity 

MRI

• CSF biological measures associated with 

neurodegeneration (decreased Ab42, increased 

CSF total tau and phosphorylated tau (ptau181) 

and oxidative damage (increased F2-

isoprostanes), and decreases in the neurotrophin, 

brain-derived neurotrophic factor

• Pain sensitivity by Quantitative Sensitivity Testing 

and impaired activation of endogenous opioids in 

response to Conditioned Pain Modulation

• Abnormalities in neuropeptides, neurotransmitters, 

hormones, and immune factors associated with 

pain and fatigue perception and sleep. 

• Cerebral glucose metabolism in brain regions 

modulating sensory pain (eg, thalamus).

• Frequency of the apolipoprotein E (APOE)-e4 - 

allele, the microtubule associated protein tau 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

(MAPT) H1 haplotype, the Met allele of the brain 

derived neurotrophic factor Val66Met variant, the 

Val allele of the catechol-O-methyl transferase 

Val158Met variant, the G allele of the mu opioid 

receptor 1 A118G single nucleotide polymorphism 

(ie, rs1799971), the Arg allele of the PON1 

Gln192Arg variant, and decreased functional 

activity of PON1. 

• DNA methylation levels in CpG Islands in the 

PON1, APOE, MAPT, and BDNF genes.

Steele, L.55

Case-control

W81XWH-14-1-0622

CDMRP

Baylor College of 

Medicine

Data collection was 

scheduled to start in 2016

Nigrostriatal pathway; 

brainstem and basal 

ganglia integrity

80 GWVs with GWI; 

50 healthy GWVs 

controls

Case definition: NR

“Well-characterized” 

sample of 1990-91 

GWVs

Corticostriatal circuit using high-resolution DTI

Sullivan, K.54

Longitudinal analysis

W81XWH-19-1-0767

CDMRP GWIRP

Boston University Medical

Brain health (all major 

aspects)

N=100, GW 

Veterans 50 cases 

and 50 controls

Case definition: Kansas

GWVs with GWI

And healthy GWVs

recruited from BBRAIN

CBF patterns, BBB permeability, and WM 

microstructural integrity using DKI and HARDI

Younger, J. W.57

Cross-sectional

W81XWH-19-1-0725

CDMRP GWIRP

University of Alabama, 

Birmingham

Neuroinflammation 40 GWVs 

(deployed): 20 GWI 

vs

20 healthy controls 

Case definition: Kansas

Recruited from existing 

database of individuals 

interested in research

Whole-brain MRSI scan metabolite concentrations: 

myoinositol, lactate, choline, NAA, and absolute 

brain temperature

GENETIC

Haley, R.58

Cross-sectional

W81XWH-15-1-0672

Whole genome geneexpression140 GW-era 

Veterans: 4 clinical 

groups (the 3 GWI 

Case definition: CDC 

definition of MSI, Factor 

Level of gene expression of the messenger RNAs 

and micro-RNAs in pure suspensions of T 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

CDMRP GWIRP

University of Texas, 

Southwestern Medical 

Center at Dallas

variants of the 

Factor case 

definition and a 

control group)

case definition subset 

(Haley definition) 

From "samples who 

participated in our prior 

studies"

lymphocytes after stimulating aliquots with LPS or 

ACh

Malanoski, A. P.60

Case-control

CDMRPL-17-0-

GW160096

CDMRP GWIRP

Naval Research 

Laboratory

Epigenetics 125 GW Veterans, 

75 GWI cases, 50 

controls

Case definition: 

Kansas, CDC case 

criteria obtained for 

comparison purposes

GWVs with GWI or 

healthy; participants of 

Boston GWIC 

biorepository

DNA methylation, micro RNA expression

Provencale, D.59

Nested case-control

VA CSP #2006

VA MVP

Genome-wide 

association study

7,500 GWI case 

participants; 7,500 

controls GWVs 

(estimated)

Case definition: NR

1990-1991 GWVs (both 

deployed and 

nondeployed) who 

participated in the MVP

Potential genetic risk factors for GWI utilizing SNP 

genotyping

IMMUNE SYSTEM

Abou Donia, M. B.61

Cross-sectional + followup after acupuncture 

treatment

W81XWH-19-1-0465

CDMRP GWIRP

Duke University

Plasma/serum 

autoantibodies; 

Neural cells

150 GWVs: 100 with 

GWI (50 with CFS 

and 50 without) and 

50 healthy GWVs 

Case definition: NR

NR

IgG-class autoantibodies for CNS markers in the 

saliva, serum, and plasma compared to correlate 

brain volumetric and microstructural alterations on 

brain imaging

James, L.62

Cross-sectional 

W81XWH-17-1-0677

USAMRDC 

Genes and biological 

measures of immune 

system dysfunction 

Large sample of 

GWVs with and 

without GWI: 

randomly selected 

from a DoD list of 

Case definition: NR

NR

Genes associated with immune system functioning, 

and biological measures of immune system 

dysfunction, inflammation, and autoimmunity
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

University of Minnesota, 

Twin Cities

Recruiting as of Oct 2018

66,229 people in MN

who served GW 

from August 2, 

1990-April 11, 1991

Klimas, N.63

Double-blind RCT

NCT02848417

SFVAFRE, Miami

Recruiting as of Mar 2020

Expected: Aug 2020

Intervention study 

(nutraceuticals) – 

homeostatic network 

biological measure

response to treatment

75 Veterans with 

GWI

Case definition: NR

Inclusion Criteria:

• Veterans with GWI

• 35-70 years old

• Good health by 

medical history prior to 

1990

• No diagnoses 

exclusionary for GWI

1. Biological measure response to therapy using a 

VO2 exercise test

2. Biological measure response to therapy using 

cytokine panel

Monson, N.64

Nested case-control (4 

groups)

W81XWH-17-1-0586

CDMRP GWIRP

University of Texas, 

Southwestern Medical 

Center, Dallas

Molecular blood 

biological measures 

Development 

sample (n=142): 96 

cases + 46 matched 

controls

Replication sample 

(n=142): cases and 

controls groupmatched to 

development sample

Longitudinal sample 

(n~120) 

Neuroinflammatory 

sample (n=100)

Case definition: CDC, 

Factor, and modified 

Kansas

Representative of the 

GW-era military 

population in the US 

Military Health Survey; 

and a sample of clinical 

patients with other 

neuroinflammatory 

diseases.

Autoantigen arrays, analyte arrays, custom Luminex 

bead-based arrays to detect neuronal, glial, 

immune, and neuroinflammatory mediators with high 

sensitivity in plasma (cytokines, chemokines, 

autoantibodies, autoantigens, analytes, and other 

proteins); genome-wide extent of methylation of 

CpG sites on banked DNA

Shungu, D. C.65

Case-control

W81XWH-15-1-0437

CDMRP GWIRP

Weill Cornell Medicine

Neuroinflammation, 

oxidative stress, and 

mitochondrial 

dysfunction

40 GWVs: 20 with 

GWI vs 20 without

GWI

Case definition: NR

Criteria not specified

Binding potential of the ligand with PET; glutathione, 

brain levels of lactate and NAA, ATP, PCr, Pi, and 

phosphomonoesters and phosphodiesters all with 

proton MRS; cerebral blood flow with arterial spinlabeling MRI. Complementary: markers of 

neuroinflammation and oxidative stress will also be 

measured in the CSF
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

AUTONOMIC NERVOUS SYSTEM

Barnes, J. N.66

Cross-sectional

W81XWH-19-1-0381

CDMRP GWIRP

University of Wisconsin, 

Madison

Impaired 

cerebrovascular and 

autonomic variables, 

and their association 

with neuroimaging 

biological measures 

of cognitive decline

NR Case definition: NR

Veterans with GWI 

compared with age and 

deployment-matched 

Veterans

Autonomic function testing and MRI to determine 

brain structure and intracranial blood flow 

measurements at rest and in response to 

physiological stress to identify cerebral circulation 

vascular dysfunction; impaired neurovascular 

coupling of blood flow with metabolic demand, 

and/or autonomic dysregulation

OTHER BIOLOGICAL SYSTEMS

Abdullah, L.69

Case-control

NCT03082638

DoD

Roskamp Institute, Inc. 

Sarasota, Florida

Recruiting as of Jun 2019

Expected completion: Dec 

2019

Lipids specific to 

inflammation and 

metabolic 

disturbances

100 GWVs: 50 

Cases with GWI, 50 

Controls without.

Case definition: Kansas

Served in GW 1990-

1991, with or without 

GWI

Lipids specific to inflammation, mitochondria and 

peroxisome function using mass spectrometry 

technologies. 

Golomb, B.68

Cross-sectional

W81XWH-15-1-0626

CDMRP GWIRP

UC San Diego

Mitochondrial/ 

bioenergetic 

impairments

54 GWVs: 27 with 

GWI vs 27 healthy 

controls (matched 

1:1 on age, sex, 

ethnicity) 

Case definition: Kansas 

and CDC

NR

(1) Appearance of mitochondria, including mt 

density/number; mt networks including fission/fusion 

or elongation, mt cristae density and patterning, plus 

capillary #/density (re: energy substrates to 

mitochondria). (2) Function of mitochondria, 

specifically energetic function and energetic reserve 

function, stages of respiratory chain function, in 

intact/skinned muscle fibers and in isolated 

mitochondria. (3) Mt Membrane and OS Measures: 

Mt membrane rigidity-fluidity, integrity, barrier 

function, vulnerability to calcium induced swelling; 

mt OS and apoptosis markers. 

Secondary: (A) Bioenergetic: Basal fasting 

respiratory exchange ratio. (B) Laboratory: Platelet 

mt assay. Tests of OS, inflammation. (C) 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

Exploratory: Quantitative amino acids. Coagulation 

activation (subset).

Keating, J. A .70

Prospective cohort study

Pilot Project #CX-001574

VA CSRD 

NLM5T15LM007359

VAMC, Madison, WI

Protocol published May 

2019

Gut microbiome 52 deployed GWVs: 

26 with GWI and 26 

without GWI

Case definition: 

Modified Kansas

Aged 43–75 years.

Deployed to Gulf as 

part of Operations 

Desert Shield and/or

Desert Storm during 1st

GW (1990–1991)

Microbiome analyses (weekly): Stool total genomic 

DNA extraction; Saliva total genomic DNA extraction

Blood analyses (2 total; 1 at enrollment & 1 at 8 

weeks): C-reactive protein; Flow cytometry

Kokkotou, E.72

Cross-sectional

W81XWH-16-1-0528

CDMRP GWIRP

BIDMC, Boston, MA

Plasma proteome GWI participants

matched to healthy 

individuals and 2 

different disease 

controls

Case definition: NR

Existing database of a 

recently completed 

RCT testing the 

effectiveness of 

acupuncture treatment 

in GWI (PI: Lisa 

Conboy).

Plasma proteome; SOMA scan

Lin, H. C.71

Cross-sectional

W81XWH-09-2-0073

CDMRP GWIRP

BRINM

Small intestinal 

microbial community

GWI vs controls (not 

specified)

Case definition: NR

Participants in a GWI 

antibiotic treatment trial

Quantitative PCR of total microbes on small 

intestinal mucosal biopsy tissue

Lipkin, W. I.73

Case-control

W81XWH-19-1-0398

CDMRP GWIRP

Columbia University

Discovery project to 

survey plasma 

metabolomes

100 pts: 50 with GWI 

and 50 GWVs and 

civilian controls 

without GWI

Case definition: NR

Criteria not specified

Primary metabolites, biogenic amines, complex 

lipids, and bioactive oxylipins

Meyer, J. N.67

Cross-sectional (Aim 2 

longitudinal)

W81XWH-16-1-0663

CDMRP GWIRP

Mitochondrial 

dysfunction

152 GWVs: 76 with 

GWI vs 76 without 

GWI

Case definition: NR

Criteria not specified

Mitochondrial parameters measured in PBMCs: 

mtDNA copy number and damage
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Details: PI

Study design

Registration/award no.

Study sponsor

Setting & status

Focus of 

Investigation

Anticipated 

Participants

GWI Case Definition

Selection criteria/ 

population Biological Measure/Outcome Measure

Duke University

Steele, L.74

Multiphase case-control

W81XWH-12-1-0382

CDMRP GWIRP

Serum analytes 2 samples of 45 

GWI vs 30 healthy 

GWVs Then 

validation in 3rd

sample (90 GWI 

cases, 60 controls)

Case definition: Kansas

1991 GWVs

Panel of ~190 serum analytes: cytokines, 

chemokines, growth factors, hormones, 

hematological measures, and neurotrophic factors

Steele, L.75

Case-control

W81XWH-11-1-0812

USAMRDC; Baylor 

College of Medicine

(Moved to Baylor in 2016

– had not yet started data 

collection)

Multiple systems 130 GWVs: 80 with 

GWI; 50 healthy 

controls

Case definition: NR

“well-characterized” 

sample of GWVs

Neuroimaging (MRI, fMRI, DTI), adrenal function 

tests, and diverse immune, inflammatory, and 

coagulation measures

Sutton, R.76

Cross-sectional

W81XWH-11-1-0825

CDMRP GWIRP

Yale University

XMRV (retrovirus) ~60 pts: 30 GWI vs 

30 matched controls

Case definition: NR

“well-characterized” 

cohort of GWI Veterans

and closely matched 

controls

XMRV in serum samples: in DNA and in XMRVrelated mRNA, antibody against XMRV

Abbreviations: ACh=Acetylcholine; ATP=Adenosine Triphosphate; BBB=Blood-Brain Barrier; BBRAIN=Boston Biorepository and Integrated Network; BIDMC=Beth 

Israel Deaconess Medical Center; BRINM=Biomedical Research Institute of New Mexico; CBF=Cerebral Blood Flow; CDC=Centers for Disease Control and Prevention; 

CDMRP=Congressionally Directed Medical Research Program; CFS=Chronic Fatigue Syndrome; CNS=Central Nervous System; CpG=Cytidine-phosphate Guanosine; 

CSF=Cerebrospinal Fluid; CSP=Cooperative Studies Program; CSRD=Clinical Sciences Research and Development Service; DKI=Diffusion Kurtosis Imaging; 

DNA=Deoxyribonucleic Acid; DoD=Department of Defense; DTI=Diffusion Tensor Imaging; fMRI=functional Magnetic Resonance Imaging; GW=Gulf War; 

GWI=Gulf War Illness; GWIC=Gulf War Illness Consortium; GWIRP=Gulf War Illness Research Program; GWV=Gulf War Veteran; HARDI=High-Angular Resolution 

Diffusion Imaging; Ig-G=Immunoglobulin G; LPS=Lipopolysaccharide; MN=Minnesota; MPF=Macromolecular Proton Fraction; MRI=Magnetic Resonance Imaging; 

mRNA=Messenger Ribonucleic Acid; MRS=Magnetic Resonance Spectroscopy; MRSI=Magnetic Resonance Spectroscopic Imaging; MSI=Multisymptom Illness; 

mt=Mitochondria; mtDNA=Mitochondrial Ribonucleic Acid; MVP=Million Veteran Program; NAA=N-acetyl Aspartate; NIH=National Institutes of Health; NR=Not 

Reported; OS=Oxidative Stress; PBMC=Peripheral Mononuclear Cell; PCr=Creatine Phosphate; PCR=Polymerase Chain Reaction; PET=Positron Emission 

Tomography; Pi=Inorganic phosphate; PI=Principal Investigator; PON1=Paraoxanase; RCT=Randomized Controlled Trial; RNA=Ribonucleic Acid; SFVAFRE=South 

Florida Veterans Affairs Foundation for Research and Education; SNP=Single-Nucleotide Polymorphism; UC=University of California; USAMRDC=United States Army 

Medical Research and Materiel Command; VA=Veterans Affairs; VAMC=Veterans Affairs Medical Center; VO2=Oxygen Volume; WM=White Matter; 

XMRV=Xenotropic Murine Leukemia Virus-related Virus
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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DISCUSSION

This systematic review provides a broad overview of the state of biomarker research in GWI. We 

found no studies assessing the diagnostic accuracy of biomarkers in GWI (KQ1). We found 56

studies assessing the potential association of a broad variety of biological measures in 

participants with and without GWI (KQ2), and 24 ongoing or unpublished studies examining

biological measures for their association with GWI (KQ3). 

Most of the GWI biomarker literature to date could be characterized as “discovery” studies (see 

Figure 2 for biomarker discovery process diagram, and Figures 4 and 5 for summary graphs of 

studies by category) and were largely designed to shed light on the potential causes of GWI.

Most studies fell into biological system categories that have been theorized as pathophysiologic 

contributors to GWI. We did not find studies assessing the relationship between biomarkers and 

severity of GWI illness. Studies in the discovery phase are an early step toward diagnostic test 

development and help identify biomarkers that are likely to be differentially expressed in 

participants with GWI. We categorized biological measure studies according to biological 

system and further identified those studies which found statistically significant associations 

between the biological measure and GWI (Figures 4 and 5). The vast majority of studies 

identified biological measures that were significantly more commonly expressed in participants

with GWI. We found it challenging to identify a particular biomarker or set of biomarkers as 

potentially promising because nearly all of these studies were “one-off” studies where the 

findings have not been replicated in additional studies.

One potential way to prioritize areas of inquiry for further study would be to focus on those areas 

which have already been tested in populations in which there is some diagnostic uncertainty.

These studies included comparator groups of deployed GWVs without GWI (either healthy 

individuals or those with health conditions other than GWI) and are the studies that were 

reviewed in the body of this report.

In Key Question 1, we searched for studies evaluating diagnostic tests for GWI. An included 

study would have been an evaluation of a diagnostic test’s ability to provide differential results 

for cases (per Kansas or CDC criteria) versus non-cases of GWI. It is not surprising that no such 

studies were found, because the case definition for GWI is still debated. In the absence of a gold 

standard definition or diagnostic test, the determination of biological measures to distinguish a 

case from a non-case is challenging.

For Key Question 2, the reviewed body of evidence included a range of studies examining 

relationships between GWI and biological measures. The range of included biological measures 

was wide, from indicators of immune function in serum samples, to general brain activity 

measured with EEG. While the biological measures tended to cluster within a few broadlydefined biological systems (eg, immune system, genetic, etc), there was significant heterogeneity 

among the specific biological measures within these groups. The marked variability in the types 

of measures that were studied, the lack of corroborating data from multiple studies, and 

methodological limitations of many of the included studies limit clinical application of this 

evidence. Instead, the current review provides (1) an overview of the range of biological 

measures that have been examined, and (2) a highlight of methodological limitations of current 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

57

studies that may be adjusted to better position future studies to identify biological measures that 

distinguish cases of GWI from other conditions.

A majority of the studies we identified were focused on the immune and central nervous systems,

with fewer on the autonomic nervous system and genetic markers (Figures 4). The emphasis on 

immune and central nervous systems is consistent with some hypotheses implicating dysfunction 

in these systems in GWI.78 The majority of the immune literature has focused on peripheral 

blood cytokines (4 studies), with the remainder focused on squalene antibodies (2 studies)

mycoplasma fermentans antibodies (1 study), phospholipids (1 study), and human leukocyte 

antigen alleles (1 study). Most of the studies measuring central nervous system activation have 

been focused on cognitive/emotional processing, with 7 of 9 of the studies measuring general 

brain activation in response to cognitive/emotional tasks/stimuli. The studies examining the 

autonomic nervous system involved both cardiovascular and neurological measurements, with 

greater emphasis on cardiovascular measures. The studies measuring genetic properties both 

examined PON1 activity. The remaining studies measured sleep parameters, cardiorespiratory 

and metabolic responses to maximal exercise, audiovestibular measurements, serum enzymes, 

neurophysiologic parameters, and presence of viruses.

The lower priority studies (Appendix D) – those that did not include comparator groups of 

deployed GWI Veterans with or without other health conditions, or that had total sample sizes of 

<25 – were also heavily focused on the central nervous (28 studies) and immune (20 studies) 

systems, with several studies in each of the genetic (10 studies), autonomic nervous (5 studies), 

general nervous system (3 studies), energy metabolism (2 studies), gastrointestinal (2 studies) 

and skeletal (2 studies) systems, as well as single studies of measures within the respiratory and 

circulatory systems, and of various measures, biochemical pathways, and bacteria (Figure 5).

Ongoing and upcoming studies of potential associations between biological measures and GWI 

(Key Question 3) include an emphasis on studies of measurements in the central nervous system 

(5 studies53-57) and immune system (5 studies61-65), which parallels the distribution of biological 

systems emphasized in the reviewed completed studies. Studies of genetic (3 studies58-60) and 

ANS measures (1 study66) are less represented. Additional upcoming studies are diverse in their 

range of proposed biological measurements, with some emphasis on mitochondrial dysfunction 

(3 studies67-69) and gut microbiome (2 studies70,71), which were not areas represented in the 

completed studies we included.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

58

Figure 4. Number of studies of GWI biological measures by biological system 

Abbreviations: ANS=Autonomic Nervous System; CNS=Central Nervous System; KQ=Key Question

Figure 5. Studies of GWI biological measures with lower priority comparator group,* no 

comparator, or inadequate sample size (N<25) by biological system and promise as a 

biomarker

* Priority comparator group=. See Appendix D for summary of studies in this figure.

Yes=Indication by statistical significance of association of a biological measure with GWI case status; No=No 

indication by statistical significance of association of a biological measure with GWI case status

LIMITATIONS

There were significant limitations in the body of evidence in regard to its applicability to the 

questions posed in this review, including limitations in study design, and heterogeneity in 

important aspects of the studies. Additionally, we limited inclusion of studies in the review based 

on the appropriateness of the comparator group for the purpose of identifying potential biological 

measures, though we tried to mitigate this limitation by providing additional details about the 

excluded studies (Appendix D). The text below evaluates limitations of the body of evidence. 

4

8

12

13

26

1

1

1

7

2

0 5 10 15 20 25 30

Autonomic Nervous System

Genetic

Other

Immune System

Central Nervous System

Number of studies

Biological System

Yes No

5

2

6

10

9

1

3

10

5

5

5

10

14

20

28

0 5 10 15 20 25 30 35 40 45

ANS

Genetic

Other

Immune

CNS

Published (KQ2) Unpublished (KQ3) Lower priority controls/N<25
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Study Design

The choice of comparator group was a key limitation of this evidence base. The intent behind 

Key Question 1 was to identify potential biological measures that identify cases of GWI, and 

distinguish cases from non-cases, including the distinction between GWI and conditions with 

overlapping symptomology (eg, CFS or depression). To establish a biological metric capable of 

making this distinction would require biological measures to be compared between cases versus 

individuals without GWI yet with other health conditions with overlapping symptomology with 

GWI. The ability of a biologic measure to distinguish GWI when comparing participants with 

symptoms to healthy participants without symptoms may not translate to its ability to distinguish 

GWI from another illness in participants presenting with symptoms (which is more typically the 

context in which a diagnostic test would be used). Therefore, the ideal comparator group to 

deployed GWVs with GWI would be deployed GWVs without GWI with a condition with 

overlapping symptoms to GWI. A promising biological measure, then, would have differential 

results in GWI (loosely defined here) and controls (per the ideal comparator group described 

above). Studies in the identified body of evidence for the current review almost exclusively 

employed comparator groups of healthy deployed GWV, with only 1 instance of an ill 

comparator among deployed GWV.21 While studies with this composition of comparator groups 

may contribute to understanding of biological differences between individuals with GWI and 

healthy individuals, their contribution to establishing biological measures of GWI that are 

clinically relevant for differential diagnostic purposes is limited.

Still, while the state of the literature may indicate limited studies with the above-described ideal 

specifications, other types of exploratory studies hold some value in the search for biological 

measure with promise for development as biomarkers. Thus, the Appendix D table that includes 

studies that did not include the ideal comparator, as described above, including those studies 

without a comparator group at all, and studies with n<25, also indicates where there are 

statistically significant associations of GWI with some biological measure. 

In addition to inadequate control groups for the purposes of the current review specifically, there 

was a general lack of reporting of several features of the study design, which limits the 

consumer’s ability to adequately interpret study results. Many studies did not report power 

calculations, details regarding how or from where the samples were selected, blinding of 

investigators to group, recruitment non-response rate, information about the distribution of the 

data, or whether corrections were made when multiple comparisons were conducted. In the 

absence of a reported power analysis, it could not be determined whether or not the sample size 

included in the study was sufficient. Some studies claimed to have insufficient sample sizes but

did not provide metrics behind the claim. It was also often not reported how, or from where, the 

sample was selected, or, in some cases when the selection criteria were reported, all participants 

were selected from the same battalion, limiting the representativeness of the sample. Further, 

non-response rate of potential participants during recruitment was also not reported, again 

limiting the ability to assess the representativeness of the sample. 

Blinding of the assessors was also often not reported. When it was reported, it was often the 

investigators conducting the test who were reported as being blinded to case status and it was 

very rarely reported that the investigators conducting the data analyses were blinded. Also rarely 

reported were details of the data analysis including whether or not features of the data, such as 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

60

the distribution of the data and outliers in the data, were evaluated, and what, if any, adjustments 

were made in the face of non-normally distributed data or the presence of outliers, for example. 

Further, when multiple comparisons were conducted, it was often not reported whether steps 

were taken to limit false positives due to chance by making corrections to statistical criteria.

Another remarkable feature of study design in the body of evidence was the use of the Haley 

criteria for case definition.4 As noted above, the Haley definition categorizes cases of GWI into 6 

syndromes based on groupings of related symptoms. In the current body of evidence, the Haley 

criteria was primarily used in studies of the central nervous system. There were instances of the 

use of CDC or Kansas criteria for case definition plus stratification of syndrome types using the 

Haley criteria. Future studies may benefit from the addition of a similar kind of symptom-based 

stratification, as it is possible that different GWI syndrome types would be associated with 

different biological measures.

Heterogeneity

In addition to limitations pertaining to study design, the current body of evidence was also 

limited for the purposes of answering our posed questions due to the heterogeneity of outcome 

measures, as well as case definition. While there were a few instances of repeated outcome 

measures – in the case of 2 instances of replication studies, for example – in general the outcome 

measures were heterogeneous, with only 1 study per outcome measure. Consequently, we were 

unable to conduct meta-analyses and were unable to otherwise draw conclusions in terms of 

biological metrics that might hold promise for further establishment as biological measures.

The heterogeneity of case definition in the current body of studies is expected given that the 

GWI diagnostic criteria is somewhat debated and there are several prevailing case definitions. 

Namely, a gold standard case definition has not been established and the pursuit of an 

identification of biological measures is intended to contribute to the development of a gold 

standard. Consequently, heterogeneity in case definition is expected and we were purposefully 

inclusive of a wide range of definitions in the current review. One consideration that arose from 

the current review that might contribute to more effective future studies of potential GWI 

biological measures is the potential use of stratified clusters of symptoms (eg, similar to Haley 

criteria syndromes). The application of stratified symptom groups may allow for the 

identification of potential biological measures that are specific to different subsets of GWI, or 

syndromes. It is possible that GWI itself is too heterogeneous in symptomology to be effectively 

categorized as 1 cohesive condition, and that stratification may help to organize the condition 

into more clinically relevant components.

RESEARCH GAPS/FUTURE RESEARCH

While the review of the current body of evidence did not provide insight into promising 

biological measures, it did provide insight into methodological features of the studies that limit 

applicability to the questions posed in this review. Future studies examining biological measures 

of GWI would benefit from using comparator groups composed of deployed GWVs with 

conditions with overlapping symptomology to GWI and reporting study methods with sufficient 

detail, including: conducting and reporting a power analysis to determine adequate sample size; 

providing details regarding how or from where the samples were selected; blinding those 

collecting and analyzing the data as to group designation, where possible; reporting recruitment 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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non-response rate; making adjustments for non-normally-distributed data or outliers, and 

reporting these methods; and potentially stratifying by syndrome sub-category. In addition, for 

funders of GWI biomarker research, requiring proposed projects to include funding to support 

the technical expertise, data management, and biostatistical assistance may help to address some 

of the methodological challenges of these studies. Importantly, the DoD Congressionally 

Directed Medical Research Program (CDMRP) has implemented a strategic funding mechanism 

pipeline composed of the following: 1) a discovery stage representing innovative biomarker 

research that is in the earliest stages of development; 2) a qualification stage representing 

research already supported by preliminary or published data in the GWI field that is ready for 

validation through expansion, replication, or comparative studies; 3) a verification stage 

representing clinical translation (testing in a GW Veteran population) of concepts previously 

replicated and validated; and 4) a confirmation stage representing large-scale confirmatory and 

pivotal trials that will transform and revolutionize the clinical management of GWI.79 This 

strategy will aid in the translation of the current state of biomarker research into the development 

of diagnostic and clinical tools.

Additionally, since the outcome measures were diverse, there was an insufficient volume of 

studies of any given biological measure to draw conclusions about any specific biological 

measure. Future studies that employ 1) appropriate control groups for GWI biological measure

investigation, and 2) methodology that allows for sufficient internal and external validity, would 

position positive findings of GWI to biological measure associations to be built upon, thus 

establishing bodies of evidence for promising biological measures. Future research might also 

consider the complexity between using biological measures to diagnose GWI in the context of 

the development of other chronic health comorbidities that may impact this aging group of 

Veterans.

We also identified ongoing or upcoming studies related to GWI biological measures that did not 

meet our inclusion criteria but are likely to contribute to our knowledge. The Gulf War Illness

Consortium has awarded funds to investigate a wide range of metrics: epigenetic DNA changes 

in Veterans with GWI; effects of exposure to cholinergic compounds; abnormalities in tau, a 

cytoskeletal protein; and probability scores of case qualification at the individual level using a 

computerized diagnostic system that incorporates multiple biological measure data.80 The US

Army Medical Research and Development Command is also involved in studies of computerbased diagnostic system for identifying brain-immune interactions in GWI.81 GWI treatments are 

being studied simultaneously, including an upcoming trial by the Gulf War Illness Research 

Program (GWIRP) of an antioxidant, Coenzyme Q10 (CoQ10).82

CONCLUSIONS

Gulf War Illness (GWI) is a chronic multisymptom illness comprised of a wide range of 

systemic symptoms and functional impairments. Hypothesized etiology includes exposure to 

anticholinergic agents, with suspected dysfunction in cellular energy metabolism with 

downstream CNS disruption related to inflammation.78 Thus, much of the literature focused on 

evaluating biomarkers along this cascade of biological processes. In the current review, we 

sought to evaluate existing studies validating existing diagnostic tests for GWI, and to determine

whether biological measurements with promise for further establishment as biomarkers either in 

completed or ongoing/upcoming studies have been demonstrated. The establishment of 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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biological measures for GWI would allow for increased accuracy in diagnosis and potential 

mechanisms for treatment.

Our review indicates that biological measures within the immune and central nervous systems 

have more often been investigated for their potential relationship with GWI, consistent with 

some dominant theories of disease etiology and dysfunction, but the literature also suggests other 

avenues of inquiry in upcoming studies, such as the gut microbiome. More importantly, our 

review revealed that existing studies are insufficient for determining promising biomarkers due 

to the extent of heterogeneity in biological measures across studies, inadequate comparator 

groups, and several other methodological limitations. Future studies that employ ideal control 

groups, reproduce findings of existing studies, and otherwise apply rigorous methodological 

practices and reporting specifically appropriate for investigating potential biomarkers would 

contribute to the establishment of a base of targeted, highly reliable studies from which lines of 

investigation could grow.
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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responses are not abnormal in symptomatic Gulf War veterans. Ann N Y Acad Sci. 

2002;966:327-342.

89. Golomb BA, Koslik HJ, Christians U, et al. Depressed prostaglandins and leukotrienes in 

veterans with Gulf War illness. Journal of Environmental Science & Health - Part B: 

Pesticides, Food Contaminants, & Agricultural Wastes. 2019:1-17.

90. Halpin P, Williams MV, Klimas NG, Fletcher MA, Barnes Z, Ariza ME. Myalgic 

encephalomyelitis/chronic fatigue syndrome and gulf war illness patients exhibit 

increased humoral responses to the herpesviruses-encoded dUTPase: Implications in 

disease pathophysiology. J Med Virol. 2017;89(9):1636-1645.

91. Hannan KL, Berg DE, Baumzweiger W, et al. Activation of the coagulation system in 

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laboratory approach to diagnosis. Blood Coagul Fibrinolysis. 2000;11(7):673-678.

92. Khaiboullina SF, DeMeirleir KL, Rawat S, et al. Cytokine expression provides clues to 

the pathophysiology of Gulf War illness and myalgic encephalomyelitis. Cytokine. 

2015;72(1):1-8.

93. Klaustermeyer WB, Kraske GK, Lee KG, Klaustermeyer WB, Kurohara ML. Allergic 

and immunologic profile of symptomatic Persian Gulf War veterans. Ann Allergy Asthma 

Immunol. 1998;80(3):269-273.

94. O'Bryan TA, Romano PJ, Zangwill BC. Human leukocyte antigens in Gulf War veterans 

with chronic unexplained multiple symptoms. Mil Med. 2003;168(12):1015-1018.

95. Parkitny L, Middleton S, Baker K, Younger J. Evidence for abnormal cytokine 

expression in Gulf War Illness: A preliminary analysis of daily immune monitoring data. 

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96. Skowera A, Stewart E, Davis ET, et al. Antinuclear autoantibodies (ANA) in Gulf Warrelated illness and chronic fatigue syndrome (CFS) patients. Clin Exp Immunol. 

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97. Smylie AL, Broderick G, Fernandes H, et al. A comparison of sex-specific immune 

signatures in Gulf War illness and chronic fatigue syndrome. BMC Immunol. 2013;14:29.

98. Tsilibary EC, Souto EP, James LM, Engdahl BE, Georgopoulos AP. Human 

Immunoglobulin G (IgG) Neutralizes Adverse Effects of Gulf War Illness (GWI) Serum 

in Neural Cultures: Paving the Way to Immunotherapy for GWI. Journal of Neurology & 

neuromedicine. 2018;3(5):23-28.

99. Vojdani A, Thrasher JD. Cellular and humoral immune abnormalities in Gulf War 

veterans. Environ Health Perspect. 2004;112(8):840-846.

100. Whistler T, Fletcher MA, Lonergan W, et al. Impaired immune function in Gulf War 

Illness. BMC Medical Genomics [Electronic Resource]. 2009;2:12.

101. Zhang Q, Zhou XD, Denny T, et al. Changes in immune parameters seen in Gulf War 

veterans but not in civilians with chronic fatigue syndrome. Clin Diagn Lab Immunol. 

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102. Alshelh Z, Albrecht DS, Bergan C, et al. In-vivo imaging of neuroinflammation in 

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103. Baraniuk JN, Casado B, Maibach H, Clauw DJ, Pannell LK, Hess SS. A Chronic Fatigue 

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104. Chao LL, Mohlenhoff BS, Weiner MW, Neylan TC. Associations between subjective 

sleep quality and brain volume in Gulf War veterans. Sleep. 2014;37(3):445-452.

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107. Clarke T, Jamieson JD, Malone P, et al. Connectivity differences between Gulf War 

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108. Concato J, Aslan M, Palmisano MM, et al. Acetylcholinesterase activity in veterans of 

the first Gulf War. J Investig Med. 2007;55(7):360-367.

109. Engdahl BE, James LM, Miller RD, et al. A Magnetoencephalographic (MEG) Study of 

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110. Georgopoulos AP, James LM, Carpenter AF, Engdahl BE, Leuthold AC, Lewis SM. Gulf 

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111. Gopinath KS, Sakoglu U, Crosson BA, Haley RW. Exploring brain mechanisms 

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112. Haley RW, Hom J, Roland PS, et al. Evaluation of neurologic function in Gulf War 

veterans. A blinded case-control study. JAMA. 1997;277(3):223-230.

113. Haley RW, Marshall WW, McDonald GG, Daugherty MA, Petty F, Fleckenstein JL. 

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Radiology. 2000;215(3):807-817.

114. Haley RW, Spence JS, Carmack PS, et al. Abnormal brain response to cholinergic 

challenge in chronic encephalopathy from the 1991 Gulf War. Psychiatry Res. 

2009;171(3):207-220.

115. Hubbard NA, Hutchison JL, Motes MA, et al. Central Executive Dysfunction and 

Deferred Prefrontal Processing in Veterans with Gulf War Illness. Clinical Psychological 

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116. Jamal GA, Hansen S, Apartopoulos F, Peden A. The "Gulf War syndrome". Is there 

evidence of dysfunction in the nervous system? J Neurol Neurosurg Psychiatry. 

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117. James LM, Christova P, Engdahl BE, Lewis SM, Carpenter AF, Georgopoulos AP. 

Human Leukocyte Antigen (HLA) and Gulf War Illness (GWI): HLA-DRB1 13:02 

Spares Subcortical Atrophy in Gulf War Veterans. EBioMedicine. 2017;26:126-131.

118. Li X, Spence JS, Buhner DM, et al. Hippocampal dysfunction in Gulf War veterans: 

investigation with ASL perfusion MR imaging and physostigmine challenge. Radiology. 

2011;261(1):218-225.

119. Menon PM, Nasrallah HA, Reeves RR, Ali JA. Hippocampal dysfunction in Gulf War 

Syndrome. A proton MR spectroscopy study. Brain Res. 2004;1009(1-2):189-194.
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120. Moffett K, Crosson B, Spence JS, et al. Word-finding impairment in veterans of the 1991 

Persian Gulf War. Brain Cogn. 2015;98:65-73.

121. Rayhan RU, Raksit MP, Timbol CR, Adewuyi O, Vanmeter JW, Baraniuk JN. Prefrontal 

lactate predicts exercise-induced cognitive dysfunction in Gulf War Illness. American 

Journal Of Translational Research. 2013;5(2):212-223.

122. Rayhan RU, Stevens BW, Raksit MP, et al. Exercise challenge in Gulf War Illness 

reveals two subgroups with altered brain structure and function. PLoS ONE [Electronic 

Resource]. 2013;8(6):e63903.

123. Rayhan RU, Stevens BW, Timbol CR, et al. Increased brain white matter axial diffusivity 

associated with fatigue, pain and hyperalgesia in Gulf War illness. PLoS ONE [Electronic 

Resource]. 2013;8(3):e58493.

124. Rayhan RU, Washington SD, Garner R, et al. Exercise challenge alters Default Mode 

Network dynamics in Gulf War Illness. BMC Neurosci. 2019;20(1):7.

125. Tillman GD, Calley CS, Buhl VI, et al. Electrophysiological correlates of semantic 

memory retrieval in Gulf War Syndrome 2 patients. J Neurol Sci. 2017;373:66-72.

126. Turner MP, Hubbard NA, Himes LM, et al. Cognitive Slowing in Gulf War Illness 

Predicts Executive Network Hyperconnectivity: Study in a Population-Representative 

Sample. NeuroImage Clinical. 2016;12:535-541.

127. Washington SD, Rayhan RU, Garner R, et al. Exercise alters cerebellar and cortical 

activity related to working memory in phenotypes of Gulf War Illness. Brain 

Communications. 2020;2(1):fcz039.

128. Wylie GR, Genova H, Dobryakova E, et al. Fatigue in Gulf War Illness is associated with 

tonically high activation in the executive control network. NeuroImage Clinical. 

2019;21:101641.

129. Falvo MJ, Chen Y, Klein JC, Ndirangu D, Condon MR. Abnormal rheological properties 

of red blood cells as a potential marker of Gulf War Illness: A preliminary study. Clin 

Hemorheol Microcirc. 2018;68(4):361-370.

130. Fiedler N, Giardino N, Natelson B, et al. Responses to controlled diesel vapor exposure 

among chemically sensitive Gulf War veterans. Psychosom Med. 2004;66(4):588-598.

131. Haley RW, Vongpatanasin W, Wolfe GI, et al. Blunted circadian variation in autonomic 

regulation of sinus node function in veterans with Gulf War syndrome. Am J Med. 

2004;117(7):469-478.

132. Peckerman A, LaManca JJ, Smith SL, et al. Cardiovascular stress responses and their 

relation to symptoms in Gulf War veterans with fatiguing illness. Psychosom Med. 

2000;62(4):509-516.

133. Stein PK, Domitrovich PP, Ambrose K, et al. Sex effects on heart rate variability in 

fibromyalgia and Gulf War illness. Arthritis Rheum. 2004;51(5):700-708.

134. Baraniuk JN, Shivapurkar N. Exercise-induced changes in cerebrospinal fluid miRNAs in 

Gulf War Illness, Chronic Fatigue Syndrome and sedentary control subjects. Sci Rep. 

2017;7(1):15338.

135. Craddock TJ, Harvey JM, Nathanson L, et al. Using gene expression signatures to 

identify novel treatment strategies in gulf war illness. BMC Medical Genomics 

[Electronic Resource]. 2015;8:36.

136. Liu G, Ye CJ, Chowdhury SK, et al. Detecting Chromosome Condensation Defects in 

Gulf War Illness Patients. Current Genomics. 2018;19(3):200-206.
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137. Mackness B, Durrington PN, Mackness MI. Low paraoxonase in Persian Gulf War 

Veterans self-reporting Gulf War Syndrome. Biochem Biophys Res Commun. 

2000;276(2):729-733.

138. NCT00810225. Study of Gulf War Illness (GWI) by Comparing GWI and Healthy 

Veterans. In. Georgetown U, Research USAM, Materiel C, trans2008.

139. Steele L, Lockridge O, Gerkovich MM, Cook MR, Sastre A. Butyrylcholinesterase 

genotype and enzyme activity in relation to Gulf War illness: preliminary evidence of 

gene-exposure interaction from a case-control study of 1991 Gulf War veterans. Environ 

Health. 2015;14:4.

140. Trivedi MS, Abreu MM, Sarria L, et al. Alterations in DNA Methylation Status 

Associated with Gulf War Illness. DNA Cell Biol. 2019;38(6):561-571.

141. Urnovitz HB, Tuite JJ, Higashida JM, Murphy WH. RNAs in the sera of Persian Gulf 

War veterans have segments homologous to chromosome 22q11.2. Clin Diagn Lab 

Immunol. 1999;6(3):330-335.

142. Vladutiu GD, Natelson BH. Association of medically unexplained fatigue with ACE 

insertion/deletion polymorphism in Gulf War veterans. Muscle Nerve. 2004;30(1):38-43.

143. Nicolson GL, Nasralla M, Nicolson NL, Haier J. High prevalence of Mycoplasma 

infections in symptomatic (chronic fatigue syndrome) family members of Mycoplasmapositive Gulf War Illness patients. Journal of Chronic Fatigue Syndrome. 2003;11(2):21-

36.

144. Naviaux RK, Naviaux JC, Li K, et al. Metabolic features of Gulf War illness. PLoS ONE 

[Electronic Resource]. 2019;14(7):e0219531.

145. Falvo MJ, Lindheimer JB, Serrador JM. Dynamic cerebral autoregulation is impaired in 

Veterans with Gulf War Illness: A case-control study. PLoS ONE [Electronic Resource]. 

2018;13(10):e0205393.

146. Chen Y, Meyer JN, Hill HZ, et al. Role of mitochondrial DNA damage and dysfunction 

in veterans with Gulf War Illness. PLoS ONE [Electronic Resource]. 

2017;12(9):e0184832.

147. Henry C. Lin M, System NMVH. Bacterial Overgrowth Associated With Chronic 

Multisymptom Illness Complex. In:2009.

148. Fletcher MA, Rosenthal M, Antoni M, et al. Plasma neuropeptide Y: a biomarker for 

symptom severity in chronic fatigue syndrome. Behavioral & Brain Functions 

[Electronic Resource]: BBF. 2010;6:76.

149. Khan F, Kennedy G, Spence VA, Newton DJ, Belch JJ. Peripheral cholinergic function in 

humans with chronic fatigue syndrome, Gulf War syndrome and with illness following 

organophosphate exposure. Clin Sci. 2004;106(2):183-189.

150. Lindheimer JB, Cook DB, Klein-Adams JC, et al. Veterans with Gulf War Illness exhibit 

distinct respiratory patterns during maximal cardiopulmonary exercise. PLoS ONE 

[Electronic Resource]. 2019;14(11):e0224833.

151. Compston JE, Vedi S, Stephen AB, et al. Reduced bone formation in UK Gulf War 

veterans: a bone histomorphometric study. J Clin Pathol. 2002;55(12):897-899.

152. Pessler F, Chen LX, Dai L, et al. A histomorphometric analysis of synovial biopsies from 

individuals with Gulf War Veterans' Illness and joint pain compared to normal and 

osteoarthritis synovium. Clin Rheumatol. 2008;27(9):1127-1134.
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153. NCT00810329. Proteomics of Cerebrospinal Fluid in Chronic Fatigue Syndrome. In. 

Georgetown University N, National Institute of Environmental Health S, University of 

South A, trans2008.

154. Broderick G, Ben-Hamo R, Vashishtha S, et al. Altered immune pathway activity under 

exercise challenge in Gulf War Illness: an exploratory analysis. Brain Behav Immun. 

2013;28:159-169.

155. Bunegin L, Mitzel HC, Miller CS, Gelineau JF, Tolstykh GP. Cognitive performance and 

cerebrohemodynamics associated with the Persian Gulf Syndrome. Toxicol Ind Health. 

2001;17(4):128-137.

156. Haley RW, Fleckenstein JL, Marshall WW, McDonald GG, Kramer GL, Petty F. Effect 

of basal ganglia injury on central dopamine activity in Gulf War syndrome: correlation of 

proton magnetic resonance spectroscopy and plasma homovanillic acid levels. Arch 

Neurol. 2000;57(9):1280-1285.

157. Latimer JJ, Alhamed A, Sveiven S, et al. Preliminary Evidence for a Hormetic Effect on 

DNA Nucleotide Excision Repair in Veterans with Gulf War Illness. Mil Med. 

2020;185(1-2):e47-e52.

158. Janulewicz PA, Seth RK, Carlson JM, et al. The Gut-Microbiome in Gulf War Veterans: 

A Preliminary Report. International Journal of Environmental Research & Public Health 

[Electronic Resource]. 2019;16(19):04.
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APPENDIX A. SEARCH STRATEGIES

Ovid MEDLINE ALL 1946 to February 20, 2020

Date searched: February 21, 2020

1 Persian Gulf Syndrome/ or Gulf War/ (1100)

2 ("Desert Saber" or "Desert Sabre" or "Desert Shield" or "Desert Storm" or "Gulf War" or "Gulf 

Conflict" or "Gulf Crisis" or "Persian Gulf Syndrome" or "Kuwait War" or "Operation 

GRANBY" or "Op GRANBY").ti,ab,kf. (2230)

3 (GWI or GWIs or GWVI or GWVIs or "Iowa Persian Gulf Study" or "War Related Illness and 

Injury Study Center*").ti,ab,kf. (222)

4 ((Kuwait or Iraq or "Persian Gulf" or "Southwest Asia" or "SW Asia") and ("air force" or 

"armed forces" or army or marines or "military personnel" or "national guard*" or naval or navy 

or "service members" or servicemembers or soldier* or Veteran*)).ti,ab,kf. (2938)

5 or/1-4 (4955)

6 exp Biological measures/ (727323)

7 (antigen or antigens or autoantibod* or auto-antibod* or antibody or antibodies or bioassay* or 

bio-assay* or biological measure* or bio-marker* or biopsy or biopsies or blood or coexpress* 

or co-express* or conduction or "CT scan*" or cytokine or cytokines or diagnos* or 

dysfunction* or electromyograph* or endoscop* or fluid or fluids or fMRI or genet* or "gene 

expression" or imaging or inflammat* or marker or markers or MRI or "Magnetic resonance 

imaging" or mechanism* or neurodegenerat* or neuro-degenerat* or neuroendocrine or neuroendocrine or neuroimag* or neuro-imag* or neuroinflammat* or neuro-inflammat* or protein or 

proteins or pulse or receptor or receptors or saliva or scan or scans or scanning or semen or 

serum or signal* or specimen* or temperature or test or tests or tissue* or tomograph* or 

ultrasound or urine or "vital signs" or x-ray*).ti,ab,kf. (14445351)

8 (bl or di or dg).fs. (4947785)

9 or/6-8 (16203250)

10 and/5,9 (2503)

11 10 not ((exp animals/ not humans/) or ("animal model" or "animal models" or cat or cats or 

dog or dogs or marmoset* or mice or mouse or pig or pigs or rat or rats or rodent or sheep or 

species or swine or bTBI or mTBI or sTBI or PTSD or TBI or "posttraumatic stress" or "posttraumatic stress" or "traumatic brain injury" or "traumatic brain injuries").ti.) (1773)

12 limit 11 to english language (1738)

13 limit 12 to yr="1990 -Current" (1736)

PsycINFO 1806 to February Week 3 2020

Date searched: February 21, 2020

1 ("Desert Saber" or "Desert Sabre" or "Desert Shield" or "Desert Storm" or "Gulf War" or "Gulf 

Conflict" or "Gulf Crisis" or "Persian Gulf Syndrome" or "Kuwait War" or "Operation 

GRANBY" or "Op GRANBY").ti,ab. (1135)

2 (GWI or GWIs or GWVI or GWVIs or "Iowa Persian Gulf Study" or "War Related Illness and 

Injury Study Center*").ti,ab. (64)

3 ((Kuwait or Iraq or "Persian Gulf" or "Southwest Asia" or "SW Asia") and ("air force" or 

"armed forces" or army or marines or "military personnel" or "national guard*" or naval or navy 

or "service members" or servicemembers or soldier* or Veteran*)).ti,ab. (2431)

4 or/1-3 (3347)
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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5 Biological Markers/ (12461)

6 (antigen or antigens or autoantibod* or auto-antibod* or antibody or antibodies or bioassay* or 

bio-assay* or biological measure* or bio-marker* or biopsy or biopsies or blood or coexpress* 

or co-express* or conduction or "CT scan*" or cytokine or cytokines or diagnos* or 

dysfunction* or electromyograph* or endoscop* or fluid or fluids or fMRI or genet* or "gene 

expression" or imaging or inflammat* or marker or markers or MRI or "Magnetic resonance 

imaging" or mechanism* or neurodegenerat* or neuro-degenerat* or neuroendocrine or neuroendocrine or neuroimag* or neuro-imag* or neuroinflammat* or neuro-inflammat* or protein or 

proteins or pulse or receptor or receptors or saliva or scan or scans or scanning or semen or 

serum or signal* or specimen* or temperature or test or tests or tissue* or tomograph* or 

ultrasound or urine or "vital signs" or x-ray*).ti,ab. (1453074)

7 or/5-6 (1453534)

8 and/4,7 (1097)

9 8 not ("animal model" or "animal models" or cat or cats or dog or dogs or marmoset* or mice 

or mouse or pig or pigs or rat or rats or rodent or sheep or species or swine or bTBI or mTBI or 

sTBI or PTSD or TBI or "posttraumatic stress" or "post-traumatic stress" or "traumatic brain 

injury" or "traumatic brain injuries").ti. (625)

10 limit 9 to english language (608)

11 limit 10 to yr="1990 -Current" (608)

EBM Reviews - Cochrane Database of Systematic Reviews 2005 to February 21, 2020

Date searched: February 21, 2020

1 ("Desert Saber" or "Desert Sabre" or "Desert Shield" or "Desert Storm" or "Gulf War" or "Gulf 

Conflict" or "Gulf Crisis" or "Persian Gulf Syndrome" or "Kuwait War" or "Operation 

GRANBY" or "Op GRANBY").ti,ab. (0)

2 (GWI or GWIs or GWVI or GWVIs or "Iowa Persian Gulf Study" or "War Related Illness and 

Injury Study Center*").ti,ab. (0)

3 ((Kuwait or Iraq or "Persian Gulf" or "Southwest Asia" or "SW Asia") and ("air force" or 

"armed forces" or army or marines or "military personnel" or "national guard*" or naval or navy 

or "service members" or servicemembers or soldier* or Veteran*)).ti,ab. (0)

4 or/1-3 (0)

5 (antigen or antigens or autoantibod* or auto-antibod* or antibody or antibodies or bioassay* or 

bio-assay* or biological measure* or bio-marker* or biopsy or biopsies or blood or coexpress* 

or co-express* or conduction or "CT scan*" or cytokine or cytokines or diagnos* or 

dysfunction* or electromyograph* or endoscop* or fluid or fluids or fMRI or genet* or "gene 

expression" or imaging or inflammat* or marker or markers or MRI or "Magnetic resonance 

imaging" or mechanism* or neurodegenerat* or neuro-degenerat* or neuroendocrine or neuroendocrine or neuroimag* or neuro-imag* or neuroinflammat* or neuro-inflammat* or protein or 

proteins or pulse or receptor or receptors or saliva or scan or scans or scanning or semen or 

serum or signal* or specimen* or temperature or test or tests or tissue* or tomograph* or 

ultrasound or urine or "vital signs" or x-ray*).ti,ab. (4210)

6 and/4-5 (0)

EBM Reviews - Cochrane Central Register of Controlled Trials January 2020

Date searched: February 21, 2020
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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1 ("Desert Saber" or "Desert Sabre" or "Desert Shield" or "Desert Storm" or "Gulf War" or "Gulf 

Conflict" or "Gulf Crisis" or "Persian Gulf Syndrome" or "Kuwait War" or "Operation 

GRANBY" or "Op GRANBY").ti,ab. (111)

2 (GWI or GWIs or GWVI or GWVIs or "Iowa Persian Gulf Study" or "War Related Illness and 

Injury Study Center*").ti,ab. (54)

3 ((Kuwait or Iraq or "Persian Gulf" or "Southwest Asia" or "SW Asia") and ("air force" or 

"armed forces" or army or marines or "military personnel" or "national guard*" or naval or navy 

or "service members" or servicemembers or soldier* or Veteran*)).ti,ab. (257)

4 or/1-3 (356)

5 (antigen or antigens or autoantibod* or auto-antibod* or antibody or antibodies or bioassay* or

bio-assay* or biological measure* or bio-marker* or biopsy or biopsies or blood or coexpress* 

or co-express* or conduction or "CT scan*" or cytokine or cytokines or diagnos* or 

dysfunction* or electromyograph* or endoscop* or fluid or fluids or fMRI or genet* or "gene 

expression" or imaging or inflammat* or marker or markers or MRI or "Magnetic resonance 

imaging" or mechanism* or neurodegenerat* or neuro-degenerat* or neuroendocrine or neuroendocrine or neuroimag* or neuro-imag* or neuroinflammat* or neuro-inflammat* or protein or 

proteins or pulse or receptor or receptors or saliva or scan or scans or scanning or semen or 

serum or signal* or specimen* or temperature or test or tests or tissue* or tomograph* or 

ultrasound or urine or "vital signs" or x-ray*).ti,ab. (836972)

6 and/4-5 (197)

7 6 not ("animal model" or "animal models" or cat or cats or dog or dogs or marmoset* or mice 

or mouse or pig or pigs or rat or rats or rodent or sheep or species or swine or bTBI or mTBI or 

sTBI or PTSD or TBI or "posttraumatic stress" or "post-traumatic stress" or "traumatic brain 

injury" or "traumatic brain injuries").ti. (117)

ClinicalTrials.gov

Date searched: February 21, 2020

( EXPAND[Concept] ( "Desert Saber" OR "Desert Sabre" OR "Desert Shield" OR "Desert 

Storm" OR "Gulf War" OR "Gulf Conflict" OR "Gulf Crisis" OR "Persian Gulf Syndrome" OR 

"Kuwait War" OR "Operation GRANBY" OR "Op GRANBY" OR "GWI" OR "GWIs" OR 

"GWVI" OR "GWVIs" ) OR AREA[ConditionSearch] ( Gulf AND ( illness OR syndrome ) ) ) | 

antigen OR autoantibody OR auto-antibody OR antibody OR bioassay OR bio-assay OR 

biological measure OR bio-marker OR biopsy OR blood OR coexpression OR co-expression OR 

conduction OR CT OR cytokine OR diagnosis OR diagnostic OR electromyography OR 

endoscopy OR fluid OR fMRI OR genetic OR gene OR imaging OR inflammation OR marker 

OR MRI OR magnetic OR mechanism OR neurodegeneration OR neuro-degeneration OR 

neuroendocrine OR neuro-endocrine OR neuroimaging OR neuro-imaging OR 

neuroinflammation OR neuro-inflammation OR protein OR pulse OR receptor OR saliva OR 

scan OR semen OR serum OR signaling OR specimen OR temperature OR test OR tissue OR 

tomography OR ultrasound OR urine OR vital or x-ray

(36)

WHO ICTRP

Date searched: February 21, 2020

Condition = "Desert Saber" OR "Desert Sabre" OR "Desert Shield" OR "Desert Storm" OR 

"Gulf Conflict" OR "Gulf Crisis" OR "Kuwait War" OR "Operation GRANBY" OR "Op 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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GRANBY" OR (Gulf AND (illness OR syndrome)) OR GWI OR GWIs OR GWVI OR GWVIs

(Without synonyms checked)

Recruitment Status = ALL

(53)
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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APPENDIX B. STUDY SELECTION

Inclusion codes, code definitions, and criteria

1. Is the full text of the article in English? 

Yes  Proceed to 2.

No  Code X1 (Non-English-language publication). STOP.

2. Does the population include Veterans with Gulf War Illness?

Include: Veterans (either U.S. or international) deployed to the Persian Gulf region 

between Aug 2, 1990 - Nov 1991, defined by the authors as having Gulf War Illness

according to a recognized case definition (CDC or Kansas), or defines cases using 

similar criteria to CDC/Kansas. Also include studies of civilian contractors present 

during the conflict, if available. Include studies where deployment status and/or time of 

deployment is unclear. 

Included illness definitions (past and present terms to identify Gulf War Illness): Chronic 

Multisymptom (or multisystem) Illness (CMI), Irritable Bowel Syndrome (IBS), Chronic 

Fatigue Syndrome (CFS)/Myalgic Encephalitis(ME), fibromyalgia (FM), Gulf War

Syndrome.

Exclude: children and birth outcomes of Gulf War Veterans.

Comparator populations may include:

• Veterans who were deployed elsewhere (other than Persian Gulf) during the Gulf 

War.

• Gulf War-deployed Veterans

• Non-deployed Gulf War era Veterans

• Civilians with other health conditions/conditions with similar symptomology to 

GWI (eg, chronic fatigue syndrome, neurodegenerative disorders, musculoskeletal 

problems)

• Healthy controls

Yes  Proceed to 3.

No  Code X2 (Excluded population). STOP.

3. Does the study examine measures of any of the following categories of biological 

functions/systems that are potential loci of dysfunction:

o Genes (eg, paraoxonase levels, enzyme butyrylcholinesterase)

o Immune activation/inflammation (eg, anti-squalene antibody, natural killer cell 

activity, humoral immune response, human leukocyte antigen, platelet function, 

plasma proteins, serum cytokines, peripheral blood lymphocyte factors)

o Neurodegeneration (eg, acetylcholinesterase activity, N-acetylaspartate-tocreatine ratio)

o Autonomic nervous system (eg, feedback regulation of the HPA axis)

o Endocrine system (eg, neuroendocrine-immune signaling)

o Energy metabolism (eg, mitochondrial dysfunction)
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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o General brain activity (eg, synchronous neural interactions, findings from brain 

imaging (eg, fMRI, PET))

o Other 

(Exclude: assessments that do not include biological measurements (eg, questionnaires, 

symptom inventories)

Yes  Proceed to 4.

No  Code X3 (Not relevant to GWI biological measures). STOP.

4. Is this study of diagnostic accuracy or a systematic review of such studies? 

Yes study of diagnostic accuracy. Code KQ1 diagnostic accuracy [specify test].

STOP.

Yes  Systematic review. Code KQ1-SR. STOP.

No  Proceed to 5.

5. Is the study a published measure of association between biological measures and GWI?

Yes  Code Bio-KQ2-[specify biological measure and biological measure

category]. STOP.

No, it is an unpublished study that otherwise meets criteria  Code -KQ3 emerging 

research [specify biological measure and biological measure category]. STOP.

No, none of the above  Code X4. STOP.

Key Questions:

KQ1: Which diagnostic tests (or test combinations) are candidates for distinguishing individuals 

diagnosed with GWI from individuals without GWI?

KQ2: Which biological measures have been examined for their potential association with GWI, 

and which among them have been shown to be associated with GWI? 

KQ3: Which ongoing or unpublished research studies examine diagnostic tests or biological 

measures for potential association with GWI? 

Exclusion Codes:

X1: Non-English-language publication

X2: Excluded population

X3: Not relevant to GWI biological measures/accuracy of tests

X4: Excluded study design or publication type

X9: Duplicate or preliminary publication of a more recent study

X99: Study terminated
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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APPENDIX C. QUALITY ASSESSMENT

Table 9. Quality Assessment of Studies of Biological Measures for Gulf War Illness

Study

Assessment Criteria*

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11

Amin, 201148 a (1) b (0) a (1) a (1) c (0) a (1) a (1) a (1) b (0) c (0) a (1)

Asa, 200023 a (1) b (0) a (1) a (1) c (0) d (0) a (1) b (0) d (0) c (0) b (0)

Blanchard, 201942 a (1) d (0) a (1) a (1) a (1) a (1) a (1) a (1) d (0) d (0) b (0)

Butterick, 201928 a (1) d (0) a (1) a (1) c (0) d (0) a (1) a (1) d (0) c (0) b (0)

Calley, 201032 b (0) b (0) a (1) a (1) c (0) a (1) a (1) b (0) c (0) c (0) b (0)

Cooper, 201633 b (0) b (0) a (1) b (0) c (0) a (1) a (1) b (0) c (0) c (0) a (1)

Davis, 200043 b (0) b (0) a (1) a (1) c (0) a (1) a (1) d (0) d (0) c (0) a (1)

Emmerich, 201724 a (1) d (0) a (1) a (1) c (0) a (1) a (1) a (1) a (1) c (0) a (1)

Georgopoulos, 201625 d (0) d (0) a (1) a (1) c (0) a (1) a (1) a (1) d (0) c (0) b (0)

Gopinath, 201234 a (1) b (0) a (1) a (1) c (0) a (1) a (1) b (0) a (1) c (0) a (1)

Haines, 201721 b (0) a (1) a (1) a (1) c (0) d (0) a (1) b (0) c (0) c (0) b (0)

Haley, 201344 b (0) a (1) a (1) a (1) c (0) a (1) a (1) a (1) a (1) c (0) a (1)

Hotopf, 200347 b (0) a (1) b (0) a (1) a (0) a (1) a (1) a (1) d (0) b (0) a (1)

James, 201627 a (1) a (1) a (1) a (1) c (0) b (1) a (1) a (1) c (0) c (0) b (0)

Johnson, 201326 a (1) b (0) a (1) a (1) c (0) d (0) a (1) a (1) d (0) c (0) b (0)

Johnson, 201629 a (1) b (0) a (1) a (1) c (0) c (0) a (1) a (1) b (0) c (0) b (0)

Li, 201445 b (0) b (0) a (1) a (1) c (0) c (0) a (1) a (1) a (1) b (0) b (0)

Liu, 201135 a (1) b (0) a (1) b (0) c (0) a (1) a (1) b (0) a (1) c (0) b (0)

Lo, 200022 b (0) a (1) b (0) a (1) c (0) a (1) a (1) b (0) c (0) c (0) a (1)

Nagelkirk, 200346 a (1) d (0) a (1) b (0) c (0) b (1) a (1) b (0) d (0) c (0) a (1)

Odegard, 201336 b (0) b (0) a (1) b (0) c (0) a (1) a (1) a (1) d (0) c (0) b (0)

Phillips, 200930 a (1) b (0) a (1) b (0) c(0) b (1) a (1) b (0) c (0) c (0) a (1)

Roland, 200049 b (0) b (0) a (1) b (0) c (0) a (1) a (1) a (1) d (0) c (0) b (0)

Sharief, 200250 b (0) a (1) a (1) a (1) c (0) c (0) a (1) a (1) a (1) b (0) a (1)

Skowera, 200431 b (0) a (1) a (1) a (1) c (0) a (1) a (1) a (1) d (0) a (1) a (1)

Tillman, 201037 b (0) d (0) a (1) a (1) c (0) a (1) a (1) b (0) d (0) c (0) b (0)

Tillman, 201238 b (0) b (0) a (1) a (1) c (0) d (0) a (1) a (1) a (1) c (0) b (0)

Tillman, 201339 b (0) d (0) a (1) b (0) c (0) a (1) a (1) b (0) d (0) c (0) b (0)

Tillman, 201940 b (0) a (1) a (1) a (1) c (0) d (0) a (1) a (1) a (1) c (0) b (0)

Wallace, 199951 a (1) d (0) a (1) a (1) c (0) a (1) a (1) b (0) b (0) c (0) a (1)

Weiner, 201141 a (1) d (0) a (1) a (1) c (0) b (1) a (1) a (1) d (0) c (0) b (0)

Zhou, 201852 b (0) b (0) a (1) a (1) c (0) b (1) a (1) b (0) d (0) c (0) b (0)

*Quality Assessment Criteria (adapted from Newcastle-Ottawa19 and BIOCROSS20):

1. Is the case definition adequate?

a. Yes: CDC or Kansas definition (+1)

b. All other definitions (0)

2. Representativeness of cases and controls:
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a. Truly representative of the population of both GWI+ and GWI- Veterans (ie, total 

pop[census] or random sampling) (+1)

b. Non-random selection of either GWI+ or GWI- subjects (0) 

c. No description of the sampling strategy (0) 

3. Selection of controls: Were D-GWVs controls selected or recruited from the same 

population as cases (including the same time period)? 

a. Yes (+1)

b. No (0) 

4. Were inclusion and exclusion criteria for being in the study prespecified and applied 

uniformly to all participants?

a. Yes (+1) 

b. No/unclear (0) 

5. Sample size/power calculation:

a. Reported having conducted a power analysis, and then used an appropriate sample 

size based on that analysis (+1)

b. Reported having conducted a power analysis, but were not able to/did not use an 

appropriate sample size (0)

c. Did not report having conducted a power analysis (0) 

6. Comparability of cases and controls on the basis of the design or analysis: 

a. Study controls for important confounders like demographics (age, gender, 

comorbidity, etc) through matching participants or statistical adjustment (+1)

b. Did not match by age or gender, nor adjust for confounders in analysis, but 

demographic analysis found no statistically significant differences on these 

variables (+1).

c. There were significant descriptive differences that were not adjusted for (0)

d. No matching and/or demographics not reported (0) 

7. Were the biological measure measurements (independent variables) clearly defined, 

valid, reliable, and implemented consistently across all study participants?

a. Yes (+1)

b. No (0) 

8. Biological measure data modeling: Was the distribution of biological measure data 

reported (if non-normal were statistical approaches used to standardize it)? Were methods 

of outlier detection and handling used? Were any possible errors resulting from 

measurement inaccuracies discussed?

a. Any of the above were addressed (+1) 

b. Unclear/did not report (0)

c. Reported but inadequate (0) 

9. If there were multiple comparisons, did they adjust appropriately (eg, Bonferroni)? 

a. Yes (1)

b. No (0) 

c. N/A (no penalty, 0)

d. Not reported (0) 

10. Non-Response rate (for enrollment): 
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a. Same rate for both groups, or rate differs but is weighted statistically (+1)

b. Unequal response rate, non-respondents are described (with no statistical 

adjustment) (0)

c. Unclear/not reported (0)

11. Blinding: Were the assessors of the outcome measurement (biological measure) blinded 

to the (case or control) status of participants?

a. Yes (+1)

b. No/not reported (0)
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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APPENDIX D. SUPPLEMENTAL MATERIAL

Table 10. Studies of Gulf War Illness Biological Measures Using Lower-priority Comparator Groups* or No Comparator 

Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

IMMUNE SYSTEM

Abou-Donia, 

201783

Screening for novel central 

nervous system biological 

measures in Veterans with Gulf 

War Illness

Autoantibodies reactive 

to specified proteins

GWVs with GWI had higher had higher levels of autoantibody 

reactivity in all proteins examined except S-100B comparted to 

healthy, non-Veterans with low back pain (GFAP p b 0.001; Tau 

p b 0.001; MAP p b 0.002; MAG

p b 0.001; PNF p b 0.006; Tubulin p b 0.003; MBP p b 0.01; S100B

p = 0.31)

Yes

Brimacombe, 

200284

Immunological variables mediate 

cognitive dysfunction in Gulf War 

Veterans but not civilians with 

chronic fatigue syndrome

Patterns of 

cytokine/symptom 

relationships

A type 2 cluster of chronic fatigue syndrome plus a T and B cell 

factor predicted CFS cases for GWVs but not civilians with CFS, 

which was modulated by reaction time

Yes

Broderick, 

201185

Altered immune pathway activity 

under exercise challenge in Gulf 

War Illness: an exploratory 

analysis

Gene expression 

pathways, cytokines in 

plasma, lymphocytes, 

cytotoxicity, with exercise 

challenge

Mutual information networks linking immune markers in GWI 

had more abundant connections but were less organized than 

non-Veteran health controls during and after exercise.

Yes

Broderick, 

201886

A pilot study of immune network 

remodeling under challenge in 

Gulf War Illness

Immune markers, with 

exercise challenge

GWI compared to control networks of immune signaling during 

exercise had more abundant connections but were less 

organized. NPY, IL-1α, TNF-α and CD2+/CD26+ nodes were 

better integrated in the GWI network at rest. Under effort (t1) 

these differences were replaced by significant restructuring 

around nodes for CD19+ B cell population, IL-5, IL-6 and 

soluble CD26 concentrations.

No

Diaz-Torne, 

200787

Absence of histologic evidence of 

synovitis in patients with Gulf War 

Veterans' illness with joint pain

Synovial biopsy samples GWVI synovia (synovitis, osteoarthritis, and rheumatoid arthritis 

scores) did not differ from normal controls. 

No

Everson, 

200288

Immunological responses are not 

abnormal in symptomatic Gulf War 

Veterans

Humoral immune 

responses

Immune response measures in antigen presenting cells, T cells, 

type 1-2 T-helper cells, and B cells did not differ between GWIsymptomatic GWVs vs matched controls (asymptomatic 

Veterans, non-Gulf War Veterans

No

Golomb, 

201989

Depressed prostaglandins and 

leukotrienes in Veterans with Gulf 

War Illness

Eicosanoids -

prostaglandins and 

leukotrienes

Several plasma eicosanoid levels were lower in GWI vs nonVeteran controls.Yes
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Halpin, 201790 Myalgic encephalomyelitis/chronic 

fatigue syndrome and Gulf War 

Illness patients exhibit increased 

humoral responses to the 

herpesviruses-encoded dUTPase: 

Implications in disease 

pathophysiology

Antibodies against 

multiple human 

herpesviruses-encoded 

dUTPases and/or the 

human dUTPase

GWI participants had higher levels of antibodies to the HHV-6 

and human dUTPases than healthy controls (p=0.0053 and 

p=0.0036, respectively).

Yes

Hannan, 

200091

Activation of the coagulation 

system in Gulf War Illness: a 

potential pathophysiologic link with 

chronic fatigue syndrome. A 

laboratory approach to diagnosis

Coagulation and platelet 

activation

More GWVs with GWI (23/33) than healthy controls (0/33) had 2

or more positive scores on the Immune System Activation of 

Coagulation panel (p<0.001), the laboratory criterion for 

activation of coagulation.

Yes

Khaiboullina, 

201592

Cytokine expression provides 

clues to the pathophysiology of 

Gulf War Illness and myalgic 

encephalomyelitis

77 serum cytokines A group of 77 cytokines identified myalgic encephalomyelitis 

(ME) and GWI with sensitivities of 92.5% and 64.9%, 

respectively. When ME and GWI were compared to healthy 

controls, the specificity was 33.3%.

No

Klaustermeyer, 

199893

Allergic and immunologic profile of 

symptomatic Persian Gulf War 

Veterans

Total serum IgE levels GWVs with allergy symptoms had higher mean IgE level (88.7 

I/U/mL) than GWVs without allergy symptoms (47.5 IU/mL)

No

O'Bryan, 

200394

Human leukocyte antigens in Gulf 

War Veterans with chronic 

unexplained multiple symptoms

Frequency of antigens: 

HLA-A, -B, -DR, -DQ 

Human Leukocyte Antigen-A28 was present in 21.9% of 

symptomatic Veterans and 6.9% of the healthy population 

(p=0.01), but not significant when corrected for number of 

antigens determined. 

No

Parkitny, 

201595

Evidence for abnormal cytokine 

expression in Gulf War Illness: A 

preliminary analysis of daily 

immune monitoring data

Serum cytokine and 

chemokine 

concentrations

No difference in serum cytokine concentrations between GWI 

and healthy GWV. GWI associated with higher variability in the 

expression of eotaxin-1 than healthy GWVs (p<0.001). 

Yes

Skowera, 

200296

Antinuclear autoantibodies (ANA) 

in Gulf War-related illness and 

chronic fatigue syndrome (CFS) 

patients

Antinuclear 

Autoantibodies

No difference in prevalence of antinuclear autoantibodies 

between symptomatic GWV, healthy GWV, symptomatic Bosnia 

and Era Veterans, chronic fatigue syndrome patients, and health 

control subjects. 

No

Smylie, 201397 A comparison of sex-specific 

immune signatures in Gulf War 

Illness and chronic fatigue 

syndrome

Cytokine markers with 

exercise challenge

No differences between GWI and controls indicated. Differences 

in cytokine markers by sex.

No

Tsilibary, 

201898

Human Immunoglobulin G (IgG) 

Neutralizes Adverse Effects of 

Gulf War Illness (GWI) Serum in 

Human IgG Cell spreading was lower in GWI than control (p=4.4 x 10-34). 

GWI apoptosis was higher than control (=-6.91 x 10-24) 

Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Neural Cultures: Paving the Way 

to Immunotherapy for GWI

Vojdani, 200499 Cellular and humoral immune 

abnormalities in Gulf War 

Veterans

Percentage of 

immunological markers

Percentage of T cells in symptomatic GWV(sGWV) v. controls 

not different. More sGWVs had elevated T cells than controls. 

More B cells in sGWVs v controls. Natural Killer cell activity 

decreased in patients (24.8 ± 16.5 lytic unit) v controls (37.3 ± 

26.4 lytic unit). Immune complexes increased in patients (53.1 ± 

18.6, mean ± SD) v controls (34.6 ± 14.3). Autoantibody titers 

directed against myelin basic protein and striated or smooth 

muscle greater in sGWVs v control.

Yes

Whistler, 

2009100

Impaired immune function in Gulf 

War Illness

Immune cell function with 

exercise challenge

Differences for 3 Natural Killer cell subsets and Natural Killer 

cytotoxicity between GWI and controls (p<0.05). 

Yes

Zhang, 1999101 Changes in immune parameters 

seen in Gulf War Veterans but not 

in civilians with chronic fatigue 

syndrome

Lymphocyte 

subpopulations, cytokine 

gene expression

Veterans with chronic fatigue syndrome had more total T cells 

and MHC II+ T cells and higher percentage of these lymphocyte 

subpopulations, and lower percentage of Natural Killer cells, 

than controls. Also had higher levels of IL-2, IL-10, IL-10, IFN- 

(symbol), and TNF-(alpha symbol) than controls.

Yes

CENTRAL NERVOUS SYSTEM

Alshelh, 

2020102

In-vivo imaging of 

neuroinflammation in Veterans 

with Gulf War Illness

[11C]PBR28 PET/MRI GWI had higher cortical [11C]PBR28 PET signal in precuneus, 

prefrontal, primary motor, and somatosensory cortices 

compared to both healthy non-Veterans and healthy Veterans. 

No group differences in inflammatory cytokines.

Yes

Baraniuk, 

2005103

A Chronic Fatigue Syndrome -

related proteome in human 

cerebrospinal fluid

Proteomes in 

cerebrospinal fluid

Pooled chronic fatigue syndrome and GWI samples contained 

proteins in the cerebrospinal fluid not detected in the control 

sample: α-1-macroglobulin, amyloid precursor-like protein 1, 

keratin 16, orosomucoid 2 and pigment

epithelium-derived factor.

Yes

Chao, 2014104 Associations between subjective 

sleep quality and brain volume in 

Gulf War Veterans

Cortical, lobar gray 

matter, and hippocampal 

volumes

Global Pittsburgh Sleep Quality Index was associated with total 

cortical and frontal gray matter volume in GWV, and, in the 

frontal lobe, total Global Pittsburgh Sleep Quality Index was 

inversely associated with the superior and middle frontal, 

orbitofrontal, anterior cingulate, and frontal pole volumes.

No

Chao, 2019105 Do Gulf War Veterans with high 

levels of deployment-related 

exposures display symptoms 

suggestive of Parkinson's 

disease?

Total basal ganglia 

volume

GWI had lower total basal ganglia volume than healthy deployed 

Veterans.

Yes

Christova, 

2017106

Subcortical brain atrophy in Gulf 

War Illness

Subcortical brain atrophy GWI had subcortical brain atrophy compared to healthy controls. Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Clarke, 2019107 Connectivity differences between 

Gulf War Illness (GWI) phenotypes 

during a test of attention

Exercise challenge: brain 

activation (fMRI BOLD 

response)

Unique brain activation connectivity patterns between control 

and GWI groups. Controls had an exercise task related network 

of right dorsolateral and left ventrolateral prefrontal cortex, 

dorsal anterior cingulate cortex, posterior insulae and frontal eye 

fields. GWI subgroup with brain stem atrophy and postural 

tachycardia after exercise had activity in the dorsal anterior 

cingulate cortex with direct links to basal ganglia, anterior 

insulae, and right dorsolateral prefrontal cortex notes. GWI 

subgroup with stress test originated phantom perception had 

submodules of basal ganglia-anterior insulae, and dorsolateral 

prefrontal executive control regions.

Yes

Concato, 

2007108

Acetylcholinesterase activity in 

Veterans of the first Gulf War

Acetylcholinesterase 

activity

Acetylcholinesterase activity was similar for Veterans with 

versus without GWI.

No

Engdahl, 

2016109

A Magnetoencephalographic 

(MEG) Study of Gulf War Illness 

(GWI)

Synchronous neural 

interactions

Differences in synchronous neural interactions between GWI 

and healthy controls centered in the cerebellum and frontal 

cortex. 

Yes

Georgopoulos, 

2017110

Gulf War Illness (GWI) as a 

neuroimmune disease

Synchronous neural 

interactions

GWI synchronous neural interactions did not differ from relapseremitting multiple sclerosis, Sjogren's syndrome, or rheumatoid 

arthritis, but did differ from control, schizophrenia, Alzheimer's 

disease, post-traumatic stress disorder, and major depressive 

disorder.

Yes

Gopinath, 

2019111

Exploring brain mechanisms 

underlying Gulf War Illness with 

group ICA based analysis of fMRI 

resting state networks

Resting state fMRI Impaired functional connectivity in GWI between language 

networks, sensory input networks, motor output networks, 

between different sensory perception and motor networks, and 

between different networks in the sensorimotor domain.

Yes

Haley, 1997112 Evaluation of neurologic function 

in Gulf War Veterans. A blinded 

case-control study

Neurophysiological, 

audiovestibular, 

neuroradiological, blood 

cell count, erythrocyte 

sedimentation rate

GWI had greater inter-side asymmetry of the wave I to wave III 

interpeak latency of brain stem auditory evoked potentials, 

greater interocular asymmetry of nystagmic velocity on 

rotational testing, increased asymmetry of saccadic velocity, 

more prolonged interpeak latency of the lumbar-to-cerebral 

peaks on posterior tibial somatosensory evoked potentials, and 

diminished nystagmic velocity after caloric stimulation bilaterally.

Yes

Haley, 2000113 Brain abnormalities in Gulf War 

syndrome: evaluation with 1H MR 

spectroscopy

N-acetyl aspartate-tocreatine ratio, measuring 

neuronal mass

N-acetyl aspartate-to creatine (NAA/Cr) ratio (functional 

neuronal mass) was lower in the basal ganglia and brainstem of 

GWVs than in control participants (p=0.007).

Yes

Haley, 2009114 Abnormal brain response to 

cholinergic challenge in chronic 

encephalopathy from the 1991 

Gulf War

Brain response to 

cholinergic challenge; 

normalized regional 

cerebral blood flow 

Baseline normalized regional cerebral blood flow in chronically ill 

GWVs was lower than controls throughout deep structures. 

Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Hubbard, 

2014115

Central Executive Dysfunction and 

Deferred Prefrontal Processing in 

Veterans with Gulf War Illness

Brain activation (BOLD 

fMRI) during working 

memory task

GWI deferred prefrontal cortex activity from encoding to retrieval 

for high demand conditions. 

Yes

Jamal, 1996116 The "Gulf War syndrome". Is there 

evidence of dysfunction in the 

nervous system?

Peripheral nerve function Three measures of peripheral nerve function were abnormal in 

Veterans compared to controls: cold threshold (p=0.0002), sural 

nerve latency (p=0.034), and median nerve sensory action 

potential (p=0.030).

Yes

James, 2017117 Human Leukocyte Antigen (HLA) 

and Gulf War Illness (GWI): HLADRB1 13:02 Spares Subcortical 

Atrophy in Gulf War Veterans

Volume of cerebellar gray 

matter

Human leukocyte allele DRB1*12:02 spared subcortical brain 

atrophy in GWVs and subcortical volume was higher in carriers 

of the allele, and in cerebellar grey matter.

Yes

Li, 2011118 Hippocampal dysfunction in Gulf 

War Veterans: investigation with 

ASL perfusion MR imaging and 

physostigmine challenge

Hippocampal regional 

cerebral blood flow

Decreased hippocampal regional cerebral blood flow with 

physostigmine challenge in control subjects (p<0.0005) and 

Veterans with syndrome 1 (impaired cognition) (p<0.05), and 

increased in syndrome 2 (confusion-ataxia) (p<0.005) and 

syndrome 3 (central neuropathic pain) (p<0.002).

Yes

Menon, 2004119 Hippocampal dysfunction in Gulf 

War Syndrome. A proton MR 

spectroscopy study

N-acetyl aspartate to 

creatine and choline to 

creatine ratios

The N-acetyl aspartate/creatine ratio of the GWI group was 

lower that control group.

Yes

Moffett, 2015120 Word-finding impairment in 

Veterans of the 1991 Persian Gulf 

War

Brain activation (BOLD 

signal fMRI) during 

cognitive task

GWI group had reduced activity in the thalamus, putamen, and 

amygdala, and increased activity in the right hippocampus 

relative to controls 

Yes

Rayhan, 

2013121

Prefrontal lactate predicts 

exercise-induced cognitive 

dysfunction in Gulf War Illness

Brain activation with 

exercise challenge

GWI who had decreased working memory performance after 

exercise had elevated prefrontal lactate levels compared to GWI 

who had increased performance.

Yes

Rayhan, 

2013122

Exercise challenge in Gulf War 

Illness reveals 2 subgroups with 

altered brain structure and function

Brain activation (BOLD 

fMRI) with exercise 

challenge

GWI subgroup with orthostatic tachycardia correlated with 

brainstem atrophy, baseline working and memory compensation 

in the cerebellar vermis. The other GWI subgroup that 

developed exercise- induced hyperalgesia was associated with 

cortical atrophy and baseline working memory compensation in 

the basal ganglia. 

Yes

Rayhan, 

2013123

Increased brain white matter axial 

diffusivity associated with fatigue, 

pain and hyperalgesia in Gulf War 

Illness

White matter diffusivity 

properties

GWI had increased axial diffusivity in the right inferior frontaloccipital fasciculus, but not in controls.Yes

Rayhan, 

2019124

Exercise challenge alters Default 

Mode Network dynamics in Gulf 

War Illness

Brain activation patterns 

with exercise

GWI had increase in deactivation patterns within the Default 

Mode Network following exercise that was not seen in controls. 

Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Tillman, 

2017125

Electrophysiological correlates of 

semantic memory retrieval in Gulf 

War Syndrome 2 patients

Brain activation (ERP) 

with cognitive task

GWI had an event-related potential difference between memory 

retrieval and no memory retrieval stimuli at the midline parietal 

region that had a scalp voltage polarity opposite from that 

recorded at the left temporal area that was not present in 

controls.

Yes

Turner, 2016126 Cognitive Slowing in Gulf War 

Illness Predicts Executive Network 

Hyperconnectivity: Study in a 

Population-Representative Sample

Brain activation (BOLD 

fMRI) during cognitive 

task 

Bilateral dorsolateral prefrontal cortex connectivity with taskrelevant notes was altered in GWI participants compared to 

healthy controls during processing speed task.

Yes

Washington, 

2020127

Exercise alters cerebellar and 

cortical activity related to working 

memory in phenotypes of Gulf War 

Illness

Brain activity with 

working memory task/ 

exercise

GWI with stress test associated reversible tachycardia has postexertional deactivation of cerebellar dentate nucleus and vermis 

regions associated with working memory. GWI stress tests 

originated phantom perception had activation of the anterior 

supplementary motor area .

Yes

Wylie, 2019128 Fatigue in Gulf War Illness is 

associated with tonically high 

activation in the executive control 

network

Brain activation (BOLD 

fMRI) with cognitive 

challenge

GWI had greater activation than healthy controls in frontal and 

parietal areas for less difficult cognitive tasks.

Yes

AUTONOMIC NERVOUS SYSTEM

Falvo, 2018129 Dynamic cerebral autoregulation is 

impaired in Veterans with Gulf War 

Illness: A case-control study

Cerebral blood flow 

responses to 

physostigmine challenge

Greater decreases in cerebral blood flow both a nadir and after 

standing and during steady state standing in GWI vs controls. 

Dynamic autoregulation was lower in GWI than controls. 

Cerebrovascular reactivity was not different between groups. 

Yes

Fiedler, 2004130 Responses to controlled diesel 

vapor exposure among chemically 

sensitive Gulf War Veterans

Responses to diesel 

vapor exposure: Heart 

rate, blood pressure, 

respiration rate, end-tidal 

CO(2)

GWI had reduced end-tidal CO2 after exposure to diesel and 

petrochemical fumes compared to controls and were 

physiologically hyporeactive in response to behavioral tasks 

administered during, but not before, exposure.

Yes

Haley, 2004131 Blunted circadian variation in 

autonomic regulation of sinus 

node function in Veterans with 

Gulf War syndrome

Heart-rate variability by 

24-hr 

electrocardiography, 

ambulatory blood 

pressure, Valsalva ratio, 

sympathetic skin 

response, sweat imprint 

test measures

GWI had less increase (1.2-fold) in high-frequency spectral 

power of heart rate variability during sleep compared to normal 

increase (2.2-fold) in controls. In GWI, it was lower at night, 

higher in morning, but no difference from controls during rest of 

the day. GWI heart rate declined less at night and corrected QT 

intervals were longer over 24 hours, particularly at night.

Yes

Peckerman, 

2000132

Cardiovascular stress responses 

and their relation to symptoms in 

Hemodynamic responses 

to stressors

Veterans with chronic fatigue had diminished blood pressure 

responses during cognitive stress tests due to unusually small 

increases in total peripheral resistance. Similar blood pressure 

Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Gulf War Veterans with fatiguing 

illness

responses to cold pressor test in Veterans with chronic fatigue 

and healthy Veterans. 

Stein, 2004133 Sex effects on heart rate variability 

in fibromyalgia and Gulf War 

Illness

Heart rate variability No group differences in heart rate variability. No

GENETIC

Baraniuk, 

2017134

Exercise-induced changes in 

cerebrospinal fluid miRNAs in Gulf 

War Illness, Chronic Fatigue 

Syndrome and sedentary control 

subjects

MicroRNAs in 

cerebrospinal fluid

No group differences in microRNAs in cerebrospinal fluid. After 

exercise, GWI Stress Test Originated Phantom Perception 

participants had lower miR-22-3p than control and GWI Stress 

Test Activated Reversible Tachycardia, but higher miR-9-3p 

than Stress Test Originated Phantom Perception participants.

Yes

Craddock, 

2015135

Using gene expression signatures 

to identify novel treatment 

strategies in Gulf War Illness

Gene Expression 

Signatures

Found 19 functional modules with significantly altered gene 

expression patterns in GWI.

Yes

Liu, 2018136 Detecting Chromosome 

Condensation Defects in Gulf War 

Illness Patients

Chromosome 

condensation defects

In GWI, 3 subtypes of Defective Mitotic Figures. Another type of 

condensation defect identified as sticky chromosomes were 

observed. 

Yes

Mackness, 

2000137

Low paraoxonase in Persian Gulf 

War Veterans self-reporting Gulf 

War Syndrome

Paraoxonase GWVs paraoxon hydrolysis was less than 50%of that found in 

controls. Serum PON1 concentration was lower in GWV. No 

group difference in rate of diazoxon hydrolysis. 

Yes

NCT00810225, 

2008138

Study of Gulf War Illness (GWI) by 

Comparing GWI and Healthy 

Veterans

CNDP1 gene, 

cerebrospinal fluid 

proteome contents

N/A N/A

Steele, 2015139 Butyrylcholinesterase genotype 

and enzyme activity in relation to 

Gulf War Illness: preliminary 

evidence of gene-exposure 

interaction from a case-control 

study of 1991 Gulf War Veterans

Butyrylcholinesterase 

Genotype and Enzyme 

Activity

No difference between GWI and controls in mean 

butyrylcholinesterase (BChE) enzyme activity level or BChE 

genotype. 

No

Trivedi, 2019140 Alterations in DNA Methylation 

Status Associated with Gulf War 

Illness

DNA methylation patterns 

in peripheral blood 

mononuclear cells

Global DNA methylation levels not different in GWI v controls. 

Genome-wide assessment indicated hypermethylation in GWI in 

88% of CpG sites across gene regulatory elements and within 

coding regions.

Yes

Urnovitz, 

1999141

RNAs in the sera of Persian Gulf 

War Veterans have segments 

homologous to chromosome 

22q11.2

Amplicons Genetic alterations in the 22q11.2 region in GWI. Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Vladutiu, 

2004142

Association of medically 

unexplained fatigue with ACE 

insertion/deletion polymorphism in 

Gulf War Veterans

Frequency of mutant 

alleles associated with 

metabolic myopathies or 

genetic variation 

associated with physical 

performance

Increased risk for chronic fatigue syndrome/idiopathic chronic 

fatigue was associated with alterations of the insertion/deletion 

polymorphism in the angiotensin-converting enzyme gene in 

GWV. The I allele frequency was decreased in affected vs 

unaffected Veterans. The II genotype was decreased 4-fold in 

affected Veterans DD genotype was increased 2-fold.

Yes

OTHER

Bacterial

Nicolson, 

2003143

High prevalence of Mycoplasma 

infections in symptomatic (chronic 

fatigue syndrome) family members 

of Mycoplasma-positive Gulf War 

Illness patients

Presence of bacterial 

infection

Over 80% of GWI who were positive for blood mycoplasma 

infections had only 1 Mycoplasma spp., M. Fermentans, vs 

healthy controls with 8.5% incidence of mycoplasma

Yes

Biochemical Pathways

Naviaux, 

2019144

Metabolic features of Gulf War 

Illness

Abnormalities in 

biochemical pathways, 

surveyed via broadspectrum serum 

metabolomics

GWI, compared to healthy controls, had abnormalities in 8 of 46 

biochemical pathways. Lipid abnormalities accounted for 78% of 

the metabolic impact. 

Yes

Circulatory System

Falvo, 2018145 Abnormal rheological properties of 

red blood cells as a potential 

marker of Gulf War Illness: A 

preliminary study

Red blood cell 

deformability and 

aggregation

Red blood cells were more deformable in GWI, as indicated by 

higher elongation indices particularly at higher shear stress 

values when compared to matched controls.

Yes

Energy Metabolism

Chen, 2017146 Role of mitochondrial DNA 

damage and dysfunction in 

Veterans with Gulf War Illness

Mitochondrial DNA 

damage and dysfunction

Mitochondrial DNA lesion frequency and mitochondrial DNA 

copy number were elevated in GWI vs controls. 

Yes

Koslik, 201412 Mitochondrial dysfunction in Gulf 

War Illness revealed by 

Phosphorus Magnetic Resonance 

Spectroscopy: a case-control 

study

Calf muscle 

phosphocreatine

Post-exercise phosphocreatine-recovery time constant was 

prolonged in GWI vs controls.

Yes

Gastrointestinal

Lin, 2009147 Bacterial Overgrowth Associated 

with Chronic Multisymptom Illness 

Complex

Hydrogen and methane 

in breath 

The proportion of Fusobacteria in the was increased in GWI vs 

controls in the jejunum. In the ileum, Proteobacteria were 

reduced in GWI vs controls.

Yes
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Author, year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?†

Nervous System

Fletcher, 

2010148

Plasma neuropeptide Y: a 

biological measure for symptom 

severity in chronic fatigue 

syndrome

Neuropeptide Y in 

plasma

Plasma neuropeptide Y elevated in chronic fatigue syndrome 

participants vs controls and GWI.

Yes

Khan, 2004149 Peripheral cholinergic function in 

humans with chronic fatigue 

syndrome, Gulf War syndrome 

and with illness following 

organophosphate exposure

Skin blood flow 

responses to 

iontophoresis of 

acetylcholine and of 

methacholine

Response to acetylcholine was higher in participants with 

chronic fatigue syndrome than controls, but normal in GWI and 

those exposed to organophosphates. The methacholine 

response was higher than acetylcholine response in all patient 

groups compared to controls except for those with chronic 

fatigue syndrome. 

Yes

Respiratory

Lindheimer, 

2019150

Veterans with Gulf War Illness 

exhibit distinct respiratory patterns 

during maximal cardiopulmonary 

exercise

Ventilatory variables 

(minute ventilation, 

respiratory frequency, 

tidal volume) in response 

to maximal 

cardiopulmonary exercise

Ventilator variables measured during exercise stress test 

indicated minute ventilation was not different but tidal volume 

was greater and respiratory frequency was lower in GWI than 

controls.

Yes

Skeletal

Compston, 

2002151

Reduced bone formation in UK 

Gulf War Veterans: a bone 

histomorphometric study

Bone measures: 

cancellous bone area, 

mineral apposition rate, 

mean wall width, bone 

formation rate at tissue 

level

Measures from iliac crest bone biopsies showed that cancellous 

bone area was lower in GWVs vs healthy controls, and this was 

associated with reduced mineral apposition rate, mean wall 

width, and bone formation rate at the tissue level.

Yes

Pessler, 

2008152

A histomorphometric analysis of 

synovial biopsies from individuals 

with Gulf War Veterans' Illness 

and joint pain compared to normal 

and osteoarthritis synovium

Histologic, 

immunohistochemical, 

and vascular measures in 

synovial biopsies

Measures from synovial biopsies indicated no difference 

between GWI and healthy controls in histologic appearance. 

No

Various

NCT00810329, 

2008153

Proteomics of Cerebrospinal Fluid 

in Chronic Fatigue Syndrome

Proteins in cerebrospinal 

fluid, cerebrospinal 

pressure, ANS function, 

pulmonary function, pain 

threshold, allergic 

response

N/A - ongoing N/A -

ongoing

* Priority comparator groups=Deployed GWVs without GWI, with or without other health conditions. See main report for studies using priority comparator groups. This table

includes studies of biological measures in GWVs with GWI (loosely defined) compared to other groups, or with no comparator group.
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† Yes=Indication by statistical significance of association of a biological measure with GWI case status; No=No indication by statistical significance of association of a biological 

measure with GWI case status

Abbreviations: ACE=Angiotensin-Converting Enzyme; ANA=Antinuclear Antibody; ANS=Autonomic Nervous System; ASL=Arterial Spin Labelling; BOLD=Blood-OxygenLevel-Dependent; CFS=Chronic Fatigue Syndrome; CNDP1=Carnosine Dipeptidase 1; CO2=Carbon Dioxide; dUTPase=Deoxyuridine Triphosphate Diphosphatase; 

DNA=Deoxyribonucleic Acid; ERP=Event Related Potential; fMRI=functional Magnetic Resonance Imaging; GWI=Gulf War Illness; HLA=Human Leukocyte Antigen;

ICA=Independent Component Analysis; IgE=Immunoglobulin E; IgG=Immunoglobulin G; MEG= Magnetoencephalograph; miRNA=Micro Ribonucleic Acid; MR=Magnetic 

Resonance; MRI=Magnetic Resonance Imaging; NCT=National Clinical Trial; PET=Positron Emission Tomography; UK=United Kingdom
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Table 11. Gulf War Illness Biological Measure Studies with Insufficient Sample Size (N<25) 

Study Author, 

Year Title

Biological Measure/ 

Outcome Measure Findings

Findings 

promising?*

IMMUNE SYSTEM

Broderick, 

2013154

Exploring the Diagnostic Potential of 

Immune Biomarker Co-expression in 

Gulf War Illness

Projection model based on 

markers of endocrine and 

immune function

Increases in neuroendocrine-immune signaling and 

inflammatory activity in GWI with decreased apoptotic 

signaling associated with exercise stress test.

Yes

CENTRAL NERVOUS SYSTEM

Bunegin, 2001155 Cognitive performance and 

cerebrohemodynamics associated 

with the Persian Gulf Syndrome

Middle cerebral artery 

blood flow velocity with 

acetone challenge

No difference in pulmonary function tests between GWI 

and controls breathing clean air or 40 ppm acetone in air. 

Middle cerebral artery blood flow velocity increases for 

each of clean air, clean air placebo, and mixture of air and 

acetone were different between groups.

Yes

Haley, 2000156 Effect of basal ganglia injury on 

central dopamine activity in Gulf War 

syndrome: correlation of proton 

magnetic resonance spectroscopy 

and plasma homovanillic acid levels

Functioning neuronal 

mass (N-acetyl-aspartate 

to creatine ratio)

Homovanillic acid: 3-methoxy-4-hydroxyphenlyglycol was 

inversely associated with functioning neuronal mass in the 

left basal ganglia but not the right.

Yes

GENETIC

Latimer, 2020157 Preliminary Evidence for a Hormetic 

Effect on DNA Nucleotide Excision 

Repair in Veterans with Gulf War 

Illness

DNA nucleotide excision 

repair capacity

Total gene expression and nucleotide excision repair 

differed between GWI and controls.

Yes

OTHER

Janulewicz, 

2019158

The Gut-Microbiome in Gulf War 

Veterans: A Preliminary Report

Gut microbiome patterns GW controls had more but firmicutes and the GWI plus 

gastrointestinal symptoms had more phyla bacteroidetes, 

actinobacteria, euryarchaeota, and proteobacteria, and 

Bacteroidaceae, Erysipelotrichaceae, and 

Bifidobacteriaceae. GWI plus gastrointestinal symptoms 

also showed greater plasma levels of the inflammatory 

cytokine TNF-RI.

Yes

*Yes=Indication by statistical significance of association of a biological measure with GWI case status; No=No indication by statistical significance of association of a biological 

measure with GWI case status. 

Abbreviations: DNA=Deoxyribonucleic Acid, GWI=Gulf War Illness; TNF-RI=Tumor Necrosis Factor-Receptor 1
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APPENDIX E. PEER REVIEW COMMENTS/AUTHOR 

RESPONSES 

Reviewer

number Comment Author Response

Are the objectives, scope, and methods for this review clearly described?

1 No - See detailed comments--needs to be 

clearer conceptually

4 Yes 

5 Yes 

6 Yes 

Is there any indication of bias in our synthesis of the evidence?

1 No

4 No

5 No

6 No 

Are there any published or unpublished studies that we may have overlooked?

1 Yes - I don't know, but I would guess that 

requiring the 2 IOM-approved definitions might 

exclude the best studies, which would be done 

in a very sick group of GWI patients vs

controls. It is impossible to get anywhere with 

GWI using the Kansas and CDC definitions 

because they include a very diverse, mostly 

not very sick, very large groups of veterans.

Regardless of case definition restrictions, 

we found no studies that could answer 

KQ1, so no studies were excluded based 

on case definition for KQ1. We agree that 

the CDC and Kansas definitions include a 

heterogeneous group of symptoms and do 

not specify symptom severity. Also, a 

larger challenge that restricts our review 

and the GWI research is that CDC and 

Kansas case definitions are currently 

recommended for use in research to 

identify GWI, so the preponderance of 

studies use one of these as their criteria. 

We acknowledge this challenge in the 

discussion.

4 No 

5 Yes 

• VA Million Veterans Program consisting of 

biological samples and clinical data from 

thousands of GW veterans.

• VA Cooperative Studies Program 585 - 

various studies using a repository including 

blood specimens (serum, buffy coat, DNA) 

from hundreds of GW veterans.

We identified one study58 from the VA 

Million Veterans Program. This study was 

also part of the VA CSP 2007.

Unfortunately, upon reviewing publications 

and products from the CSP 585 program, 

we were unable to identify published 

studies meeting our selection criteria. 

6 No 

Additional suggestions or comments can be provided below. If applicable, please indicate the 

page and line numbers from the draft report.

1 OVERVIEW

Clearly a thorough report and largely a good 

review of the included studies. However, the 

report could be improved in several ways, and 

some of the methods used are not wellThank you.
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justified. More detail might help, but the biggest 

concern is that the report is conceptually weak. 

Some concerns are:

1) using adherence to the CDC and Kansas 

definitions as a criterion for inclusion; 

conceptually, if you are after a biomarker 

related to "symptoms" or severity or course, 

you would not want to use a general sample of 

the very nonspecific CDC and Kansas 

definitions 

Our justification for this requirement was 

that there needs to be a gold standard of 

case definition to evaluate diagnostic 

accuracy. We recognize that there are 

limitations to these case definitions, but 

they are currently what is recommended 

and widely used. Because we did not find 

any studies to include for KQ1, this 

restriction of case definition did not result 

in the exclusion of any studies. For KQ2, 

we were very inclusive of diagnostic 

criteria. 

2) as above, the investigators do not seem to 

have a coherent conceptual approach to 

evaluating biomarkers. The conceptual 

framework (Figure 1) is not really conceptual--it 

is merely a graphic saying who the 

populations, interventions, and measures are.

We have clarified that Figure 1 is not a 

conceptual model, rather a graphic 

showing our PICOTS and KQs. We have 

added an additional figure (Figure 2), 

which provides an overview of the 

diagnostic test/biomarker development 

process, which guided our 

conceptualization of how our KQs and 

report fit into the biomarker development 

pipeline. 

The use of "measures of diagnostic accuracy" 

as an outcome is out of date; diagnostic tests 

should be evaluated based on a framework 

that considers technical, diagnostic, and 

therapeutic impacts, not just "measures of 

diagnostic accuracy".

We agree that a diagnostic test should be 

able to both accurately diagnose a 

condition as well as give insight to potential 

therapies to use and therapeutic impacts. 

The latter two, however, are far beyond the 

discovery phase of how a given biomarker 

(or group of biomarkers) are associated 

with the presence or absence Gulf War 

Illness. While conceptually including 

therapeutic impacts would be an important 

property of a biomarker practically, there 

were no studies that were far enough 

along the diagnostic test development 

pipeline to be able to comment on clinical 

utility.

Similarly, "association between symptoms and 

biological measures" is not a valid basis for 

evaluating a biomarker. (This sentence is also 

in the background section, p7) What does 

"association with symptoms" mean? 

Association with the severity of illness? 

Association with the types of symptoms? This 

is conceptually unclear and incomplete.

To date there have only been studies that 

have taken a biological measurement and 

assessed its association with GWI or its 

symptoms. The literature is not at a stage 

where we can evaluate the diagnostic and 

clinical utility of biomarkers for GWI. 

Furthermore, studies were somewhat 

broad in how they identified GWI and 

examined associations. For example, 

some studies may simply categorize as 

“symptomatic” GWV. For clarity, we have 

changed “symptoms” to “GWI” in the 

report.
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Because the report isn't clear about the type of 

evaluation they are interested in, it is also 

unclear what an ideal study of the question 

they have in mind would be. Would it evaluate 

a biomarker to predict a response to 

treatments or the risk of a complication? Before 

criticizing the studies that were included (and 

excluded) it is important to lay out what the 

target is, and I could not figure out what the 

target was.

KQ1: Target was accurate case 

identification between those with GWI and 

those with some other illness (which no 

studies existed, so we could not critique 

them). We specified in the Study Selection 

which studies we included which identifies 

which studies we were interested in.

KQ2 : Target was biological markers that 

are associated with GWI case status and

should thus be validated or researched 

further for potential GWI diagnostic test 

candidates (many of these studies existed 

so we could critique them on 

methodological rigor). The parameters of 

the included studies for KQ2/3 are also 

described in the Study Selection section. 

The inclusion criteria lay out specifically 

what types of studies we were looking for.

The background section, then, should provide 

a much clearer description of what the authors 

are looking for in a biomarker--the sentence 

"...studies of associations between biological 

measures and GWI status for potential 

development of biomarker tests.." should end 

with at least one possible use of a biomarker 

other than distinguishing GWI from non-GWI. I 

think a reasonable goal would be to find a 

marker that was associated with the severity of 

illness, its course, or suitability for various 

treatments, but I cannot tell what the authors 

had in mind.

Severity of illness, illness course, and 

suitability for various treatments are all 

important outcomes, but per the agreed 

upon a priori KQ?s, we were focused on a 

test’s ability to identify the presence or 

absence of GWI, not to predict its course. 

Conceptually, the report should also distinguish 

between "discovery" studies and validation 

studies of biomarkers.

We agree, this is an important distinction 

and have added language and a figure to 

help us clarify this difference.

1. p12 line 7 "meeting inclusion criteria" line 45 

"initial inclusion criteria" : Why "initial" criteria? 

Were there additional versions of the criteria? It 

is unclear what "initial" is meant to convey.

Thank you for pointing this out. We have 

removed “initial” so that the sentence now 

reads: “Those studies that met inclusion 

criteria other than including a priority 

comparator group (72 studies), and/or...”

2. p14 line 11 "We did not identify any studies 

that met the criteria for inclusion for Key 

Question 1." This may be the least informative 

way to convey the results of search and 

selection! For anyone but systematic 

reviewers, this sentence would make more 

sense if it spelled out what you mean--eg, "We 

did not find any studies that compared a test's 

classification of GWI to a reference standard 

and reported measures of diagnostic 

accuracy." As it stands, the literature flow chart 

provides no information about which studies 

were candidates for KQ1 and why they didn't 

qualify--the KQs are not distinguished until the 

Thank you. We spelled out, as suggested, 

and indicated that no studies addressing 

the validity of diagnostic tests, regardless 

of comparator type, were found. 

We agree that the lack of an agreed upon 

gold standard makes finding the ideal 

comparator group difficult. We did identify 

one prospective case-control study, all 

others were cross-sectional.

We have also updated the literature flow 

chart to more clearly portray the studies 
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last step in the flow diagram. The audience for 

this report needs to understand whether there 

were studies that aspired to be about diagnosis 

but did not meet your criteria, and why. A 

clearer type of flow for KQ1 would itemize the 

characteristics of these candidate studies so a 

reader could see, eg, that among studies in the 

right population, that evaluated an intervention, 

how many dropped because of a lack of an 

appropriate comparator or measure 

(outcomes). Also, it is concerning that the 

"comparator" is a disputed reference standard. 

Where there is no adequate gold standard test 

or diagnostic criteria, a better "comparator" is 

what happened to the patient over time.

removed for lack of priority comparator or 

small sample size.

3. The section about quality assessment lacks 

important details. It would be helpful for the 

authors to describe the ideal study for each of 

the key questions. Then explain or justify the 

choice of instrument they used. For KQ1, the 

relevance of the Newcastle-Ottawa tool 

escapes me. If the plan was to evaluate 

diagnostic accuracy studies, why wouldn't 

something like QUADAS-2 be appropriate? 

Also, BIOCROSS is not a quality appraisal 

("risk of bias") tool, it assesses the quality of 

reporting, not of the science or study itself, so it 

should not be described as " the quality 

appraisal tool for cross-sectional studies using 

biological..." Overall I could not make out how 

the tool (Newcastle-Ottawa+items from 

BIOCROSS) could be used to assess the 

quality of either diagnostic accuracy studies or 

cross sectional studies of biomarkers. 

Considering that the end of the report 

summarizes limitations of the studies, there 

seems to be a mismatch between the 

instruments you used to assess the studies 

and the problems you found with them. The 

mismatch might be because you included 

studies that might be described as "discovery" 

studies but assessed "risk of bias" as if they 

were clinical studies.

Regarding the ideal study, the material on 

pp54ff describing the problems of the literature 

would be much stronger if, up front, you 

described a study that would be strong.

The QUADAS-2 tool is used for diagnostic 

tests, though none of our studies actually 

examined a diagnostic test, so the 

QUADAS-2 would be largely irrelevant. We 

added to the Quality Assessment section 

indicating that had we found studies of 

validity of diagnostic tests, we would have 

used the QUADAS-2. 

We modified the language associated with 

BIOCROSS, as suggested.

Most of the studies that were identified 

were cross-sectional or case-control 

studies and looked for associations 

between GWI case status and a specified 

set of biomarkers. For this reason, we 

believe the Newcastle-Ottawa items were 

the most applicable. We did note some 

limitations with the Newcastle-Ottawa tool, 

which is why we used this descriptive 

approach rather than a definitive rating of 

ROB.

We agree with your comment about 

describing the ideal study and have added 

a description of what an ideal diagnostic 

test study would like.

4. The report doesn't give me confidence that 

what was excluded was not of interest and 

what was included was of interest; that is, the 

authors need to show why applying these 

PICOTS does not exclude material of interest. 

It is by no means obvious that these PICOTS 

make sense. Why would studies that don't 

For KQ1, we did not find any studies 

testing validity of diagnostic tests, 

regardless of the case definition used for 

the comparator group, so we did not miss 

any studies by using these criteria. We 

have added language in the report to 

describe this as well.
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relate to CDC or Kansas be excluded? 

Wouldn't that close off research that could 

demonstrate there is a better definition? Those 

instruments are said to be the "best" for what 

the IOM was interested in, but case definition, 

research definition, and other criteria might be 

best for studying biomarkers. 

The approach of "included" studies vs 

"excluded" studies also doesn't serve the 

purpose of the review very well, at least without 

more detail about what was excluded. A 

landscape of the 270 potentially relevant 

studies could be useful--make a table of how 

many of these evaluated each biomarker 

(similar to Figure 3, but for the 270 studies). In 

a review intended to inform a state of the 

science conference, it is important to describe 

what has been studied. You might show, eg, 

that there were 30 studies of energy 

metabolism, only 2 of which were included.

For KQ2/3, we have expanded upon the 

table of 72 studies that were not included 

in the body of the report either due to 

n<25, a non-ideal comparator group (i.e., 

comparator groups other than deployed 

GWV without GWI and with or without 

other health conditions. We have added to 

the table heading this description of what 

was included.

5. p54 "To establish a biological metric capable 

of making this distinction would require 

biological measures to be compared between 

cases versus individuals without GWI and with 

other health conditions with overlapping 

symptomology with GWI. The ability of a 

biologic measure to distinguish GWI when 

comparing patients with symptoms to healthy 

patients without symptoms may not translate to 

its ability to distinguish GWI from another 

illness in patients presenting with symptoms 

(which is more typically the context in which a 

diagnostic test would be used)." These 

sentences are confusing. You have 2 goals 

here--one is to explain when healthy controls 

are not appropriate, and the other is what to do 

instead. Again, this section should start with 

your view of what a good study would look like, 

then contrast what you found with it. This must 

be done because putting out there what a good 

study would look like will establish conceptually 

what you are measuring the actual studies 

against. Do healthy controls have any role in 

evaluation at all? I would say they do--as an 

early test, a discovery test, it could be useful to 

see which markers differ from sick and well 

people. Next, you would want to do a different 

kind of study, perhaps still retrospective, with 

comparisons to other illnesses (as you say). 

Then, if a biomarker passes these phases, the 

best design is prospective and in a prospective 

study one doesn't pick cases and controls at 

all--one identifies a cohort of patients in whom 

GWI is suspected, and then applies the 

marker, and then follows up to see who is 

We have added to the description of the 

ideal study. We also added a description of 

the potential utility of the table of other 

studies. 
GWI Biological Measures and Diagnostic Tools Evidence Synthesis Program

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actually diagnosed with GWI (preferably 

without knowledge of the biomarker result). 

The people who are not diagnosed with GWI 

may be diagnosed with something else or may 

be undiagnosed. So these sentences are really 

only about studies that pick cases and controls, 

and they imply that instead of healthy controls, 

investigators should pick people with (known) 

other illnesses. That isn't really always the 

case--it is only a step in the early evaluation of 

a biomarker.

Minor comments 

1. p24 lines 13-17. Do phospholipids come in 

"species"? This may be the right term but it is 

new to me.

Thank you. Yes, it seems that different 

phospholipids can be referred to as 

“species”. This was the terminology used 

in the study.

2. The actual writeups of studies on pages 23-

24 and pp29-30 is quite good, but some 

studies, particularly ref 28 and 42, merit a more 

detailed critique in the text.

For reference 28 and 42 we have added 

additional detail.

3. p24 line 42 "reporting' should be 'report" I 

think

Thank you. We changed “reporting” to 

“reported” where indicated.

4 1. I'm concerned you threw some of the baby 

out with the bath water by excluding the 72 

studies with non-priority comparator groups. Is 

there nothing that can be learned by including 

those studies in this review, perhaps in a 

separate category and then triangulating the 

findings with findings from the studies with 

better comparator groups? This is especially 

important in the field of GWI research given the 

paucity of data, the frustration with the lack of 

progress in its understanding, and the amount 

of resources expended.

We have expanded on the table of 76 

studies to include study findings and 

indication of whether or not there were 

statistically significant findings related to 

associations between GWI and biological 

measures.

2. Almost all included studies were cited for not 

providing adequate power calculations. When 

differences were reported between groups 

(KQ2), was there not, empirically speaking, 

adequate power to detect a difference? When 

there was no difference, I understand how a 

power calculation is critical in assessing 

whether the study contributes to our 

understanding. Also, is it sometimes possible 

to calculate the power from the results and 

methods reported in the publication? If so, did 

the review team do this?

We request from future studies certain 

information that would increase the 

consumer’s ability to determine level of 

confidence in the findings. Our conclusions 

were not greatly influenced by lack of 

methodological information like this. More 

heavily weighted factors were the great 

heterogeneity in biological measures and 

the comparator group.

3. I concur that subgroup analysis is a vital 

strategy to better understanding the diverse 

symptoms afflicting Gulf War Veterans with 

Gulf War Illness. I do not think the Haley 

subsyndromes should be promoted as a 

standard approach for doing this, however. The 

subsyndromes were developed on a small 

cohort and have not been replicated. I do not 

recall exactly at the moment, but I believe the 

Thank you. We changed the language to 

recommend a stratification similar to the 

Haley categorizations, without 

recommending Haley categorizations 

specifically.
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sample size was so small that even if randomly 

selected from the population (which they may 

not have been), they are likely not 

representative of the population. The 2014 

VA/DoD Clinical Practice Guideline for the 

Management of Chronic Multisymptom Illness 

used the labels fatigue-, GI- and painpredominant CMI which correspond to CFS, 

IBS, and FM. This labelling has been 

abandoned in the current draft of the 2020 

update to that CPG, but still has clinical 

relevance. I'm not suggesting this approach, 

but merely highlight that the subgroups of GWI 

are also far from settled.

4. I appreciate the discussion (pp. 54-55) of the 

need for a comparison group with similar 

symptoms to GWI, but have some questions. 

Many such potential comparison groups have 

biomarkers that would differentiate them from 

GWI. Why then would a biomarker for GWI 

established compared to a healthy group, not 

be of value if it is different from the biomarker 

for a condition with similar symptoms? For 

example, if we were to select multiple sclerosis 

(MS) as the appropriate symptomatic 

comparison group for GWI, there are already 

biomarkers that differentiate GWI from MS. If 

we found a marker for GWI compared to 

healthy comparators that is different from the 

markers for MS, we wouldn't confuse GWI and 

MS with that biomarker any more than we 

currently do. It would be a remarkable advance 

in the diagnosis and care of Veterans with 

GWI. Also, what other conditions would be 

suitable comparison groups for GWI in

general? Using this approach, one would likely 

only find a biomarker for pain, or fatigue, or 

cognitive deficits or whatever the symptom of 

focus is in GWI, not for the constellation of 

chronic multiple symptoms. Do your findings 

lead you to conclude that is the best approach?

We agree that a study to examine a 

healthy control compared to GWI could 

shed some light on biomarkers that could 

be researched further for a diagnostic 

biomarker for GWI. Thus, we have 

expanded upon the Appendix Table D to 

provide more information about studies 

including healthy controls, including 

findings. Still, we prioritized studies with 

comparators that would give us the most 

useful information about a biomarker as a 

GWI diagnostic tool. Specifically, an ideal 

biomarker would enable us to differentiate 

GWI from another illness.

5. On p. 55 there is a discussion of the general 

lack of information in the included studies 

about the distribution of the data and outliers 

and how they were handled. I assume this is a 

matter of degree, but most of these were peer 

reviewed; are you holding these studies to too 

high a standard? Or are you extracting 

information from studies that focused on 

reporting other findings and therefore these 

were not held to a high enough standard? In 

other fields/conditions, are comparable studies 

reported in a manner more consistent with the 

standards you applied?

We hope that our synthesis provides some 

guidance for how biological marker 

research might be more transparent in 

their reporting, that would increase the 

consumer’s ability to determine level of 

confidence in the findings. Our conclusions 

were not greatly influenced by lack of 

methodological information such as the 

handling of outliers. More heavily weighted 

factors were the great heterogeneity in 

biological measures and the comparator 

groups included in the studies
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6. Finally, are there NO specific areas that 

seem promising for differentiating, even at a 

group level, Veterans with GWI from Veterans 

without GWI after this review? If you had to 

pick one or two where we should invest 

resources, which would they be?

The emphasis of our review was to map 

out what biomarkers have been studied. 

Had there been strong enough evidence in 

any one direction, we would highlight that. 

We attempted to synthesize the larger 

biological systems in which the majority of 

the extant research had focused,, but we 

were unable to identify any specific 

biomarkers with sufficient strength of 

evidence. 

5 Minor correction on p.51 row 37, NR should be 

Steele, L.

Thank you. We made the suggested 

adjustment.

Minor correction on p.56 row 57, The US Army 

Medical Research and Material Command 

should read The US Army Medical Research 

and Development Command.

Thank you. We made the suggested 

adjustment.

General Comments:

Included studies were limited to those with 1) a 

comparator population of deployed healthy or 

deployed with health conditions other than GWI 

and 2) greater than 25 participants. The 

following are concerns with this limitation:

1) Because there is currently no objective, 

evidenced-based case definition of GWI, 

selection of the reported “ideal” comparator 

group (GWV without GWI and with a condition 

with overlapping symptoms) is problematic.

We agree the lack of a gold standard is 

problematic and has both hindered the 

field’s ability to develop and validate a 

biological test for GWI and hindered our 

ability to comment on the ability of such a 

biomarker to distinguish those with GWI 

from those without GWI (KQ1). We were 

very inclusive of case definitions of GWI for 

KQ2 and 3. Further, we have expanded 

upon the table of excluded studies to 

provide additional information such as 

findings from studies that included a 

comparator besides a healthy, deployed 

GWV.

2) Participants selected from the same 

battalion was considered a limitation; however, 

given reported exposure differences depending 

on deployment location, branch of service, etc. 

subgrouping may in fact be a reasonable 

approach to biomarker research rather than a 

one size fits all approach to this multi-symptom 

illness. It is likely there will not be a single 

diagnostic criteria or tool. Future consideration 

of further GW Veteran subgrouping, including 

by molecular characteristics, may facilitate 

biomarker discovery and would allow for use of 

smaller sample sizes.

Thank you. We agree that inclusion of 

individuals from the same battalion, some 

who developed GWI and sone who did not, 

could provide important and nuanced 

insights about how GWI develops, etc. Our 

rationale for calling this a limitation, was in 

the context of considering how the findings 

of one study might be applicable or 

generalizable to a larger group (in 

evidence synthesis, this is known as 

applicability). We do acknowledge in the 

discussion, the importance of subgrouping 

this complex and heterogenous illness. 

3) Many clinical biomarker studies in the GWI 

field are exploratory, as the underlying 

pathobiology of the illness is still being 

discovered (primarily in preclinical systems). 

An evidence-based framework is necessary 

We have expanded upon the table of 

excluded studies to include findings and 

indication of statistical significance in the 

association between biological measures 

and GWI.
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prior to pursuing larger scale clinical validation. 

Therefore, a goal of many early clinical 

biomarker investigations is refinement of 

existing hypotheses rather than testing validity. 

These investigations provide a platform to 

generate preliminary data and give direction to 

future investigations. It would be worthwhile to 

pursue an evaluation of excluded studies that 

did not meet the comparator and group size 

threshold, but that show promise for replication 

and future validation. This would allow 

prioritization of the most promising 

pathways/potential diagnostics going forward.

4) The GW Veteran population is limited with 

respect to recruitment. Unlike disease fields 

where there are new cases each year, the 

deployed 1990-1991 Gulf War population is 

relatively small and difficult to recruit. 

Compounding effects of aging in this 

population create additional obstacles. These 

confounding variables should be taken into 

consideration when determining an appropriate 

sample size, particularly for exploratory and 

pilot translational studies. Again, appropriate 

subgrouping (and potentially smaller group 

sizes) may be the most reasonable approach.

We have expanded upon the table of 

excluded studies to include findings and 

indication of statistical significance in the 

association between biological measures 

and GWI.

Observation - The lack of outcome assessor 

blinding may reflect financial shortfalls in 

technical expertise, database management, 

and biostatistical assistance. This is an 

important consideration for GWI research 

funders.

Thank you for this insight. We have added 

a comment about this in our discussion.

The strategic and specific funding mechanism 

pipeline implemented by the DoD CDMRP 

GWIRP in FY19 will aid translation of research 

in this area. A description of this strategy could 

be considered for the Future Research section. 

FY20 and beyond, the GWIRP will be 

continuing this funding pipeline composed of 1) 

a discovery stage representing innovative 

biomarker research that is in the earliest 

stages of development; 2) a qualification stage 

representing research already supported by 

preliminary or published data in the GWI field 

that is ready for validation through expansion, 

replication, or comparative studies; 3) a 

verification stage representing clinical 

translation (testing in a GW Veteran 

population) of concepts previously replicated 

and validated; and 4) a confirmation stage

representing large-scale confirmatory and 

pivotal trials that will transform and 

revolutionize the clinical management of GWI. 

Objective biomarkers to measure the biological 

effect of an intervention or predictive/cohortThank you for notifying us of this strategic 

funding pipeline. We have added this in the 

future research section of the discussion.
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selective biomarkers are required in the 

conformation stage. This promotes biomarker 

and diagnostic assay development and 

validation simultaneously with testing of new 

treatments instead of as separate steps in the 

development process.

6 This report is a comprehensive review of 

studies of potential biomarkers of the Gulf War 

Illness, reviewing studies of GWI patients 

compared to deployed veterans without GWI. 

The report is very well written, easy to follow, 

and detailed and concise enough. Intro and 

methods look good to me. I just have a few 

suggestions re results and discussion:

Thank you.

Results:

In table 2, all the fMRI studies measure the 

same thing: BOLD signal during a task, which 

is an indicator of changes in the activation in 

different areas of the brain in response to a 

task. So for avoiding confusion, “The biological 

measures examined” should be the same for 

all those, and can be “brain activation.”

Thank you. We have modified so that the 

data collection column for all fMRI studies 

refers to ‘brain activation’.

Unsure why Zhou et al, examining pain is 

classified under “ANS”. There are several 

different mechanisms involved in pain 

tolerance, and ANS does not seem to be the 

major one. This work can come under the 

“Other Biological Systems”

Thank you. We moved the Zhou et al study 

from the ANS section to the Other 

Biological Systems section, as suggested.

Weiner 2011, An spectroscopy study should be 

under “CNS” category, and not genes.

We have moved Weiner 2011 from the 

genetic to the CNS category, as 

suggested.

Nagelkirk, 2003, could come under ANS. We have moved Nagelkirk, 2003 into the 

ANS category.

Results:

It might be helpful to add a brief paragraph in 

the beginning of each section (or in the intro) 

about why researching each of these systems 

(immune, ANS, CNS, etc) sounded reasonable 

for this illness.

We agree that it is important to put into 

context the involved biological systems, we 

included a sentence and some additional 

language in the introduction about the 

rationale and hypotheses about the 

involvement of each of these systems in 

GWI.

Similarly, it might be helpful to add a couple 

sentences about each (or some of the less 

commonly known by general readers) measure 

addressed here. For instance: what is 

squalene antibody? Or what is the function of 

the candidate genes in genetic studies, and 

why were they selected?

Due to the extensive heterogeneity of 

studies, this level of information was not 

feasible to include.

Discussion: 

I understand the studied biomarkers are all 

over the place and the findings are 

inconsistent, but it will be helpful to address the 

consistencies in findings of these limited 

studies. For instance, HRV and ANS seem 

As you point out, there were very few 

studies of any one biological measure. 

Because of that, the review is meant to be 

a map of what has been studied.
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more consistent, although very limited number 

of studies.

Since the ongoing studies are mentioned in 

this report, it might be helpful to discuss how 

those studies might be informed by, and 

following (or not following) trajectory of the 

published studies. Is there a direction where 

the research seem to have been following? 

And what are the new areas which have not 

been addressed before, and why are those 

selected? To me there seems to be a heavier 

focus on neuroinflammation, plus addition of 

mitochondrial and gut microbiome studies.

I think the above is important as an aim of this 

report is to help inform the future research.

We agree and have tried to synthesize this 

by listing the respective frequency of 

extant studies for each of the biological 

systems, to provide insight to what has and 

has not been studied thus far. 

I think one of the inherent limitations of such 

studies, and challenges of this field which 

might be worth mentioning is the time x illness 

interaction. The chronic illness over many 

years since the Persian Gulf War might have 

led to differences in health behaviors and 

lifestyle (e.g. chronically reduced activity and 

exercise due to fatigue, medications side 

effects, etc.) among GWI veterans compared 

to healthy controls. These differences could 

lead to some of the current or future detected 

differences (e.g. cardiovascular) which could 

not have been a part of the illness itself, but a 

consequence. In that sense, it will be important 

for future studies to consider such confounding 

variables (BMI, level of activity, comorbid 

psychiatric and cardiovascular disorder, 

medications) when researching differences 

between GWI and control groups.

Thank you. This is a good suggestion; we 

have added a sentence about this in the 

future research section. In addition, one of 

our quality assessment rating questions 

examined whether important confounders 

such as the presence of other chronic 

illnesses were considered during sample 

selection. For the immune section, there 

were many exclusions of those who might 

have chronic health conditions that would 

muddy the interpretation of the 

immunological measures included in the 

study. On the other hand, exclusion of 

those with comorbid chronic illness might 

become increasingly challenging as this 

populations ages and may miss an 

important part of the diagnostic test utility, 

which is to differentiate GWI from other 

conditions.