Pharmacogenomics of Thiazolidinediones
Phase: NA
Conditions: Type 2 Diabetes
Sponsor: University of Maryland, Baltimore

 
 
 
 
 
 
 
 
Pharmacogenomics of Thiazolidinediones 
NCT01135394 
2006-11-27 

2006-11-27 
 Principal Investigator/Program Director (Last, first, middle): 
 Snitker, Soren 
PHS 398/2590 (Rev. 09/04) 
Page __54__
 Continuation Format Page
RESEARCH PLAN 
A.
SPECIFIC AIMS
The Thiazolidinediones (TZDs) are a class of insulin sensitizing drugs used in the treatment of
type 2 diabetes (T2DM). In addition, trials suggest that the TZDs may be useful in the prevention of 
progression from impaired glucose tolerance to T2DM and have salutary effects on other aspects of the 
metabolic syndrome. However, clinical and mechanistic studies indicate that not all patients exhibit the 
expected response to TZDs. Approximately 1/3 of those treated show little or no improvement in insulin 
sensitivity or HbA1c. 
The TZDs are ligands for the nuclear receptor peroxisome proliferator activated receptor-
gamma (PPAR). When this receptor is activated by TZDs, many genes involved in adipogenesis and 
insulin signaling are up- or down-regulated.  
The overall objective of this proposal is to define the molecular and genetic basis of TZD 
responsiveness using a phenotype to genotype approach and state-of-the-art functional genomics and 
high-throughput genetics. Our hypotheses are:  
(1) Differences in clinical response (primarily improvement in insulin sensitivity) can be defined at
the molecular level as differences in expression of genes involved in TZD-activated pathways.
(2) Genes whose expression change is correlated to clinical response can be identified through
cDNA microarrays of muscle and fat, the two target tissues for TZDs.
(3) Sequence variants in these candidate genes will determine TZD response, particularly
sequence variants within regulatory regions.
To directly test these hypotheses, the Specific Aims of this proposal are to: 
1.
Perform comprehensive testing in 75 individuals before and after a 3-month rosiglitazone
intervention and characterize the variability in TZD response at the physiological, cellular, and
molecular levels. The primary TZD response trait will be changes in insulin sensitivity as measured
by an intravenous glucose tolerance test (IVGTT). We will also define secondary response traits,
which will include body fat composition and distribution (DXA and abdominal CT), serum lipid levels
(HDL, LDL, triglycerides, lipoprotein particle sizes), inflammatory markers (hsCRP, SAA, IL1, IL6,
TNF), prothrombotic factors (plasminogen activator inhibitor 1; PAI-1), adipokines (leptin,
adiponectin), and adipocyte metabolism (glucose uptake, lipolysis). From these data, we will:
a. Identify the baseline factors (e.g., age, gender, body mass index, etc.) that are predictive of TZD
response to the measured traits.
b. Determine if the response of the primary trait (i.e., insulin sensitivity) to TZDs is correlated with
the response of each of the secondary traits to TZDs.
c.
Perform cDNA microarray analysis of muscle and fat before and after 3 months of rosiglitazone
therapy to identify the genes and their corresponding pathways that are correlated to TZD
response.
d. Perform quantitative real-time PCR to accurately quantify changes observed in the microarray
analysis and to study expression of other genes of interest.
2.
Define genes whose regulation correlates to TZD response. One-hundred of these TZD-response
candidate genes will be extensively studied by:
a. Identifying common sequence variants through DNA sequencing and in silico SNP discovery.
b. Validating these SNPs and defining allele frequencies through genotype analysis of
approximately 2000 SNPs in the 100 candidate genes in DNA samples from Caucasian and
African American subjects.
3.
Identify the SNPs and haplotypes in these 100 candidate genes that influence TZD response by
performing association analysis in (1) the 75 subjects recruited for this study, (2) a subset of 597
individuals from the Diabetes Prevention Program study who were treated with the TZD,

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
          Snitker, Soren 
PHS 398/2590 (Rev. 09/04) 
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troglitazone, and (3) 97 gestational diabetic Mexican American women who were treated with 
troglitazone in TRIPOD (and more recently with pioglitazone in PIPOD). 
In summary, this project will seek to determine the genetic underpinnings of inter-individual 
variation in TZD response. Since TZDs are agonist for the nuclear receptor, PPAR, we will use a 
functional genomics approach to identify the molecular phenotype of TZD response from which 
candidate genes will be selected and studied in several populations. This proposal combines powerful 
functional genomics and high-throughput genetic approaches in a unique and innovative fashion to 
study physiologically well-characterized subjects in whom a controlled TZD intervention is performed.   
 
B.  
BACKGROUND AND SIGNFICANCE 
1. 
Thiazolidinediones (TZDs), a Novel Class of Insulin Sensitizers 
a. 
PPAR and TZD mechanism of action 
The thiazolidinediones (TZDs) are synthetic ligands for the nuclear receptor PPAR (peroxisome 
proliferator activated receptor-gamma). Agents approved in the United States include rosiglitazone, 
pioglitazone, and troglitazone (the latter is no longer marketed due to liver toxicity). PPAR is an 
important regulator of adipocyte differentiation and insulin signaling (22, 33, 46, 64, 72, 78), which 
forms heterodimers with RXR. When a ligand binds to the PPAR-RXR heterodimer, this event 
promotes association of the heterodimer to specific DNA sequences within regulatory regions of target 
genes (so-called PPREs) to activate or repress transcription, thereby promoting adipogenesis and 
increasing insulin sensitivity. The expression of many (hundreds of) genes is affected by 
PPARactivation (35, 40, 47, 58).  
Human trials have shown that TZDs decrease insulin resistance, improve glycemic control and 
decrease hypertriglyceridemia, and they are now a mainstay of treatment for people with T2DM (10, 32, 
54, 56, 57, 69). The mechanisms whereby TZDs increase insulin sensitivity in humans likely include up-
regulation of cellular pathways involved in lipid oxidation, glucose uptake, and glucose utilization 
(reviewed in 22, 43, 64). Improved insulin sensitivity induced by TZDs also appears related to the ability 
of TZDs to increase adiponectin expression as well as to decrease the expression and insulin-
antagonistic effects of tumor necrosis factor- (TNF)(34, 44, 50, 59), and possibly other inflammatory 
factors. TZDs are also known to promote changes in body fat distribution from visceral to subcutaneous 
fat, which may increase insulin sensitivity (2, 3, 41, 53). 
b. 
Anti-inflammatory and other actions of TZDs  
Insulin resistance is thought to be a mainstay of the metabolic syndrome, which is clinically 
defined by the NCEP ATP-III guidelines based on specific cut points for waist circumference, 
triglycerides, HDL-cholesterol, fasting glucose, and blood pressure (21). In addition to insulin 
resistance, it is becoming increasingly evident that the syndrome also involves chronic inflammation 
and a prothrombotic state manifest in elevations of factors such as C-reactive protein (CRP) and PAI-1. 
CRP is an acute phase reactant that has been associated with all the traditional components of the 
metabolic syndrome (60, 79, 86). In addition to insulin resistance, CRP levels are correlated to the 
number of elements of the metabolic syndrome present (23). 
TZDs have anti-inflammatory effects that include the suppression of CRP levels (63), PAI-1, 
serum amyloid A (SAA), and complement C (20). When TZDs are used in obese subjects with and 
without diabetes, it results in reduced NFB-binding activity in mononuclear cell nuclear extract and 
reduced plasma monocyte chemoattractant protein-1 (MCP-1) and CRP. Plasma TNF- and SAA 
concentrations are additionally inhibited in those without diabetes (52). In addition to these anti-
inflammatory effects, TZDs reduce blood pressure (63), and possibly have direct effects on the vascular 
endothelium, which expresses PPAR. Lastly, PPARis also expressed in pancreatic -cells and 
appears to play a role in maintenance of -cell function and insulin secretion. Although much remains to 
be learned about the relation between insulin resistance and chronic inflammation, it is likely that TZD 
treatment, offering a combination of insulin sensitization and anti-inflammatory properties, may be 
especially promising for the large group of individuals predisposed to the metabolic syndrome.  

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
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2. 
TZD Response is Variable and Influenced by Genetics 
TZDs induce clinically significant improvements in insulin sensitivity, glucose tolerance, lipid profile, and 
blood pressure compared to placebo (7, 10, 29, 32, 36, 54, 56, 69, 82). However, the efficacy of TZD 
therapy is highly variable among subjects as shown by x lines of evidence. First, premarketing studies 
sponsored by drug companies to prepare for FDA approval. In these studies, as much as 25% to 50% 
of TZD-treated subjects were considered non-responders (1, 6, 29, 45, 66, 68, 76). That the estimates 
differed between the drugs is likely explained by differences in definitions of non-response and the 
eligibility criteria of the studies, because when rosiglitazone, pioglitazone, and troglitazone were 
compared side-by-side they did not differ in efficacy (42). Second, in the TRIPOD study, nondiabetic 
Hispanic women with previous gestational diabetes were randomly assigned to troglitazone (400 
mg/day) or placebo to test the hypothesis that troglitazone treatment would protect the subjects against 
the development of T2DM. Intravenous glucose tolerance tests were performed at randomization and 
after 3 months of treatment. Troglitazone/placebo treatment continued for a median duration of 30 
months. As the investigators had hypothesized, assignment to troglitazone was associated with a 
reduced incidence of diabetes compared to placebo for the duration of the study. The change in insulin 
sensitivity between baseline and the 3 month treatment point was highly variable among TZD treated 
subjects. Indeed, in the bottom third of the TZD treated subjects it was no different from placebo-treated 
controls (60; Fig. 1). Moreover, as a group, this stratum did not enjoy the protection from T2DM 
otherwise associated with troglitazone treatment.  
 
 
Fig. 1. rank ordered 
delta Si (post minus pre 
value) among troglitazone-
treated TRIPOD subjects 
(n=108). In the interest of 
visual resolution on the 
vertical 
axis, 
only 
the 
central 
95% 
of 
the 
distribution 
(n=102) 
is 
shown (lowest measured 
value was -2.1, highest 
measured value was 7.7). 
The box shows the mean 
and 
SD 
among 
the 
placebo-treated 
subjects. 
Unit for delta Si is 10-4 min-1 
per μU/ml (with permission 
from TA Buchanan).  
 
In the Tripod study, the change in insulin sensitivity and the 3 month time point was normally distributed 
in the placebo group Physical and biochemical patient characteristics that predict who will respond to 
TZD treatment have not yet been recognized. Variation in drug response is conceptually divided into 
pharmacokinetic (metabolic) and pharmacodynamic (action on receptors and pathways) variability. 
Pharmacokinetic variability is unlikely to play a great role because, as stated in the FDA-approved 
prescription information, response (or lack thereof) was not associated with plasma drug concentrations 
in premarketing trials (66). On the other hand, there is evidence that more than one pharmacodynamic 
aspect of TZD response is genetically determined. Two candidate genes have been examined for 
putative associations with clinical TZD response, the PPAR2 receptor and adiponectin (a.k.a. ACDC or 
APM1). Despite the fact that the Pro12Ala variant in the PPAR2 receptor is inactivating in vitro, several 
studies have failed to demonstrate a relation between this variant and TZD effects on insulin action (1, 
71 – see Preliminary Studies). However, consistent with biological predictions, one report indicates that 
-2
-1
0
1
2
3
4
5
0
20
40
60
80
100
Centile
Delta Si

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
          Snitker, Soren 
PHS 398/2590 (Rev. 09/04) 
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this variant is protective against fluid retention with ensuing edema and congestive heart failure (31), a 
serious adverse effect that limits the usefulness of the TZDs and is caused by PPARinduced sodium 
reuptake in the distal nephron (87). Expansion of this knowledge may enable physicians to genetically 
stratify patients into categories of risk for fluid retention. Dr. Watanabe (consultant on this proposal) and 
collaborators have demonstrated association between other variants in the gene encoding the PPAR2 
receptor and TZD response in the TRIPOD study (81), but these findings await biological confirmation. 
In the adiponectin gene, the two variants SNP45 and SNP276 both confer a blunted reduction in fasting 
glucose in response to a TZD (38). Further implications of the above findings are that variants that 
determine drug response may be located not in a drug’s immediate target molecule (i.e., PPAR) but in 
numerous downstream mediators (e.g., adiponectin). Especially when studying drugs that operate at 
the transcription level (as TZDs do), the multitude of putative pharmacogenetic candidates supports the 
use of gene expression arrays to determine genes that are up- or down-regulated in response to 
treatment, as we do in this proposal.  
3.  
Significance 
TZDs are insulin-sensitizing drugs that are useful in the treatment of T2DM, potentially having a 
future application in prophylaxis. Moreover, they appear to have salutary effects on a host of other 
medical conditions. Unfortunately, response to TZDs is very variable. The proposed studies will define 
the molecular and genetic underpinnings of TZD response. This information will have important 
implications for patient care. Physicians will be able to identify patients who are more likely to respond 
to TZDs for the treatment of insulin resistance and the prevention of CVD. In addition, some of the gene 
variants we identify may turn out to be determinants of T2DM risk.   
C.  
PRELIMINARY STUDIES 
Over the years, Dr. Snitker, Dr. Shuldiner, and other members of Dr. Shuldiner’s group have 
worked intensively to understand the genetic and molecular mechanisms of insulin resistance and the 
complex relationship between obesity and insulin resistance, applying state-of-the art physiological, 
molecular and cellular, genetic, pharmacogenetic, and functional genomic approaches to the questions 
at hand. As an experienced member of Dr. Shuldiner’s multidisciplinary group, Dr. Snitker is well suited 
to perform the proposed pharmacogenomic study. Below are highlights of previous work by the group 
leading up to this proposal, including some multi-center network studies.  
1. 
Identification of a Common Amino Acid Variant in the Human PPAR2 Gene 
Because PPAR is an important mediator of insulin signaling and adipogenesis, we 
hypothesized that mutations in the PPAR gene might predispose to obesity and/or type 2 diabetes. 
Therefore, we isolated and characterized the human PPAR gene and determined its chromosomal 
localization (8). We found that the coding region of PPAR is encoded by seven exons. The 
downstream six exons are common to both the 1 and 2 isoforms, while the 2 isoform has an 
additional upstream exon that encodes the additional 28 amino acids (8). We screened PPARG for 
mutations in obese subjects and were the first to identify a CG nucleotide change in the 2-specific 
exon that predicted the substitution of proline at position 12 to alanine (Pro12Ala PPAR2)(85). This 
genetic variant is common in diverse populations, especially Caucasians and Mexican Americans with 
a prevalence of 20% to 25% (14).  
Because proline residues are known to disrupt -helix formation whereas alanine easily 
participates in -helix formation, this alanine substitution could have profound effects on this protein’s 
structure and function. Proline at position 12 of PPAR2 is invariant between human, rhesus, mouse, 
and rat sequences, which further suggests that it may be structurally and functionally important (85). 
Indeed, others have shown that the Ala12 allele leads to a decrease in protein function; Deeb and 
coworkers (16) found that Ala12 PPAR2 had 1.6 to 2.5-fold lower affinity for the PPAR response 
element (PPRE) than Pro12 PPAR2. Similar defects in signaling of Ala12 PPAR2 were reported by 
Masugi and coworkers (49). 
2. 
Pro12Ala PPAR2 Is Associated with Increased Insulin Sensitivity  
a. 
African Americans of the Atherosclerosis Risk in Communities (ARIC) Study 
To investigate the relevance of the Pro12Ala PPAR2 variant in African Americans, we characterized 

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
          Snitker, Soren 
PHS 398/2590 (Rev. 09/04) 
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this variant in 1,441 middle-aged African American individuals with respect to diabetes, obesity, and 
other cardiovascular-related traits, both cross-sectionally and prospectively. Subjects were participants 
in the ARIC Study, a prospective, population-based study investigating the etiology and natural history 
of atherosclerosis and its clinical manifestations (5). Approximately 16,000 persons aged 45-64 at 
baseline (Visit 1: 1987-89) from four U.S. communities:  Forsyth County, NC (12% African Americans); 
Jackson, MS (all African Americans); suburban Minneapolis, MN (mostly white); and Washington 
County, MD (mostly white); were enrolled in ARIC. During baseline exam, data on health status, 
selected risk factors, family medical history, employment and educational status, diet, and physical 
activity were collected in an interviewer-administered questionnaire. Participants completed 4 clinic 
examinations, conducted every 3 years. The follow-up for each visit ranged from 80% to 90%. The 
overall frequency of the Ala12 allele in African Americans was 1.9% (95% confidence interval: 1.5 - 
2.5%). Consistent with previous findings in Caucasians (6), African American carriers of the Ala12 allele 
tended to be protected from the development of T2DM (odds ratio [OR] 0.64, 95% CI 0.34-1.20); 
however, this OR was not statistically significant. Among non-obese individuals, the Pro/Ala genotype 
was associated with significantly lower (insulin) (p = 0.001), lower (HOMA-IR) (homeostasis model 
assessment of insulin resistance) (p = 0.002), higher fasting glucose-to-insulin ratio (p = 0.005), and 
lower diastolic blood pressure (p = 0.02). Among overweight individuals (BMI 25-29.9 kg/m2), the 
Pro/Ala genotype was associated with greater BMI (p = 0.02), waist-to-hip ratio (p = 0.01), and waist 
circumference (p = 0.04). These studies support the notion that the Pro12Ala PPAR2 variant is 
associated with markers of insulin sensitivity and may be protective against risk of T2DM, and is 
consistent with several other studies in humans (14, 74, 75). Although the mechanism of increased 
insulin sensitivity with an allele that has decreased activity is unclear, these findings are consistent with 
studies in mice in which heterozygous PPAR knockout animals have increased insulin sensitivity 
relative to their wild type littermates (51, 83). 
b.  
Pro12Ala PPAR2 Associates with Improved Insulin Sensitivity after Weight Loss and Predicts 
Weight Regain in Obese Postmenopausal Caucasian Women 
We performed retrospective PPAR2 genotype analysis in 49 obese postmenopausal women 
who had undergone metabolic testing and weight loss (55). Forty-one subjects were Pro12 
homozygotes (Pro/Pro), 7 subjects were heterozygotes (Pro/Ala), and 1 subject was an Ala12 
homozygote (Ala/Ala). Before weight loss, there were trends toward increased BMI and lower fasting 
glucose in those with the Ala variant as reported in other populations (data not shown). Subjects 
underwent a weight loss program, which included a hypocaloric diet (deficit = 250 - 350 calories per 
day), low intensity exercise (walking 45 min per day three times per week), and psychobehavioral 
treatment. The intervention similarly decreased body weight by 8 ± 1% in women homozygous for the 
Pro12 allele and by 7 ± 1% in women with the Ala12 allele (p < 0.0001), but women with the Ala12 allele 
decreased their insulin area during the OGTT more than women homozygous for the Pro12 allele (p < 
0.05). Fat oxidation did not change in Pro/Pro women but decreased 19 ± 9% in women with the Ala12 
allele (p < 0.05). Reciprocally, carbohydrate oxidation increased to a greater extent in Ala12 carriers (p < 
0.05). Lower fat oxidation and increased insulin sensitivity in weight reduced individuals is a well-known 
metabolic milieu for weight gain. Indeed, weight regain during follow-up was significantly greater in 
women with the Ala12 allele than women homozygous for the Pro12 allele (5.4 ± 0.9 vs. 2.8 ± 0.4 kg, p < 
0.01). PPAR2 genotype was the best predictor of weight regain (r = 0.50, p < 0.01), followed by the 
change in fat oxidation (partial r = 0.35, p < 0.05; cumulative r = 0.58). These studies support the notion 
that the Pro12Ala variant in PPAR2 has clinical significance in influencing insulin sensitivity and in 
predicting recidivism after weight loss. It also provides an example of an interventional study in which 
careful phenotyping was performed to dissect significant differences in response to the intervention 
between genotypes, similar to the study design of this proposal.  
3. 
Pharmacogenetics of Pro12Ala PPAR2 (TRIPOD Cohort)  
Because Pro12Ala PPAR  2 is an inactivating variant, we hypothesized that the Ala12 allele 
would explain TZD non-response. In collaboration with Drs. Tom Buchanan and Richard Watanabe of 
the University of Southern California, Dr. Snitker explored this question in participants in the TRIPOD 
(Troglitazone in the Prevention of Diabetes) study (71).  
Nondiabetic Hispanic women with previous gestational diabetes were randomly assigned to 

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
          Snitker, Soren 
PHS 398/2590 (Rev. 09/04) 
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troglitazone (400 mg/day) or placebo for a median 
duration of 30 months. Assignment to troglitazone was 
associated with a reduced incidence of diabetes for the 
duration of the study compared to placebo. To explore 
the 
mechanism 
of 
action, 
intravenous 
glucose 
tolerance tests were performed at randomization and 
after 3 months of treatment. Troglitazone-treated 
subjects were divided into thirds based on their change 
in minimal model insulin sensitivity (Si) during these 3 
months. The mean change in insulin sensitivity in the 
top, middle, and bottom third of Si was 2.58 ± 1.32, 
0.91 ± 0.26, and - 0.21 ± 0.57  10-4 min-1 per μU/ml, 
respectively. The Si of those in the bottom tertile was 
not statistically different from that of placebo-treated 
subjects whose mean Si was - 0.17  10-4 min-1 per 
μU/ml (Fig. 1; see Background and Significance, 
Section 2). Therefore, the bottom third of treated 
subjects was considered non-responders. The non-responders also did not differ from placebo in terms 
of T2DM incidence, suggesting that troglitazone’s diabetes-preventive effect is mediated by improved 
insulin sensitivity. 
In the TRIPOD population, 30% were Ala12 carriers. This frequency coincides with the 1/3 of the 
troglitazone-treated participants who failed to improve insulin sensitivity. In combination with the in vitro 
and clinical studies of others, this observation suggested the validity of the hypothesis that the Ala12 
variant would explain non-response. However, In contrast with the hypothesis, the distribution of Si in 
response to troglitazone (by thirds) did not differ by Pro12 Ala PPAR2 genotype  (n=93, p = 0.77; see 
Figure 2). Of note, the sole Ala/Ala subject was in the middle third. Thus, despite convincing in vitro 
data, the Pro12Ala PPAR2 variant did not explain non-response. These results support the view that a 
much broader approach must be applied to dissect the genetic underpinnings of TZD response. 
Consequently, we applied a pharmacogenomic (as opposed to pharmacogenetic) approach in our 
ensuing studies and the present proposal.   
4. 
The Pharmacogenomics of PPAR
 
The TRIPOD analysis taught us that we would need to pursue a broader approach to 
understand the genetic underpinnings of TZD response. We therefore initiated the Pharmacogenomics 
of PPAR Study at the University of Maryland. This project, which uses a phenotype-to-genotype 
approach, began recently and is ongoing. Non-diabetic, insulin resistant subjects are being treated with 
rosiglitazone for 12 weeks. We chose rosiglitazone since this agent is a “pure” PPAR agonist, not 
cross-reacting with other PPARs. Insulin sensitivity and other clinical parameters are measured before 
and after the 12-week treatment period. As a novel aspect, we obtained fat and muscle biopsies before 
and after treatment to quantify changes in gene expression on a genome-wide level and to perform in 
vitro functional assays. At present, 11 subjects have completed the protocol. Additional details of the 
study design and preliminary results from these 11 subjects are provided below. 
a. 
Ascertainment and phenotyping 
Subjects are recruited without regard to genotype. Eligible subjects are age 35-65 years with a 
BMI ≥ 25 and < 40 kg/m2. This group of overweight and obese subjects is likely to have some degree of 
insulin resistance. Subjects with frank diabetes are excluded due to the potential confounding effects of 
metabolic instability, medications, and diabetic complications. All subjects are treated with rosiglitazone 
for 12 weeks; there is no placebo group or blinding. Genetic material is obtained at the beginning of the 
study. Before and after the rosiglitazone intervention subjects undergo extensive testing, including: 
 
Frequently sampled intravenous glucose tolerance test to determine insulin sensitivity (using 
minimal modeling) and acute insulin response.  
 
Vital signs, including rigorous measures of blood pressure 
 
Blood for lipid profile, inflammatory factors, adipokines, and PAI-1 
Pro/Pro 
Ala/ - 
 S i < 0.44 
0.44 <   S i < 1.33 
1.33 <   S i 
0 
5 
10 
15 
20 
25 
N 
Pro/Pro 
Ala/ - 
 S i < 0.44 
0.44 <   S i < 1.33 
1.33 <   S i 
0 
5 
10 
15 
20 
25 
N 
Unit for  Si is min - 1 per  μ U /ml  · 10 - 4 
p  = 0.77 (  2 test) 
(Non - responders) 
Figure 2. Change in insulin sensitivity (Si) in 
response to troglitazone by Pro12Ala PPAR2 
genotype in women of the TRIPOD study. 

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
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 
Imaging tests to determine total body fat and its distribution 
o Total body fat by dual energy X-ray absorptiometry (DEXA).  
o Two single slice CT scans (abdomen and thigh) for intraabdominal and subcutaneous fat 
area 
 
Muscle biopsies (vastus lateralis) 
o mRNA expression profile of 47,400 genes and ESTs using the Affymetrix U-133 Plus 2.0 
chip. 
o Confirmation of the above findings by Q-RT-PCR  
 
Fat biopsies (abdomen) 
o In vitro functional studies performed by Dr. Susan Fried  
 
Tissue studies: secretion of TNF and IL-6 
 
Cellular studies: lipolysis, glucose transport, lipoprotein lipase activity 
 
Fat cell sizing 
o mRNA expression profile of 47,400 genes and ESTs using the Affymetrix U-133 Plus 2.0 
chip 
o Confirmation of the above findings by Q-RT-PCR  
 
b. 
Preliminary results of the Pharmacogenomics of PPAR Study 
To date, 11 subjects (8 female and 3 male) have completed the protocol.  
 
Physical characteristics of completed subjects are shown in Table 1. During the 3-month 
intervention, body weight did not change, but there may have been a shift in fat distribution, although 
further enrollment is needed for the observed differences to reach statistical significance. Rosiglitazone 
treatment resulted in marked decreases in fasting insulin levels, consistent with an overall improvement 
in insulin sensitivity. There was a slight reduction in fasting glucose levels and no change in HbA1c, as 
one would expect in this non-diabetic cohort. Notably, there was a marked decrease in both systolic 
and diastolic blood pressure as well as the inflammatory markers CRP and SAA (serum amyloid A, an 
adipokine). As with changes in insulin sensitivity, there was great individual variation in the magnitude 
of change in each of these traits to rosiglitazone therapy.  
Table 1. Phys. Characteristics/ Results from Pharmacogenomics of PPARγ  
Pre 
Post 
Age (y) 
45.8 ± 9.0 
 
BMI 
30.8 ± 3.1 
 
Body weight (kg) 
88.0 ± 14.4 
88.0 ± 15.1 
CT Intraabdominal fat (cm2) 
140.0 ± 59.4 
134.6 ± 51.3 
CT Subcutaneous fat (cm2) 
396.2 ± 168.0 
408.7 ± 180.6 
DXA total body fat (%) 
37.4 ± 4.5 
37.5 ± 6.1 
SBP (mm Hg) 
128.6 ± 13.4 
122.9 ± 9.6 
DBP (mm Hg) 
76.3 ± 7.1 
71.6 ± 6.1 
Fasting plasma glucose (mg/dl) 
98.5 ± 3.8 
94.8 ± 6.5 
Fasting plasma insulin (U/ml) 
16.9 ± 5.4 
10.1 ± 2.2 
HbA1c (%) 
5.2 ± 0.5 
5.3 ± 0.4 
Plasma hsCRP (mg/L) 
4.2 ± 3.5 
1.6 ± 1.1 
Plasma Serum Amyloid A (SAA) (g/ml) 
14.0 ± 10.4 
10.2 ± 7.7 

2006-11-27              Principal Investigator/Program Director (Last, first, middle):    
 
 
 
          Snitker, Soren 
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0
2
4
6
8
10
12
Baseline
Rosiglitazone
SI (10(-4) min(-1)/(uIU/ml))
 
 
Insulin sensitivity data are available for 9 of the 11 
subjects and appears consistent with the reported non-
response rates in previous clinical trials (Figure 3). 
 
Studies of muscle and fat tissue including mRNA 
expression. 
In 
addition 
to 
studies 
of 
whole-body 
metabolism, tissue samples were obtained and studied 
because it is thought that individual response is better 
understood if it is characterized at lower levels. Fig. 4 
shows results of our in vitro lipolysis studies. Again, 
although the number of subjects is small, on average, 
rosiglitazone increased insulin’s ability to inhibit lipolysis, 
consistent with increased insulin sensitivity in adipocytes.  
Microarray studies 
To date, we have performed a preliminary 
microarray analysis of two pairs of pre/post fat and 3 pairs 
of pre/post muscle samples from our subjects. These 
experiments were conducted using Affymetrix U-133 Plus 2.0 chips to measure the expression of 
~47,400 transcripts. Comparisons were made pre- versus post-rosiglitazone in the same subject, and 
changes in each gene were averaged. Fat tissue was analyzed separate from muscle tissue. At 
threshold p-values of p < 0.05 and 1.5 fold-change, we found 167 genes in subcutaneous fat and 141 
genes in muscle that were significantly up- or down-regulated. Table 3 shows the 10 genes that had the 
largest changes in expression in fat (left) and muscle (right). In addition to our human data, the table 
shows the changes in  same genes in mice fed a high-fat diet with or without rosiglitazone (5 
mice/group, 30 mmol rosiglitazone/kg diet, 4 weeks). A comparison between the human genes 
responsive to rosiglitazone and their mouse HomoloGenes in Table 3 shows that the genes generally 
change in the same direction despite differences in magnitude. We acknowledge that with the small 
number of samples examined, these findings must be interpreted with great caution. Nevertheless, the 
comparison of genes responsive to rosiglitazone between humans and mice will disclose the genes that 
are co-regulated in the two species in a same manner. These genes are likely important for TZD-
← Figure 3. Insulin sensitivity (Si) before and after 
rosiglitazone treatment in 9 subjects. Note that in 3 
subjects, Si did not improve. 
ADA+PIA
ADA+PIA+ISO
0
1
2
3
PRE-TZD
POST-TZD
* p=0.023
* p=0.008
* p=0.006
n=5
Glycerol (umol/10^6 cells*2h)

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mediated insulin sensitivity. These studies demonstrate that our group has the expertise and 
infrastructure to perform the proposed studies. They also show that these studies are likely to provide 
valuable information regarding genes and pathways that are associated with TZD response, which will 
be very useful in our choice and prioritization of candidate genes of importance to drug response.   
 
Q-RT-PCR studies of gene expression. Steady state mRNA levels of selected genes were 
determined by quantitative real time PCR (Q-RT-PCR) using a Lightcycler (Roche Diagnostics). Total 
RNA extracted from omental and subcutaneous adipose tissue was converted into cDNA using 1st 
Strand cDNA Synthesis Kit for RT-PCR from Roche. cDNA was quantified for CD68 levels using an 
ASSAYS ON DEMAND Taqman primer/probe set purchased from ABI and the LightCycler Fast Start 
DNA Master Hybridization Probe Kit from Roche Diagnostics (Fig. 5). These studies show that we have 
the ability to confirm microarray results and study other genes of interest by Q-RT-PCR. 
Fig. 5. mRNA expression in adipocytes before and after 12 weeks of rosiglitazone treatment. Each 
vertical bar represents one subject. Expression after 12 wk. rosiglitazone treatment is expressed relative to 
baseline (subject’s baseline = 1.00). In some, rosiglitazone stimulated expression of its receptor, PPAR-gamma. 
Also adiponectin (ACDC) was often stimulated. Expression of the inflammatory adipocytokine, SAA, was 
generally decreased consistent with the drop in plasma SAA levels. The enzymes lipoprotein lipase (LPL) and 
hormone sensitive lipase (HSL) were both upregulated in response to treatment. Adding interest to this proposal, 
changes in expression levels of all genes were widely variable between individuals.  
Table 3. Five genes most up-regulated and five genes most down-regulated in fat and muscle tissue pre- versus 
post-rosiglitizaone in humans. Mouse column shows expression changes in mice (see text). N/A, not on chip. 
Fat 
Muscle 
Gene Name 
Fragment 
Name 
Fold change 
Gene Name 
Fragment 
Name 
Fold change 
 
 
Human  Mouse  
 
Human Mouse 
Mitochondrial solute carrier protein 
222529_at 
20.39 
3.8  
ATPase, Class I, type 8B, 
member 1 
241534_at 
8.7 
4.52 
Leukocyte specific transcript 1 
211582_x_at 
9.84 
N/A 
Similar to protein sp:P39188 
222375_at 
5.31 
N/A 
T cell receptor beta chain BV20S1 
BJ1-5 BC1 
211796_s_at 
8.09 
3.09 
FGF 18 
206986_at 
3.64 
1.58 
Amylase, alpha 2B; pancreatic, 
hypothetical protein FLJ25070 
226999_at 
7.27 
N/A 
SGT1, suppressor of G2 allele 
of SKP1 (S. cerevisiae) 
244368_x_a
t 
3.61 
1.22 
Neurogranin (protein kinase C 
substrate, RC3) 
204081_at 
6.09 
2.01 
Paired-like homeodomain 
transcription factor 1 
209587_at 
3.43 
1.38 
RasGEF domain family, member 
1A 
230563_at 
-4.31 
-1.59 
FLJ20533 
217220_at 
-4.73 
N/A 
Stathmin-like 2 
203001_s_at -4.54 
-1.29 
DKFZP586K1520 
233415_at 
-5.08 
1.2 
AAA-type ATPase family protein 
233039_at 
-4.83 
-2.11 
IMAGE Clone 246773 
241863_x_a
t 
-5.17 
N/A 
hypothetical protein FLJ20712   
237678_at 
-5.89 
N/A 
FLJ20890 
216155_at 
-5.24 
-1.47 
G protein-coupled receptor 115 
237690_at 
-10.94 
1.4 
Dachshund homolog 2 
(Drosophila) 
239738_at -12.05 
-1.30 
0
1
2
3
4
5
6
PPARg ACDC
SAA
IL-6
TNF-a
PAI-1
LPL
HSL

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Genotyping of candidate genes 
To test hypotheses regarding the putative effect of selected candidate genes, we have genotyped our 
subjects for these genes using a pyrosequencer. As we have only 11 completed subjects, the statistical 
power to test hypotheses is limited, but as recruitment progresses power will increase.  These results 
demonstrate that we have the ability to genotype our subjects for genes of interest. 
 
5. 
Functional Genomic Studies of Adipose Tissue: Defining the Micro-Architecture of 
Adipose Tissue 
As mentioned, an intense ongoing interest of several members of our research team is the link 
between obesity (increased adipose mass) and insulin resistance. A number of studies are now 
underway to elucidate the molecular pathways of this link. It is increasingly recognized that adipose 
tissue is a dynamic endocrine organ that participates actively in energy homeostasis. In addition to 
adipocytes, adipose tissue contains other cell types whose function is unknown. To further define the 
micro-architecture of adipose tissue, Dr. Da-Wei Gong, a co-
investigator on this proposal, separated human adipocytes from 
supporting stromal vascular cells by collagenase treatment followed 
by gravity separation. Cells were isolated, RNA prepared, and 
microarray analyses performed using the Affymetrix U133 chips 
described above. Figure 6 shows an example of the chip analysis 
revealing 
marked 
differences 
in 
gene 
expression 
between 
adipocytes and stromal vascular cells (SVCs). Table 5 shows 
summary statistics of this experiment. For example, the table shows 
that 592 genes are differentially regulated in fat cells compared to 
SVCs with fold changes (FC) > 3 and p-values < 0.001. These 
studies again demonstrate our team’s ability to perform state-of-the-
art functional genomics studies. Although we do not plan to separate 
fat cells from stromal vascular cells in this project, knowledge of 
gene expression profiles of the different adipose tissue cell types will 
aid in the interpretation of our pharmacogenomic results and may aid 
in our selection of candidate genes.  
Table 4. Sex, race, age, and selected genotypes of enrolled subjects 
     ID      
Sex 
Race 
Age 
PPAR-gamma 
Pro12Ala 
ACDC 
+2019  
ACDC 
SNP45 
ACDC 
SNP276 
PPAR001 
F 
Caucasian 
45 
Pro/Pro 
ins/ins 
T/T 
G/G 
PPAR008 
F 
Caucasian 
36 
Pro/Pro 
ins/del 
T/T 
G/T 
PPAR009 
F 
Caucasian 
35 
Pro/Pro 
ins/del 
G/T 
G/G 
PPAR011 
F 
Caucasian 
37 
Pro/Pro 
del/del 
T/T 
T/T 
PPAR012 
M 
Caucasian 
59 
Pro/Pro 
ins/ins 
T/T 
G/G 
PPAR013 
F 
Caucasian 
43 
Pro/Pro 
ins/del 
T/T 
G/T 
PPAR014 
F 
Caucasian 
57 
Pro/Pro 
ins/del 
G/T 
G/G 
PPAR016 
M 
Asian 
45 
Pro/Ala 
ins/del 
T/T 
G/T 
PPAR018 
M 
Caucasian 
60 
Pro/Pro 
del/del 
G/T 
G/T 
PPAR022 
F 
Caucasian 
40 
Pro/Ala 
del/del 
G/T 
G/T 
PPAR023 
F 
African American 
36 
Pro/Pro 
ins/del 
T/T 
G/T 
Fat
Cells
SVCs
Figure 
6. 
Example 
of  
microarray comparison of fat 
cells 
and 
adipose 
tissue 
stromal vascular cells isolated 
from the same person 
Table 5. Summary statistics for Fat Cells vs SVC
                p  All
p < 0.05 p < 0.01 p  < 0.005 p  < 0.001
FC all
4988
2319
1693
1477
1116
FC >1.1
4473
2319
1693
1477
1116
FC >1.5
2728
2244
1689
1476
1116
FC >2
1613
1571
1398
1286
1049
FC >3
666
665
644
632
592

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6. 
Omentin, a Novel Candidate 
Gene for Insulin Resistance and the 
Metabolic Syndrome  
Because of these and other high-
throughput 
functional 
genomics 
analyses, Dr. Gong and his colleagues 
identified a gene that was preferentially 
expressed in visceral compared to 
subcutaneous fat. He named this gene 
omentin 
(84). 
Immunohistochemistry 
revealed that omentin is expressed in 
stromal vascular cells of visceral, but not 
subcutaneous fat. Immunoprecipitation 
experiments 
showed 
that 
omentin 
circulates in blood, thus defining it as a 
novel adipokine produced by visceral fat 
tissue.  
Given the important role that visceral fat 
plays in the development of insulin 
resistance and metabolic syndrome, Dr. Gong expressed omentin to study its biological properties. Like 
adiponectin, omentin enhanced glucose uptake in mouse 3T3-L1 adipocytes in vitro and increased 
glucose disposal when injected into mice (84). A bioinformatics search revealed that there are two 
omentin genes (that we named omentin 1 and 2). Both are located within 100 kb on chromosome 1q21, 
a region that we and others have found linked to T2DM in genome-wide scans (26). 
Since omentins 1 and 2 are excellent positional candidate genes for T2DM and insulin 
resistance, we sequenced 20.3 kb of DNA encompassing the promoter regions, exons and introns of 
omentin 1 and omentin 2 (26). For omentin 1, we identified 25 polymorphisms (4 novel polymorphisms), 
including 1 SNP that changed the amino acid sequence (D109V). In omentin 2, we identified 23 
polymorphisms (8 novel polymorphisms), including 2 SNP that changed the amino acid sequence 
(R103H in exon 4 and S168P in exon 5). Twenty-three haplotype tagging SNPs (Figure 7) were typed 
in Amish subjects with T2DM (n = 145), impaired glucose tolerance (IGT)(n = 148), and normal glucose 
tolerance (NGT)(n = 358). Om-74938 and rs1556519, both in intron 6 of omentin 2, were strongly 
associated with IGT (p = 0.003, 0.005) and IGT/T2DM (p =0.031, 0.048). Om-81237 and om-81873 in 
the omentin 2 promoter and om-79443 in intron 2 of omentin 2 were also associated with IGT (p = 
0.001, 0.012). Haplotype analysis revealed a six-marker haplotype within omentin 2 was associated 
with both IGT and IGT/T2DM (p = 0.004, 0.018). In nondiabetic subjects (n = 754), the haplotype and T 
allele of om-74938 associated with higher glucose area under the curve during an OGTT, leptin levels, 
and BMI. We conclude that these variants in omentin 2 may play a role in susceptibility to IGT and 
T2DM in the Old Order Amish (26). These studies illustrate our ability to perform state-of-the art SNP 
discovery and high-throughput genotype and association analysis of candidate genes, approaches that 
will be key to the successful execution of the Aims of this proposal.  
 
7. 
The Effects of Olmesartan Medoxomil, Losartan Potassium, and Atenolol on Insulin 
Sensitivity in Overweight and Obese Subjects with Hypertension  
 
Dr. Snitker was the site principal investigator of this multi-center study. The purpose of the study 
was to quantify the insulin-sensitizing effects of olmesartan medoxomil, a drug approved for the 
treatment of hypertension. This question has merit given the large number of individuals in whom 
hypertension coincides with impaired glucose tolerance or diabetes. Insulin sensitivity was measured  
with a hyperinsulinemic, euglycemic clamp twice in each subject, before and after treatment with 
blinded study drug. Consistent with his leading role in the pharmacogenomics of PPAR-gamma study, 
this study demonstrates that Dr. Snitker is qualified to oversee clinical investigation of a complicated 
nature.   
Figure 7. Haplotype structure of omentins 1 and 2 as imaged 
by Haploview. 

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D.  
RESEARCH DESIGN AND METHODS 
1. 
Study Overview and Rationale  
The overall objective of this proposal is to define the molecular and genetic basis of the inter-
individual variation in response to TZDs. We will test the hypothesis that inter-individual variability in 
TZD response is due, in part, to genetic influences, more specifically sequence variants in one or more 
candidate genes. The general approach we will use is a phenotype to genotype approach. We will not 
only be looking at phenotypes on the whole-body level, but also obtain muscle and fat tissue samples 
to study more immediate phenotypes all the way down to the gene expression level. Because TZDs act 
by binding to the nuclear receptor PPAR to elicit direct and indirect changes in gene expression that 
ultimately exert their phenotypic effects, we reason that a phenotype to molecular (gene expression) 
profile to genotype approach would have the highest likelihood of identifying the responsible genes and 
their variants. 
  
 
The proposed study will expand upon an ongoing study of the pharmacogenomics of PPAR in 
which sophisticated phenotyping and microarray studies of fat and muscle are already well underway 
(see Preliminary Studies). A schematic of the study design and timeline are shown in Figures 8 and 9. 
We will define the genes whose regulation correlates to TZD response. Comparisons of gene 
expression profiles of the same tissue in the same person pre- and post- intervention should minimize 
many of the problems associated with most microarray experimental designs and should minimize the 
number of false positive (and false negative results).  
Furthermore, we will confirm these differences by real-time PCR on RNA from the same sample 
in which the microarray studies were performed. Confirmed genes whose up- or down-regulation is 
Screening/Clinical Evaluation
Rosiglitazone Intervention
Identify genes differentially regulated in 
responders vs. nonresponders as 
candidate genes for drug response
Define sequence variants and haplotype 
structure of candidate genes 
Metabolic Testing 
cDNA Microarrays 
of fat and muscle
Association analysis of SNPs/haplotypes in 
candidate genes in DPP, TRIPOD
Prospective clinical studies 
of subjects recruited by 
genotype/hapolotype
In vitro functional analysis 
of consequence of 
sequence variant(s)
Animal transgenic/KO
Future studies
Genome-wide association 
analysis (changes in mRNA 
levels, physiological traits)
Specific Aim 1
Specific Aim 2
Specific Aim 3
Q-RT-PCR 
Fig. 8. Schematic of overall study design
Metabolic Testing 
cDNA Microarrays 
of fat and muscle
Q-RT-PCR 

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correlated to TZD response will be defined as candidate genes and will be pursued using state-of-the-
art molecular genetic approaches to define all common sequence variation and haplotype structure of 
the gene. We will then genotype these SNPs in our subjects to determine if these SNPs or haplotypes 
associate with TZD response. Although the 75 subjects we plan to recruit for this study will provide very 
rich phenotype and microarray information for defining TZD response and for selecting candidate genes 
to study, they will not be particularly powerful to discern the effect of specific gene variants on clinical 
response to TZDs.  
However, they may be useful in discerning associations of gene variants with mRNA levels encoded by 
that gene (or within the same regulatory pathway). Thus, in the next stage of our study, we will take 
advantage of established collaborations with investigators of large prospective studies which involved a 
TZD intervention and for which relevant response traits have been collected, the DPP Study, and the 
TRIPOD Study (now continued as PIPOD). 
 
High throughput genotyping of 1536 SNPs in 100 candidate genes will be performed in these cohorts 
and association of SNPs and haplotypes with TZD response will be examined. It is our hope that in a 
subset of these 100 candidate genes we will find consistent evidence for a role in TZD response, e.g., 
altered mRNA levels in those with the variant SNP/haplotype and association with TZD response in 
both populations.  
 
2. 
Specific Aim 1. Defining interindividual variability in TZD response 
a.   
Subject recruitment and screening 
 
Seventy-five subjects will be recruited for this study from the following sources: 
 
A large database belonging to the Division comprising over 500 subjects recruited at health 
fairs. These subjects have provided basic health information, given blood for genotyping, 
and indicated that we may contact them to inform them about future genetic studies. 
Additionally, some subjects have had oral glucose tests. We have previously successfully 
recruited subjects from this collection. This collection will also allow rapid identification of 
potential volunteers for our study if the focus should later change to a particular genotype or 
clinical profile. 
 
Database of the Center for Clinical Trials (CCT) at the University of Maryland School of 
Medicine. Our CCT maintains a database of several thousand individuals who have 
expressed an interest in participating as research volunteers into clinical trials and other 
clinical research studies.  
 
Flyers on the University of Maryland Baltimore campus and notices in the campus 
newsletter. In the past, this approach has been very successful.  
Planning phase
Rosiglitazone intervention
Analysis of cDNA microarray 
data
Genotype 1536  ht-SNPs in 100 
candidate genes in DPP and TRIPOD; 
perform association analysis
Refine candidate gene list and 
define sequence variants and 
haplotype structure of candidate 
genes
1
2
3
Year 
4
5
Fig.  9. Timeline of the study

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 
The website of the University of Maryland Medical Center and the Division of Endocrinology, 
Diabetes and Nutrition.  
 
Newspaper advertisements. In addition, volunteers for this study will be recruited by 
advertisements in local and regional newspapers. 
Initial screening for eligibility will be assessed by brief communication (telephone or in person) 
and include the following: 
Inclusion criteria:  
 
Subjects will be overweight (25 kg/m2  BMI < 40 kg/m2) but otherwise healthy men and 
women between the ages of 35 and 65 years who self-identify as (in NIH classification 
terms) White. As shown in a similar population in Section C4, the ability to show an 
improved insulin sensitivity in response to TZD treatment does not appear influenced by 
initial insulin sensitivity. Thus, no insulin sensitivity criteria will be applied.  
Exclusion criteria:  
 
Diabetes (history of diabetes or as per screening tests) 
 
Medications that the investigator judges will make interpretation of the results difficult or 
increase the risk of participation (e.g., anticoagulants). 
 
Medical history that the investigator judges will make interpretation of the results difficult or 
increase the risk of participation (e.g., anemia, cardiac decompensation, intolerance to 
rosiglitazone, lidocaine or other agents used).  
 
Pregnancy 
 
Physical dimensions exceeding the limits of any equipment used 
We will obtain written informed consent from all subjects. Further screening will include history, 
vital signs, physical exam, and blood tests. Please see Section E for detailed screening procedures 
and exclusion criteria. 
b. 
Metabolic testing: 
Subjects who qualify for the study will then enter into the metabolic testing phase of the study. 
The identical metabolic testing will be performed before and after the 12-week rosiglitazone 
intervention. Methods for performing these tests have been standardized and are essentially identical to 
those being performed in our ongoing Pharmacogenetics of PPAR study. All testing will be performed 
at the University of Maryland General Clinical Research Center (see Letter from Dr. Carol Tacket, 
GCRC Program Director).  
Metabolic testing will include: 
 
Blood for fasting lipid profile and lipoprotein particle size, C-peptide, leptin, adiponectin, IL1, 
IL6, TNF, hsCRP, SAA, adiponectin, and rosiglitazone concentrations. 
 
Imaging tests to determine total body fat and its distribution 
o Total body fat by dual energy x-ray absorptiometry (DEXA). We will use a Hologic QDR 
4500 W scanner with Windows XP-based software allowing for region-of-interest 
analysis. The scans will be performed and analyzed by trained operators. 
o A single slice CT scan will be taken mid-way between the L4 and L5 lumbar vertebrae to 
determine intraabdominal fat area, subcutaneous fat area, and sagittal diameter. 
Another single slice CT scan will taken at the thigh (halfway between the femoral head 
and the top of the patella) to assess limb fat and muscle area.  
 
Frequently sampled intravenous glucose tolerance test (IVGTT) to determine insulin sensitivity 
(using minimal modeling) and acute insulin response. As in our previous studies, data will be 
analyzed to provide parameters of insulin sensitivity and glucose effectiveness by the minimal 
model using a modified version of the computer program MINMOD. 
 
Muscle biopsy (100-250 mg) will be obtained under local anesthesia from the vastus lateralis. 
Connective tissue will be removed. A representative portion of the tissue will be formalin fixed, 

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paraffin embedded, sectioned, and stained with hematoxylin and eosin using standard 
histological methods. Slides will be examined visually to assess cell populations/tissue 
composition in order to ensure that morphologically similar tissue biopsies were sampled before 
and after rosiglitazone therapy. The remainder of the tissue will be snap frozen in liquid nitrogen 
for subsequent RNA extraction and microarray analyses (see below). 
 
Subcutaneous adipose tissue (3-5 g) will be removed from the abdominal region by aspiration 
with a 16-gauge needle under local anesthesia. A representative portion of the tissue will be 
handled as noted above. A portion of the tissue will be snap frozen in liquid nitrogen for 
subsequent RNA extraction and microarray analyses (see below). An aliquot of fresh adipose 
tissue will be transported immediately to Dr. Susan Fried’s laboratory for the in vitro adipocyte 
studies (see below). 
c. 
Rosiglitazone intervention: 
Following the initial metabolic testing and sample acquisition, the subjects will initiate 
rosiglitazone therapy at 4 mg/day. At 4 weeks following the initiation, subjects will be evaluated by Dr. 
Stuart Haines, Pharm.D., in order to ensure that there are no adverse effects from rosiglitazone 
therapy. Although liver dysfunction is much less common with rosiglitazone than troglitazone, we will 
measure serum ALT concentrations monthly. Medication compliance will be monitored using Trackcap 
(Medication Event Management Systems; Aprex Corp.), and by pill counting at each monthly visit. 
Frequent telephone contact will be maintained. If rosiglitazone is tolerated well after 4 weeks of 
treatment, the dose will be increased to the maximum dose of 8 mg (4 mg twice per day). We have 
chosen the maximum dose for rosiglitazone since to maximize the differences between responders and 
nonresponders. At each monthly visit, rosiglitazone drug levels will be measured to control for 
differences in drug when analyzing responsiveness.  
d. 
Metabolic testing after rosiglitazone therapy:  
After 12 weeks of rosiglitazone therapy, subjects will repeat metabolic testing identical to that 
described above taking place before the rosiglitazone intervention. Three months of therapy was 
chosen as the end point for the metabolic studies since by this time, rosiglitazone will have had its 
maximal effects on insulin sensitivity, the primary response outcome. Results of metabolic testing and 
clinical outcomes will be compared to the identical tests performed 12 weeks earlier (prior to the 
initiation to rosiglitazone) in the same subject.  
e. 
Analysis plan: 
We will define the response variable for each of the 75 individuals studied to be the relative 
change in the primary (insulin sensitivity) and secondary (lipid, body fat composition, inflammatory 
markers, etc.) endpoints, expressed as the (post-intervention value minus the pre-intervention value) 
divided by the pre-intervention value. We will then begin with a series of descriptive analyses to identify 
the correlates of response to TZD therapy.   
Specific Aim 1a. We will use conventional regression analysis and analysis of variance 
approaches to determine if baseline subject characteristics, including age, gender, ethnicity, and body 
mass index, are correlated with response. We will perform separate analyses for the primary and each 
of the secondary response variables. All analyses will also be adjusted for the baseline (pre-TZD 
treatment) value. 
Specific Aim 1b. After identifying baseline variables that are significantly associated with 
response, we will then will evaluate, as Specific Aim 1b, the pairwise relationships between the insulin 
sensitivity response and each of the secondary response variables. We predict that the insulin 
sensitivity responses will be correlated with the secondary trait responses, although the degree of 
correlation will not necessarily be equal in magnitude across all pairs (e.g., the correlation between the 
insulin sensitivity response and HDL cholesterol response may be greater than the correlation between 
the insulin sensitivity response and SBP response). We will estimate these correlations while adjusting 
for the effects of baseline variables identified in Specific Aim 1.   
These correlation analyses will provide important descriptive information on this novel 
phenotype. For example, we predict that response in the secondary traits will generally be driven by the 

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insulin sensitivity response. Hence, we would expect there to be very limited little mean change in the 
secondary responses among those with limited insulin sensitivity response. Our proposed sample of 75 
subjects should provide us with 80% power to detect correlations of size (population r) = 0.31. 
Specific Aim 1c. We will perform cDNA microarray analysis of muscle and fat before and after 3 
months of rosiglitazone therapy for the purpose of identifying genes (and their corresponding pathways) 
whose changes in expression correlate with the change in insulin sensitivity in response to TZDs. This 
proposed experiment extends our ongoing work from the Pharmacogenetics of PPARγ Study (see 
Preliminary Data).   
We will initially compute the change in expression levels for each of the 40,000 examined 
genes. We will then use correlation analysis to determine the genes whose changes in expression 
levels best correlate with change in insulin sensitivity and the other clinical outcomes. We anticipate 
that some genes will be up-regulated in response to TZD therapy and others will be down-regulated, 
and the magnitude of these changes will correlate with clinical response. Our goal will be to identify the 
100 genes whose differential in up (or down) regulation most closely correlate with clinical response.  
We recognize that the results of microarray experiments must be interpreted cautiously, 
especially in light of the multiple comparisons issue. However, the major goal of our experiment is to 
identify genes that might mediate the response to TZDs and which hence might be candidates for 
further studies. Our selection of genes will therefore be guided by several factors, including the strength 
of the statistical evidence, and our best understanding of the likely function of the differentially 
expressed genes. Please see the Microarrays section below for a description of the statistical analysis 
of the microarray data. 
f. 
Laboratory methods: 
Serum/plasma assays. Cytokines will be measured by the Cytokine Core Laboratory at the 
University of Maryland by ELISA (see Letter from Dr. Jeffrey Hasday). C-peptide, leptin and adiponectin 
will also be measured by ELISA at the GCRC Biochemistry Core Laboratory. Lipid profile will be 
measured by Quest and lipoprotein particle sizes will be measured by NMR (Liposciences, Raleigh, 
NC). 
Adipocyte assays. Secretion of TNF and IL6 from adipose tissue fragments will be determined 
after 3-hour incubations of tissue fragments as we have previously described (67). Total tissue TNF 
and IL6 content will be measured from tissue extracts after lysis in buffer containing 1% Triton-X-100 
and protease inhibitors. TNF and IL6 in the media and in lysates will be measured by ELISA (R&D 
Systems, Minneapolis, MN). TNF and IL6 mRNA levels will be determined by quantitative real time 
PCR using the Lightcycler (Roche Diagnostics). Data will be normalized to expression of 18S RNA or 
cyclophilin. Lipolysis will be measured in isolated fat cells under four conditions: 1) adenosine 
deaminase (ADA); 2) 20, and 100 nM phenylisopropyl adenosine (PIA); 3) ADA+ 20 nM PIA + 1000 nM 
isoproterenol; 4) ADA + PIA (20 nM) + 8-bromocyclic AMP + insulin (0, 25, 50, 100, 200, 400, 2000 
pM). Glycerol concentrations will be determined as a measure of lipolysis as previously described (48, 
80). To measure glucose transport, fat cells will be incubated with tracer U-14C-glucose and varying 
insulin concentrations as described by Kashiwagi et al. (39). Fat cell size, number of fat cells per ml of 
incubation, fat cell surface, and fat cell diameter will be determined using standard methods. All fat cell 
metabolic data will be expressed relative to the number and surface area of the fat cells. Within- 
individual comparisons (pre- versus post-rosiglitazone) will be made for all adipose biology traits. 
Absolute and percent changes will be used as quantitative traits for the proposed analyses (See 
Specific Aims 2 and 3). 
Microarrays. The Biopolymer/Genomics Core and its Scientific Director Dr. Stine have the 
capacity (see Resources and Environment) and the expertise (see letter of collaboration) to perform all 
of the microarray experiments described in this proposal. The reproducibility of microarray data from 
the Affymetrix system depends in part on the use of standardized methods developed by the 
manufacturer. Our laboratory follows the manufacturer guidelines for each of the protocols.  
Briefly, total RNA is isolated from frozen tissues using Trizol (Invitrogen). The RNA is cleaned 
using Qiagen RNeasy mini spin columns and eluted with DEPC treated water. The amount and 
preliminary estimate of the quality of total RNA is determined using the OD 260/280 ratio.  A ratio of 1.2 

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to 2 is indicative of RNA. A ratio of 1.5 to 1.8 is considered good, while a ratio of 1.8 to 2.0 is 
considered excellent. We routinely achieve ratios above 1.9. 
We further test the quality of the RNA using capillary electrophoresis in our Bioanalyzer 
(Agilent). This method is comparable to agarose gels, but much more sensitive, i.e. we can test as little 
as 5 nanograms of total RNA.  
The labeling and hybridization is performed following Affymetrix standard protocols. For the 
following reverse transcription, transcription, labeling and hybridization steps, there are bacterial genes 
that are added to the sample to serve as controls and are examined as part of the low level dCHIP 
analyses. 15 micrograms of RNA is reverse transcribed using SuperScript and the other reagents from 
the One-cycle cDNA Synthesis Kit (Affymetrix). The next step is in vitro transcription and biotin labeling 
using the IVT Labeling Kit (Affymetrix). The sample is cleaned and hybridization controls are added. 
After hybridization, the microarrays are washed, stained with streptavidin phycoerythrin and scanned in 
our Affymetrix scanner. The quality of our hybridizations is very good; our normalization ratios range 
from 1 to 1.6. 
 
Power considerations for the microarray studies. Although there is no consensus about which 
sample-size determination procedures for microarray comparison study are best, evidence indicates 
that a minimum of 5 biological cases per group for a two-group comparison study should be analyzed 
(4). Therefore, our current microarray comparison (before and after a 3-month rosiglitazone 
intervention) study with 75 subjects should have an adequate statistical power to detect most 
differentially expressed genes. 
Statistical approach. To study the gene expression profiles that are related to the 3-month 
rosiglitazone intervention, we will compare the expression profile of the muscle (and fat) tissues from 
pretreatment subjects to that from post-treatment subjects. We will calculate and normalize gene 
expression levels by the Robust Multichip Average method (RMA) (37) and quantile normalization (11). 
The statistical significance of these comparisons will be assessed using LIMMA package software (27) 
(a linear model approach implemented in Bioconductor) or non-parametric methods (e.g., Kruskal-
Wallis- or Mann-Whitney U-tests), using SAS software. A critical issue in microarray analysis is to avoid 
a high “false discovery rate” (9) (FDR; the probability that a gene is incorrectly identified as differentially 
expressed), due to the large number of genes compared. We will consider genes to be differentially 
expressed only if they meet criterion values for fold-change and p-value (for example, >1.5 and <0.05, 
respectively). For each analysis of differential expression, we will experiment with different 
combinations of criterion values and calculate the resultant FDR (using MULTTEST package in 
Bioconductor (61)) for each set of criteria, until we obtain acceptable stringency for fold-change, 
statistical significance and FDR (we anticipate using conservative FDRs in the range of 1-5%). 
 To study the association between these differentially expressed (DE) genes and the response 
of the primary and secondary traits to TZDs, for each DE gene, we will generate linear regression 
models using the change in the response of the primary (i.e., insulin sensitivity) and secondary 
responses to TZDs as dependent variables and the gene expression change in response of the 
treatment and demographic and clinical measures as the independent variables. For the regression 
models, we will report the adjusted squared correlation coefficient (R2adj), which corrects for the 
degrees of freedom. The association between each DE gene and each trait in response to TZDs can be 
measured by the adjusted squared correlation coefficient. The overall association with primary and 
secondary traits can be based on the score that is calculated as the weighted sum of the corresponding 
the adjusted squared correlation coefficients. The weights can be determined by principal component 
analysis. The importance of each gene can be determined by its score. 
Unsupervised clustering techniques will also be used to cluster the subjects according to the 
similarity of their gene expression profiles (before and after treatment), independently of clinical 
diagnosis or other investigator-designated categories. To limit noise and increase the strength of our 
findings, only genes with ample variability are used for cluster analysis (criterion values for variability 
will be determined empirically). The Hierarchical Ordered Partition and Collapsing Hybrid (HOPAH) (27) 
algorithm for building a hierarchical tree of clusters will be used for our cluster analysis. The number of 
clusters will be determined by Median Split Silhouette (MSS) (62) and biological consideration. The 
non-parametric bootstrap technique will be used for validating the HOPAH clusters. The clustering 
analyses will be assessed by using HOPAH package software in Bioconductor (27). Clustering of the 

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subjects based on gene expression would suggest a distinct, common underlying biological basis for 
disease in these subjects. Such an outcome could be a significant start in achieving one of our goals – 
a biology-based, rather than a clinical outcome-based classification of TZD response. Comparison of 
subject symptoms and clinical outcomes within- and across subject clusters based defined by gene 
expression profiling could be indicative of differences among subjects in response of TZD treatment.  
Because the regulation of gene expression is a dynamic process, it is also important to identify 
and characterize changes in gene expression in response of TZD treatment among subject subgroups 
defined by expression profiling. Storey, et al. (73) proposed a general statistical method that identifies 
genes differentially expressed over time among different classes and have developed freely available 
software called EDGE. It is applicable to detecting differences in the behavior of expression in response 
to TZD treatment among different subgroups of the subjects. In addition to the pre-post comparison for 
each subgroup described above, the EDGE program will be used to identify genes that show significant 
differential expression in response to TZD treatment among different subjects.   
We will then analyze the biological significance of the genes which are identified by the above 
analyses, using GenMAPP (15) (Gene MicroArray Pathway Profiler) and MAPPFinder (19). GenMAPP 
facilitates the viewing and analysis of gene expression data in the context of biological pathways. The 
pathways are represented in a special file format called a “MAPP”. The MAPPs are independent of the 
gene expression data and can be used to group genes and view data by any organizing principle. 
MAPPFinder uses GenMAPP and the Gene Ontology (GO) database (http://www.geneontology.org) to 
identify biological trends in gene expression data. MAPPfinder can use the terminology and organizing 
principles of the GO database, or user-defined MAPPs. The three organizing principles of GO are 
“molecular function”, “biological process” and “cellular component”, which are attributes of gene 
products. Each of these terms has sub-terms (“children”) organized in a tree-like structure. The terms 
are assigned independently, such that each gene can fall into all three groups. Our analysis will identify 
(on the basis of z-score and permuted p-value) molecular functions, biological processes or cellular 
components that are potentially influential in the subjects in regard to TZD treatment. Based on 
microarray and functional genomic analyses and Q-RT-PCR validation, we will select 100 candidate 
genes that influence TZD response for Aim 2 study. 
Software. The statistical software packages SAS, Bioconduct and R (27) are the primary tools 
for most statistical analysis. SAS software, which is installed on our computers, is a combination of 
programs originally designed to perform statistical analysis of data. R is a free software environment for 
statistical computing and graphics. Bioconductor is an open source and open development software 
project for the analysis and comprehension of genomic data. These approaches as well as others that 
emerge during the course of this project in the continually evolving field of microarray analysis will be 
utilized to process the data obtained in our microarray experiments.  
Quantitative Real time-PCR. Due to the limited dynamic range of the gene array expression 
analysis, we will more precisely quantify up- and down-regulated genes by Q-RT-PCR of the transcripts 
using the Lightcycler (Roche Diagnostics) and standard methods as described under Preliminary 
Studies. The same RNA samples as were used in the original microarray analysis will be used for Q-
RT-PCR. 
 
3. 
Specific Aim 2. Define and Characterize Genes that are Differentially Expressed 
between Responders and Non-Responders to TZDs  
We will identify sequence variants within the 100 genes targeted for study on the basis of the 
cDNA microarray experiments and will use this information to characterize the haplotype structure of 
the candidate gene. We will then identify haplotype-tagging SNPs (htSNPs) to be used for the 
association analyses described under Specific Aim 3. 
a. 
Specific Aim 2a 
We will identify common sequence variants by sequencing 20 individuals (10 European 
Caucasian and 10 African American) and through in silico SNP discovery. The 20 individuals will be 
unrelated individuals selected from our existing DNA repository at the Shuldiner laboratory.   
We will use a combination of in silico and laboratory-based approaches to SNP discovery and 

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validation. We will choose validated SNPs from the multiple public databases (e.g., dbSNP, CELERA, 
Hap-Map) as well as from SNPs that have already been converted into Illumina assays for their linkage 
and fine mapping panels (http://www.illumina.com/snp_linkage.htm). SNPs that appear independently 
in more than one database or SNPs that have been experimentally validated and have allele 
frequencies greater than 5%. As these databases become more complete (the next version of dbSNP 
will have >10 million SNPs; David Altshuler, personal communication), we expect to be able to obtain 
the great majority of SNPs that we will initially genotype from these databases. In genes that do not 
have adequate sequence variant information from these sources (i.e., less than a 1 SNP per 3-5 kb 
density), we will perform DNA sequence analysis to identify new SNPs. The regions of greatest interest 
will be potentially functional regions, including exons, exon-intron junctions, regulatory regions, and 
other intragenic regions that are phylogenically conserved. These regions and other regions in which 
the inter-marker distance exceeds 5 kb will be sequenced in 10 European Caucasians and 10 African 
Americans (40 alleles). This number of subjects will provide >87% power to detect gene variants with 
an allele frequency of 10% or greater. When considering the density of SNPs within candidate genes, 
we will also take into account what is known about the haplotype structure of the gene/region based 
upon information from Hap-Map and other sources. Indeed, Dr. David Altshuler, consultant on this 
proposal, is a member of the Hap-Map project and will be able to provide expert advice regarding SNP 
selection. 
b. 
Specific Aim 2b 
We will validate the SNPs identified by sequencing and/or in silico SNP discovery by genotyping 
a subsample of 96 individuals (48 European Caucasians and 48 African Americans) selected also from 
our DNA repository. We anticipate that on average, we will have identified 20 SNPs per gene, requiring 
us to genotype  2,000 SNPs/person (100 genes  20 SNPs/gene). Allele frequencies of SNPs and 
linkage disequilibrium (LD) between intragenic SNPs will be calculated. This information will be used to 
choose haplotype-tagging SNPs for genotype analysis in the larger sample. With allele frequency and 
linkage disequilibrium information, we will be able to eliminate some SNPs from subsequent analysis. 
For example, SNPs with allele frequencies < 5% or SNPs in high LD (e.g., r2 > 0.7) with other SNPs will 
not be genotyped in the larger cohort. We will use algorithms to select haplotype-tagging SNPs to 
maximize the likelihood that we have captured most or all of the sequence variation. Haplotype-tagging 
SNPs (ht-SNPs) will be genotyped in three different population groups as described in the following 
section. The Illumina platform will be used for this large-scale genotyping effort, which will be performed 
at the Broad Institute of Harvard and MIT (see Letter from David Altshuler). 
4. 
Specific Aim 3. Association Analysis of Candidate Genes with TZD Response in 
Three Populations 
We will genotype the htSNPs identified in Specific Aim 2 in three separate populations who 
have undergone a TZD intervention. Approximately 1,500 htSNPs from 100 genes will be genotyped in: 
(1) 75 subjects recruited for this study; (2) 597 troglitazone-treated individuals from the Diabetes 
Prevention Program, or DPP; and (3) 97 gestational diabetic Hispanic women who were treated with 
troglitazone (and more recently pioglitazone) from the TRIPOD/PIPOD study.  
Subjects were eligible for DPP if their age was ≥ 25 years, BMI was ≥ 24 (≥ 22 for Asian-
Americans), and they had impaired glucose tolerance plus a fasting plasma glucose of 95-124 mg/dl (or 
≤ 124 mg/dl for American Indians). Among randomized subjects, 55% were Caucasian, 20% were 
African-American, 16% were Hispanic, 5% were American Indian, and 4% were Asian-American. Their 
average age at entry was 51 +/- 10.7 years (mean ± SD), and 67.7% were women. Of the women, 48% 
were postmenopausal. Overall, BMI averaged 34.0 ± 6.7 at baseline with 57% of the men and 73% of 
women having a BMI ≥ 30. Average fasting plasma glucose (108 ± 9 mg/dl) and HbA1c (5.9 ± 0.5%) in 
men were comparable with values in women (106 ± 7 mg/dl and 5.9 ± 0.5%, respectively). For 
TRIPOD, Hispanic women (of Mexican and Central American origin) in the Los Angeles area were 
eligible if they were ≥ 18 years of age, had GDM in the previous 4 years, and were willing to use 
effective contraception. They were offered participation in the study if they had no evidence of chronic 
disease; a serum alanine aminotransferase concentration < 1.5 times the laboratory upper normal; no 
diabetes; and a sum of five oral glucose tolerance test (OGTT) plasma glucose concentrations ≥ 625 
mg/dl, predicting a 70% risk of diabetes in the next 5 years. Enrollment continued until 266 women had 

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been randomized. Mean age was 34.9 ± 6.6 yrs, BMI 30.6 ± 6.1, fasting glucose 98.7 ± 10.2 mg/dl, 2-h 
glucose 154.4 ± 27.0 mg/dl. We have successfully extracted DNA on 217 women who completed the 3-
month IVGTT assessment. After termination of the study, women who had not developed diabetes 
were invited to participate in an extension involving pioglitazone, PIPOD.  
Dr. Shuldiner (co-investigator) and Dr. Altshuler (consultant) are co-PIs on an NIH-funded grant 
to relate candidate genes to DPP data (see letter from Dr. Altshuler), while Dr. Pollin (co-investigator) is 
PI of the data management and analysis subcontract for the study. The DPP genetics study includes 
the typing of population markers, which will be used in the proposed replication study. Dr. Snitker has 
previously preformed a pharmacogenetic study involving the TRIPOD subjects.  
In each population separately, we will compare mean insulin sensitivity according to genotype 
using analysis of variance. We will include age and sex in the analysis as covariates. In additional 
models, we will add multiple genotypes from the same or related pathways and test their potential 
interaction effects in a multiplicative fashion. Prior to analysis, we will perform a series of routine quality 
control checks on the genotype data that will include a repeatability analysis of a panel of 5% duplicate 
samples, and chi-square tests to assess compliance of the genotype distributions with Hardy-Weinberg 
Equilibrium. These procedures are routinely carried out in our laboratory. The primary analysis will be to 
determine if there is a dose response effect of genotype on the outcome. This analysis is conducted by 
assigning codes of 0, 1, or 2 to the genotype, with the value of the code reflecting the number of (minor) 
alleles. In addition, we will also carry out secondary analyses of the dominant and recessive genetic 
models to evaluate the effects of the respective homozygotes on TZD response.   
a. 
Haplotype-disease association analysis 
Haplotypes may potentially provide more information about an unobserved causal variant than 
single SNPs because of the increased resolution of multiple SNPs considered jointly to tease out the 
unobserved ancestral haplotype background on which mutation arose and which is shared among 
affected individuals. We plan to use Helix Tree, a commercially available software package designed 
for managing and analyzing data from large-scale genome-wide association studies, for our haplotype 
analysis. 
The haplotype trend regression analysis in Helix Tree regresses the response variable using the 
estimated haplotype probabilities as the regression matrix, thereby properly incorporating the 
uncertainty in individual haplotype assignment inherent in population-based data. We will compute 
permutation-derived empirical significance levels for each candidate gene to eliminate bias due to 
incorrect distributional assumptions. 
b. 
Population stratification 
One potential pitfall of association studies in population-based samples is false-positive signals 
resulting from population stratification. We will use an Illumina panel of 278 ancestry informative 
markers from the HumanHap 550K chip (Illumina; San Diego, CA) to account for potential effects of 
population stratification in our analyses either by adjusting the variance of the statistic or by adjusting 
the populations (88, 89). We attempt to eliminate false positives by assigning higher priority to signals 
that are replicated in multiple populations either at the same SNP, same haplotype block or same gene. 
We recognize that these methods represent global adjustments based on genome-wide averages of 
admixture, thus do not provide information about the chromosomal region containing SNPs of interest. 
However, newer methods are beginning to appear that attempt to define the ancestral blocks to 
potentially allow for local admixture adjustment in case-control analyses (90). We plan to evaluate new 
methods as they become available and integrate those that will add power to our analyses. 
  
c. 
Power calculation for the candidate gene analyses 
We estimated the effect sizes we could reasonably expect to detect at 80% power in these three 
samples. The calculations were made across a range of allele frequencies, and for sample sizes of 75, 
97, and 585, in the current study, TRIPOD, and the Diabetes Prevention Program (DPP), respectively. 
For purposes of these calculations, we further assumed a dominant genetic model in which mean TZD 
response levels were to be compared between two genotype groups (e.g., AA vs. Aa/aa). The effect 
sizes are parameterized to reflect the difference between the group means, expressed in terms of the 

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standard deviation units. By convention, differences between groups of 0.8 SD, 0.5 SD and 0.2 SD are 
considered large, medium, and small effects, respectively.  
For the current study (n = 75 subjects) and TRIPOD studies (n = 97 subjects), we would have 
80% or higher power to detect medium size effects (0.7 SD unit differences between means) across a 
wide range of allele frequencies (i.e., minor allele frequencies 0.1-0.5). For a putative subgroup of the 
current study with n=38, we would have 80% or higher power to detect large effect sizes (1.0-1.2) 
across the same allele frequencies. For the DPP (n = 597 subjects), we would have 80% or higher 
power to detect small size effects (0.3 SD unit differences between means) also across a wide range of 
allele frequencies. It is also worth noting that subjects to be recruited from this study will have mRNA 
levels measured, a very proximal phenotype that can be used as an additional outcome for the genetic 
association studies. One might expect a very large effect of an associated SNP on this particular 
phenotype.  
E.  
HUMAN SUBJECTS RESEARCH 
 
Protection of Human Subjects 
 
1. 
Risks to the Subjects 
 
a. 
Human subjects involvement and characteristics 
 
Inclusion criteria 
 
 
Male or female who self-identifies as (in NIH classification terms) White. 
 
Age 35 - 65 years  
 
BMI 25 - 40 kg/m² 
 
The age and BMI criteria were chosen to favor subjects with a relatively high likelihood 
of having insulin resistance and chronic inflammation, which makes intervention clinically 
meaningful.  
 
Exclusion criteria 
 
 
History of diabetes (Doctor told pt. they had diabetes or pt. is taking diabetes medication) 
 
Fasting glucose value diagnostic for diabetes 
 
2-h oral glucose tolerance test diagnostic for diabetes 
 
Medications that the investigator judges will make interpretation of the results difficult or 
increase the risk of participation (e.g., anticoagulants) 
 
Disease that the investigator judges will make interpretation of the results difficult or 
increase the risk of participation (e.g., anemia, cardiac decompensation, intolerance to 
rosiglitazone, lidocaine or other agents used) 
 
Pregnancy 
 
Physical dimensions exceeding the limits of any equipment used 
 
Screening will consist of medical history, vital signs, physical exam, and blood tests (a 
comprehensive chemistry profile, CBC, TSH, pregnancy test, 75 gram oral glucose tolerance test). 
Based upon this testing, subjects will be excluded if they have (1) stage III or greater congestive heart 
failure, symptomatic peripheral vascular disease, or stroke; (2) severe hypertension (BP>170/100); (3) 
anemia; (4) thyroid, pituitary, kidney or liver disease; (5) diabetes; (6) weight loss or gain of >5 kg in the 
previous six months; (6) hormone replacement therapy; (7) excessive use of caffeine (greater than 
three caffeinated drinks per day), or alcohol (greater than 3 ounces of liquor, or 3 x 4 ounce glasses of 
wine, or  3 x 12 ounce cans of beer per day); (8) use of medications that may affect glucose or lipid 
metabolism including beta blockers, thiazide diuretics, hypolipidemic agents, thyroid hormone, or 

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weight loss medications or formulas; (9) smoking >1/2 pack per day of cigarettes; and (10) pregnancy 
by urine testing. 
 
Individuals with diabetes will be excluded because their metabolic control may vary, their likely 
use of multiple of multiple medications, and possible complications. From a statistical point of view, 
each source of variation can be regarded as an additional error term, requiring a much larger sample 
size for the same amount of power. 
 
b. 
 Sources of materials 
 
Blood samples, urine samples, tissue samples, and results of physiological tests. 
 
c. 
Potential risks 
 
We expect no physical, psychological, social, or legal risks from a subject's participation in this 
protocol. Subjects who are screened, but not selected for participation for any reason will be referred to 
their primary health care provider for follow-up. Some of the procedures (DXA and CT scans) involve 
exposure to low doses of radioactivity; so pregnant women will be excluded from the study.  
 
Body composition: Dual energy x-ray absorptiometry (DXA) and CT scan entail exposure to 
radiation. The cumulative total body dose throughout the study is up to 0.015 rem from the DEXA scans 
and up to 0.040 rem from the CT scans. However, some organs will receive a larger dose from the CT 
scans: 0.092 rem to the ovaries (women only), 0.054 rem to the testes (men only), 0.060 rem to the 
bladder, and 0.058 rem to the rectum. Other organs and tissues will receive lesser radiation doses. 
These radiation doses are within institutional and federal guidelines for radiation exposure to research 
subjects and have never been associated with any definite adverse effects.  
 
  
Phlebotomy and oral glucose tolerance test: The placement of intravenous needles and 
catheters may cause transient pain, and occasionally infection or bruising at the needle insertion site. 
The frequency and severity of these potential adverse events will be minimized by using sterile 
technique, universal precautions, and trained personnel. Blood volumes to be drawn are minimal, and 
therefore these tests are unlikely to cause anemia. For the oral glucose tolerance test, an indwelling 
catheter will be placed in an antecubital vein. Normal saline will be infused to maintain venous patency 
during the 2-h test. A solution containing 75 g of glucose is ingested by the fasting subject, which 
occasionally causes nausea. 
 
   
Frequently sampled IV glucose tolerance test: Indwelling catheters will be placed in both 
antecubital veins. Glucose is given IV at T=0 minutes and regular insulin is given IV at T=20 minutes. 
The amount of glucose and insulin given are based on the subject’s weight (glucose 0.3 g/kg; insulin 
0.025 U/kg). Blood will be drawn from one of the IVs for glucose and insulin measurements at -15, -10, 
-5, -1, 2, 3, 4, 5, 6, 8, 10, 14, 19, 22, 25, 30, 40, 50, 70, 100, 140, and 180 minutes (3 ml/time point x 22 
= 66 ml). Additional glucose levels will be drawn at 35, 45, 55, 60, 80, and 90 minutes (1.5 ml/time point 
x 6 = 9 ml) to monitor for possible hypoglycemia. Proinsulin will be measured at -1, 10 and 60 minutes. 
The total amount of blood drawn for the FSIGTT will be 255 ml. In addition to the risks described above 
for phlebotomy, IV administration of glucose may sometimes result in lightheadedness, tingling in the 
arm or shoulder, and rarely phlebitis. To prevent phlebitis from occurring, a large volume of saline is run 
in through the IV used for the glucose bolus immediately after the bolus in order to decrease the 
likelihood of glucose irritating the vein. IV insulin may cause hypoglycemia with symptoms of dizziness, 
diaphoresis, palpitations and fatigue. Blood glucose will be monitored frequently and the test will be 
terminated if hypoglycemia develops and a glucose drink will be given. 
 
   
Adipose and muscle biopsies: There may be slight discomfort and burning when the local 
anesthetic is injected prior to the needle biopsy, but subjects are not expected to experience discomfort 
during the biopsy procedure. Following the biopsies, there may be tenderness, bleeding, bruise 
formation, or infections. Ice will be applied to the site immediately after the procedure to limit bruising, 

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swelling and tenderness. Aseptic technique will be used to minimize chances of infection. 
 
   
Rosiglitazone: 
(1) Mechanism of action. Rosiglitazone is a potent and highly selective agonist of peroxisome 
proliferator-activator receptor-gamma (PPAR)(70). As a member of the thiazolidinedione antidiabetic 
class of medications, rosiglitazone improves glycemic control by increasing the action of insulin in 
tissues that express PPAR. In animal studies, rosiglitazone reduced blood glucose concentrations and 
demonstrated significant insulin-sensitizing activity in skeletal muscle, adipose tissue, and the liver. In 
addition, rosiglitazone decreased hyperinsulinemia and increased GLUT-4 transporter expression in 
adipose tissue. While the exact onset of action has not been established in humans, glucose-lowering 
effects are generally evident 2 to 3 weeks after initiating rosiglitazone (70). Maximal effects are 
observed after 6 to 12 weeks of therapy. Since rosiglitazone effects gene expression, its 
pharmacological half-life is much longer and not closely related to its elimination half-life. In vitro data 
suggests that the pharmacodynamic half-life of rosiglitazone is greater than 24 hours. 
 
(2) Pharmacokinetics and drug metabolism 
Absorption. Rosiglitazone is well absorbed from the gastrointestinal tract, with an absolute 
bioavailability of 99% (70). Peak plasma concentrations are seen approximately 1 hour following an oral 
dose. While food appears to slow the rate of absorption of rosiglitazone (Tmax delayed by 1.75 hours), 
it does not alter the extent of absorption (AUC unchanged)(25). Rosiglitazone displays linear 
pharmacokinetics over a wide dose range (0.2 to 20 mg)(70). 
 
Distribution. Rosiglitazone is highly protein bound (99.8%) and its mean volume of distribution 
(Vss/F) is 17.6 liters (70). 
 
Metabolism. Rosiglitazone is extensively metabolized in the liver by N-demethylation and 
hydroxylation, primarily through the cytochrome P450 CYP2C8 isoenzyme pathway. The CYP2C9 
isoenyzme pathway is a minor route. No unchanged drug is excreted in the urine. None of the 
metabolites contributes significantly to the insulin-sensitizing activity of rosiglitazone (70). 
 
Excretion. The majority (64%) of labeled rosiglitazone appears in the urine following oral or 
intravenous administration. A substantial portion (23%) is eliminated in the feces (70). 
 
Half-life/plasma concentrations. The single-dose elimination half-life of rosiglitazone is 
approximately 3.3 ± 0.7 hours and is independent of dose (70). The approximate rate of clearance is 3 
± 0.7 L/hour. In a dose ranging pharmacokinetic study, peak plasma concentrations of 182 ± 48.3 ng/ml 
were observed 1 hour following a single 4 mg oral dose of rosiglitazone. The elimination half-life may 
become significantly shorter following repeated doses of rosiglitazone (70). A reduction in t½ of 0.75 to 
1.35 hours was observed in one study after 10 days of therapy in obese patients given rosiglitazone 
daily. The mechanism for this observed increase in clearance is unknown. Based on these 
observations, steady state trough plasma concentrations are anticipated to be 5 to 20 ng/ml in most 
patients given 4 mg twice daily. 
 
Special populations. Based on a population-based analysis from clinical trials, the 
pharmacokinetics of rosiglitazone appears to be similar in young (<65 years old) and elderly (>65 years 
old) patients (18, 70). Females clear rosiglitazone at a slightly slower rate than males and, in 
monotherapy studies, demonstrated a slightly greater therapeutic response. However, in obese 
patients, differences between genders were less apparent. Neither race nor smoking status influences 
the pharmacokinetics of rosiglitazone. Furthermore, patients with mild to severe renal impairment or 
who are dependent on hemodialysis do not exhibit altered pharmacokinetic properties of rosiglitazone 
(77). 
 
(3) Clinical effects 
In clinical trials, rosiglitazone resulted in improved glycemic control as measured by hemoglobin 
A1c and fasting plasma glucose concentrations (24, 30, 70). Improvements in glycemic control were 

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sustained for 52 weeks in clinical trials. The maximum recommended dose is 8 mg daily. No additional 
blood glucose lower effects were observed when a total daily dose of 12 mg was used. Twice daily 
dosing of rosiglitazone produces greater reductions in HgbA1c and fasting plasma glucose when 
compared to equivalent doses given once a day. When compared to placebo, the reductions in HgbA1c 
observed with rosiglitazone therapy were 1.2% for once daily administration and 1.5% for twice-daily 
administration. The percentage of patients treated with rosiglitazone 4 mg BID considered responders, 
defined as a 0.7% or greater decrease in HgbA1c from baseline, was approximately 50% in clinical 
trials. 
 
When used in combination with other oral antidiabetic agents or insulin, rosiglitazone 
demonstrates additive effects.  In combination with metformin, rosiglitazone 8 mg daily reduced 
HgbA1c by 0.8% when compared to baseline and 52% of patients were considered responders (28, 
70). Similar reductions in HgbA1c and response rates have been observed in trials combining 
rosiglitazone and a sulfonylurea (28) or insulin therapy (65). 
 
(4) Adverse effects/precautions 
Rosiglitazone therapy is associated with weight gain (70). In clinical trials, the mean increase in 
weight was 3.5 kg in patients treated with 8 mg per day when given as monotherapy. Metformin 
attenuates the amount of weight gain associated with rosiglitazone therapy. Some of this weight gain 
may be attributable to an increased plasma volume and mild to moderate lower extremity edema 
(incidence 5%). Mild anemia, reported in 1.9% of patients on rosiglitazone monotherapy, may also be 
attributable to increased plasma volume. The incidence of all other reported side effects were similar to 
placebo. 
 
In clinical trials and during post-marketing surveillance, rosiglitazone has not been associated 
with hepatotoxicity. Troglitazone, another member of the thiazolidinedione class of drugs, has been 
implicated in numerous cases of severe hepatotoxicity, thus requiring meticulous liver enzyme (ALT) 
monitoring. In clinical trials, significant elevations in serum ALT (greater than 3 times the upper limit of 
normal) was observed in only 0.2% of patient treated with rosiglitazone compared with a 0.2% 
incidence with placebo and a 0.5% incidence with metformin, insulin, and sulfonylureas. Evidence to 
date suggests that rosiglitazone is a safer medication than troglitazone with regard to liver toxicity. 
However, given their structural similarities, periodic serum ALT monitoring is recommended. 
 
(5) Use in non-diabetic individuals 
Rosiglitazone is FDA approved for use in diabetic subjects to improve glycemic control. As 
demonstrated in TRIPOD (see Preliminary Studies) and other clinical studies it also ameliorates insulin 
resistance and chronic inflammation in overweight individuals, the population eligible for this study. 
Thus, the genetic determinants of drug response can be studied in this population. However, because 
the use of rosiglitazone in non-diabetic subjects is an unlabeled use of a marketed drug, we need to 
ensure the safety of the intended use. Precedence established by other studies is reassuring. In 
particular, rosiglitazone does not appear to have the potential to elicit hypoglycemia in a non-diabetic 
person. The Diabetes Prevention Project (DPP) was a federally funded, multicenter trial to assess 
various strategies to prevent or delay conversion from impaired glucose tolerance to diabetes. One 
treatment arm received troglitazone. The troglitazone arm was discontinued two years into the study 
due to liver toxicity, a problem inherent to troglitazone but not to other TZDs. The exposure of 585 
impaired glucose tolerance subjects to troglitazone in the DPP did not lead to study-terminating side 
effects other than liver toxicity (17). In the TRIPOD study, troglitazone was administered to 133 women 
with previous gestational diabetes without any reports of hypoglycemia (12, 13). This cohort is now 
receiving pioglitazone and the trial continued as PIPOD. With the FDA's approval, a multicenter trial, 
the DREAM study, is being conducted to test the use of rosiglitazone in impaired glucose tolerance to 
prevent diabetes.  
 
On the occasion of the ongoing PPAR study (see Preliminary Studies), we petitioned the FDA 
via IND 67,070 to use rosiglitazone in non-diabetic individuals. The FDA informed us that all 
requirements for an FDA exemption were met. We have not observed any drug-related adverse events 

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in PPAR. 
 
   
We will heed the WARNINGS included in the rosiglitazone Product Information. We will monitor 
every four weeks for adverse events, including those mentioned in the Product Information, i.e., lower 
extremity edema and anemia. Patients will be questioned and examined at each visit regarding leg 
swelling. Rosiglitazone therapy will be decreased or discontinued in case of adverse events that are 
likely to be related to the drug. A complete blood count will be obtained at baseline and after 12 weeks 
to monitor for a decline in red blood cell concentrations as well as any other hematological 
derangements. Individuals with preexisting fluid retention or heart failure are ineligible for the study.  
 
Although evidence indicates that rosiglitazone does not share the hepatotoxicity of troglitazone, 
we will monitor alanine transaminase every four weeks.  
 
   
Subjects will be cautioned about the possibility of rosiglitazone to produce ovulation in some 
premenopausal women who might think themselves infertile. Adequate contraception in premenopausal 
women will be recommended. 
 
   
Rosiglitazone will not be used in patients in whom it is CONTRAINDICATED (known 
hypersensitivity to the product or any of its components). 
 
Genetic testing: Since the clinical relevance of the genetic testing is not yet proven, subjects will 
not be given any genetic information. If, in the future as a result of these studies or studies of others, 
gene variants that have clinical relevance are detected, all subjects who were genotyped will be 
contacted and first asked if they wish to know the result of their genotype analysis. If they do wish to be 
informed of their genotype, they will be informed by the investigators with the assistance of a genetics 
counselor, and with advice from an ethicist. However, subjects are given the option to decline all future 
genetic information when signing the consent form. 
 
2. 
Adequacy of Protection Against Risks 
 
a. 
Recruitment and informed consent  
Written informed consent will be obtained from all subjects. The IRB approved consent form will 
be given to each subject, and informed written consent will be obtained by one of the investigators or a 
study coordinator following a personal interview with each subject. Subjects will be encouraged to read 
the consent form closely and the rationale, methods, potential risks and benefits of the interventions will 
be explained.  
 
b. 
Protection against risks 
Please see potential risks section above.  
 
3.  
Potential benefits of the proposed research to the subjects and others 
Anticipated benefits to health include screening for anemia, liver and kidney dysfunction and 
cardiovascular risk factors (lipids and blood pressure). The primary beneficiaries will be future patients 
in whom treatment with a TZD may be an option. Our study may help determine whether a given 
patient stands to benefit from treatment based on his/her genetic makeup or clinical presentation. 
 
 
 
4.  
Importance of the knowledge to be gained 
The metabolic – or insulin resistance – syndrome is a clustering of cardiovascular risk factors. 
Although weight reduction and physical activity are first-line treatments for this condition, drug therapy 
with TZDs may improve not only insulin resistance, but also – directly or indirectly – inflammatory and 
prothrombotic conditions. Because 1/3 of subjects treated with TZDs typically do not respond to the 
insulin sensitizing effects of TZDs, it is important to know whether this non-response also extends to 
other effects of the drug, and to understand the drugs’ pharmacogenetic profile. 
 
 

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5. 
 Data and Safety Monitoring Plan  
An independent Data Safety and Monitoring Board (DSMB) will be convened on a quarterly 
basis to insure protection of human subjects. The DSMB will consist of at least five individuals with 
expertise in clinical investigation, clinical cardiology and research subject advocacy. Dr. Shuldiner and 
the Steering Committee will prepare accurate and timely data tables and reports for DSMB meetings 
and will present regular updates on the research projects. The objectives of the DSMB will be: 
 
To ensure that that research in conducted in a safe and ethical manner 
 
To ensure those adverse events (AEs) that occur while conducting this trial is adequately 
reported and investigated 
 
To conform with UM IRB and NIH policies and procedures 
 
To assist the UM IRB and the NIH in their mission to protect subjects participating in study 
protocols under their jurisdiction 
 
To ensure that any medical-coverage-related problems are addressed if an AE occurs 
 
The PI will uphold the following responsibility to the DSMB: 
 
The Principal Investigator (PI) and members of the staff will be responsible for reporting all 
new clinical experiences, exacerbations, and/or deterioration of any existing clinical 
condition occurring after a study subject has entered the study. 
 
The PI will be responsible for reporting Serious Adverse Events (SAEs) to the UM IRB and 
the DSMB. 
 
The PI will be responsible for determining the causality of all AEs/SAEs subject to review by 
the UM-IRB and DSMB. 
 
The PI will be responsible for determining whether test results are “clinically significant” (CS) 
or “not clinically significant” (NCS). The PI will sign and date all lab reports as “CS” or “NCS”.  
 
The PI and staff will be responsible for follow-up information on all AEs until resolution or an 
appropriate endpoint is reached.   
The DSMB in concert with UMB-IRB will be responsible for reviewing all reports of SAEs which occur in 
this clinical trial and for determining whether any corrective actions need to be taken regarding: 
communication issues between the PI and the DSMB, management decisions of the PI and staff, 
whether protocol violations are congruous with patient welfare taking precedence over protocol, 
whether there are any issues among the research staff which need to be addressed, etc. The DSMB 
will be responsible to determine if the results of its review require a revision/modification of the RPN 
and/or the consent form.   
 
6.  
Inclusion of women and minorities 
Women are eligible for this research and will be actively recruited. We will halt recruitment of 
men or women if we reach more than 2/3 of either sex. To avoid analytical problems related to the 
inclusion of a highly stratified sample, only Caucasians will be eligible for Aim 1. However, because 
non-response to TZDs has never been associated with a particular race, we believe that the genes 
identified will be relevant to minorities as well, which we will be able to test in Aim 3, using banked DNA 
from the DPP and TRIPOD studies. Please see body of proposal for demographic description of the 
DPP and TRIPOD samples, which do not meet the requirements for listing in the Targeted/Planned 
Enrollment Table. The analysis strategy will address hypotheses related to sex and ethnicity/race. 
Please see enrollment table below. 
 
 
 
 
 
 

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TARGETED/PLANNED ENROLLMENT: Number of Subjects 
Ethnic Category 
Sex/Gender 
Females 
Males 
Total 
Hispanic or Latino 
0 
0 
0 
Not Hispanic or Latino 
37 
38 
75 
Ethnic Category: Total of All Subjects * 
37 
38 
75 
Racial Categories 
 
American Indian/Alaska Native 
0 
0 
0 
Asian 
0 
0 
0 
Native Hawaiian or Other Pacific Islander  
0 
0 
0 
Black or African American  
0 
0 
0 
White 
37 
38 
75 
Racial Categories: Total of All Subjects  
37 
38 
75 
 
 
7. 
Inclusion of children 
NIH policy provides that applicants must include children in research unless there are scientific 
and ethical reasons not to include them. Children are not included because (1) rosiglitazone treatment 
in children is still undocumented, and (2) even if it were, the protocol involves risks and discomforts 
(radiation, tissue biopsies) that are not justified considering that we have little reason to expect that the 
genetic determinants of rosiglitazone response differ between adults and children.  
 
F.  
VERTEBRATE ANIMALS – not applicable. 
 
G.  
SELECT AGENT RESEARCH – not applicable 
 

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H.  
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I.  
MULTIPLE PI LEADERSHIP PLAN -Not applicable 
 
J.  
CONSORTIUM/CONTRACTUAL AGREEMENTS -Not applicable 
 
K. 
RESOURCE SHARING  (This item is gladly addressed although not a formal requirement for 
investigators that do not seek $500,000 or more in direct costs in any year).                                             
 
Dr. Snitker’s commitment to collaboration with other groups goes back to his appointment at the 
NIH as demonstrated in his publication record. He has an ongoing relationship with the TRIPOD study 
group at the University of Southern California. Dr. Watanabe of this group is a collaborator on this 
project. It is expected that data from this study will enter into the pool of data we share with the TRIPOD 
group (while observing HIPAA privacy rules).   
Dr. Snitker and the other University of Maryland investigators participate in multiple collaborative 
(U01) projects. These efforts have taught us to collaborate closely with the NIH Program Officers to 
promote data sharing. We envision that the data may be of interest to the Pharm-GKB database, an 
NIH effort.

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L.  
LETTERS OF SUPPORT  
 
Consultants (alphabetical; letters attached): 
 
1. David Altshuler, MD, PhD; Consultant.  Dr. Altshuler is an Associate Professor of Genetics 
and Medicine at Harvard Medical School and Director of the Medical and Population 
Genetics at the Broad Institute of Harvard and MIT. Dr. Altshuler is an expert in the area of 
genetics of complex diseases and traits, high-throughput SNP genotyping and defining 
haplotype diversity in populations. He is a member of the Hap-Map project and is also 
collaborating with Dr. Shuldiner and Dr. Pollin (co-investigators) on genetic studies of the 
Diabetes Prevention Program (DPP) cohort. Dr. Altshuler will provide consultative services 
regarding the SNP selection and genotype analysis. He will also provide expertise in 
haplotype construction and in interpretation of association and linkage results.  He will meet 
at least once per year with the University of Maryland group, either in Baltimore and or at the 
annual meeting of either the American Society of Human Genetics or the American Diabetes 
Association.  
 
2. Richard Watanabe, Ph.D.; Consultant.  Dr. Watanabe is an Assistant Professor of Medicine 
at the University of Southern California Keck School of Medicine. Dr. Watanabe is an expert 
of genetic empidemiology. He will collaborate with us in regard to candidate gene analyses 
of the TRIPOD (now PIPOD) sample. He will meet at least once per year with the University 
of Maryland group, either in Baltimore or at the annual meeting of either the American 
Society of Human Genetics or the American Diabetes Association.  
 
 
Other Letters: 
 
1. Jeffrey Hasday, M.D.; Professor of Medicine, University of Maryland School of Medicine; 
Director, University of Maryland Cytokine Core Laboratory 
 
2. Colin Stine, Ph.D.; Associate Professor of Epidemiology, University of Maryland School of 
Medicine; Director, University of Maryland Biopolymer/Genomics Core 
 
3. Carol Tacket, M.D.; Professor of Medicine, University of Maryland School of Medicine; 
Program Director of the University of Maryland General Clinical Research Center 
 
4. Sonia Anand, M.D., Ph.D, FRCPc; Chair of Genetics Subcommittee, DREAM 
 
5. Memo of Understanding regarding Dr. Shuldiner’s joint VA-University appointment