Pharmacogenomics of the Variability in the In Vivo Response to Glucocorticoids
Phase: PHASE1
Conditions: Glucocorticoids Toxicity, Glucose Intolerance
Sponsor: Vanderbilt University Medical Center

10/23/17 
Pharmacogenomics of the Variability in the In 
Vivo Response to Glucocorticoids 
NCT03023891
10/23/17 
Vivian K. Kawai, M.D. 
Division of Clinical Pharmacology 

 
 
 
 
10/23/17 
 
 
 
 
 
 
 
Table of Contents: 
 
Study Schema 
1.0 
Background 
2.0 
Rationale and Specific Aims 
3.0 
Animal Studies and Previous Human Studies 
4.0 
Inclusion/Exclusion Criteria 
5.0 
Enrollment/Randomization 
6.0 
Study Procedures 
7.0 
Risks of Investigational Agents/Devices (side effects) 
8.0 
Reporting of Adverse Events or Unanticipated Problems involving Risk 
to Participants or Others 
9.0 
Study Withdrawal/Discontinuation 
10.0 Statistical Considerations 
11.0 Privacy/Confidentiality Issues 
12.0 Follow-up and Record Retention 
 
 

 
 
 
 
10/23/17 
1.0 
Background 
 
Glucocorticoids (GCs) are commonly used worldwide to treat several conditions, 
including asthma, lymphomas, and chronic inflammatory disorders.1,2 It is estimated that 
between 1-3% of the general population in developed countries have used GCs, and 
that 65-75% of these patients used GCs chronically (≥ 3 months).1,3 Although GCs have 
dramatic anti-inflammatory and immunosuppressive effects, they exhibit large inter-
individual variability in GC response in terms of efficacy and toxicity. This 
variability in GC response explains why nearly 30% of patients with different pathological 
conditions have a suboptimal response to the anti-inflammatory effects of GCs4-7 
resulting in morbidity and substantial economic costs.8 Moreover, it explains why 
between 10 to 80% of patients who receive GCs chronically develop serious side 
effects.9-11 
2.0 
Rationale and Specific Aims 
 
 
The large inter-individual variability in GC response has a genetic component, but 
the genetic architecture of GC response is still unknown.  A few studies have linked a 
small number of genetic variants with GC therapeutic efficacy.12-15 One reason for the 
lack of progress in identifying the genetic determinants of GC efficacy has been the 
measurement of response in clinically heterogeneous phenotypes (e.g., asthma, 
inflammatory bowel disease). These phenotypes require weeks to respond to GCs, and 
the pharmacogenetic signal is susceptible to confounding by disease heterogeneity and 
co-therapies. A more attractive approach is to study a “clean” intermediate phenotype — 
a pharmacodynamic measure of GC efficacy. GC-induced lymphopenia is such a 
measure; this is a rapid, reproducible, dose- and concentration-dependent response that 
has been quantified in human pharmacokinetic and pharmacodynamic models.16-19 
Moreover, lymphocytes are major determinants of inflammation and GCs exert part of 
their anti-inflammatory effects by altering lymphocyte kinetics and survival. Thus, GC-
induced lymphopenia is a robust and relevant intermediate phenotype to study the 
genetic predictors of GC efficacy. 
In addition to variability in GC efficacy, there is also marked variability in the 
frequency and severity of GC toxicity. One of the most common metabolic side effects of 
GC therapy is GC-induced glucose intolerance.20-22 Although GC impairs glucose 
metabolism through insulin resistance and by β-cell dysfunction, there is large variability 
in the severity of the metabolic effects of GCs that is explained by clinical factors. 
Evidence suggest that metabolic effects of GC has a genetic component, but the genetic 
determinants of metabolic toxicity remains unknown. 
As other complex traits, genome-wide association studies (GWAS) have identified 
some risk variants for GC response;12-15 however the functional relevance for most of 
these variants are not known. In complex traits, the combination of GWAS information 
and large gene expression data has shown that top hits on GWAS were enriched for 
polymorphisms that regulate expression levels of genes (regulatory polymorphisms),23 
and that these regulatory variants explained a large proportion of the phenotypic 
variability for complex phenotypes including drug response.24, 25  
GCs are known to exert their effect through changes in gene expression and also by 
interacting with several transcription factors,26,27 but we still do not know which genes are 
involved in the response to in vivo administration GC in humans, and which genetic 
variants are more likely to affect the expression of these genes. Thus, we propose to 
perform a hypothesis-generating pilot study to identify potential set of genes and 

 
 
 
 
10/23/17 
regulatory variants that are likely to be involve in the response to GC in terms of 
efficacy and metabolic toxicity.  
For this pilot study we plan to use a robust pharmacodynamic effect of GCs (GC-
induced lymphopenia) and a common metabolic complication of GC therapy (GC-
induced glucose intolerance) after in vivo administration of GC as our clinical 
phenotypes to: 
Specific Aim: To characterize gene expression patterns that are predictive of in vivo GC 
response, and identify regulatory variants that are associated with in vivo GC response 
by challenging 40 healthy volunteers with 60 mg of oral prednisone. We will measure 
changes in lymphocyte counts and changes in glucose regulation (using the 75 gr 
glucose tolerance test ~ OGTT) as surrogates of GC efficacy and metabolic toxicity. We 
will perform transcriptome-wide next generation RNA sequencing to measure changes in 
gene expression due to in vivo administration of GC and study the association of gene 
expression patterns with our clinical phenotypes. Once we identify a set of genes that 
are significantly changed after in vivo GC administration, we will select potential 
regulatory variants that have been associated with changes in expression of these 
genes in public available expression quantitative trait loci (eQTL) datasets to perform 
genome interrogation and eQTL analysis. This will allow us to identify variants that are 
associated with gene expression changes due to in vivo administration of GC. 
 
3.0 
Animal Studies and Previous Human Studies 
 
Pharmacodynamic measures of GC efficacy: One of the mechanism through which 
GC exerts its anti-inflammatory effects is by modifying the traffic and function of immune 
cells.18,28,29 GCs markedly increase the number of circulating neutrophils and decrease 
the number of circulating white blood cells (WBC).30,31 Among WBC, neutrophils and 
lymphocytes have been largely use to study the pharmacodynamics of GCs;16,17,32,33 
mostly because the other leukocytes represent a small percentage of the circulating 
WBC and the administration of GCs reduced greatly their number (sometimes to 
undetectable levels) making difficult to establish a dose-concentration response.31 Not 
only the changes induced by GCs in circulating neutrophil and lymphocyte counts are 
dose- and concentration-dependent,34 reproducible,35 and obtained with equivalent 
doses of commonly used GCs;16 but also the same response can be evoked in healthy 
individuals and in patients with inflammatory disorders.34,36,37 Thus, changes in WBC 
counts are well-established and reproducible pharmacodynamic phenotypes to study the 
genetics of GC response.  
 
Metabolic side effects of GCs – glucose intolerance: There is also large variability in 
GC toxicity. Glucose intolerance is a frequent and clinically relevant metabolic 
complication of GC therapy21,22 that can lead to transient hyperglycemia38 or to a 
permanent condition called GC-induced diabetes.39,40 The mechanisms through which 
GCs induce glucose intolerance are similar to those involved in the development of type 
2 diabetes mellitus (T2DM),39,41,42 and include insulin resistance43 and β-cell 
dysfunction.44,45 Hyperglycemia occurs when β-cell failure can no longer compensate for 
the degree of insulin resistance. Clinical studies have shown that short-term 
administration of GCs impairs glucose tolerance in individuals with normal glucose 
tolerance;44,46-48 while a high single doses of GCs have shown to increase insulin 
resistance44,46,48,49 and consequently increase insulin secretion44,46  a compensatory 
mechanism that is reduced in individuals at risk for T2DM.44,48,50-53 Evidence suggest that 

 
 
 
 
10/23/17 
glucose intolerance during GC therapy is in part due to a genetic component since the 
degree of glucose intolerance varies markedly among patients who received the same 
dose of GCs;54 there is large variability in the incidence of glucose intolerance due to GC 
therapy among races;55,56 and variants known to affect GC sensitivity have been 
associated with metabolic phenotypes57-65 Despite these observations, the genetic 
determinants of GC–induce metabolic toxicity are not known and we cannot predict who 
will develop a metabolic complication during GC therapy. 
 
GC exert their effects through the regulation of gene expression by regulatory 
variants: GCs are known to exert their effect through changes in gene expression and 
by interacting with several transcription factors. Thus, they represent an excellent tool to 
study gene-environment interactions.66 Accordingly, using immortalized and cultured 
human peripheral blood mononuclear cells (PBMCs), several studies have shown that 
GC response is associated with different patterns of transcriptional response,14,27,67 and 
that variants in regulatory binding sites at GC-targeted genes may play a role in GC 
response variabilty.12,14,26,27 However, the process of immortalization and the culture of 
cells are known to affect the gene expression response and likely differ from in vivo 
expression.68-70 Moreover, the eQTL studies using immortalized cells have little overlap 
with eQTL from cultured cells.27 To our knowledge, there is only one small study that has 
examine gene expression changes with in vivo administration with GC but have not 
performed eQTL analysis. Olnes explored genome transcription response after in vivo 
administration of GCs using whole transcriptome microarrays,71 and found that peak 
changes in gene expression were observed 4 hours after GC administration with large 
variability in the gene expression changes. We propose to extend Olnes’ work by 
studying the role that genetic variants have in the observed variability of gene 
expression response during in vivo administration of GC. Besides, we propose to study 
PBMCs gene expression response to a glucose challenge due to in vivo administration 
of GCs to study the genetic architecture of the metabolic complications of GC 
administration,  which has not be done.  
 
4.0 
Inclusion/Exclusion Criteria 
 
Inclusion criteria: 
• 
18 to 45 years old (to exclude the effect of age on glucose tolerance)  
• 
BMI between 20 and 30 kg/m2 (to exclude individuals that are likely to have 
impaired insulin response) 
• 
Normal fasting glucose 
• 
Stable weight for three months before participation 
  
Exclusion criteria: 
• 
BMI ≥30kg/m2 
• 
Fasting glucose ≥126 mg/dl 
• 
Shift work or disordered sleep (to exclude individuals with alterations in the 
hypothalamus-adrenergic axis) 
• 
Any active diseases (requiring current treatment) 
• 
Use of any medication regularly (including over-the-counter such as aspirin, 
ibuprofen, etc)  
• 
Previous exposure to GCs (within the last year) 
• 
Diabetes  
• 
Pregnant or breastfeeding 

 
 
 
 
10/23/17 
.  
 
5.0 
Enrollment/Randomization 
 
We plan to recruit participants by sending notices through ResearchMatch.org, 
Research notifications email, word of mouth, and fliers. 
 
6.0 
Study Procedures 
 
Potential participants will meet with the study team to discuss the purpose, inclusion 
and exclusion criteria, procedures and risk/benefits of the study.  Those who sign the 
consent form will come for a screening visit, and if they  fulfill inclusion and exclusion 
criteria, they will come to Vanderbilt University Medical Center (VUMC) for two study 
visits in the following 30 days. The study visits will take place within a one-month 
window, will start early in the morning and will last approximately 6 hours. For all the 
study visits participants will be asked to fast overnight and avoid alcohol consumption 
and exercise (except walking<30 minutes) 48 hours before the visit, and be free of any 
illness.  
• 
Screening visit: The participant will arrive fasting early in the morning to VUMC. In 
this visit, the study team will ask questions about his/her health and past medical history. 
The study team will perform a brief physical exam that include  participant’s height and 
weight to calculate the body mass index (BMI) and waist and hip circumference. Blood 
samples will be collected for safety reasons and to culture peripheral blood mononuclear 
cells in vitro with glucocorticoid. We will request a urine and stool sample for future 
research. Women of child-bearing potential will have a urine pregnancy test. Women 
who are pregnant will not be allowed to participate. The study team will provide 
instructions about fasting and activities before the study visits. If any of the test results 
on the blood collected at the screening suggests that the participant may have a 
condition that can alter the study (e.g., random glucose>200 mg/dL) or put him/her at 
risk, the study team will contact the participant to explain the reason why he/she cannot 
participate in the study.  
• 
Study visit 1: The participant will arrive fasting early in the morning to VUMC. A 
standardized clinical interview and brief physical examination will be performed. For 
female participants, a urine sample will be collected for pregnancy testing. A fingerstick 
will be perform to check fasting glucose level. An intravenous catheter will be placed in 
one arm for continuous sampling around 31/2 hours after the participant arrival. Once 
venous access is secure, blood samples will be collected at different times. If the fasting 
glucose is ≥126 mg/dl, the participant will be excluded from the oral glucose tolerance 
test study, and blood will be collected approximately 4 and 6 hours after the fingerstick. If 
blood glucose is below 126 mg/dl; we will collect blood samples around 4 hours after the 
patient’s arrival. Immediately afterwards,  a 75-gram glucose solution will be 
administered as part of the oral glucose tolerance test (OGTT), and blood samples will 
be collected at  10, 20, 30, 60, 90, and 120 minutes after glucose ingestion. Participants 
with 2-hour blood glucose ≥200mg/dl will be excluded. A standard meal will be provided 
at the end of the study visit. 
• 
Study visit 2: Visit 2 will occur within one month after visit 1. An interview, physical 
exam, and urine testing in the fasting participant will be performed, as in Study visit 1. A 
single dose of 60 mg of oral prednisone will be administered immediately after a 
fingerstick for blood glucose is performed. Approximately 31/2 hours later, an 
intravenous catheter will be placed in one arm for continuous sampling. The first blood 

 
 
 
 
10/23/17 
collection will be perform around 4 hours after the ingestion of prednisone, and 
immediately after an OGTT will be performed (if tolerated on study visit 1). Blood 
collection will occur at 10, 20, 30, 60, 90, and 120 minutes after glucose ingestion. A 
standard meal will be provided at the end of the study visit. 
Each visit will take approximately 6 hours and the participant should stay at VUMC 
during the study visits. The study team will contact the participant by phone for the next 
two consecutive days after the last visit to inquire about any problem related to the 
study. 
 
Measurements: Blood samples will be used for the following: 
• Basic metabolic screening  
• Glucose, insulin, and C-peptide to study insulin sensitivity and β cell function72, 73 at 
both study visits. 
• Blood cell counts.  
• RNA extraction will be performed from PBMCs collected in both study visits. 
• Genotyping 
• 
Urine and stool samples will be stored for future research. Female participants will 
have a urine pregnancy test at each study visit. 
 
RNA sequence and Genotyping: RNA will be extracted from PBMC lysates following 
standard procedures and send to the Vanderbilt Technologies for Advance Genomics 
(VANTAGE) Core for RNA-seq. VANTAGE will perform RNA sequencing including small 
RNAs. Once sequencing data is available, VANGARD will align the reads to a reference 
genome, count the reads, compared gene expression levels before and after GC 
exposure and obtain a list of genes that are differential expressed. Genotyping will be 
performed at the VANTAGE Core using Megachip 
 
7.0 
Risks 
 
Drawing of venous blood: Puncturing of a vein may result in minor bleeding or 
bruising. Because some people may faint during blood draw, we will ask the participants 
to lie down during blood collection. 
Fasting: Dizziness, light-headedness, or nausea can occur as result of fasting.  
75gr OGTT: nausea, vomiting, or light-headedness may occur after drinking the glucose 
solution. A drop in blood glucose levels may occur during the test which may cause 
nausea, dizziness, sweating, weakness, or restlessness. A significant drop in glucose 
levels during the test is likely to occur during illnesses, alcohol consumption, dieting, in 
patients using medications or long periods of bed rest (hospitalized patients), but rarely 
in healthy people. The study team will monitor the blood glucose several times during 
the test using a glucometer. If blood glucose levels fall out of safe ranges, the test will 
be stopped and oral (or intravenous) glucose solutions will be administered if needed. 
Transient hyperglycemia during OGTT is likely to occur in people with undiagnosed 
diabetes, and it resolves spontaneously once the test is complete.  
Prednisone is a FDA-approved drug for several disorders. Glucocorticoids have 
several side effects (e.g., increase in blood pressure, electrolytes abnormalities, 
adrenocortical suppression, risk of infection, mood and behavioral disturbance, 
gastrointestinal problems, etc.), which are more likely to occur with repeated and 
high doses. We plan to administer a single dose of prednisone 60 mg to healthy 
individuals and monitor them for the next 6-6½ hours after the administration of the 
drug. It is unlikely that any harmful side effects will occur with a single exposure to 

 
 
 
 
10/23/17 
glucocorticoids. Prednisone has a half-life of 18-36 hours; thus, we will instruct all the 
participants to contact us if they experience any discomfort within the next 72 hours 
after the study conclusion. 
 
8.0 
Reporting of Adverse Events or Unanticipated Problems involving Risk 
to Participants or Others 
 
The proposed study is low risk. These studies do not fall into the traditional Phase 
I, II, or III clinical trial concepts. Nevertheless it is important to monitor the data 
obtained and subject safety. Monitoring should be commensurate with the risks, which 
are small. The principal investigator proposes to review the data weekly and adverse 
events if they occur. During the conduct of the study, any serious adverse event will be 
reported to the IRB within 7 days of the PI’s notification of the event. Should the risk-
benefit ratio of the study change the principal investigator will make alterations to the 
procedures of the study and submit them to the IRB. 
 
9.0 
Study Withdrawal/Discontinuation 
 
Withdrawal of participants from any part or the whole study is under the discretion of the 
PI and will be done if: 
• 
Performing a specific study procedure may be harmful for the participant or 
he/she cannot tolerate the procedure. 
• 
The participant does not meet the study requirements 
• 
The study is stopped 
• 
Participant is not willing to continue the study 
 
Participants who may not tolerate the OGTT may continue in the study to assess GC 
efficacy but not GC metabolic toxicity. The study team will explain to the participant why 
he/she is taken out from specific procedures or from the whole study.  
 
10.0 Statistical Considerations 
 
Although this is a pilot study that will provide preliminary information of the effect size 
and variability of our outcomes (clinical and gene expression changes), we have 85% 
power to detect a minimum fold change of 2.5 for a total number of 20,000 tested genes 
with the top 100 being prognostic with a FDR of 0.1 using exact test, assuming a 
minimum average read counts of 10 for prognostic genes, a maximum dispersion of 0.5, 
and a ratio of the geometric mean of normalization factor of 1. The main analysis of the 
gene expression data will be carried out by Dr. Yan Guo (VANGARD), who will perform 
the following procedures: 
• Detect genes that on average change expression levels due to GC treatment: we will 
normalized gene expression data. We will use the log2 fold change in expression as a 
measure of response, and perform multiple linear regression controlling for potential 
covariates (batch, age, and BMI) with a FDR < 0.1. 
• Identify a set of genes with expression patterns are predictive of GC response: to test 
the association between changes in our clinical outcomes (lymphocyte counts and 
metabolic indexes) and log2 fold change for each gene, we will perform linear regression 
analysis using a FDR of 0.1. We will stratify our analysis by sex. To control for the effect 
of estrogens levels in GC response among females, we will register the day of the 
menstrual cycle that the study visit occurred and include it as a covariate. For women 

 
 
 
 
10/23/17 
who are using a hormonal birth control method, we will also include the type of hormone 
birth control method (systemic or local), and active ingredient (progesterone with and 
without estrogen) as covariates. 
• Select a list of candidate of regulatory variants that are likely to explain the observed 
variability in gene expression patterns: using public available eQTL datasets (GTEx, 
seeQTL, NCBI eQTL Browser, Genevar, SCAN, ExSNP, etc), we will query genes that 
their gene expression profile are predictive of GC response to identify a candidate list of 
variants for genome interrogation. We will select variants that: (a) have shown to be 
significantly associated with the expression of the respective gene (P<5x10-4), (b) are 
located within 1 megabase on either site of the gene’s transcriptional start site, and (c) 
have MAF >0.10. We will combined genome information and the gene expression data 
using a Bayes factor calculation27 to identify which variants do affect gene expression 
profile with GC administration with and without OGTT. 
Additionally, to determine which mechanisms are shared between in in vivo and in vitro 
GC exposure, we plan to culture PBMCs with GC in normoglycemic and hyperglycemic 
media. PBMCs will be isolate from blood samples collected at screening visit and culture 
for 4 hours with GC in a normoglycemic and hyperglycemic media to simulate the effect 
of OGTT and measure changes in gene expression. By comparing gene expression 
profiles from in vivo and in vitro administration of GC on PBMCs from the same 
individual, we can identify which mechanisms are shared in both conditions. This will 
largely improve our ability to perform clinical studies in the future that involve GC 
exposure by reducing the risk and costs related to drug exposure for specific 
mechanisms that are shared in in vitro and in vivo response. 
 
11.0 Privacy/Confidentiality Issues 
 
Confidentiality and ethical considerations will be addressed as follows. All identifying 
documents, data, and specimens collected as a result of this study will be retained by 
the investigator. The information collected will be stored in a secure, password-protected 
database. All samples collected will be labeled with a unique code. Access to this 
material will be available only to the research investigator and/or her research staff. If 
results of this study are to be published, only code numbers will be used for identification 
purposes. Participants will not be identified by name. 
 
12.0 Follow-up and Record Retention 
 
For this pilot study, we expect to finish participant enrollment within 2 years. However, 
we expect that the sequencing process, genome interrogation, and data analysis will be 
completed within 3 years. Research data that results from this study will be archive for 
unknown length of time.    

 
 
 
 
10/23/17 
Reference List 
 
 
1.  Fardet L, Petersen I, Nazareth I. Prevalence of long-term oral glucocorticoid 
prescriptions in the UK over the past 20 years. Rheumatology (Oxford) 
2011;50(11):1982-1990. 
 
2.  Barnes PJ, Adcock IM. Glucocorticoid resistance in inflammatory diseases. Lancet 
2009;373(9678):1905-1917. 
 
3.  Overman RA, Yeh JY, Deal CL. Prevalence of oral glucocorticoid usage in the 
United States: A general population perspective. Arthritis Care Res (Hoboken ) 
2012. 
 
4.  Leung DY, Bloom JW. Update on glucocorticoid action and resistance. J Allergy 
Clin Immunol 2003;111(1):3-22. 
 
5.  Chan MT, Leung DY, Szefler SJ, Spahn JD. Difficult-to-control asthma: clinical 
characteristics 
of 
steroid-insensitive 
asthma. 
J 
Allergy 
Clin 
Immunol 
1998;101(5):594-601. 
 
6.  Faubion WA, Jr., Loftus EV, Jr., Harmsen WS, Zinsmeister AR, Sandborn WJ. The 
natural history of corticosteroid therapy for inflammatory bowel disease: a 
population-based study. Gastroenterology 2001;121(2):255-260. 
 
7.  van SD, Valkema R, Dijkmans BA et al. Prednisone treatment of elderly-onset 
rheumatoid arthritis. Disease activity and bone mass in comparison with 
chloroquine treatment. Arthritis Rheum 1995;38(3):334-342. 
 
8.  Barnes PJ, Jonsson B, Klim JB. The costs of asthma. Eur Respir J 1996;9(4):636-
642. 
 
9.  Mazzantini M, Torre C, Miccoli M et al. Adverse events during longterm low-dose 
glucocorticoid treatment of polymyalgia rheumatica: a retrospective study. J 
Rheumatol 2012;39(3):552-557. 
 10.  Saag KG, Koehnke R, Caldwell JR et al. Low dose long-term corticosteroid 
therapy in rheumatoid arthritis: an analysis of serious adverse events. Am J Med 
1994;96(2):115-123. 
 11.  Proven A, Gabriel SE, Orces C, O'Fallon WM, Hunder GG. Glucocorticoid therapy 
in giant cell arteritis: duration and adverse outcomes. Arthritis Rheum 
2003;49(5):703-708. 
 12.  Tantisira KG, Lasky-Su J, Harada M et al. Genomewide association between 
GLCCI1 and response to glucocorticoid therapy in asthma. N Engl J Med 
2011;365(13):1173-1183. 

 
 
 
 
10/23/17 
 13.  Tantisira KG, Damask A, Szefler SJ et al. Genome-wide association identifies the 
T gene as a novel asthma pharmacogenetic locus. Am J Respir Crit Care Med 
2012;185(12):1286-1291. 
 14.  Qiu W, Rogers AJ, Damask A et al. Pharmacogenomics: novel loci identification 
via integrating gene differential analysis and eQTL analysis. Hum Mol Genet 
2014;23(18):5017-5024. 
 
 15.  McGeachie MJ, Wu AC, Chang HH, Lima JJ, Peters SP, Tantisira KG. Predicting 
inhaled corticosteroid response in asthma with two associated SNPs. 
Pharmacogenomics J 2012. 
 16.  Mager DE, Lin SX, Blum RA, Lates CD, Jusko WJ. Dose equivalency evaluation of 
major corticosteroids: pharmacokinetics and cell trafficking and cortisol 
dynamics. J Clin Pharmacol 2003;43(11):1216-1227. 
 17.  Magee MH, Blum RA, Lates CD, Jusko WJ. Pharmacokinetic/pharmacodynamic 
model for prednisolone inhibition of whole blood lymphocyte proliferation. Br J 
Clin Pharmacol 2002;53(5):474-484. 
 18.  Fauci AS. Mechanisms of corticosteroid action on lymphocyte subpopulations. I. 
Redistribution of circulating T and b lymphocytes to the bone marrow. 
Immunology 1975;28(4):669-680. 
 19.  Dale DC, Fauci AS, Guerry D, IV, Wolff SM. Comparison of agents producing a 
neutrophilic leukocytosis in man. Hydrocortisone, prednisone, endotoxin, and 
etiocholanolone. J Clin Invest 1975;56(4):808-813. 
 20.  Fardet L, Flahault A, Kettaneh A et al. Corticosteroid-induced clinical adverse 
events: frequency, risk factors and patient's opinion. Br J Dermatol 
2007;157(1):142-148. 
 21.  Fardet L, Kassar A, Cabane J, Flahault A. Corticosteroid-induced adverse events 
in adults: frequency, screening and prevention. Drug Saf 2007;30(10):861-881. 
 22.  Gurwitz JH, Bohn RL, Glynn RJ, Monane M, Mogun H, Avorn J. Glucocorticoids 
and the risk for initiation of hypoglycemic therapy. Arch Intern Med 
1994;154(1):97-101. 
 23.  Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated 
SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. 
PLoS Genet 2010;6(4):e1000888. 
 24.  Albert FW, Kruglyak L. The role of regulatory variation in complex traits and 
disease. Nat Rev Genet 2015;16(4):197-212. 
 
 25.  Li H, Pouladi N, Achour I et al. eQTL networks unveil enriched mRNA master 
integrators downstream of complex disease-associated SNPs. J Biomed Inform 
2015;58:226-234. 

 
 
 
 
10/23/17 
 26.  Luca F, Maranville JC, Richards AL, Witonsky DB, Stephens M, Di RA. Genetic, 
functional and molecular features of glucocorticoid receptor binding. PLoS One 
2013;8(4):e61654. 
 
 27.  Maranville JC, Luca F, Richards AL et al. Interactions between glucocorticoid 
treatment and cis-regulatory polymorphisms contribute to cellular response 
phenotypes. PLoS Genet 2011;7(7):e1002162. 
 28.  Fauci AS, Dale DC, Balow JE. Glucocorticosteroid therapy: mechanisms of action 
and clinical considerations. Ann Intern Med 1976;84(3):304-315. 
 29.  Fauci AS, Dale DC. Alternate-day prednisone therapy and human lymphocyte 
subpopulations. J Clin Invest 1975;55(1):22-32. 
 30.  Cupps TR, Fauci AS. Corticosteroid-mediated immunoregulation in man. Immunol 
Rev 1982;65:133-155. 
 31.  Saavedra-Delgado AM, Mathews KP, Pan PM, Kay DR, Muilenberg ML. Dose-
response studies of the suppression of whole blood histamine and basophil 
counts by prednisone. J Allergy Clin Immunol 1980;66(6):464-471. 
 32.  Barth J, Damoiseaux M, Mollmann H, Brandis KH, Hochhaus G, Derendorf H. 
Pharmacokinetics and pharmacodynamics of prednisolone after intravenous and 
oral administration. Int J Clin Pharmacol Ther Toxicol 1992;30(9):317-324. 
 33.  Chakraborty A, Blum RA, Cutler DL, Jusko WJ. Pharmacoimmunodynamic 
interactions of interleukin-10 and prednisone in healthy volunteers. Clin 
Pharmacol Ther 1999;65(3):304-318. 
 34.  Mishler JM, Emerson PM. Development of Neutrophilia by serially increasing 
doses of dexamethasone. Br J Haematol 1977;36(2):249-257. 
 35.  Czock 
D, 
Keller 
F, 
Rasche 
FM, 
Haussler 
U. 
Pharmacokinetics 
and 
pharmacodynamics 
of 
systemically 
administered 
glucocorticoids. 
Clin 
Pharmacokinet 2005;44(1):61-98. 
 36.  Shoenfeld Y, Gurewich Y, Gallant LA, Pinkhas J. Prednisone-induced leukocytosis. 
Influence of dosage, method and duration of administration on the degree of 
leukocytosis. Am J Med 1981;71(5):773-778. 
 37.  Serra-Bonett N, Al SS, Rodriguez MA. Effect of low-dose prednisone on leukocyte 
counts and subpopulations in patients with rheumatoid arthritis. J Clin Rheumatol 
2009;15(3):148-149. 
 38.  Donihi AC, Raval D, Saul M, Korytkowski MT, DeVita MA. Prevalence and 
predictors of corticosteroid-related hyperglycemia in hospitalized patients. Endocr 
Pract 2006;12(4):358-362. 

 
 
 
 
10/23/17 
 39.  Kwon S, Hermayer KL. Glucocorticoid-induced hyperglycemia. Am J Med Sci 
2013;345(4):274-277. 
 40.  Clore JN, Thurby-Hay L. Glucocorticoid-induced hyperglycemia. Endocr Pract 
2009;15(5):469-474. 
 41.  Bergman RN, Finegood DT, Kahn SE. The evolution of beta-cell dysfunction and 
insulin resistance in type 2 diabetes. Eur J Clin Invest 2002;32 Suppl 3:35-45. 
 42.  DeFronzo RA. Pathogenesis of type 2 diabetes mellitus. Med Clin North Am 
2004;88(4):787-835, ix. 
 43.  Yuen KC, Chong LE, Riddle MC. Influence of glucocorticoids and growth hormone 
on insulin sensitivity in humans. Diabet Med 2013;30(6):651-663. 
 44.  van Raalte DH, Nofrate V, Bunck MC et al. Acute and 2-week exposure to 
prednisolone impair different aspects of beta-cell function in healthy men. Eur J 
Endocrinol 2010;162(4):729-735. 
 45.  Hollingdal M, Juhl CB, Dall R et al. Glucocorticoid induced insulin resistance 
impairs basal but not glucose entrained high-frequency insulin pulsatility in 
humans. Diabetologia 2002;45(1):49-55. 
 46.  Kauh E, Mixson L, Malice MP et al. Prednisone affects inflammation, glucose 
tolerance, and bone turnover within hours of treatment in healthy individuals. 
Eur J Endocrinol 2012;166(3):459-467. 
 47.  Abdelmannan D, Tahboub R, Genuth S, Ismail-Beigi F. Effect of dexamethasone 
on oral glucose tolerance in healthy adults. Endocr Pract 2010;16(5):770-777. 
 48.  Taheri N, Aminorroaya A, Ismail-Beigi F, Amini M. Effect of dexamethasone on 
glucose homeostasis in normal and prediabetic subjects with a first-degree 
relative with type 2 diabetes mellitus. Endocr Pract 2012;18(6):855-863. 
 49.  Plat L, Byrne MM, Sturis J et al. Effects of morning cortisol elevation on insulin 
secretion and glucose regulation in humans. Am J Physiol 1996;270(1 Pt 1):E36-
E42. 
 50.  Larsson H, Ahren B. Insulin resistant subjects lack islet adaptation to short-term 
dexamethasone-induced 
reduction 
in 
insulin 
sensitivity. 
Diabetologia 
1999;42(8):936-943. 
 51.  Wajngot A, Giacca A, Grill V, Vranic M, Efendic S. The diabetogenic effects of 
glucocorticoids are more pronounced in low- than in high-insulin responders. 
Proc Natl Acad Sci U S A 1992;89(13):6035-6039. 
 52.  Henriksen JE, Alford F, Ward GM, Beck-Nielsen H. Risk and mechanism of 
dexamethasone-induced deterioration of glucose tolerance in non-diabetic first-
degree relatives of NIDDM patients. Diabetologia 1997;40(12):1439-1448. 

 
 
 
 
10/23/17 
 53.  Grill V, Pigon J, Hartling SG, Binder C, Efendic S. Effects of dexamethasone on 
glucose-induced insulin and proinsulin release in low and high insulin responders. 
Metabolism 1990;39(3):251-258. 
 54.  Iwamoto T, Kagawa Y, Naito Y, Kuzuhara S, Kojima M. Steroid-induced diabetes 
mellitus and related risk factors in patients with neurologic diseases. 
Pharmacotherapy 2004;24(4):508-514. 
 55.  Tornatore KM, Biocevich DM, Reed K, Tousley K, Singh JP, Venuto RC. 
Methylprednisolone pharmacokinetics, cortisol response, and adverse effects in 
black and white renal transplant recipients. Transplantation 1995;59(5):729-736. 
 56.  Fennell RS, Van DJ, Riley WJ. Steroid-induced diabetes in pediatric renal 
transplant recipients. Int J Pediatr Nephrol 1983;4(2):103-107. 
 57.  van Rossum EF, Koper JW, Huizenga NA et al. A polymorphism in the 
glucocorticoid receptor gene, which decreases sensitivity to glucocorticoids in 
vivo, is associated with low insulin and cholesterol levels. Diabetes 
2002;51(10):3128-3134. 
 58.  Syed AA, Halpin CG, Irving JA et al. A common intron 2 polymorphism of the 
glucocorticoid receptor gene is associated with insulin resistance in men. Clin 
Endocrinol (Oxf) 2008;68(6):879-884. 
 59.  van Raalte DH, van LN, Simonis-Bik AM et al. Glucocorticoid receptor gene 
polymorphisms are associated with reduced first-phase glucose-stimulated insulin 
secretion and disposition index in women, but not in men. Diabet Med 
2012;29(8):e211-e216. 
 60.  Trementino L, Appolloni G, Concettoni C, Cardinaletti M, Boscaro M, Arnaldi G. 
Association of glucocorticoid receptor polymorphism A3669G with decreased risk 
of developing diabetes in patients with Cushing's syndrome. Eur J Endocrinol 
2012;166(1):35-42. 
 61.  Giordano R, Marzotti S, Berardelli R et al. BClI polymorphism of the 
glucocorticoid receptor gene is associated with increased obesity, impaired 
glucose metabolism and dyslipidaemia in patients with Addison's disease. Clin 
Endocrinol (Oxf) 2012;77(6):863-870. 
 62.  Di Blasio AM, van Rossum EF, Maestrini S et al. The relation between two 
polymorphisms in the glucocorticoid receptor gene and body mass index, blood 
pressure and cholesterol in obese patients. Clin Endocrinol (Oxf) 2003;59(1):68-
74. 
 63.  Huizenga NA, Koper JW, de LP et al. Interperson variability but intraperson 
stability of baseline plasma cortisol concentrations, and its relation to feedback 
sensitivity of the hypothalamo-pituitary-adrenal axis to a low dose of 
dexamethasone in elderly individuals. J Clin Endocrinol Metab 1998;83(1):47-54. 

 
 
 
 
10/23/17 
 64.  Dobson MG, Redfern CP, Unwin N, Weaver JU. The N363S polymorphism of the 
glucocorticoid receptor: potential contribution to central obesity in men and lack 
of association with other risk factors for coronary heart disease and diabetes 
mellitus. J Clin Endocrinol Metab 2001;86(5):2270-2274. 
 65.  Goracy I, Goracy J, Safranow K, Skonieczna-Zydecka K, Ciechanowicz A. 
Association of glucocorticoid receptor gene NR3C1 genetic variants with 
angiographically documented coronary artery disease and its risk factors. Arch 
Med Res 2013;44(1):27-33. 
 66.  Grundberg E, Adoue V, Kwan T et al. Global analysis of the impact of 
environmental perturbation on cis-regulation of gene expression. PLoS Genet 
2011;7(1):e1001279. 
 
 67.  Maranville JC, Baxter SS, Torres JM, Di RA. Inter-ethnic differences in 
lymphocyte sensitivity to glucocorticoids reflect variation in transcriptional 
response. Pharmacogenomics J 2013;13(2):121-129. 
 68.  Gordon K, Clouaire T, Bao XX et al. Immortality, but not oncogenic 
transformation, of primary human cells leads to epigenetic reprogramming of 
DNA methylation and gene expression. Nucleic Acids Res 2014;42(6):3529-3541. 
 
 69.  Lorsch JR, Collins FS, Lippincott-Schwartz J. Cell Biology. Fixing problems with 
cell lines. Science 2014;346(6216):1452-1453. 
 
 70.  Zaitseva M, Vollenhoven BJ, Rogers PA. In vitro culture significantly alters gene 
expression profiles and reduces differences between myometrial and fibroid 
smooth muscle cells. Mol Hum Reprod 2006;12(3):187-207. 
 
 71.  Olnes MJ, Kotliarov Y, Biancotto A et al. Effects of Systemically Administered 
Hydrocortisone on the Human Immunome. Sci Rep 2016;6:23002. 
 
 72.  Dalla MC, Campioni M, Polonsky KS et al. Two-hour seven-sample oral glucose 
tolerance test and meal protocol: minimal model assessment of beta-cell 
responsivity and insulin sensitivity in nondiabetic individuals. Diabetes 
2005;54(11):3265-3273. 
 73.  Cobelli C, Toffolo GM, Dalla MC et al. Assessment of beta-cell function in 
humans, simultaneously with insulin sensitivity and hepatic extraction, from 
intravenous and oral glucose tests. Am J Physiol Endocrinol Metab 
2007;293(1):E1-E15.