Metabolic Phenotyping During Stress Hyperglycemia in Cardiac Surgery Patients
Phase: PHASE4
Conditions: Stress Hyperglycemia
Sponsor: Emory University

Study Protocol 
 
Title: Metabolic Phenotyping During Stress Hyperglycemia in Cardiac 
Surgery Patients 
 
NCT Number: NCT03743025 
 
Protocol date: July 30, 2020 

Title:
Metabolic Phenotyping During Stress Hyperglycemia in Cardiac Surgery 
Patients
 
Principal Investigator:        
1Francisco J. Pasquel, MD, MPH*
                                           
Assistant Professor of Medicine
                                           
Department of Medicine/Endocrinology
                                           
Emory University 
Co-Investigators: 
1Guillermo Umpierrez, MD,* 2Michael Halkos, MD*, 3Elizabeth Duggan, MD
Collaborators/Advisors:    1Dean Jones, PhD; 1Jessica Alvarez, PhD; 1Thomas Ziegler MD; 1Karan Uppal, 
4Richard Kibbey, MD, PhD; 4Silvio Inzucchi, MD
 
Statistician: 
5Liming Peng, PhD   
1 Department of Medicine, Emory University
2 Department of Surgery, Emory University 
3 Department of Anesthesiology 
4Yale School of Medicine  
5Rollins School of Public Health 
*Investigators who will have access to identifiers and conduct research under this protocol (consenting, etc.)
Correspondence:
       
Francisco J. Pasquel, M.D., M.P.H.
                                         
Assistant Professor of Medicine
       
Emory University School of Medicine
       
       
       
       
       

Version 6 July 30, 2020
2
ABSTRACT: 
Research: Stress hyperglycemia is common in the perioperative period and is associated with 
increased risk of postoperative mortality. Counterregulatory hormones and inflammatory mediators 
appear to modulate the acute biological response to stress; however, the pathophysiological 
pathways that result in stress hyperglycemia and its link to poor clinical outcomes are not well 
understood. Comprehensive metabolic profiling (metabolomics) represents a novel tool to examine 
metabolic changes during stress conditions. The current approach to treat hyperglycemia with insulin 
has major limitations including high resource utilization and high risk of hypoglycemia. The 
overarching hypotheses of this research proposal are that: 1) Stress-hyperglycemia is associated with 
changes in unknown and known metabolites (i.e. branched-chain amino acids, acylcarnitines, 
ceramides) and path-specific biomarkers (representing inflammation, coagulation, cell stress, and 
immune modulation) in the perioperative period in cardiac surgery patients, and 2) with a low risk of 
iatrogenic hypoglycemia, exposure to a long acting glucagon like peptide-1 receptor agonist (GLP-1 
RA) will ameliorate the hyperglycemic and inflammatory response to surgical stress. Using 
comprehensive metabolic profiling methods, this study will provide new insights into the mechanisms 
underlying the metabolic and inflammatory responses to surgical stress (Aim 1). This study will also 
examine whether exposure to a long acting GLP-1 RA can improve glycemic control and ameliorate 
the inflammatory response to acute surgical stress (Aim 2). These findings will provide proof-of 
principle to support the use of novel therapies to prevent and manage stress hyperglycemia in the 
inpatient setting.

Version 6 July 30, 2020
3
SPECIFIC AIMS    About 400,000 patients undergo coronary artery bypass graft surgery (CABG) yearly. 
Stress hyperglycemia (blood glucose [BG] >140 mg/dl) occurs in ~60-80% of non-diabetic patients during 
CABG and is associated with increased perioperative complications and mortality. The underlying mechanisms 
that lead to hyperglycemia in response to stress during cardiac surgery are not well understood. 
Even though stress hyperglycemia and diabetes are primarily disorders of glucose metabolism, other 
dimensions including lipids (i.e. acylcarnitines, ceramides) and amino acids [i.e. branched-chain amino-acids 
(BCAA)] have been identified in association with hyperglycemia and diabetes through high-resolution 
metabolomics (HRM) techniques. Furthermore, HRM has identified BCAA, ceramides, and acylcarnitines as 
important markers of cardiovascular disease (CVD) risk.  In acutely ill patients presenting with severe 
hyperglycemia we observed a positive association between BCAA and delayed resolution of hyperglycemia 
during intravenous insulin therapy. These relationships suggest that circulating and increased release of 
amino-acids and acylcarnitines during hormone-mediated catabolism during stress could mediate insulin 
resistance, beta-cell dysfunction, and cardiovascular (CV) risk in patients with CAD undergoing surgery. In 
addition, path-specific biomarkers representing inflammation [C-reactive protein (CRP)], hyper-coagulation 
[fibrin degradation products (FDP)], cell stress [heat shock protein-70 (HSP-70)], and immune modulation 
[soluble urokinase-type plasminogen activator receptor (SuPAR)] have emerged as strong predictors of death 
in patients with CVD. Previous studies suggest hyperglycemia can lead to activation of these pathways. 
Continuous insulin infusion therapy (CII) is the treatment of choice of stress hyperglycemia. Although 
effective in improving BG control, CII is costly and labor intensive requiring hourly measurements of glucose 
levels; and carries a high risk of iatrogenic hypoglycemia (12% to 35%), which is associated with increased risk 
of complications and death. Incretin-based therapies have emerged as an effective alternative to insulin 
therapy in improving glycemic control with minimal risk of hypoglycemia and additional cardiovascular benefits. 
By augmenting glucose-dependent insulin secretion and inhibiting glucagon secretion, glucagon-like peptide-1 
receptor agonists (GLP1-RA) target the underlying pathophysiology of stress-induced hyperglycemia and could 
represent a novel prevention and treatment approach with a very low risk of hypoglycemia. 
This project will fill several knowledge gaps by characterizing the underlying mechanisms and metabolic 
signatures of stress hyperglycemia with the use of comprehensive metabolic profiling methods and provide 
proof-of principle with the use of novel therapies for the prevention and management of stress-hyperglycemia.
Aim 1. To examine baseline and postoperative metabolic profiles of non-diabetic CABG patients with 
stress hyperglycemia. With a case-control design we will conduct the following analyses: 
1.1 Targeted Approach: To determine cross-sectional and longitudinal changes of BCAA, ceramides, 
acylcarnitines, inflammatory markers, and a biomarker panel for CV risk [CRP + FDP + HSP-70, SuPAR] and 
their relationship with stress hyperglycemia (BG >140mg/dl) and insulin resistance estimates. We hypothesize 
that elevated ceramides, acylcarnitines, branched-chain amino acids (BCAAs) are associated with incident 
stress hyperglycemia, and that hyperglycemia leads to changes in path-specific biomarkers related to CV risk.  
1.2 Untargeted Analysis: Utilizing untargeted HRM analyses, we aim to identify additional candidate 
metabolites and metabolic pathways associated with incident stress hyperglycemia during cardiac surgery. We 
hypothesize that known (candidate metabolites) and previously unrecognized metabolites and pathways are 
associated with hyperglycemia during surgical stress. 
1.3 Integrative analysis: Metabolome-wide association studies (xMWAS) and integrative network analyses will 
be conducted to find multilevel associations between metabolites, biomarkers, and stress-hyperglycemia. We 
hypothesize that multiple factors associated with diabetes and cardiovascular disease are interrelated and 
predict hyperglycemia during stress conditions. 
Aim 2. To obtain preliminary estimates of the effect of a long-acting GLP-1 RA on the prevention of 
stress-hyperglycemia and modulation of metabolic stress during cardiac surgery. We propose a 
randomized study with assignment to dulaglutide or placebo three days prior to surgery in non-DM obese 
patients (BG<126 mg/dL, HbA1c <6.5%, BMI ≥25, age≥40 years) undergoing CABG surgery.  
2.1 Primary endpoint: to determine if dulaglutide can prevent stress-hyperglycemia.  
2.2 Secondary endpoints: to determine if dulaglutide can result in a lower mean BG during ICU and hospital 
stay. We will also explore the need of rescue therapy with insulin and differences in clinical outcomes. 
2.3 Comparison of changes in candidate metabolites and path-specific biomarkers, as well as beta-cell 
function and insulin resistance estimates in response to dulaglutide or placebo. 

Version 6 July 30, 2020
4
We will test the hypothesis that exposure to a single dose of dulaglutide can ameliorate the hyperglycemic 
and inflammatory response to surgical stress with a low risk of iatrogenic hypoglycemia. 
A) SIGNIFICANCE 
Coronary artery 
disease (CAD) remains a predominant 
cause of death globally1,2 About 400,000 
patients undergo coronary artery bypass 
graft surgery (CABG) each year in the 
United States5 and more than 80% of 
patients with a known history of diabetes 
(DM) and ~60% of non-DM patients 
develop stress-hyperglycemia [blood 
glucose (BG) >140 mg/dl] after surgery.6-9 
Perioperative hyperglycemia is associated 
with higher rates of wound infections,10,11 
kidney injury,8 longer hospital stay,12,13 
and mortality.10,12,14-17 In non-DM subjects, 
stress-hyperglycemia is associated with 
up to 4-fold increase in complications and 
a 2-fold increase in death compared to 
patients with normoglycemia and to 
subjects with DM.18-27 
The mechanisms leading to stress-
hyperglycemia and the association with 
poor clinical outcomes are not well 
understood. Acute metabolic and 
hormonal changes associated with the response to injury can result in insulin resistance, increased hepatic 
glucose production, and relative insulin deficiency.28-31 Elevated Inflammatory mediators can also contribute to 
hyperglycemia during injury; conversely, hyperglycemia itself can promote inflammation, oxidative stress, and 
a pro-thrombotic state 32-36 (Fig. 1). Metabolomics analysis of human biosamples has undergone a rapid 
technological evolution in recent years and represents a powerful tool to investigate the complexity of all the 
interactions within a complex individual. Even though hyperglycemia and diabetes are primarily considered 
disorders of glucose metabolism, other dimensions including lipids and amino acids have been consistently 
identified in association with prediabetes and diabetes through high-resolution metabolomics techniques 
(HRM).37,38 Analyses of large cohorts using HRM have reported positive associations of branched-chain amino 
acids (BCAAs) and aromatic amino acids (AAAs) with incident prediabetes or diabetes.37-39 Products of fatty 
acid metabolism (i.e. acylcarnitines) are also associated with insulin resistance, insulin secretion defects, and 
incident diabetes.40-42 These relationships suggest that circulating and increased release of amino acids and 
acylcarnitines during hormone mediated catabolism during stress could mediate insulin resistance and beta-
cell dysfunction in surgical patients, Table 1 and Fig 1. Furthermore, HRM has identified BCAA, ceramides, 
and acylcarnitines as important markers of CVD risk.43-46
Table 1.
Candidate metabolites 
and known biomarkers
Rationale / Clinical relevance
Protein Metabolism 
BCAA (Isoleucine, leucine, 
valine) 
Aromatic Acids (tyrosine, 
phenylalanine) 
Other: glycine, 
glutamine/glutamate ratio 
Meta-analyses of multiple cohorts show that BCAA, AAA increase the risk of chronic 
hyperglycemia. Some AA are protective (glyceine, glutamine/glutamate ratio)37,47. BCAA 
lead to mTOR/S6K1 kinase pathway activation and serine phosphorylation of IRS1 leading 
to insulin resistance. BCAA catabolic flux can lead to glutamate transamination to alanine 
and increased gluconeogenesis, as well as insulin secretion defects.48-50 BCAA are also 
associated with CVD.42 The product of valine catabolism, 3-HIB has emerged as a 
potential mediator linking BCAA to regulation of fatty acid flux and insulin resistance 
(IR).51,52 We observed higher abundance of 3-HIB in subjects with hyperglycemic crises 
and delayed response to insulin suggesting IR.  Recently, 3-HIB was associated with 
incident diabetes.53 
Lipid Metabolism 
Acylcarnitines, ceramides / 
sphingosine, 
triacylglycerols (TAGs), 
Elevated medium chain acylcarnitines lead to pancreatic β-cell dysfunction.40 Ceramides, 
and acylcarnitines are associated with insulin resistance and incident CVD41,44-46,54,55 
Sphingosine, a ceramide derivate is elevated in prolonged ketoacidosis, and is associated 
with insulin resistance.56 
Carbohydrate 
Metabolism 
Glucose, Insulin
Hyperglycemia can lead to inflammation, oxidative stress, and a pro-thrombotic state 32-36
HOMA-IR and HOMA-B are well-described surrogates of insulin resistance and insulin 
secretion derived from insulin and glucose levels. 
Figure 1. Study overview. Aim 1 is focused on understanding the underlying mechanisms that lead 
to stress hyperglycemia and increased cardiovascular risk in cardiac surgery patients (see Table 1). 
Green box: main groups of candidate metabolites to be assessed with metabolomics techniques. 
Red box: inflammatory markers and biomarker risk score components known to predict CV 
outcomes. Blue box: assessments of glycemia, insulin secretion (HOMA-B) and insulin resistance 
(HOMA-IR). Aim 2  is focused on obtaining preliminary estimates on stress hyperglycemia 
prevention and glycemic control (RCT) with a strategy with low risk of iatrogenic hypoglycemia. 

Version 6 July 30, 2020
5
Hormonal response 
 
Cortisol, glucagon
Counterregulatory hormones are expected to increase during cardiovascular surgery 
(GLUCO CABG).3,4
Inflammatory pathways 
TNF-a, IL-6, CRP 
TNF-a, IL-6 are Inflammatory markers known to cause insulin resistance (TNF-a, IL-6) and 
insulin secretion defects57-62 and associated with cardiovascular risk. Inflammatory cytokine 
concentrations are acutely increased by hyperglycemia in humans.36 Levels of 
inflammatory markers are increased for days after CABG surgery.3 CRP is an established 
CVD risk factor.63 
Cell stress 
Heat shock protein (HSP) 
Hyperglycaemia upregulates HSP60 & HSP70.64 HSP70 is associated with incident 
CVD.2,65,66 (Quyyumi) 
Immune pathways
suPAR 
SuPAR is a marker of immune activation associated with all-cause death and myocardial 
infarction in patients with CVD. 
Pro-thrombosis 
Fibrin degradation products 
(FDP) 
Hyperglycemia and diabetes are associated with a prothrombotic state.  FDP associated 
with incident cardiovascular disease in patients with and without diabetes.2,65,66
BCAA: branched-chain amino acids, AAA aromatic amino acids, 3-HIB 3-Hydroxyisobutyric acid
Recently, four biomarkers identified susceptible individuals and predicted meaningful clinical endpoints.65-
68 A simultaneous evaluation of biomarkers representing inflammation (C-reactive protein), coagulation (fibrin 
degradation products), cell stress (HSP-70), and immune pathways (suPAR) significantly predicts risk of death 
in patients with coronary artery disease (CAD).65,66 Previous research suggests that hyperglycemia can lead to 
activation of these pathways36,64,69,70, however; no longitudinal assessment of the relationship between 
biomarkers and stress-hyperglycemia has been conducted. It is important to characterize the molecular 
changes that occur during stress hyperglycemia to identify preventive strategies and maximize the potential to 
find cost-effective interventions. To fill this knowledge gaps, we will conduct targeted and untargeted analyses 
to further understand the underlying biology of metabolic changes during surgical stress. 
Large number of observational and randomized control studies have shown that improvement of 
glycemic control reduces perioperative complications in critically and non-critical ill patients but is 
also associated with high risk of hypoglycemia. Glycemic control with continuous insulin infusion therapy 
(CII) can reduce complications and mortality,11,71-76 particularly among non-DM subjects.77 In the GLUCO-
CABG trial4, we reported a significant reduction in a composite of perioperative complications in non-DM 
patients with stress-hyperglycemia treated with intensive vs conservative glycemic control. Although 
effective,71,72,76 the use of CII is costly and labor intensive, and carries a 12-35% risk of hypoglycemia.78-82 
Hypoglycemia after cardiac surgery has been associated with increased risk of complications and mortality.83-85
Hospital use of incretin-based therapies is a safe alternative to complex insulin regimens. To 
simplify the management of hyperglycemia and avoid the risk of hypoglycemia we have examined the role of 
DPP-4 inhibitors86  and GLP-1 receptor analogs (RA) in the hospital setting (see preliminary results). GLP-1 is 
an incretin hormone secreted in the gut in response to meal ingestion, targeting multiple tissues. One of its 
main targets is the pancreatic β-cell, resulting in increased insulin secretion and inhibition of glucagon 
production.87 In patients with and without diabetes,88-90 native GLP-1 and GLP1-RA treatments have shown to 
improve glycemic control similar to insulin administration during corticosteroid-induced hyperglycemia,91 acute 
cardiovascular (CV) events,91 in the perioperative period of patients undergoing CABG surgery,92 and mixed 
ICU populations.93 In patients with CV disease, compared to insulin therapy native GLP-1 resulted in better 
glycemic control, fewer arrhythmias,94 and improved cardiac function,95,96  CV events.97,98 
Dulaglutide, a long-acting GLP-1RA suitable for once-weekly administration,99 have demonstrated 
improved glycemic control compared to other antidiabetic agents.100-102 Phase 1 pharmacodynamics analysis in 
non-DM subjects showed median maximum plasma concentrations of dulaglutide between 24-48 hours after a 
single dose.103 Evaluation with stepped glucose infusion 3 days after dulaglutide administration revealed a 
dose-dependent rise in insulin secretion and associated decline in serum glucose concentration.103 Compared 
to short-acting GLP-1 RA, dulaglutide results in fewer and dose-dependent gastrointestinal side effects.103,104 
Dulaglutide has also been shown to be better tolerated compared to other long-acting GLP-1RA.105 GLP-1 RA 
can be used before surgery in patients with diabetes.106,107 The above data suggest that a single subcutaneous 
administration of dulaglutide can improve glucose-dependent insulin secretion and decrease glycemic 
excursions that may result in stress-hyperglycemia prevention with low risk of hypoglycemia.
B) INNOVATION  
1) The underlying mechanisms leading to stress hyperglycemia and poor hospital outcome are not well 
understood. It is unknown if hyperglycemia is a mediator in the development of hospital complications, or a 
bystander marker only representing severity of disease. Serial assessments of relevant candidate metabolites 
and biomarkers before and after the acute stress episode will facilitate our understanding of potential 
mediators of stress hyperglycemia and the metabolic response to stress. CABG surgery represents a unique 
human model to study in depth these relationships. 

Version 6 July 30, 2020
6
2) Metabolomics (HRM) is an emerging scientific field that enables profiling of thousands of metabolites to 
provide a functional readout of cellular metabolism in plasma and other bio samples. The use of metabolomics 
can identify metabolites and metabolic pathways implicated in the development of stress hyperglycemia. We 
expect to find an association of metabolites derived from protein and fatty acid catabolism (such as BCAA, and 
specifically 3-HIB) with stress hyperglycemia. In addition, serial assessments of glucose levels, candidate 
metabolites and biomarkers as well as an integrative network analysis to find multilevel associations between 
them will facilitate for the first time an approximation to causality interpretations. No previous studies have 
applied metabolomics analysis to understand stress-hyperglycemia. Novel bioinformatics tools such as 
metabolome-wide association studies (xMWAS) have the capability to facilitate an integrative network analysis 
to find multilevel associations and identify metabolic pathways and network structures in association with 
stress-hyperglycemia. 
3) We will examine biomarkers associated with meaningful cardiovascular disease outcomes that represent 
inflammation (C-reactive protein), coagulation (fibrin degradation products), cell stress (heat shock protein-70), 
and immune activation (suPAR). Hyperglycemia can lead to activation of these pathways. This study will for 
the first time examine longitudinally the relationship between clinically relevant biomarkers and stress-
hyperglycemia in patients with established cardiovascular disease. 
4) Current approach to manage hyperglycemia is a reactive care model. In a setting associated with high 
incidence of stress-hyperglycemia, we will test for the first time (Aim 2) if the use of a GLP-1 RA can prevent 
the onset of stress-hyperglycemia (proactive care) during the perioperative period of cardiac surgery. The use 
of novel agents with prolonged action is ideal for this purpose. 
5) The proposed study will provide critical first steps aiming at understanding and preventing stress-
hyperglycemia and will lay the groundwork to study the links between stress-hyperglycemia and hospital 
complications (career goal). This strategy has the potential to impact millions of patients undergoing cardiac 
surgery and to other scenarios associated with high risk of stress hyperglycemia and related complications. 
6) Next steps (R03/R01 applications): Results from Aim 1: once the most relevant metabolites are identified I 
plan to conduct primed continued tracer infusions to better understand if concentration changes are related to 
faster, slower production or both. I will conduct metabolic flux analysis in vivo to further examine the dynamic 
flow of specific metabolites through metabolic pathways during stress conditions. To understand what tissues 
are responsible for these changes, I will collaborate with scientists such as Dr. Kibbey to design additional 
studies. I also plan to integrate multi-omics approaches (metabolomics along with genomics/epigenomics) and 
reproduce findings in independent cohorts. Results from Aim 2 will provide preliminary estimates on 
hyperglycemia prevention, insights into risk stratification, and characterization of subjects most likely to 
respond to GLP-1RA to design additional RCTs.  
7) In summary: this project will under the guidance of a mentoring/advisory team: a) leverage the state-of-the-
art innovative technology available at Emory by applying new high-resolution metabolic profiling capabilities 
developed by co-mentor Dr. Jones and collaborators Dr. Yu, Dr. Uppal and Dr. Ziegler; b) integrate data with 
clinically meaningful biomarkers; c) help understand the underlying biology of stress hyperglycemia (under the 
guidance of Dr. Kibbey and Dr. Alvarez); and e) test potential solutions along with experts in inpatient diabetes 
(Dr. Umpierrez and Dr. Inzucchi).
C) PRELIMINARY DATA     Our research team has extensive research experience in inpatient management of 
hyperglycemia and has published several randomized controlled trials in ICU and non-ICU settings.24,27,86,108-114 
C.1. Impact of perioperative stress-hyperglycemia: In a retrospective analysis of glycemic control and hospital 
outcomes in 1971 patients with confirmed preoperative 
normoglycemia, we observed an increase in complications 
and mortality in patients developing stress-hyperglycemia.115 
Compared to patients with normoglycemia (<140 mg/dl), 
patients with stress-hyperglycemia had a longer length of 
hospital stay (LOS) and higher rates of complications and 
mortality (all, p<0.001). After adjusting for age, gender, BMI, 
race, and Charlson comorbidity score, compared to patients 
with normoglycemia, those with postoperative BG 140-180 
mg/dl had higher odds for both complications [odds ratio 
(OR) 1.7 (1.2 – 2.4)] and mortality [OR 1.7 (0.4-7.1)]. The OR 
Figure 2. Non-DM patients randomized to the intensive group had 
a lower composite of complications compared to subjects in the 
conservative target.4
!!
Intensive (BG 100-140 mg/dl) 
Conservative (BG 100-140 mg/dl) 
P=0.008!

Version 6 July 30, 2020
7
for complications and mortality in patients with BG >180 mg/dl were 3.5 (2.2-5.4) and 6.6 (2.1-20.3), 
respectively. 
C.2. GLUCO-CABG Trial:24 in this RCT we aimed to determine whether intensive BG control (target: 100-140 
mg/dl) could reduce perioperative complications compared to conservative BG control (141-180 mg/dl) in 
hyperglycemic patients undergoing CABG. A total of 302 patients were randomized to intensive (n=151) or 
conservative (n=151) glucose control. The mean ICU daily BG was 132±14 mg/dl (IQR 124-139) in the 
intensive and 154±20 mg/dl (IQR 142-164) in the conservative group (p<0.001). Overall, we observed a similar 
number of patients in the intensive and conservative groups experiencing ≥ 1 complications (42% vs. 52%, 
p=0.08). We observed no differences in the composite or individual complications in patients with DM; 
however, the composite of complications was lower in non-DM patients with stress-hyperglycemia assigned to 
the intensive group, p=0.008, (Fig.2). Since all patients enrolled GLUCO-CABG patients had stress-
hyperglycemia, we recently analyzed phenotypic and biological characteristics of non-diabetic individuals at 
risk for perioperative stress hyperglycemia during CABG. The two most important features in patients who 
developed >2 episodes of BG >140 mg/dL were older age (67.0 ± 9.2 vs 59.1± 9.4, p=0.004) and higher BMI 
(28.9 ± 5.8 vs 26.1 ± 4.3, p=0.06). All six patients older than 50 years and BMI>30kg/m2 developed 
hyperglycemia. These characteristics were confirmed in an additional cohort (i.e. age >45 years). Older age 
and obesity are well-established risk factors for insulin resistance and DM. 
C.3. Inflammatory markers during CABG: Circulating 
levels of cortisol, inflammatory and oxidative stress 
markers increase for several days in the perioperative 
period (Fig. 3).  No difference was observed between 
conservative and intensive insulin therapy groups in the 
perioperative period of patients undergoing CABG 
surgery. Changes in these markers did not explain the 
benefits associated with tighter glycemic control with 
insulin therapy.3 
C.4. GLP-1 RA safety and non-inferiority to insulin 
therapy. In a recent RCT, we observed that a similar proportion of subjects reached goal (70-180mg/dL) with 
exenatide alone (61%) compared to basal bolus (63%) in the hospital. Exenatide (twice a day) was well 
tolerated, with higher, but not significant incidence of gastrointestinal side effects. Longer acting (once a week) 
GLP-1 RAs have been shown to be non-inferior or superior to insulin therapy and have a lower incidence of  
gastrointestinal side effects compared to exenatide or other short acting GLP-1 RAs.116-119 Recently dulaglutide 
was shown to have lower incidence of side effects compared to another long acting GLP-1 RA, semaglutide. 
Native GLP-1 continuous infusion (very short half-life) was well tolerated in patients undergoing CABG.120 GLP-
1 RA may be used before surgery in patients with DM.106,107 
C.5. Biomarkers and CV Death: Dr. Quyyumi et al. have demonstrated that a biomarker risk score (BRS) 
comprising suPAR (a biomarker of immune system activation and of inflammation) in addition to CRP 
(inflammation), FDP (coagulation), and HSP-70 (cell stress) significantly predicts 
all major outcomes in patients with coronary disease independently of traditional 
risk factors (Fig. 4). Correlations between biomarkers are modest suggesting 
that they act independently involving separate biological pathways.30 This 
findings have been recently reproduced in CABG patients (BARI-2 trial).2 
 C.6. Metabolomics in patients with hyperglycemic crisis: Following a 
metabolomics analysis flow developed in the lab of Dr. Jones (Fig. 7) we 
examined plasma samples from 29 adults with diabetic ketoacidosis (DKA). Out 
of 4,144 metabolites, we found 
216 significant baseline 
features that differed between 
patients with severe and non-
severe DKA; 87 features were 
down-regulated and 129 were 
up-regulated in severe 
DKA. A total of 373 
metabolites differed 
according to duration of 
continuous insulin 
infusion therapy. 
C.7. Protein and lipid 
catabolism: We observed that 
Figure 3. Cortisol and inflammatory markers during CABG surgery (Source:  
GLUCO-CABG trial)3,4
Figure 4. Path-specific biomarkers predict 
Mortality in patients with CAD. 30 
Figure 5. 3-Hydroxyisobutyrate (Panel A) a product of BCAA and 
sphingosine (Panel B), a product of lipid metabolism was associated with 
prolonged resolution of hyperglycemic crises in patients with diabetes, 
suggesting prolonged resistance to insulin (Pasquel et al. preliminary data)

Version 6 July 30, 2020
8
3-Hydroxyisobutyrate (3-HIB), and sphingosine were associated with prolonged resolution of ketoacidosis 
suggesting resistance to insulin therapy (Fig. 5) BCAA are implicated in the development of insulin resistance 
based on epidemiological and experimental data.49 3-HIB a catabolic intermediate of the BCAA valine is 
secreted from muscle cells, activates endothelial fatty acid transport, stimulates muscle fatty acid uptake in 
vivo and promotes lipid accumulation in muscle, leading to insulin resistance.52 Recently, 3-Hydroxyisobutyrate 
has been associated with insulin resistance in humans and has also been identified as a predictor of incident 
type 2 diabetes.52,53,121  In addition, sphingosine, a sphingolipid derived from ceramide was also associated 
with prolonged resolution of ketoacidosis. Ceramides and acylcarnitines are associated with insulin resistance 
and incident CVD. 41,44-46,54,55  These initial metabolomics analyses represent an innovative approach in 
diabetes related research. To better understand mechanisms leading to stress-hyperglycemia in hospitalized 
patients (career goal) we will apply these technologies in a setting were a baseline and longitudinal 
assessment is more feasible (elective CABG surgery) and very clinically relevant.  
 
In summary, our preliminary studies indicate that stress-hyperglycemia is common and associated with 
increased rate of complications. Plasma BCAA, ceramides and acylcarnitines provide an intermediate link 
between metabolic stress, overnutrition, and mechanisms underlying insulin resistance and cardiovascular 
disease (CVD). Metabolic changes during hyperglycemia are complex and further explorations before and after 
stress in patients with and without perioperative stress-hyperglycemia are needed. Previous studies suggest 
that protein catabolism related products are associated with increased risk of pre-diabetes and diabetes. Our 
results suggest that 3-Hydroxyisobutyrate (3-HIB) a product of valine catabolism, and sphingosine are 
associated with prolonged response to insulin in acutely ill patients. A simultaneous and longitudinal 
assessment of biomarkers and relevant metabolites before and after surgery is essential to understand these 
complex relationships.  
D) APPROACH
 General overview: The proposed research will 
test in a nested case- control study 
associations of individual metabolites and 
broader metabolomics profiles as well as 
counter-regulatory hormones and biomarkers 
with stress-hyperglycemia (Aim 1). For this 
analysis patients will be stratified according to 
the outcome of stress-hyperglycemia (cases) 
and compared to controls (no hyperglycemia). 
For Aim 2 a randomized clinical trial will be 
conducted to examine the effects of exposure 
to dulaglutide on the prevention of stress-
hyperglycemia and the metabolic inflammatory 
response in the perioperative period, (Fig. 6).
Specific Aims
D1. Case – control study
Aim 1. To examine baseline and postoperative metabolic profiles of non-diabetic CABG patients with 
stress hyperglycemia.
Rationale: the underlying mechanisms leading to stress-hyperglycemia and its association with poor outcomes 
in cardiac surgery patients are not well understood. We aim to study metabolic signatures associated with 
hyperglycemia and path-specific biomarkers. We will test the temporal relationships between candidate 
metabolites/pathways (Table 1) and specific biomarkers with stress-hyperglycemia. 
Study design: In a nested case-control study (N=50) we will compare the characteristics of patients developing 
postoperative stress-hyperglycemia (cases) compared to patients that do not develop stress-hyperglycemia 
(age-matched controls). A proportion of patients will be derived from the placebo arm of the randomized trial. 
This design will allow efficient recruitment from our cohort of CABG patients to allow substantial savings in cost 
Figure 6. General overview. Aim 1 will be tested with a case-control study design. In Aim 2 we 
will conduct a clinical trial.  

Version 6 July 30, 2020
9
and time. Case definition: stress-hyperglycemia is defined as a BG >140mg/dl on arrival to the ICU. Please 
see additional details in Human Subjects section.  
Study Population: we will include patients with ages between 18-80 years without a history of diabetes [BG 
≤125 mg/dl and HbA1c <6.5%] undergoing CABG surgery. Patients will be recruited from three academic 
hospitals, including Emory University Hospital, Emory Hospital Midtown (EUHM), and Grady M Hospital in 
Atlanta, GA. Dr. Halkos (Chief of Division and Director of the CTS Center for Clinical Research) is a key 
collaborator and will facilitate recruitment and perform CABG surgeries. 
Aim 1.1 Targeted Approach: We will determine cross-sectional and longitudinal changes of ceramides, 
acylcarnitines, BCAA, and a biomarker panel for CVD outcomes and their relationship with stress 
hyperglycemia (BG >140mg/dl on ICU arrival). Path-specific biomarkers include: TNF-α and C-reactive protein 
(inflammation), fibrin degradation products (pro-thrombosis), heat shock protein-70 (cell stress), and suPAR 
(immune modulation and inflammation). Counterregulatory hormones include: cortisol, glucagon. Glucose, 
insulin and indexes of insulin sensitivity/insulin resistance: will be derived from glucose, insulin and c-peptide 
values. Methods: Plasma samples will be stored at − 80 °C until analysis. Serum CRP and FDP including 
fragments D and E, D -dimer, and additional intermediate cleavage products will be determined (FirstMark, 
San Diego, CA). Serum HSP-70 will be measured with a sandwich enzyme-linked immunosorbent assay (R&D 
Systems, Minneapolis, MN). Plasma suPAR levels will be measured using commercially available kits 
(suPARnostic kit; Virogates, Copenhagen, Denmark). Minimum detectable CRP, FDP, HSP-70, and suPAR 
have been reported at 0.1 mg/L, 0.06 μg/mL, 0.313 ng/ml, and 0.1 ng/ml, as described previously.65-68 Liquid 
chromatography tandem mass spectrometry techniques will be used to quantitatively profile ceramides, 
acylcarnitines, branched chain amino acids (leucine, isoleucine, valine), and aromatic amino acids (tyrosine 
and phenylalanine) in plasma samples along with standard reference samples. Glucose levels will be analyzed 
by the hexokinase method (Beckman-Coulter, Los Angeles, CA). Counterregulatory hormones, insulin, C-
peptide levels by two-site sequential chemiluminescent immunometric assays. Surrogates of beta cell function 
(HOMA-B) and insulin resistance (HOMA-IR) will be derived from fasting glucose, c-peptide and insulin. 
Analysis: Student’s t test and χ2 tests will be used when appropriate. Mann–Whitney U or Kruskal–Wallis 
nonparametric tests will be used for non-normally distributed variables. Analysis will account for age-matching. 
The relationship between biomarkers and stress-hyperglycemia and surrogates of insulin sensitivity and beta 
cell function will be determined using the Cox proportional hazards regression in unadjusted models and in 
models adjusted by gender, and smoking status to evaluate the impact of each 1 U increase in a biomarker 
risk score based on previously defined cut points for CRP, FDP, HSP-70, and suPAR (3 mg/L, 1.0 
μg/mL,0.313 ng/ml, and 3.5 ng/ml, respectively) as previously described.65,66,68

Version 6 July 30, 2020
10
1.2 Untargeted Analysis: Utilizing untargeted 
LCMS-derived high-resolution metabolomics (HRM) 
analysis we aim to identify additional candidate 
metabolites and new metabolic pathways associated 
with incident stress hyperglycemia during cardiac 
surgery. Methods: HRM analysis is a major 
component of my career development plan. Data 
analysis will be conducted under the supervision of 
Dr. Jones. Data Interpretation will be conducted 
under the guidance of Dr. Richard Kibbey (expert in 
metabolism). I will also have local support from Dr. 
Alvarez for interpretation of metabolomics results, as 
well as statistical support from Dr. Tianwei Yu. Dr. 
Uppal will provide guidance for hands-on use of 
metabolomics tools. Co-mentors and collaborators are 
experts in the data management requirements of the 
LCMS-derived high-resolution metabolomics (HRM) 
analysis proposed in this project. Serial samples will be 
collected: at baseline (up to seven days before surgery), and intra-op at 15 minutes (after cardiac bypass has 
started for on pump surgeries) or 2hrs after sternum incision (for off pump surgeries), 24hrs, 72-96hrs and 30 
days after surgery. Plasma samples will be stored at −80°C until analysis. The methods use UPLC with ultra-
high resolution mass spectrometry with a dual chromatography, dual ionization approach with each sample 
being analyzed with three technical replicates.122-124 With advanced data extraction provided by xMSanalyzer 
and apLCMS, I will be able to also measure a spectrum of high and low abundance metabolites. We will utilize 
a well-established computational metabolomics workflow with rigorous standard operating procedures outlined 
in Fig. 7.124 Data Selection and Annotation (Biostatistics and Bioinformatics Tools): HRM data will be 
processed using apLCMS with xMSanalyzer 125,126. Feature annotation will be performed using 
xMSannotator.127 xMSannotator provides confidence scores for more than 2,000 metabolites, with more than 
300 metabolites having confirmed identities and including most major pathways. The quality of data will be 
judged based on established rigorous criteria (e.g. coefficient of variation within replicatesand QC samples, the 
percentage of missing values, the percentage of data exceeding detection limits of the machinery, batch-
effects, and the percentage of outliers, etc.). Principal Component Analysis (PCA) will be used for batch-effect 
evaluation and ComBat122 will be used for batch- effect correction if necessary. Identifying expressed 
metabolites across different time points: we will use univariate and multivariate statistical methods, such as 
linear mixed effects models with post-hoc Tukey tests, LIMMA, and multilevel sparse Partial Least Squares 
Discriminant Analysis (msPLSDA).128,129 Multiple hypotheses testing correction will be performed using the 
Benjamini-Hochberg method.130 Identifying putative biomarkers: we will use a consensus feature selection 
approach such that metabolic signatures identified by both univariate and multivariate approaches will be used 
for downstream analyses. K-fold and leave-one-out cross-validation using Support Vector Machine (SVM) will 
be performed to evaluate predictive accuracy of putative biomarkers. Two-way hierarchical clustering analysis 
will be performed to identify clusters of correlated metabolic features and to evaluate the ability of the 
discriminatory features to separate samples based on stress-hyperglycemia. Pathway enrichment analysis: 
Pathway analysis will be performed with pathway enrichment software for untargeted metabolomics data 
(Mummichog and MetabNet)131,132 Where appropriate, additional chemical verification will be performed for 
significant metabolites of interest according to retention time and characteristic ion dissociation (MS/MS) 
patterns relative to authentic standards. Metabolome-wide association study (MWAS) of specific metabolites 
will be conducted (MetabNet) to facilitate detection of their related metabolic pathways and network structures 
in association with glucose levels (continuous) and stress-hyperglycemia as a discrete dichotomous variable. 
1.3 Integrative Analysis: A metabolome-wide association study (MWAS) of specific metabolites will be 
conducted (using MetabNet) to facilitate detection of their related metabolic pathways and network structures 
in association with stress-hyperglycemia. In addition, we will use xMWAS133 for integrative network analysis to 
find multilevel associations between glucose levels, metabolites and biomarkers.
Expected outcomes of Aim 1: During our analysis of candidate metabolites and discovery phase in Aim 1 we 
expect to find an association between candidate metabolites (i.e. BCAA, ceramides, acylcarnitines) and 
pathways associated with stress-hyperglycemia. We also expect to find an association between pathway-
xMSanalyzer
Data Table
m/z, RT, Intensity
Quality metrics
apLCMS, XCMS,
MZmine options
Biostatistics
Bioinformatics
Annotation, pathway
and network analysis: 
Mummichog, MetabNet, 
xMSannotator, others
Metabolite
forensics
Unequivocal 
Identification
MSn; UHRAM MS; 
1-D, 2-D NMR; ion
Mobility; Chemical
Reactivity, etc
MWAS 
PLS-DA
Data
extraction
ESI LC-MS 
Ultra-high resolution MS 
LTQ-FT
LTQ-Velos Orbitrap
Q-Exactive HF
Thermo Fusion 
GC-Orbitrap
Metabolites of 
interest 
Figure 7. Workflow for high-resolution metabolomics.  Samples are processed 
and analyzed in triplicate using liquid chromatography coupled to ultra-high 
resolution mass spectrometry.  Data extraction and quality assessment is 
automated with stringent quality control requirements.  Data are analyzed with 
well-established univariate and multivariate methods, with false discovery rate 
adjustment as appropriate.  Selected features are tested for pathway 
enrichment, and suspect metabolites are confirmed by coelution and MS/MS 
with authentic standards.  Reference standardization is used for quantification 
of metabolites of interest. (Uppal, Jones et al)

Version 6 July 30, 2020
11
specific biomarkers and stress-hyperglycemia with a higher degree of association according to the number of 
biomarkers based on pre-specified cut points (biomarker risk score).66 We expect to find multilevel associations 
between glucose, biomarkers and candidate metabolites. Serial measurements including measurements 
before the acute stress episode will facilitate causal interpretations. 
Potential Pitfalls, and Alternative Considerations: It is possible that some comparisons do not yield false 
discovery rate (FDR)-significant metabolites. In this scenario, and to provide a balance between type I and type 
II statistical error, metabolites with raw p<0.05 will be used for subsequent analyses, with statistical tests for 
pathway enrichment. Pathway enrichment analysis is less susceptible to type II error.134 We hypothesize that 
stress-hyperglycemia leads to elevation of path-specific biomarkers, however it is possible that pre-surgical 
elevation of biomarkers predicts the development of stress-hyperglycemia. Elucidating this scenario is 
important as identifying susceptible subjects before surgery (baseline elevation of biomarkers) will facilitate the 
design of cohort studies as well as recruitment of at-risk patients in future trials. 
D2. Randomized Clinical Trial 
Aim 2. To obtain preliminary estimates of the effect of a long-acting GLP-1 RA on the prevention of 
stress-hyperglycemia and modulation of metabolic stress during cardiac surgery.
Rationale: At least half of non-DM patients undergoing cardiac surgery develop stress hyperglycemia,8,9,72,135 
shown to be an independent risk factor of morbidity and mortality.10,12,14-16 To maximize exposure to subjects at 
high risk of stress hyperglycemia, we will enroll obese individuals older than 50 years of age. Most patients 
with stress-hyperglycemia require insulin infusion in the ICU and about half continue to receive subcutaneous 
insulin after transition to regular wards.24,136 GLP1 treatment has been shown to improve glycemic control 
similar to insulin administration in critically ill patients,88-90 CABG surgery,92 and in mixed ICU populations.93 
Dulaglutide is effective in improving glycemic control in patients with DM,100-102 with lower rate of side effects 
compared to other long acting GLP-1-RA.105 Phase 1 pharmacodynamics analysis in non-DM participants 
demonstrated median maximum plasma concentrations of dulaglutide between 24-48 hours after single-dose 
administration103 with a dose-dependent rise in insulin secretion and progressive reduction in glucose 
concentration.103 The rapid onset and sustained action, safety and efficacy with low-rate of hypoglycemia,102,137 
indicate that dulaglutide is an ideal agent for the prevention of stress-hyperglycemia.
Study Design: This randomized, placebo controlled study will include 60 obese subjects with ages between 40-
80 years of age without a history of diabetes [BG ≤125 mg/dl and HbA1c <6.5%] undergoing CABG surgery. 
Due to the study design (i.e. need of surgical care), there will be no run-in period.  Women in childbearing 
potential will have urine β-HCG measured before participating in the study.   
Study population: 
Inclusion criteria:  Males or females between the ages of 40 and 80 years and BMI ≥25kg/m2 undergoing 
elective CABG surgery. No previous history of diabetes or hyperglycemia. 
Exclusion criteria: a) Hyperglycemia (BG>125 mg/dl or HbA1c > 6.5%) or previous treatment with antidiabetic 
agents; b) impaired renal function (GFR < 30 ml/min) or clinically significant hepatic failure; c) subjects with 
gastrointestinal obstruction expected to require gastrointestinal suction; d) patients with clinically relevant 
pancreatic or gallbladder disease; e) treatment with oral or injectable corticosteroid; f) mental condition 
rendering the subject unable to understand the possible consequences of the study; g) pregnancy or 
breast-feeding at time of enrollment. Additional considerations are outlined in the Human Subjects section. 
Aim 2.1 Prevention of hyperglycemia (primary endpoint): 
The primary endpoint of this exploratory study is to compare the frequency of stress-hyperglycemia defined as 
any post-CABG hospital-obtained BG > 140 mg/dl between patients randomized to dulaglutide or placebo. 
Aim 2.2 Secondary Endpoints: To determine the difference between groups in mean BG level in the ICU.  
Additional endpoints include differences between treatment groups on: 1) the number of patients with stress-
hyperglycemia requiring rescue therapy with SC insulin after discontinuation of CII; 2) need for CII treatment of 
hyperglycemia in the ICU; 3) mean ICU and floor BG concentration; 4) mean insulin dose during ICU (insulin 
infusion per hour (unit/hour) and per day); 5) duration of CII (hours); 6) days of SC insulin after discontinuation 
of CII; 7) amount of SC insulin in ICU and non-ICU stay; 8) hyperglycemic events (BG > 200 mg/dl) in ICU and 
non-ICU; 9) hypoglycemic events in ICU and non-ICU (<70, < 54, and <40 mg/dl); 10) composite of mortality 
and complications including sternal wound infection, bacteremia, pneumonia], acute kidney injury, and acute 
myocardial infarction; 11) gastrointestinal adverse events (nausea, vomiting, ileus, pancreatitis); 12) ICU and 
hospital LOS, and ICU readmissions; 13) cerebrovascular events; 14) ICU and hospital mortality. 

Version 6 July 30, 2020
12
Methods for Aims 2.1 and 2.2: 
Treatment Groups: Patients will be randomized at pre-
surgery/anesthesia visit. Patients without a history of DM will 
receive a single injection of dulaglutide/placebo 1-3 days prior 
to surgery (Fig. 8). 
Inform consent and randomization: Patients will be consented 
during the preoperative evaluation on admission at least 24-72 
hours prior to scheduled surgery. The investigators or study 
coordinators will review and explain the contents of the 
informed consent document to the eligible patient. Additional 
details are outlined in the Human Subjects section. 
Study Sites: This pilot study will be conducted at Emory University Hospital, Emory Midtown Hospital, and 
Grady Memorial Hospital, Atlanta.
ICU Stay: Study patients who develop stress-hyperglycemia (BG >140 mg/dl) in the ICU will be started on 
intravenous CII adjusted to maintain a BG target between 100-140 mg/dl following a standard hospital protocol. 
Transition from ICU to Regular Floor: Patients with fasting and/or premeal BG >140 mg/dl will receive 
coverage with sliding scale insulin (supplements), those with 2 consecutive BG >180 mg/dl, or average daily 
BG >180 mg/dl will be started on rescue therapy with SC basal insulin (glargine or levemir) once daily.111 
Rescue Therapy with Basal Insulin in Non-ICU Settings111:
  BG between 140-200 mg/dl= start basal insulin at 0.1 units per kg weight per day. 
  BG between 201-400 mg/dl= start basal insulin at 0.2 units per kg weight per day.
  Basal insulin will be given once daily, at the same time of the day.  
Insulin adjustment and supplemental (correction) insulin: we will use a standard insulin treatment protocol.4,86
Investigational drugs: 

Dulaglutide injection: 0.75 mg/0.5 mL solution in a single-dose pen will be provided to patients 

Saline injection/0.5 mL pre-drawn solution, a tuberculin syringe will be provided to patients

A research pharmacist at the site-specific Clinical Research Network Unit will provide dulaglutide and 
saline. Patients will be instructed on self-injection technique. 
Withdrawal Criteria (please see additional details in Human Subjects and DSMP sections):

The subject may withdraw at any time during the study by primary care provider or research team.

The subject may be withdrawn at the investigator’s discretion due to a safety concern or for contravention 
to the inclusion and/or exclusion criteria.

Subject diagnosed with acute pancreatitis by clinical and/or radiographic criteria.
Analysis: The primary endpoint of Aim 2 is the frequency of stress-hyperglycemia (BG >140 mg/dl) requiring 
insulin therapy during the postoperative period (Aim 2.1). We will use two-sided Chi-square test or Fisher’s 
exact test for the primary outcome. In Aim 2.2 we will compare the difference in mean BG upon ICU arrival and 
the frequency of rescue therapy with SC insulin after transition to regular floor. We will also explore differences 
in complications between groups. Secondary outcomes of interest can be a binary outcome (e.g. whether CII is 
needed in ICU), or a count outcome (e.g. number of perioperative complications), or a continuous outcome 
(e.g. mean ICU BG concentration). We will adopt the same strategy proposed for the primary outcome to 
analyze any binary secondary outcomes. For secondary outcomes measured as counts, we plan to use 
nonparametric tests such as Krustal-Wallis tests. Univariate Poisson regression (or Negative Binomial 
regression) will be performed to estimate the marginal treatment effect. In addition, we will also conduct 
multivariate Poisson regression (or Negative Binomial regression) to assess the outcome differences between 
the two study groups with potential confounders considered. For secondary endpoints that produce continuous 
outcomes, we will use two-sample t-tests or nonparametric Wilcoxon tests to compare study groups. 
Transformations will be applied if normality violation is detected.  
Aim 2.3. To compare changes in metabolites and biomarkers in response to dulaglutide or placebo 
(measured in Aims 1.1 and 1.2), as well as beta-cell function and insulin resistance estimates. 
Methods: Biomarkers and assessment of candidate metabolites will follow the same procedures as specified 
in Aim 1. Surrogates of beta cell function and insulin resistance will be derived from glucose, insulin, and c-
peptide measurements acquired during the perioperative period. 
Pre-
Surgery/
anesthesia
visit
Dulaglutide
Placebo
-3
-2
-1
SURG
1
2
3
4
5
6
7
Day -10 to -3
Randomization
Surgery
Study
Drug
Treatment Period (days)
Figure 8. Treatment groups in Aim 2. Patients will be assigned to 
receive dulaglutide or placebo 1-3 days before surgery. 

Version 6 July 30, 2020
13
Analytic Techniques: Glucose levels will be analyzed by the hexokinase method (Beckman-Coulter, Los 
Angeles, CA). Insulin and C-peptide levels will be measured by two-site sequential chemiluminescent immuno- 
metric assays as previously described. Surrogates of beta cell function (HOMA-B) and insulin resistance 
(HOMA-IR) will be derived from fasting glucose, c-peptide and insulin. Only glucose and c-peptide will be 
measured serially after surgery (given high likelihood of exposure to exogenous insulin).
Analysis: We will integrate analyses from Aims 1 and Aims 2 to evaluate the role of dulaglutide on metabolites, 
metabolic pathways and biomarkers. We will use univariate and multivariate statistical methods, such as linear 
mixed effects models with post-hoc Tukey tests, LIMMA, and multilevel sparse Partial Least Squares 
Discriminant Analysis (msPLSDA), as described in Aim 1.
Expected outcomes in Aim 2: In Aim 2.1 we expect to observe a decrease in the number of patients with 
stress-hyperglycemia with low risk of hypoglycemia with dulaglutide therapy. We expect to find a least an 18 
mg/dl (1 mmol/l) mean difference in BG between groups during ICU stay. In Aim 2.2 we don’t expect to find 
differences in hospital outcomes. In Aim 2.3 We expect to observe differential changes in up or down 
regulation of metabolic pathways in response to dulaglutide compared to placebo. 
Potential Pitfalls, and Possible Solutions, Alternative Consideration: Recruitment and retention of patients 
are important challenges in RCTs; however, Emory University and affiliated hospitals serve as cardiovascular 
referral center to a large population in the city of Atlanta, with over 1,000 major cardiac surgeries performed 
yearly. In our previous trial we recruited 340 patients over 3 years, therefore we expect to have enough time 
during the award period to complete enrollment (please see research timeline). It is possible that dulaglutide 
does not have an effect on the prevention of hyperglycemia. This information will help us understand the 
contribution of the incretin pathway to glucose metabolism during surgical stress. 
E. Risks to Human Subjects
E.1.1 Human Subjects Involvement, Characteristics, and Design

Description and justification for proposed involvement of human subjects: 
Extensive data from observational and prospective randomized controlled trials (RCT) in hospitalized 
patients have reported a strong association between stress hyperglycemia and poor clinical outcome, 
such as increased mortality, morbidity, hospital length of stay (LOS), infections and overall 
complications. This relationship is well established, and a dose response is consistently observed. This 
association is more robust among non-diabetic subjects compared to patients with diabetes or 
normoglycemia. Millions of cardiac surgeries are conducted globally and there is an urgent need to 
understand the mechanisms that lead to hyperglycemia during stress conditions and strategies to 
improve meaningful clinical outcomes. Cardiac surgery, in particular coronary artery bypass graft 
(CABG), is a unique human model for the study of metabolic changes during surgical stress. In our own 
study we have shown a reduction in a composite score of complications in subjects without diabetes 
undergoing CABG surgery randomized to a more intensive glycemic target. Our current approach to 
treat hyperglycemia, however, is reactive, it demands high resource utilization (staff needs to monitor 
glucose levels hourly and adjust intravenous insulin doses frequently), and if not done carefully is 
associated with a very high risk of iatrogenic hypoglycemia (up to 35%), which also carries a high risk 
of complications. This novel, exploratory study will characterize metabolic signatures during stress 
hyperglycemia, integrate findings with clinically meaningful biomarkers, and provide preliminary 
estimates of the effect of a long acting GLP-1 receptor agonist on the prevention of stress 
hyperglycemia. 

Study Population Characteristics: Clinically stable adults scheduled to undergo coronary artery 
bypass graft surgery (CABG) will be studied. We expect that a total of 90 subjects will complete this 
project. A proportion of patients will be derived from the placebo arm of the randomized trial. This 
design will allow efficient recruitment from our cohort of CABG patients to allow substantial savings in 
cost and time. For Aim 1 a total of 50 patients will be included. In Aim 2 patients will be randomly 
assigned to dulaglutide (N=30) or placebo (N=30) one to three days before surgery. For both aims 
subjects must meet the inclusion and exclusion criteria listed below: 

Version 6 July 30, 2020
14
Inclusion criteria: 1) Males or females between the ages of 40 and 80 years and BMI ≥25kg/m2 
undergoing elective CABG surgery; no previous history of diabetes or hyperglycemia. 
Exclusion criteria: 1) Hyperglycemia (BG>125 mg/dl or HbA1c > 6.5%) or previous treatment with 
antidiabetic agents; 2) Severely impaired renal function (GFR < 30 ml/min) or clinically significant 
hepatic failure; 3) Subjects with gastrointestinal obstruction expected to require gastrointestinal suction; 
4) patients with clinically relevant pancreatic (including history of pancreatitis) or gallbladder disease; 5) 
treatment with oral or injectable corticosteroid; 6) mental condition rendering the subject unable to 
understand the possible consequences of the study; 7) Pregnant female subjects or breast feeding at 
time of enrollment. 
•
Sampling Plan: Potential study subjects will be identified and pre-screened among patients scheduled 
for CABG at Emory University affiliated hospitals. Enrollment will be coordinated with cardiovascular 
surgeons. Dr. Halkos, Scientific Director, of the Cardiothoracic Surgery Center for Clinical Research 
(CCR) will facilitate enrollment in the study. A study coordinator will conduct informed consent in eligible 
subjects prior to any study procedures. Screening and recruitment reports will be generated monthly 
that include actual and expected recruitment statistics.  Strategies to address potential problems with 
recruitment will include biweekly communications between Dr. Pasquel, Dr. Umpierrez, and Dr. Halkos. 
After informed consent, baseline and post CABG blood sampling will be performed. Samples will be 
stored at Clinical Research Network (CRN) sites of the NIH-funded Georgia Clinical and Translational 
Science Alliance (CTSA).  
•
Special Populations: This study does not involve special vulnerable populations. It will, however, 
include elderly subjects with ages between 65 and 80 years of age, who are more likely to have 
advanced cardiovascular disease and require CABG surgery. Older patients are also more likely to 
develop hyperglycemia during surgery. 
•
Group Assignment / Case Definition: Clinically stable adults scheduled to undergo coronary artery 
bypass graft surgery (CABG) will be studied. For logistic purposes recruitment will favor assignment to 
the clinical trial in the beginning of the award period (Aim 2, N=60). We expect a total of 30 subjects will 
be randomized to each group (dulaglutide or placebo). Thirty patients assigned to placebo in the clinical 
trial will contribute to the cohort of patients to be included in the nested case-control study. Cases and 
controls will be categorized prospectively on arrival to the surgical intensive care unit (SICU) (Cases are 
defined as BG >140mg/dL on ICU arrival). To further avoid selection bias, 25 age-matched controls will 
be selected for comparison. Once the number of cases is completed, additional potential controls will 
be selected (+/- 3 years) based on the characteristics of the population of cases. If additional patients 
develop stress hyperglycemia no further samples will be collected.  This design will allow for efficient 
recruitment from our cohort of CABG patients to allow substantial savings in cost and time. 
•
Research Sites: All human subjects’ research will be performed on the campus of Emory University, 
Atlanta, GA, and at Emory Affiliated Hospitals. Sites include Clinical Research Units (CTSA) where 
samples will be stored. Data collected are kept confidential and protected as per HIPAA regulations. 
Data collected at each facility will be shared only with the study team.
E.1.2. Study Endpoints and Sources of Materials: 

In the case control study (Aim 1) we will compare metabolic and hormonal profiles between subjects 
that develop stress hyperglycemia compared to controls. In an open-label format (Aim 2) we will assess 
whether exposure to dulaglutide is associated with a reduction in the frequency of stress hyperglycemia 
compared to placebo (primary endpoint). Prospectively, we will assess the association of candidate 
metabolites and metabolic pathways with the onset of stress hyperglycemia. Potential mechanisms 
underlying these associations will be explored by examining whether the associations of exposure to 
dulaglutide and prevention/improvement of glycemic control can be explained by changes in metabolic 
pathways (and key metabolites within these pathways) measured in plasma that occur between 3 days 
prior to surgery (baseline) and during the perioperative period. Addressing these research questions 

Version 6 July 30, 2020
15
will be possible using innovative ultra-high resolution mass spectrometry (HRMS) with liquid 
chromatography (LC) and gas chromatography (GC) and advanced data extraction algorithms. LC-
HRMS will also measure >2000 metabolomic intermediates in plasma at the time of surgery and at 
follow-up, providing coverage of >85% of human metabolic pathways. The overall hypotheses are 
that candidate metabolites such as: ceramides, acylcarnitines, and branched-chain amino acids 
(BCAAs) are associated with incident stress hyperglycemia, and that hyperglycemia leads to changes 
in path-specific biomarkers related to CV risk. In addition, exposure to an agent associated with low risk 
of hypoglycemia can positively affect the trajectory of metabolic changes during acute surgical stress. 
Baseline measurements: Samples will be collected up to seven days prior to scheduled CABG surgery. 
Blood samples will be processed by the CRN Laboratory and stored at −80°C until ready for batch 
analysis. Fasting plasma metabolomics analysis will be performed using high-resolution metabolomics 
(HRM) methods established in Co-mentor Dean Jones’ laboratory. Samples for measurement of 
biomarkers, glucose, cortisol, glucagon, insulin and c-peptide will also be collected. Indexes of insulin 
sensitivity/insulin resistance: will be derived from glucose, insulin and c-peptide values. Indexes of 
insulin secretion: pancreatic β-cell insulin secretion capacity will be assessed with HOMA-B. 
Sources of materials: Research material will include medical history, demographic information, and 
complications obtained both from subjects, medical record, and study blood samples. 
Data collected: Data to be collected is detailed in Table 1 below, along with the source of the data. 
Baseline and daily information will be entered by Dr. Pasquel or a study coordinator into data collection 
paper forms and into an electronic database (REDCap) provided by the Emory Research Information 
Technology Department. 
Table. Data to be collected and source
Data Item
Source 
-Vitals (e.g. blood pressure, pulse, temperature)
Medical Exam
-Height, weight, BMI
Manual stadiometer and digital scale
-Demographic Information (e.g. age, race)
Self-report and medical records
-Medical history (e.g., comorbidities, surgeries)
Self-report and medical records
-Social history (e.g., education, smoking, alcohol use)
Self-report and medical records
-Indexes of insulin secretion, resistance and sensitivity
serial blood draws
-Plasma metabolomics, biomarkers, hormones 
-Dates of hospitalization and operation
-Daily laboratory results (e.g. blood glucose, creatinine, eGRF)
-Treatment (IV or SC insulin dosage, use of corticosteroids)    
Blood draw
Medical records
Medical records
Medical records 
-Complications and adverse events (defined below) 
-Length of ICU and hospital stay
Self-report and medical records 
Medical records
Data Collection on Complications 
o
Surgical wound infection (superficial and deep sternal wound infection).
o
Pneumonia (CDC criteria).
o
Acute kidney injury defined as an increment in serum creatinine ≥ 0.3 mg/dL from baseline or  
≥1.5 times baseline creatinine (KDIGO) 
o
Acute myocardial infarction (AMI): (1) typical increase and gradual decrease (troponin) or (2) 
more rapid increase and decrease (creatine kinase MB) of biochemical markers of myocardial 
necrosis with at least one of the following: (a) ischemic symptoms, (b) development of 
pathologic Q waves on the electrocardiogram, (c) electrocardiographic changes indicative of 
ischemia (ST-segment elevation or depression), or (d) coronary artery intervention (e.g., 
coronary angioplasty). 

Version 6 July 30, 2020
16

Clinical Management Guidelines: The primary care team will provide care regarding the use of 
vasopressors, ventilator support, sedation, antibiotics, and treatment of co-morbid conditions.  
a.
Assessment and monitoring of BG concentration.  Information on BG measurements both at 
bedside by glucose meter and by hospital laboratory will be collected.  BG will be measured every 
1-2 hour during continuous insulin infusion (CII) in the ICU, and before meals and at bedtime after 
transition to regular wards.  Hypoglycemia is the main adverse event and safety concern in the 
study. The number of mild (≤70 mg/dL) and severe hypoglycemia (≤ 40 mg/dL) and clinical 
consequences (neurological and cardiovascular) will be compared between groups. 
b.
Assessment and monitoring of hospital mortality.  Investigators will follow study subjects daily and 
the date and presumed cause of death will be recorded.  Information on the attending physician’s 
summary of events surrounding subject’s demise will also be documented.
c.
Assessment and monitoring of wound infections.  The investigators will review each subject’s 
records daily from Monday to Friday regarding potential new infections.  Data from the weekends 
will be collected the following Monday.  Definitions: Deep Sternal Wound Infection (DSWI): chest 
wound infection involving the sternum or mediastinal tissues, including mediastinitis; Superficial 
Sternal Wound Infection (SSWI) (not involving the sternal bone or wires): chest wound infections 
involving the skin or subcutaneous tissues, or both. 
d. Assessment and monitoring of nosocomial infections.  The following daily information will be 
collected for nosocomial infection surveillance: 1) temperature curve; 2) white blood cell counts; 3) daily 
progress notes; 4) daily clinical microbiology laboratory data; 5) all relevant radiographic reports; 6) 
orders for antimicrobial agents; 7) communication, as needed, with primary physicians and site 
infectious disease consultants; 8) use of the CDC guidelines for diagnosis of specific nosocomial 
infections. 
e. Assessment and monitoring of days on mechanical ventilation.  The need for mechanical ventilation 
will be monitored daily. The day the subject is weaned from the ventilator will be recorded.
f. Assessment and monitoring of length of SICU and hospital stay.  The morning location of the study 
subject (in either the SICU or surgical ward) and date of hospital discharge will be recorded daily. 
g. Assessment and monitoring of renal function.  Acute kidney injury - new-onset abnormal renal 
function is defined as an increment of creatinine level above 50% from baseline.  
h. Cerebrovascular accident (CVA).  Defined as central neurologic deficit persisting more than 72 hours 
(permanent stroke), transient ischemic attack, deficit resolving within 24 hours, or deficit lasting more 
than 24 hours but less than 72 hours (reversible ischemic neurologic deficit).
   Data collection and data entry.  Study members will enter baseline and daily data for this study into 
data collection paper forms and into an electronic database provided by the Emory Research 
Information Technology Department. Baseline data will include demographics/history form (subject 
gender, date of birth, ethnicity, dates of hospitalization and operation, history of diabetes, and comorbid 
conditions, body weight, BMI, smoking status, type of surgery, APACHE score. Daily information will be 
collected on treatment (insulin IV or SC and dosage, antibiotics, use of corticosteroids), nutrition 
support, BG and laboratory values, hospital complications and adverse events, and length of ICU and 
hospital stay.
E.1.3. Potential Risks

Potential Risks/Discomforts: 
a.
Venipuncture: There is some minor discomfort and risk of mild bruising during venipuncture. 
Standard sterile techniques will be used during phlebotomy; thus, infection is unlikely. Disposable 
pre-sterilized needles and syringes will be used for all blood drawing in this study; needles and 
syringes will not be reused. Discomforts associated with venipuncture are rapidly reversible. 
Approximately 40 mL of blood will be obtained for all the blood measurements proposed.

Version 6 July 30, 2020
17
b.
Hypoglycemia: In our previous ICU studies, we observed that <10% of cardiac surgery subjects 
during CII and 20-30% during SC insulin experienced hypoglycemia. No episodes of severe 
hypoglycemia were observed during CII in our study. The use of insulin in combination with GLP1-
RA may increase the risk of hypoglycemia.  The number of mild (≤70 mg/dL) and severe 
hypoglycemia (≤ 40 mg/dL) and clinical consequences (neurological and cardiovascular) will be 
compared between groups. Treatment of hypoglycemia: hypoglycemia, defined as a BG <70 mg/dL 
will be treated by a standard hypoglycemia protocol available at each hospital.
c.
Hyperglycemia: it is very likely that a high proportion of patients randomized to placebo develop 
hyperglycemia. All subjects with hyperglycemia (in both groups) will be treated by standard 
protocols in the ICU units at each hospital. We expect that ~50-60% of subjects randomized to 
placebo will experience one or more episodes of hyperglycemia. The frequency of severe 
hyperglycemia will be analyzed statistically.  
d.
Gastrointestinal adverse effects: include nausea and vomiting, which are more common in patients 
treated with dulaglutide compared to placebo. The frequency of nausea and vomiting is reported in 
up to 10% of patients receiving dulaglutide in combination to oral agents compared to 3.5% in 
patients receiving placebo plus oral agents. If a participant developed nausea during hospitalization 
the research team will perform a phone call visit on post-discharge day 2 to ensure safety. There 
have been few reported events of acute pancreatitis with the use of GLP1-RA (typically reported 
among patients at higher risk of pancreatitis).  Subjects should be informed of the characteristic 
symptoms of acute pancreatitis: persistent, severe abdominal pain. If pancreatitis is suspected, 
potentially suspect medicinal products should be discontinued. Patient will receive a single 0.75mg 
dose of dulaglutide. If the investigator suspects acute pancreatitis, all suspected drugs should be 
discontinued until confirmatory test have been conducted and appropriate treatment should be 
initiated. Subjects diagnosed with acute pancreatitis (as a minimum 2 of 3: characteristic abdominal 
pain, amylase and/or lipase >3xUNR or characteristic findings on CT scan/ MRI) should be 
withdrawn from the study.

Alternative treatments: The alternative is for a participant to not participate in the trial.
E.1.4 Adequacy of Protection Against Risks
a.
Recruitment and Informed Consent

Recruitment and Informed consent: Potential study subjects will be identified and pre-screened among 
patients undergoing CABG surgery at Emory affiliated hospitals. 

Dr. Pasquel or the study coordinator will conduct informed consent in eligible subjects prior to any 
study procedures. Screening and recruitment reports will be generated monthly that include actual and 
expected recruitment statistics.  

Description of informed consent process: A written and signed informed consent will be obtained in 
person in a private area. After identification of a person as potentially eligible for the study, approved 
study personnel will explain the study in detail in the consent form as well as verbally. Potential 
participants will be informed of the purpose of the study, the study protocol, and the routine and 
potential risks associated with the study procedures. All risks, costs, and benefits will be discussed.  
Participants will be assured of their right to withdraw at any time without prejudice to their care. They 
will be assured of confidentiality in maintaining records and reporting of results. A separate consent 
will also be obtained for permission to store biological samples for future studies. Any additional 
information regarding questions or concerns from potential or confirmed study participants will be 
provided. Participants will have ample time to call the study team with questions/concerns and make 
an informed decision about their participation in the study. 
An Institutional Review Board (IRB) approved study designee will provide verbal and written consent. 
The study is discussed thoroughly with each potential participant, and he/she will be encouraged to 
ask questions regarding the study and her participation. If the potential participant decides to enroll, 
he/she will sign the written informed consent, as will the study team member conducting informed 
consent. A copy of the signed consent form will be provided to enrolled study participants. The 

Version 6 July 30, 2020
18
consent process will be documented using a template provided by the Emory IRB in the "Consent 
Clinical Trial Tools" section of the Emory IRB website.
Subjects must be able to read, write and understand the consent documents. Study personnel will 
subjectively assess the capacity to give informed consent through the use of questions to ensure 
understanding and comprehension of the study. Only adults will participate in this study.

Waivers: We will request a partial HIPAA waiver to be able to access medical records for screening.
b.
Protections Against Risk

Protections against risks: Adverse events are expected to be uncommon and not pose more than 
minimal risk to the study participants. 
o
Subject identification and screening for eligibility: Our strict inclusion and exclusion criteria for 
entry (outlined above) will help to minimize potential risks. All subjects will be monitored closely 
by frequent clinical assessments and review of safety laboratory tests by experienced 
clinicians. In addition, we will carefully monitor capillary BG at the bedside using the hospital 
certified meter, b) only experienced nurses/or phlebotomist will draw blood samples, c) patients 
will be closely monitored in the ICU, d) no patients with history of significant pancreatic, renal 
or hepatic failure will be recruited in this study.  
o
Blood sampling: Experienced research nurses will use aseptic techniques for all study blood 
draws. Participants will be asked to stay hydrated with water the day before the study visit. 
Universal precautions will be employed in all instances involving human specimens and all 
blood processed under at least BSL2 conditions. Discomforts associated with venipuncture are 
rapidly reversible. During surgery trained personnel will collect the blood samples. 
o
Hyperglycemia: All patients that develop hyperglycemia will be treated by standard insulin 
protocols in the ICU and non-ICU units available at each hospital. 
o
Hypoglycemia: In our previous ICU studies, we observed that <10% of cardiac surgery 
subjects during CII and 20-30% during SC insulin experienced hypoglycemia. No single 
episode of severe hypoglycemia was observed during CII in our study in CABG patients. The 
use of insulin in combination with GLP1-RA may increase the risk of hypoglycemia.  The 
number of mild (≤70 mg/dL) and severe hypoglycemia (≤ 40 mg/dL) and clinical consequences 
(neurological and cardiovascular) will be compared between groups. Any episode of 
hypoglycemia (BG <70 mg/dL) will be treated by a standard hypoglycemia protocol available at 
each hospital.
o
Privacy/Confidentiality: The subjects’ extracted medical records and data from the study 
will be initially stored on paper prior to transfer to electronic format. The encrypted 
electronic record will remain on the principal investigator’s password protected computer 
in his locked office. Confidentiality will be assured by the use of subject codes rather than 
personal identifiers. The master list connecting the subject codes to identifying information 
will be secured in the Emory’s computerized database. All paper subject records will be 
kept in locked file cabinets in the PI’s research office and will be accessible only to the PI 
and the investigative team. Only institutional review board (IRB)-approved study 
personnel will have access to individually identifiable information about human subjects. 
This may include the study PI, co-investigators, research coordinators, and other 
approved study personnel. All information and materials will be obtained for research 
purposes only and the data will be kept in strict confidence for use in this proposed 
research only.
o
Other: Since the proposed tests are not inherently hazardous, hazard is likely to occur 
only as the result of impaired participant confidence or sudden unwillingness to complete 
a test. To avoid this possibility, study personnel will thoroughly explain all tests to potential 
participants prior to them signing the consent form. Potential participants will have the 
opportunity to see all test equipment and facilities before giving consent or undergoing 
testing. Every effort will be taken to prevent injury or distress that may result from this 

Version 6 July 30, 2020
19
study. Dr. Pasquel, or a study physician will be on call for support. A qualified physician 
will have primary responsibility for inpatient care.
o
Data and Safety Monitoring: A senior faculty Safety Officer, with expertise in randomized 
controlled clinical trials will be designated as the Safety Officer for this study. He/she will 
review semi-annual reports created by Dr. Pasquel and make recommendations regarding the 
continuation of study. Dr. Pasquel will review this protocol on a continuing basis for subject 
safety and include the results of the review in semi-annual reports to the Safety Officer and 
annual progress reports submitted to the IRB. A full Data and Safety Monitoring Plan (DSMP) 
is provided in a separate document. 

Medical intervention: If a participant suffers from distress during a procedure, the procedure will 
be discontinued. The participant will then be managed in the Emory affiliated Hospital surgical suite 
or in the CRN unit, which, if applicable or warranted, have immediate access to EKG monitoring, 
cardiac resuscitation equipment and are located in close proximity to Emergency Departments. 

Every effort will be taken to prevent injury or distress that may result from this study. A study 
physician will be on call for support. A qualified physician will have primary responsibility for 
inpatient care.
E.1.5   Potential Benefits of the Proposed Research to Human Subjects and Others

Potential Benefits: There may be no specific health benefit to patients’ participation in the study. 
However, participants will receive information on their blood glucose levels during the hospital stay, 
which they may use to inform their own health decisions/activities. 

Risk/Benefit Assessment: The risks incurred are minimal relative to the potential benefit of gaining 
information about individuals’ health. Our group has extensive experience in glycemic control in the 
hospital and we will follow closely all patients for timely and appropriate management of 
hyperglycemia and hypoglycemia. 
E.1.6. Importance of the Knowledge to be Gained
•
Importance of Knowledge: The long-term goal of this study is to identify underlying 
mechanisms that lead to hyperglycemia and their association with established biomarkers 
known to predict risk of death in patients with coronary disease. This study has the potential to 
identify clinically meaningful metabolites to guide the development of important biomarkers (to 
be assessed in independent cohort studies) that can help to stratify patients at risk for hospital 
complications. The findings that arise from this research will also inform scientists in developing 
new technologies and designing future therapies to better understand and treat metabolic 
derangement during acute stress. We also aim to prevent hyperglycemia with an agent 
associated with lower risk of iatrogenic hypoglycemia. If our hypothesis is correct, such 
information could guide future clinical trials aimed at preventing hyperglycemia with the goal of 
impacting meaningful endpoints (cost, hospital complications, and mortality). 

Risk/Importance Assessment: The anticipated benefits of the proposed research study outweigh the 
potential risks to participating subjects. The potential for serious adverse effects is small and the 
potential benefit to society is high enough for the studies to proceed. 
E.1.7 Data and Safety Monitoring Plan (DSMP): Please see DSMP provided as a separate document. 
E.1.8 ClinicalTrials.gov Requirement: This clinical trial will be registered on ClinicalTrials.gov. Final 
results will be published on this site. 
E.1.9 Statistical Considerations 

Version 6 July 30, 2020
20
Sample Size Calculation and Power Analysis: with a Wilcoxon test, for an effect size >1 at alpha=0.05 and 
power=0.80, the proposed sample size of N=50 for this experiment will be more than adequate for the main 
objective of identifying differentially expressed metabolites and pathways in Aim 1, as shown in previous 
studies.134,138 For Aim 2 we assumed N=60. No preliminary data on prevention of hyperglycemia is available. 
Assuming an effect size of ~0.50 on hyperglycemia prevention with X2 goodness of fit for contingency tables, 
power 80%, alpha=0.05, a total of 52 patients would be needed. In on our previous RCT (GLUCO-CABG4) 
non-DM patients in the conservative group had a mean BG 143mg/dl vs. 127mg/dl in the intensive group. This 
difference was associated with a lower composite score of complications in the intensive group. It is 
reasonable to assume the standard deviation of the mean BG is bounded around 40 mg/dl. With two-sample t 
tests or Wilcoxon tests, alpha=0.05, 25 subjects for each arm would be needed to ensure 80% power to reject 
the hypothesis that the mean BG in patients treated with dulaglutide is not 16 mg/dl lower compared to 
placebo. Accounting for ~15% attrition rate this leads to a final total sample size estimate of 60 patients for 
both considerations. This study is not powered to assess differences in hospital complications between groups. 
E.1.10 Confidentiality 

Access: Only institutional review board (IRB)-
approved study personnel will have access to 
individually identifiable information about human 
subjects. This may include the Emory study 
Investigators who are directly involved with human 
subjects (Francisco J. Pasquel, Guillermo E. 
Umpierrez, Michael Halkos), and other approved 
study personnel (e.g. research coordinators).

Protection and Management of data: All 
information and materials will be obtained for research purposes only and the data will be kept in strict 
confidence for use in this proposed research only. Confidentiality will be assured by the use of subject 
codes rather than personal identifiers on questionnaires, blood collection and storage tubes. All subject 
paper records will be kept in locked file cabinets and will be accessible only to the investigative team. 
The master list connecting the subject codes to identifying information (name, birth date, phone 
number, address, clinic visit dates, study dates) will be secured in a computer database (REDCap). All 
data maintained in computerized databases will be accessible only with a login and protected 
password. After the study is completed, all data will be kept according to NIH and FDA regulations in a 
locked file.
E.1.1 References
1.
Pasquel FJ, Gregg EW, Ali MK. The Evolving Epidemiology of Atherosclerotic Cardiovascular 
Disease in People with Diabetes. Endocrinology and metabolism clinics of North America. Mar 
2018;47(1):1-32. 
2.
Ghasemzadeh N, Brooks MM, Vlachos H, Hardison R, Sikora S, Sperling L, Quyyumi AA, 
Epstein SE. An Aggregate Biomarker Risk Score Predicts High Risk of Near-Term Myocardial 
Infarction and Death: Findings From BARI 2D (Bypass Angioplasty Revascularization Investigation 2 
Diabetes). Journal of the American Heart Association. Jul 3 2017;6(7). 5586254
3.
Reyes-Umpierrez D, Davis G, Cardona S, Pasquel FJ, Peng L, Jacobs S, Vellanki P, Fayfman 
M, Haw S, Halkos M, Guyton RA, Thourani VH, Umpierrez GE. Inflammation and Oxidative Stress in 
Cardiac Surgery Patients Treated to Intensive Versus Conservative Glucose Targets. J Clin 
Endocrinol Metab. Jan 01 2017;102(1):309-315. 
4.
Umpierrez G, Cardona S, Pasquel F, Jacobs S, Peng L, Unigwe M, Newton CA, Smiley-Byrd 
D, Vellanki P, Halkos M, Puskas JD, Guyton RA, Thourani VH. Randomized Controlled Trial of 

Version 6 July 30, 2020
21
Intensive Versus Conservative Glucose Control in Patients Undergoing Coronary Artery Bypass Graft 
Surgery: GLUCO-CABG Trial. Diabetes care. Sep 2015;38(9):1665-1672. PMC4542267
5.
Alexander JH, Smith PK. Coronary-Artery Bypass Grafting. The New England journal of 
medicine. Sep 08 2016;375(10):e22. 
6.
McAlister FA, Man J, Bistritz L, Amad H, Tandon P. Diabetes and coronary artery bypass 
surgery: an examination of perioperative glycemic control and outcomes. Diabetes care. May 
2003;26(5):1518-1524. 
7.
Carvalho G, Moore A, Qizilbash B, Lachapelle K, Schricker T. Maintenance of normoglycemia 
during cardiac surgery. Anesth Analg. Aug 2004;99(2):319-324, table of contents. 
8.
Schmeltz LR, DeSantis AJ, Thiyagarajan V, Schmidt K, O'Shea-Mahler E, Johnson D, Henske 
J, McCarthy PM, Gleason TG, McGee EC, Molitch ME. Reduction of surgical mortality and morbidity 
in diabetic patients undergoing cardiac surgery with a combined intravenous and subcutaneous 
insulin glucose management strategy. Diabetes care. Apr 2007;30(4):823-828. 
9.
Moghissi ES, Korytkowski MT, DiNardo M, Einhorn D, Hellman R, Hirsch IB, Inzucchi SE, 
Ismail-Beigi F, Kirkman MS, Umpierrez GE. American Association of Clinical Endocrinologists and 
American Diabetes Association consensus statement on inpatient glycemic control. Diabetes care. 
Jun 2009;32(6):1119-1131. 2681039
10.
Carson JL, Scholz PM, Chen AY, Peterson ED, Gold J, Schneider SH. Diabetes mellitus 
increases short-term mortality and morbidity in patients undergoing coronary artery bypass graft 
surgery. J Am Coll Cardiol. Aug 7 2002;40(3):418-423. 
11.
Furnary AP, Gao G, Grunkemeier GL, Wu Y, Zerr KJ, Bookin SO, Floten HS, Starr A. 
Continuous insulin infusion reduces mortality in patients with diabetes undergoing coronary artery 
bypass grafting. J Thorac Cardiovasc Surg. May 2003;125(5):1007-1021. 
12.
Thourani VH, Weintraub WS, Stein B, Gebhart SS, Craver JM, Jones EL, Guyton RA. 
Influence of diabetes mellitus on early and late outcome after coronary artery bypass grafting. Ann 
Thorac Surg. Apr 1999;67(4):1045-1052. 
13.
Weintraub WS, Stein B, Kosinski A, Douglas JS, Jr., Ghazzal ZM, Jones EL, Morris DC, 
Guyton RA, Craver JM, King SB, 3rd. Outcome of coronary bypass surgery versus coronary 
angioplasty in diabetic patients with multivessel coronary artery disease. J Am Coll Cardiol. Jan 
1998;31(1):10-19. 
14.
Morricone L, Ranucci M, Denti S, Cazzaniga A, Isgro G, Enrini R, Caviezel F. Diabetes and 
complications after cardiac surgery: comparison with a non-diabetic population. Acta Diabetol. Jun 
1999;36(1-2):77-84. 
15.
Herlitz J, Wognsen GB, Karlson BW, Sjoland H, Karlsson T, Caidahl K, Hartford M, Haglid M. 
Mortality, mode of death and risk indicators for death during 5 years after coronary artery bypass 
grafting among patients with and without a history of diabetes mellitus. Coron Artery Dis. Jun 
2000;11(4):339-346. 
16.
Bucerius J, Gummert JF, Walther T, Doll N, Falk V, Onnasch JF, Barten MJ, Mohr FW. Impact 
of diabetes mellitus on cardiac surgery outcome. Thorac Cardiovasc Surg. Feb 2003;51(1):11-16. 
17.
Inzucchi SE. Clinical practice. Management of hyperglycemia in the hospital setting. The New 
England journal of medicine. Nov 02 2006;355(18):1903-1911. 
18.
Kotagal M, Symons RG, Hirsch IB, Umpierrez GE, Dellinger EP, Farrokhi ET, Flum DR, 
Collaborative S-C. Perioperative hyperglycemia and risk of adverse events among patients with and 
without diabetes. Ann Surg. Jan 2015;261(1):97-103. 4208939
19.
Buehler L, Fayfman M, Alexopoulos AS, Zhao L, Farrokhi F, Weaver J, Smiley-Byrd D, 
Pasquel FJ, Vellanki P, Umpierrez GE. The impact of hyperglycemia and obesity on hospitalization 
costs and clinical outcome in general surgery patients. J Diabetes Complications. Nov-Dec 
2015;29(8):1177-1182. 

Version 6 July 30, 2020
22
20.
Kwon S, Thompson R, Dellinger P, Yanez D, Farrohki E, Flum D. Importance of perioperative 
glycemic control in general surgery: a report from the Surgical Care and Outcomes Assessment 
Program. Ann Surg. Jan 2013;257(1):8-14. 
21.
Szekely A, Levin J, Miao Y, Tudor IC, Vuylsteke A, Ofner P, Mangano DT, Investigators of the 
Multicenter Study of Perioperative Ischemia Research G, the Ischemia R, Education F. Impact of 
hyperglycemia on perioperative mortality after coronary artery bypass graft surgery. J Thorac 
Cardiovasc Surg. Aug 2011;142(2):430-437 e431. 
22.
Ascione R, Rogers CA, Rajakaruna C, Angelini GD. Inadequate blood glucose control is 
associated with in-hospital mortality and morbidity in diabetic and nondiabetic patients undergoing 
cardiac surgery. Circulation. Jul 8 2008;118(2):113-123. 2847615
23.
Falciglia M, Freyberg RW, Almenoff PL, D'Alessio DA, Render ML. Hyperglycemia-related 
mortality in critically ill patients varies with admission diagnosis. Crit Care Med. Dec 
2009;37(12):3001-3009. 
24.
Mendez CE, Mok KT, Ata A, Tanenberg RJ, Calles-Escandon J, Umpierrez GE. Increased 
glycemic variability is independently associated with length of stay and mortality in noncritically ill 
hospitalized patients. Diabetes care. Dec 2013;36(12):4091-4097. 3836112
25.
Frisch A, Chandra P, Smiley D, Peng L, Rizzo M, Gatcliffe C, Hudson M, Mendoza J, Johnson 
R, Lin E, Umpierrez GE. Prevalence and clinical outcome of hyperglycemia in the perioperative 
period in noncardiac surgery. Diabetes care. Aug 2010;33(8):1783-1788. 2909062
26.
Kotagal M, Symons RG, Hirsch IB, Umpierrez GE, Dellinger EP, Farrokhi ET, Flum DR, for the 
S-CC. Perioperative Hyperglycemia and Risk of Adverse Events Among Patients With and Without 
Diabetes. Ann Surg. Apr 17 2014. 
27.
Umpierrez GE, Isaacs SD, Bazargan N, You X, Thaler LM, Kitabchi AE. Hyperglycemia: an 
independent marker of in-hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol 
Metab. Mar 2002;87(3):978-982. 
28.
Umpierrez GE, A.E. K. ICU Care for Patients with Diabetes. Current Opinions Endocrinol. 
2004;11:75-81. 
29.
McCowen KC, Malhotra A, Bistrian BR. Stress-induced hyperglycemia. Crit Care Clin. Jan 
2001;17(1):107-124. 
30.
Chan TM. The permissive effects of glucocorticoid on hepatic gluconeogenesis. Glucagon 
stimulation of glucose-suppressed gluconeogenesis and inhibition of 6-phosphofructo-1-kinase in 
hepatocytes from fasted rats. J Biol Chem. Jun 25 1984;259(12):7426-7432. 
31.
McMahon M, Gerich J, Rizza R. Effects of glucocorticoids on carbohydrate metabolism. 
Diabetes Metab Rev. Feb 1988;4(1):17-30. 
32.
Evans JL, Goldfine ID, Maddux BA, Grodsky GM. Oxidative stress and stress-activated 
signaling pathways: a unifying hypothesis of type 2 diabetes. Endocrine reviews. Oct 2002;23(5):599-
622. 
33.
Gresele P, Guglielmini G, De Angelis M, Ciferri S, Ciofetta M, Falcinelli E, Lalli C, Ciabattoni G, 
Davi G, Bolli GB. Acute, short-term hyperglycemia enhances shear stress-induced platelet activation 
in patients with type II diabetes mellitus. J Am Coll Cardiol. Mar 19 2003;41(6):1013-1020. 
34.
Lang CH, Dobrescu C, Bagby GJ. Tumor necrosis factor impairs insulin action on peripheral 
glucose disposal and hepatic glucose output. Endocrinology. Jan 1992;130(1):43-52. 
35.
Stentz FB, Umpierrez GE, Cuervo R, Kitabchi AE. Proinflammatory cytokines, markers of 
cardiovascular risks, oxidative stress, and lipid peroxidation in patients with hyperglycemic crises. 
Diabetes. Aug 2004;53(8):2079-2086. 
36.
Esposito K, Nappo F, Marfella R, Giugliano G, Giugliano F, Ciotola M, Quagliaro L, Ceriello A, 
Giugliano D. Inflammatory cytokine concentrations are acutely increased by hyperglycemia in 
humans: role of oxidative stress. Circulation. Oct 15 2002;106(16):2067-2072. 

Version 6 July 30, 2020
23
37.
Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J, Hu 
FB. Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis. Diabetes 
care. May 2016;39(5):833-846. 4839172
38.
Menni C, Fauman E, Erte I, Perry JR, Kastenmuller G, Shin SY, Petersen AK, Hyde C, Psatha 
M, Ward KJ, Yuan W, Milburn M, Palmer CN, Frayling TM, Trimmer J, Bell JT, Gieger C, Mohney RP, 
Brosnan MJ, Suhre K, Soranzo N, Spector TD. Biomarkers for type 2 diabetes and impaired fasting 
glucose using a nontargeted metabolomics approach. Diabetes. Dec 2013;62(12):4270-4276. 
3837024
39.
Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques 
PF, Fernandez C, O'Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, 
Gerszten RE. Metabolite profiles and the risk of developing diabetes. Nature medicine. Apr 
2011;17(4):448-453. 3126616
40.
Batchuluun B, Al Rijjal D, Prentice KJ, Eversley JA, Burdett E, Mohan H, Bhattacharjee A, 
Gunderson EP, Liu Y, Wheeler MB. Elevated Medium Chain-Acylcarnitines are Associated with 
Gestational Diabetes, and Early Progression to Type-2 Diabetes, and Induce Pancreatic beta-Cell 
Dysfunction. Diabetes. Feb 7 2018. 
41.
Sun L, Liang L, Gao X, Zhang H, Yao P, Hu Y, Ma Y, Wang F, Jin Q, Li H, Li R, Liu Y, Hu FB, 
Zeng R, Lin X, Wu J. Early Prediction of Developing Type 2 Diabetes by Plasma Acylcarnitines: A 
Population-Based Study. Diabetes care. Sep 2016;39(9):1563-1570. 
42.
Ruiz-Canela M, Toledo E, Clish CB, Hruby A, Liang L, Salas-Salvadó J, Razquin C, Corella D, 
Estruch R, Ros E. Plasma branched-chain amino acids and incident cardiovascular disease in the 
PREDIMED trial. Clinical chemistry. 2016;62(4):582-592. 
43.
Wang DD, Toledo E, Hruby A, Rosner BA, Willett WC, Sun Q, Razquin C, Zheng Y, Ruiz-
Canela M, Guasch-Ferré M. Plasma Ceramides, Mediterranean Diet, and Incident Cardiovascular 
Disease in the PREDIMED Trial (Prevención con Dieta Mediterránea) Clinical Perspective. 
Circulation. 2017;135(21):2028-2040. 
44.
Shah SH, Bain JR, Muehlbauer MJ, Stevens RD, Crosslin DR, Haynes C, Dungan J, Newby 
LK, Hauser ER, Ginsburg GS, Newgard CB, Kraus WE. Association of a peripheral blood metabolic 
profile with coronary artery disease and risk of subsequent cardiovascular events. Circulation. 
Cardiovascular genetics. Apr 2010;3(2):207-214. 
45.
Guasch-Ferre M, Zheng Y, Ruiz-Canela M, Hruby A, Martinez-Gonzalez MA, Clish CB, Corella 
D, Estruch R, Ros E, Fito M, Dennis C, Morales-Gil IM, Aros F, Fiol M, Lapetra J, Serra-Majem L, Hu 
FB, Salas-Salvado J. Plasma acylcarnitines and risk of cardiovascular disease: effect of 
Mediterranean diet interventions. The American journal of clinical nutrition. Jun 2016;103(6):1408-
1416. 4881000
46.
Shah SH, Sun JL, Stevens RD, Bain JR, Muehlbauer MJ, Pieper KS, Haynes C, Hauser ER, 
Kraus WE, Granger CB, Newgard CB, Califf RM, Newby LK. Baseline metabolomic profiles predict 
cardiovascular events in patients at risk for coronary artery disease. American heart journal. May 
2012;163(5):844-850 e841. 
47.
Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, Palma MJ, Roberts LD, 
Dejam A, Souza AL, Deik AA, Magnusson M, Fox CS, O'Donnell CJ, Vasan RS, Melander O, Clish 
CB, Gerszten RE, Wang TJ. Metabolite profiling identifies pathways associated with metabolic risk in 
humans. Circulation. May 8 2012;125(18):2222-2231. 3376658
48.
Krebs M, Krssak M, Bernroider E, Anderwald C, Brehm A, Meyerspeer M, Nowotny P, Roth E, 
Waldhausl W, Roden M. Mechanism of amino acid-induced skeletal muscle insulin resistance in 
humans. Diabetes. Mar 2002;51(3):599-605. 
49.
Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, 
Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS, Jr., Eisenson H, 
Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP. A branched-chain amino acid-related 

Version 6 July 30, 2020
24
metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. 
Cell metabolism. Apr 2009;9(4):311-326. 3640280
50.
Tremblay F, Krebs M, Dombrowski L, Brehm A, Bernroider E, Roth E, Nowotny P, Waldhausl 
W, Marette A, Roden M. Overactivation of S6 kinase 1 as a cause of human insulin resistance during 
increased amino acid availability. Diabetes. Sep 2005;54(9):2674-2684. 
51.
Haufe S, Engeli S, Kaminski J, Witt H, Rein D, Kamlage B, Utz W, Fuhrmann JC, Haas V, 
Mahler A, Schulz-Menger J, Luft FC, Boschmann M, Jordan J. Branched-chain amino acid catabolism 
rather than amino acids plasma concentrations is associated with diet-induced changes in insulin 
resistance in overweight to obese individuals. Nutrition, metabolism, and cardiovascular diseases : 
NMCD. Oct 2017;27(10):858-864. 
52.
Jang C, Oh SF, Wada S, Rowe GC, Liu L, Chan MC, Rhee J, Hoshino A, Kim B, Ibrahim A, 
Baca LG, Kim E, Ghosh CC, Parikh SM, Jiang A, Chu Q, Forman DE, Lecker SH, Krishnaiah S, 
Rabinowitz JD, Weljie AM, Baur JA, Kasper DL, Arany Z. A branched-chain amino acid metabolite 
drives vascular fatty acid transport and causes insulin resistance. Nature medicine. Apr 
2016;22(4):421-426. 4949205
53.
Mardinoglu A, Gogg S, Lotta LA, Stancakova A, Nerstedt A, Boren J, Bluher M, Ferrannini E, 
Langenberg C, Wareham NJ, Laakso M, Smith U. Elevated Plasma Levels of 3-Hydroxyisobutyric 
Acid Are Associated With Incident Type 2 Diabetes. EBioMedicine. Jan 2018;27:151-155. 5828558
54.
Perreault L, Newsom SA, Strauss A, Kerege A, Kahn DE, Harrison KA, Snell-Bergeon JK, 
Nemkov T, D'Alessandro A, Jackman MR, MacLean PS, Bergman BC. Intracellular localization of 
diacylglycerols and sphingolipids influences insulin sensitivity and mitochondrial function in human 
skeletal muscle. JCI insight. Feb 8 2018;3(3). 5821197
55.
Qiu G, Zheng Y, Wang H, Sun J, Ma H, Xiao Y, Li Y, Yuan Y, Yang H, Li X, Min X, Zhang C, 
Xu C, Jiang Y, Zhang X, He M, Yang M, Hu Z, Tang H, Shen H, Hu FB, Pan A, Wu T. Plasma 
metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective 
cohorts of Chinese adults. International journal of epidemiology. Oct 2016;45(5):1507-1516. 
56.
Choi S, Snider AJ. Sphingolipids in High Fat Diet and Obesity-Related Diseases. Mediators of 
inflammation. 2015;2015:520618. 4663345
57.
Vozarova B, Weyer C, Hanson K, Tataranni PA, Bogardus C, Pratley RE. Circulating 
interleukin-6 in relation to adiposity, insulin action, and insulin secretion. Obesity research. Jul 
2001;9(7):414-417. 
58.
Zhang S, Kim KH. TNF-alpha inhibits glucose-induced insulin secretion in a pancreatic beta-
cell line (INS-1). FEBS letters. Dec 18 1995;377(2):237-239. 
59.
Southern C, Schulster D, Green IC. Inhibition of insulin secretion by interleukin-1 beta and 
tumour necrosis factor-alpha via an L-arginine-dependent nitric oxide generating mechanism. FEBS 
letters. Dec 10 1990;276(1-2):42-44. 
60.
Steinberg GR, Michell BJ, van Denderen BJ, Watt MJ, Carey AL, Fam BC, Andrikopoulos S, 
Proietto J, Gorgun CZ, Carling D, Hotamisligil GS, Febbraio MA, Kay TW, Kemp BE. Tumor necrosis 
factor alpha-induced skeletal muscle insulin resistance involves suppression of AMP-kinase signaling. 
Cell metabolism. Dec 2006;4(6):465-474. 
61.
Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-
alpha: direct role in obesity-linked insulin resistance. Science. Jan 1 1993;259(5091):87-91. 
62.
Hotamisligil GS, Peraldi P, Budavari A, Ellis R, White MF, Spiegelman BM. IRS-1-mediated 
inhibition of insulin receptor tyrosine kinase activity in TNF-alpha- and obesity-induced insulin 
resistance. Science. Feb 2 1996;271(5249):665-668. 
63.
Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. Mar 5 
2002;105(9):1135-1143. 
64.
Hall L, Martinus RD. Hyperglycaemia and oxidative stress upregulate HSP60 & HSP70 
expression in HeLa cells. SpringerPlus. 2013;2:431. 3777022

Version 6 July 30, 2020
25
65.
Eapen DJ, Manocha P, Patel RS, Hammadah M, Veledar E, Wassel C, Nanjundappa RA, 
Sikora S, Malayter D, Wilson PW, Sperling L, Quyyumi AA, Epstein SE. Aggregate risk score based 
on markers of inflammation, cell stress, and coagulation is an independent predictor of adverse 
cardiovascular outcomes. J Am Coll Cardiol. Jul 23 2013;62(4):329-337. 4066955
66.
Ghasemzedah N, Hayek SS, Ko YA, Eapen DJ, Patel RS, Manocha P, Al Kassem H, Khayata 
M, Veledar E, Kremastinos D, Thorball CW, Pielak T, Sikora S, Zafari AM, Lerakis S, Sperling L, 
Vaccarino V, Epstein SE, Quyyumi AA. Pathway-Specific Aggregate Biomarker Risk Score Is 
Associated With Burden of Coronary Artery Disease and Predicts Near-Term Risk of Myocardial 
Infarction and Death. Circulation. Cardiovascular quality and outcomes. Mar 2017;10(3). 
67.
Hayek SS, Sever S, Ko YA, Trachtman H, Awad M, Wadhwani S, Altintas MM, Wei C, Hotton 
AL, French AL, Sperling LS, Lerakis S, Quyyumi AA, Reiser J. Soluble Urokinase Receptor and 
Chronic Kidney Disease. The New England journal of medicine. Nov 12 2015;373(20):1916-1925. 
4701036
68.
Eapen DJ, Manocha P, Ghasemzadeh N, Patel RS, Al Kassem H, Hammadah M, Veledar E, 
Le NA, Pielak T, Thorball CW, Velegraki A, Kremastinos DT, Lerakis S, Sperling L, Quyyumi AA. 
Soluble urokinase plasminogen activator receptor level is an independent predictor of the presence 
and severity of coronary artery disease and of future adverse events. Journal of the American Heart 
Association. Oct 23 2014;3(5):e001118. 4323820
69.
Ceriello A. Acute hyperglycaemia and oxidative stress generation. Diabetic medicine : a journal 
of the British Diabetic Association. Aug 1997;14 Suppl 3:S45-49. 
70.
Nordt TK, Klassen KJ, Schneider DJ, Sobel BE. Augmentation of synthesis of plasminogen 
activator inhibitor type-1 in arterial endothelial cells by glucose and its implications for local 
fibrinolysis. Arteriosclerosis and thrombosis : a journal of vascular biology. Dec 1993;13(12):1822-
1828. 
71.
Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van 
Wijngaerden E, Bobbaers H, Bouillon R. Intensive insulin therapy in the medical ICU. The New 
England journal of medicine. Feb 2 2006;354(5):449-461. 
72.
van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers 
D, Ferdinande P, Lauwers P, Bouillon R. Intensive insulin therapy in the critically ill patients. The New 
England journal of medicine. Nov 8 2001;345(19):1359-1367. 
73.
Lazar HL. How important is glycemic control during coronary artery bypass? Adv Surg. 
2012;46:219-235. 
74.
Clement S, Braithwaite SS, Magee MF, Ahmann A, Smith EP, Schafer RG, Hirsh IB. 
Management of diabetes and hyperglycemia in hospitals. Diabetes care. Feb 2004;27(2):553-597. 
75.
Lazar HL, Chipkin SR, Fitzgerald CA, Bao Y, Cabral H, Apstein CS. Tight glycemic control in 
diabetic coronary artery bypass graft patients improves perioperative outcomes and decreases 
recurrent ischemic events. Circulation. Mar 30 2004;109(12):1497-1502. 
76.
Kitabchi AE, Freire AX, Umpierrez GE. Evidence for strict inpatient blood glucose control: time 
to revise glycemic goals in hospitalized patients. Metabolism. Jan 2008;57(1):116-120. 
77.
Van den Berghe G, Wouters PJ, Kesteloot K, Hilleman DE. Analysis of healthcare resource 
utilization with intensive insulin therapy in critically ill patients. Crit Care Med. Mar 2006;34(3):612-
616. 
78.
Brunkhorst FM, Engel C, Bloos F, Meier-Hellmann A, Ragaller M, Weiler N, Moerer O, 
Gruendling M, Oppert M, Grond S, Olthoff D, Jaschinski U, John S, Rossaint R, Welte T, Schaefer M, 
Kern P, Kuhnt E, Kiehntopf M, Hartog C, Natanson C, Loeffler M, Reinhart K. Intensive insulin 
therapy and pentastarch resuscitation in severe sepsis. The New England journal of medicine. Jan 10 
2008;358(2):125-139. 
79.
De La Rosa Gdel C, Donado JH, Restrepo AH, Quintero AM, Gonzalez LG, Saldarriaga NE, 
Bedoya M, Toro JM, Velasquez JB, Valencia JC, Arango CM, Aleman PH, Vasquez EM, Chavarriaga 

Version 6 July 30, 2020
26
JC, Yepes A, Pulido W, Cadavid CA. Strict glycaemic control in patients hospitalised in a mixed 
medical and surgical intensive care unit: a randomised clinical trial. Crit Care. 2008;12(5):R120. 
80.
Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V, Bellomo R, Cook D, Dodek P, 
Henderson WR, Hebert PC, Heritier S, Heyland DK, McArthur C, McDonald E, Mitchell I, Myburgh JA, 
Norton R, Potter J, Robinson BG, Ronco JJ. Intensive versus conventional glucose control in critically 
ill patients. The New England journal of medicine. Mar 26 2009;360(13):1283-1297. 
81.
Preiser JC, Brunkhorst F. Tight glucose control and hypoglycemia. Crit Care Med. Apr 
2008;36(4):1391; author reply 1391-1392. 
82.
Inzucchi SE, Siegel MD. Glucose control in the ICU--how tight is too tight? The New England 
journal of medicine. Mar 26 2009;360(13):1346-1349. 
83.
Stamou SC, Nussbaum M, Carew JD, Dunn K, Skipper E, Robicsek F, Lobdell KW. 
Hypoglycemia with intensive insulin therapy after cardiac surgery: predisposing factors and 
association with mortality. J Thorac Cardiovasc Surg. Jul 2011;142(1):166-173. 
84.
D'Ancona G, Bertuzzi F, Sacchi L, Pirone F, Stringi V, Arcadipane A, Bellazzi R, Pilato M. 
Iatrogenic hypoglycemia secondary to tight glucose control is an independent determinant for 
mortality and cardiac morbidity. European journal of cardio-thoracic surgery : official journal of the 
European Association for Cardio-thoracic Surgery. Aug 2011;40(2):360-366. 
85.
Finfer S, Liu B, Chittock DR, Norton R, Myburgh JA, McArthur C, Mitchell I, Foster D, Dhingra 
V, Henderson WR, Ronco JJ, Bellomo R, Cook D, McDonald E, Dodek P, Hebert PC, Heyland DK, 
Robinson BG. Hypoglycemia and risk of death in critically ill patients. The New England journal of 
medicine. Sep 20 2012;367(12):1108-1118. 
86.
Pasquel FJ, Gianchandani R, Rubin DJ, Dungan KM, Anzola I, Gomez PC, Peng L, Hodish I, 
Bodnar T, Wesorick D, Balakrishnan V, Osei K, Umpierrez GE. Efficacy of sitagliptin for the hospital 
management of general medicine and surgery patients with type 2 diabetes (Sita-Hospital): a 
multicentre, prospective, open-label, non-inferiority randomised trial. The lancet. Diabetes & 
endocrinology. Feb 2017;5(2):125-133. 
87.
Nauck MA, Kleine N, Orskov C, Holst JJ, Willms B, Creutzfeldt W. Normalization of fasting 
hyperglycaemia by exogenous glucagon-like peptide 1 (7-36 amide) in type 2 (non-insulin-dependent) 
diabetic patients. Diabetologia. Aug 1993;36(8):741-744. 
88.
Deane AM, Chapman MJ, Fraser RJ, Burgstad CM, Besanko LK, Horowitz M. The effect of 
exogenous glucagon-like peptide-1 on the glycaemic response to small intestinal nutrient in the 
critically ill: a randomised double-blind placebo-controlled cross over study. Crit Care. 
2009;13(3):R67. 
89.
Mussig K, Oncu A, Lindauer P, Heininger A, Aebert H, Unertl K, Ziemer G, Haring HU, Holst 
JJ, Gallwitz B. Effects of intravenous glucagon-like peptide-1 on glucose control and hemodynamics 
after coronary artery bypass surgery in patients with type 2 diabetes. Am J Cardiol. Sep 1 
2008;102(5):646-647. 
90.
Ritzel RA, Kleine N, Holst JJ, Willms B, Schmiegel W, Nauck MA. Preserved GLP-1 effects in 
a diabetic patient with Cushing's disease. Exp Clin Endocrinol Diabetes. Feb 2007;115(2):146-150. 
91.
van Raalte DH, van Genugten RE, Linssen MM, Ouwens DM, Diamant M. Glucagon-like 
peptide-1 receptor agonist treatment prevents glucocorticoid-induced glucose intolerance and islet-
cell dysfunction in humans. Diabetes care. Feb 2011;34(2):412-417. 
92.
Kohl BA, Hammond MS, Schwartz S, Ochroch EO. Intravenous exenatide (Byetta) for the 
treatment of perioperative hyperglycemia. Society of Critical Care Medicine’s 40th Critical Care 
Congress; January 15-19, 2011, 2011; San Diego, CA.
93.
Abuannadi M, Kosiborod M, Riggs L, House JA, Hamburg MS, Kennedy KF, Marso SP. 
Management of hyperglycemia with the administration of intravenous exenatide to patients in the 
cardiac intensive care unit. Endocr Pract. Jan 1 2013;19(1):81-90. 
94.
Sokos GG, Bolukoglu H, German J, Hentosz T, Magovern GJ, Jr., Maher TD, Dean DA, Bailey 
SH, Marrone G, Benckart DH, Elahi D, Shannon RP. Effect of glucagon-like peptide-1 (GLP-1) on 

Version 6 July 30, 2020
27
glycemic control and left ventricular function in patients undergoing coronary artery bypass grafting. 
Am J Cardiol. Sep 1 2007;100(5):824-829. 
95.
Sokos GG, Nikolaidis LA, Mankad S, Elahi D, Shannon RP. Glucagon-like peptide-1 infusion 
improves left ventricular ejection fraction and functional status in patients with chronic heart failure. J 
Card Fail. Dec 2006;12(9):694-699. 
96.
Nikolaidis LA, Mankad S, Sokos GG, Miske G, Shah A, Elahi D, Shannon RP. Effects of 
glucagon-like peptide-1 in patients with acute myocardial infarction and left ventricular dysfunction 
after successful reperfusion. Circulation. Mar 2 2004;109(8):962-965. 
97.
Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jodar E, Leiter LA, Lingvay I, Rosenstock J, 
Seufert J, Warren ML, Woo V, Hansen O, Holst AG, Pettersson J, Vilsboll T, Investigators S-. 
Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. The New England 
journal of medicine. Nov 10 2016;375(19):1834-1844. 
98.
Marso SP, Daniels GH, Brown-Frandsen K, Kristensen P, Mann JF, Nauck MA, Nissen SE, 
Pocock S, Poulter NR, Ravn LS, Steinberg WM, Stockner M, Zinman B, Bergenstal RM, Buse JB, 
Committee LS, Investigators LT. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. The 
New England journal of medicine. Jul 28 2016;375(4):311-322. 4985288
99.
Glaesner W, Vick AM, Millican R, Ellis B, Tschang SH, Tian Y, Bokvist K, Brenner M, Koester 
A, Porksen N, Etgen G, Bumol T. Engineering and characterization of the long-acting glucagon-like 
peptide-1 analogue LY2189265, an Fc fusion protein. Diabetes Metab Res Rev. May 2010;26(4):287-
296. 
100.
Nauck M, Weinstock RS, Umpierrez GE, Guerci B, Skrivanek Z, Milicevic Z. Efficacy and 
safety of dulaglutide versus sitagliptin after 52 weeks in type 2 diabetes in a randomized controlled 
trial (AWARD-5). Diabetes care. Aug 2014;37(8):2149-2158. PMC4113177
101.
Wysham C, Blevins T, Arakaki R, Colon G, Garcia P, Atisso C, Kuhstoss D, Lakshmanan M. 
Efficacy and safety of dulaglutide added onto pioglitazone and metformin versus exenatide in type 2 
diabetes in a randomized controlled trial (AWARD-1). Diabetes care. Aug 2014;37(8):2159-2167. 
102.
Umpierrez G, Tofe Povedano S, Perez Manghi F, Shurzinske L, Pechtner V. Efficacy and 
safety of dulaglutide monotherapy versus metformin in type 2 diabetes in a randomized controlled trial 
(AWARD-3). Diabetes care. Aug 2014;37(8):2168-2176. 
103.
Barrington P, Chien JY, Tibaldi F, Showalter HD, Schneck K, Ellis B. LY2189265, a long-acting 
glucagon-like peptide-1 analogue, showed a dose-dependent effect on insulin secretion in healthy 
subjects. Diabetes Obes Metab. May 2011;13(5):434-438. 
104.
Burness CB, Scott LJ. Dulaglutide: A Review in Type 2 Diabetes. BioDrugs. Dec 
2015;29(6):407-418. 
105.
Pratley RE, Aroda VR, Lingvay I, Ludemann J, Andreassen C, Navarria A, Viljoen A, 
investigators S. Semaglutide versus dulaglutide once weekly in patients with type 2 diabetes 
(SUSTAIN 7): a randomised, open-label, phase 3b trial. The lancet. Diabetes & endocrinology. Jan 31 
2018. 
106.
Dhatariya K, Flanagan D, Hilton L, Kilvert A, Levy N, Rayman G, Watson B. Management of 
adults with diabetes undergoing surgery and elective procedures: improving standards. NHS, UK, 
April. 2011. 
107.
Draznin B. Managing Diabetes and Hyperglycemia in the Hospital Setting: A Clinician's Guide. 
American Diabetes Association; 2016.
108.
Umpierrez GE, Smiley D, Zisman A, Prieto LM, Palacio A, Ceron M, Puig A, Mejia R. 
Randomized study of basal-bolus insulin therapy in the inpatient management of patients with type 2 
diabetes (RABBIT 2 trial). Diabetes care. Sep 2007;30(9):2181-2186. 
109.
Umpierrez GE, Gianchandani R, Smiley D, Jacobs S, Wesorick DH, Newton C, Farrokhi F, 
Peng L, Reyes D, Lathkar-Pradhan S, Pasquel F. Safety and Efficacy of Sitagliptin Therapy for the 
Inpatient Management of General Medicine and Surgery Patients With Type 2 Diabetes: A pilot, 
randomized, controlled study. Diabetes care. Nov 2013;36(11):3430-3435. 3816910

Version 6 July 30, 2020
28
110.
Umpierrez GE. Basal versus sliding-scale regular insulin in hospitalized patients with 
hyperglycemia during enteral nutrition therapy. Diabetes care. Apr 2009;32(4):751-753. 
111.
Umpierrez GE, Smiley D, Hermayer K, Khan A, Olson DE, Newton C, Jacobs S, Rizzo M, 
Peng L, Reyes D, Pinzon I, Fereira ME, Hunt V, Gore A, Toyoshima MT, Fonseca VA. Randomized 
study comparing a Basal-bolus with a basal plus correction insulin regimen for the hospital 
management of medical and surgical patients with type 2 diabetes: basal plus trial. Diabetes care. 
Aug 2013;36(8):2169-2174. 3714500
112.
Umpierrez GE, Hor T, Smiley D, Temponi A, Umpierrez D, Ceron M, Munoz C, Newton C, 
Peng L, Baldwin D. Comparison of inpatient insulin regimens with detemir plus aspart versus neutral 
protamine hagedorn plus regular in medical patients with type 2 diabetes. J Clin Endocrinol Metab. 
Feb 2009;94(2):564-569. 
113.
Umpierrez GE, Kosiborod M. Inpatient dysglycemia and clinical outcomes: association or 
causation? J Diabetes Complications. Jul-Aug 2014;28(4):427-429. 
114.
Pasquel FJ, Spiegelman R, McCauley M, Smiley D, Umpierrez D, Johnson R, Rhee M, 
Gatcliffe C, Lin E, Umpierrez E, Peng L, Umpierrez GE. Hyperglycemia during total parenteral 
nutrition: an important marker of poor outcome and mortality in hospitalized patients. Diabetes care. 
Apr 2010;33(4):739-741. 2845017
115.
Davis G, Fayfman M, Reyes-Umpierrez D, Hafeez S, Pasquel FJ, Vellanki P, Haw JS, Peng L, 
Jacobs S, Umpierrez GE. Stress hyperglycemia in general surgery: Why should we care? J Diabetes 
Complications. Mar 2018;32(3):305-309. 
116.
Trujillo JM, Nuffer W. Albiglutide: a new GLP-1 receptor agonist for the treatment of type 2 
diabetes. The Annals of pharmacotherapy. Nov 2014;48(11):1494-1501. 
117.
Gurung T, Shyangdan DS, O'Hare JP, Waugh N. A novel, long-acting glucagon-like peptide 
receptor-agonist: dulaglutide. Diabetes, metabolic syndrome and obesity : targets and therapy. 
2015;8:363-386. 4541559
118.
Thompson AM, Trujillo JM. Advances in the treatment of type 2 diabetes: impact of dulaglutide. 
Diabetes, metabolic syndrome and obesity : targets and therapy. 2016;9:125-136. 4861603
119.
Aroda VR, Bain SC, Cariou B, Piletic M, Rose L, Axelsen M, Rowe E, DeVries JH. Efficacy and 
safety of once-weekly semaglutide versus once-daily insulin glargine as add-on to metformin (with or 
without sulfonylureas) in insulin-naive patients with type 2 diabetes (SUSTAIN 4): a randomised, 
open-label, parallel-group, multicentre, multinational, phase 3a trial. The lancet. Diabetes & 
endocrinology. May 2017;5(5):355-366. 
120.
Kohl BA, Hammond MS, Cucchiara AJ, Ochroch EA. Intravenous GLP-1 (7-36) amide for 
prevention of hyperglycemia during cardiac surgery: a randomized, double-blind, placebo-controlled 
study. Journal of cardiothoracic and vascular anesthesia. Jun 2014;28(3):618-625. 
121.
Harris LLS, Smith GI, Patterson BW, Ramaswamy RS, Okunade AL, Kelly SC, Porter LC, 
Klein S, Yoshino J, Mittendorfer B. Alterations in 3-Hydroxyisobutyrate and FGF21 Metabolism Are 
Associated With Protein Ingestion-Induced Insulin Resistance. Diabetes. Jul 2017;66(7):1871-1878. 
5482083
122.
Johnson JM, Yu T, Strobel FH, Jones DP. A practical approach to detect unique metabolic 
patterns for personalized medicine. Analyst. 2010;135(11):2864-2870. 
123.
Soltow QA, Strobel FH, Mansfield KG, Wachtman L, Park Y, Jones DP. High-performance 
metabolic profiling with dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) for 
study of the exposome. Metabolomics. 2013;9(1):132-143. 
124.
Walker D, Go Y, Liu K, Pennell K, Jones D, Darzi A, Holmes E, Lindon J. Population screening 
for biological and environmental properties of the human metabolic phenotype: implications for 
personalized medicine. Metabolic Phenotyping in Personalized and Public Healthcare Elservier. 
2016. 

Version 6 July 30, 2020
29
125.
Uppal K, Soltow QA, Strobel FH, Pittard WS, Gernert KM, Yu T, Jones DP. xMSanalyzer: 
automated pipeline for improved feature detection and downstream analysis of large-scale, non-
targeted metabolomics data. BMC bioinformatics. Jan 16 2013;14:15. 3562220
126.
Yu T, Park Y, Johnson JM, Jones DP. apLCMS--adaptive processing of high-resolution LC/MS 
data. Bioinformatics. Aug 01 2009;25(15):1930-1936. 2712336
127.
Uppal K, Walker DI, Jones DP. xMSannotator: An R Package for Network-Based Annotation of 
High-Resolution Metabolomics Data. Analytical chemistry. Jan 17 2017;89(2):1063-1067. 5447360
128.
Liquet B, Le Cao KA, Hocini H, Thiebaut R. A novel approach for biomarker selection and the 
integration of repeated measures experiments from two assays. BMC bioinformatics. Dec 06 
2012;13:325. 3627901
129.
Smyth GK, Ritchie M, Thorne N, Wettenhall J. LIMMA: linear models for microarray data. In 
Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology 
and Health. 2005. 
130.
Chong EY, Huang Y, Wu H, Ghasemzadeh N, Uppal K, Quyyumi AA, Jones DP, Yu T. Local 
false discovery rate estimation using feature reliability in LC/MS metabolomics data. Sci Rep. Nov 24 
2015;5:17221. PMC4657040
131.
Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, Jones DP, Pulendran B. 
Predicting network activity from high throughput metabolomics. PLoS computational biology. 
2013;9(7):e1003123. 3701697
132.
Uppal K, Soltow QA, Promislow DE, Wachtman LM, Quyyumi AA, Jones DP. MetabNet: An R 
Package for Metabolic Association Analysis of High-Resolution Metabolomics Data. Frontiers in 
bioengineering and biotechnology. 2015;3:87. 4464066
133.
Uppal K, Ma C, Go YM, Jones DP, Wren J. xMWAS: a data-driven integration and differential 
network analysis tool. Bioinformatics. Feb 15 2018;34(4):701-702. 
134.
Alvarez JA, Chong EY, Walker DI, Chandler JD, Michalski ES, Grossmann RE, Uppal K, Li S, 
Frediani JK, Tirouvanziam R, Tran VT, Tangpricha V, Jones DP, Ziegler TR. Plasma metabolomics in 
adults with cystic fibrosis during a pulmonary exacerbation: A pilot randomized study of high-dose 
vitamin D3 administration. Metabolism. May 2017;70:31-41. 5407388
135.
Yeldandi RR, Lurie A, Baldwin D. Comparison of once-daily glargine insulin with twice-daily 
NPH/regular insulin for control of hyperglycemia in inpatients after cardiovascular surgery. Diabetes 
Technol Ther. Dec 2006;8(6):609-616. 
136.
Schmeltz LR, DeSantis AJ, Schmidt K, O'Shea-Mahler E, Rhee C, Brandt S, Peterson S, 
Molitch ME. Conversion of intravenous insulin infusions to subcutaneously administered insulin 
glargine in patients with hyperglycemia. Endocr Pract. Nov-Dec 2006;12(6):641-650. 
137.
Lund A, Knop FK, Vilsboll T. Glucagon-like peptide-1 receptor agonists for the treatment of 
type 2 diabetes: differences and similarities. Eur J Intern Med. Jun 2014;25(5):407-414. 
138.
Deo RC, Hunter L, Lewis GD, Pare G, Vasan RS, Chasman D, Wang TJ, Gerszten RE, Roth 
FP. Interpreting metabolomic profiles using unbiased pathway models. PLoS computational biology. 
Feb 26 2010;6(2):e1000692. 2829050