Obesity Genes, Energy Regulation in Response to Weight-Loss
Phase: NA
Conditions: Overweight and Obesity
Sponsor: Tulane University

 
Department of Epidemiology 
Tulane University 
School of Public Health and Tropical Medicine 
1440 Canal Street, Suite 1724 
New Orleans, LA 70112 
Tel. 504-988-7259 
Fax 504-988-3548 
 
Unique Protocol ID: R01DK091718 
DATE: April 25, 2017 
 
 
 
 
Gene-Environment 
Interaction 
on 
Adiposity, Glycemic, Lipids Changes 
and Bone Health: Study Protocol with 
Statistical Analysis 

1. Objective 
 
The purpose of this study is to test the interactions between the genes and diet 
interventions varying in macronutrient intake on glycemic, waist circumference, body 
weight, visceral fat, lipids changes and bone health. In the POUNDS LOST, all of the 
811 participants DNA was extracted from the buffy coat fraction of centrifuged 
blood using the QIAmp blood kit (Qiagen, Chatsworth, CA). Single nucleotide 
polymorphisms were genotyped using the OpenArray™ SNP genotyping system 
(BioTrove, Woburn, MA). The study is only accepting participants in the Boston, 
Massachusetts or Baton Rouge, Louisiana area. For further enrollment information in 
Boston or Baton Rouge, see Eligibility Criteria or Design Narrative. 
 
 
Besides, the investigators integrated novel pathway analyses in large cohorts and 
comprehensive genetic analyses on long-term weight loss and mechanisms in 
randomized diet intervention trials would provide very important evidence to unravel 
the etiology of obesity, and have significant public health and clinical implications. 
Establishing relationship between genetic variants and diets in determining weight 
change will help identify individuals at high risk for obesity especially when adherent 
to specific diet. 
 
 
The epidemic of obesity has become a major public health problem. Obesity is a 
multifactorial abnormality that has a genetic basis but requires environmental 
influences to manifest. Common-form obesity is underpinned by both environmental 

and genetic factors. Recent genome-wide association studies have identified several 
genes convincingly related to obesity risk. Testing gene-environment interaction is a 
relatively new field. Evidence from association studies and intervention trials 
continues to mount, indicating that genetic components may modify lifestyle effects 
on the development of obesity. However, these findings are at most preliminary. The 
interplay between genetic and environmental components may facilitate the choice of 
more effective and specific measures for obesity prevention based on the personalized 
genetic make-up. 
 
2. Design and Methods 
 
The investigators aimed to examine whether the genetic variation affected 
glycemic, waist circumference, body weight, visceral fat, lipids, and bone mineral 
density (BMD) changes in response to weight-loss dietary intervention varying in 
macronutrient intake (target percentages of energy derived from fat, protein, and 
carbohydrate in the 4 diets were as follows 20%, 15% and 65%; 20%, 25%, and 
55%; 40%, 15%, and 45%; and 40%, 25%, and 35%). 
 
The primary outcome is to test the interactions between the genes and diet 
interventions varying in macronutrient intake on body weight changes. The secondary 
outcome is to test the interactions between the genes and diet interventions varying in 
macronutrient intake on glycemic, waist circumference, visceral fat, lipids, and 
BMD changes. Understanding gene–diet interactions in relation to weight loss 
holds great promise for delivering more efficient prevention and treatment on obesity 
and related metabolic disorders. 
 

We assessed overall genetic variation by calculating a genetic risk score (GRS). 
The GRS was calculated based on the SNPs by summing risk allele’s numbers, and 
weighted by their effect sizes (β-coefficients) derived from genome-wide association 
meta-analyses data (Supplementary Table 1). The GRS was computed using the 
equation: GRS = (β1 × SNP1+β2 × SNP2+… +βn × SNPn) × (n/sum of the 
β-coefficients), where β is the β-coefficient of each SNP for higher levels of 
fasting glucose, SNP1, SNP2 … and SNP n indicate the number of risk alleles (0, 1 or 
2) for each SNP. 
 
The Hardy-Weinberg equilibrium and comparison of categorical variables at 
baseline were assessed with chi-square test. Differences in continuous variables 
according to tertiles of the GRS(genetic risk score) at baseline were tested using 
general linear models, with adjustment for age, sex, and race. The investigators used 
general linear models to test changes in adiposity, glycemic and lipids traits in high- 
fat and low-fat diets groups according to tertiles of the GRS. Covariates adjustment 
included age, sex, race, physical activity, smoking, alcohol, seasonal variation, and 
the baseline value for the respective outcome in model. Moreover, to analyze the 
potential interactions between GRS and diet interventions, an interaction product term 
of GRS-diet was included in the models. As a secondary analysis, with the use of time 
as a repeated variable, linear mixed models were used to test the GRS effect on the 
trajectory of changes in adiposity, glycemic and lipids traits by including a GRS-by- 
time interaction term. Because our analysis is hypothesis-driven and primarily 
focused on the GRS (rather than individual SNPs), we did not adjust for multiple 
testing. The level of significance for all tests was set to P<0.05. Statistical analyses 
were performed with SAS version 9.2 (SAS Institute, Inc., Cary, NC). 
 

 
3. Participant Flow 
 
 
 
 
 
 
Recruitment Details: The included participants were a group of 811 overweight or 
obese individuals with ages and BMI being 30-70y and 25-40 kg/m2, respectively. 
The major exclusion criteria were the presence of diabetes treated with oral 
medications or insulin, unstable cardiovascular disease, cancer, the use of medications 
that influence body weight and inadequate motivation as estimated by an interview 
and questionnaire. 
 
Arm/Group Information and title: 
 
diet 1:diet with moderate in fat (40% energy) and protein(15%) 
diet 2:diet with moderate in fat (40% energy) and protein(25%) 
diet 3:diet with low in fat (20% energy) and protein(15%) 
diet 4:diet with low in fat (20% energy) and protein(25%) 
 
A total of 811 participants 
were included. 
Assessed for eligibility 
(n = 1638) 
Major criteria for exclusion were the presence of 
diabetes or unstable cardiovascular disease, cancer, the 
use of medications that affect body weight, and 
insufficient motivation as assessed by interview and 
questionnaire. 
Excluded (n = 827) 
Ineligible (n = 461) 
Declined to participate (n = 366) 
Randomized 
(n = 811) 
Genotype data available in 811 
participants 

Arm/Group Description : 
 
diet 1: The study tests the effectiveness for weight loss and weight maintenance of 
four diets differing in macronutrient composition: moderate in fat (40% energy) with 
two different protein levels (15% and 25%), and low in fat (20% energy), also with 
15% and 25% protein levels. 
diet 2:The study tests the effectiveness for weight loss and weight maintenance of 
four diets differing in macronutrient composition: moderate in fat (40% energy) with 
two different protein levels (15% and 25%), and low in fat (20% energy), also with 
15% and 25% protein levels. 
diet 3:The study tests the effectiveness for weight loss and weight maintenance of 
four diets differing in macronutrient composition: moderate in fat (40% energy) with 
two different protein levels (15% and 25%), and low in fat (20% energy), also with 
15% and 25% protein levels. 
diet 4:The study tests the effectiveness for weight loss and weight maintenance of 
four diets differing in macronutrient composition: moderate in fat (40% energy) with 
two different protein levels (15% and 25%), and low in fat (20% energy), also with 

15% and 25% protein levels. 
 
Period(s): a 2-year randomized multicenter clinical trial . 
Period Title: A 2-year randomized multicenter clinical trial 
Started: September 2003 
Completed: December 2010 
 
4. Baseline Characteristics 
 
Arm/Group Information: Participants were randomly distributed to one of four diets 
that composed a two-by-two factorial design: 1) a low-fat, average-protein diet (20% 
fat, 15% protein, and 65% carbohydrate, 2) a low-fat, high-protein diet (20% fat, 25% 
protein, and 55% carbohydrate), 3) a high-fat, average-protein diet (40% fat, 15% 
protein, and 45% carbohydrate), and 4) a high-fat, high- protein diet (40% fat, 25% 
protein, and 35% carbohydrate). 
Arm/Group Description : 
 
diet 1: The study tests the effectiveness for weight loss and weight maintenance of 
four diets differing in macronutrient composition: moderate in fat (40% energy) with 
two different protein levels (15% and 25%), and low in fat (20% energy), also with 
15% and 25% protein levels. 
diet 2: The study tests the effectiveness for weight loss and weight maintenance of 
four diets differing in macronutrient composition: moderate in fat (40% energy) with 
two different protein levels (15% and 25%), and low in fat (20% energy), also with 
15% and 25% protein levels. 
diet 3: The study tests the effectiveness for weight loss and weight maintenance 
of four diets differing in macronutrient composition: moderate in fat (40% energy) 
with two different protein levels (15% and 25%), and low in fat (20% energy), 
also with 15% and 25% protein levels. 

diet 4:  The study tests the effectiveness for weight loss and weight maintenance 
of four diets differing in macronutrient composition: moderate in fat (40% energy) 
with two different protein levels (15% and 25%), and low in fat (20% energy), 
also with 15% and 25% protein levels. 
Overall Number of Baseline Participants: 811 
 
Baseline Measure Information: After an overnight fast, body weight, height and 
waist circumference were measured in the morning on 2 nonconsecutive days at 
baseline, 6 months, and 2 years. 
5. Outcome Measures 
 
Outcome Measure Information: Anthropometric measures were collected at 
baseline and 6, and 2 y according to standard protocols. BMI was calculated by 
dividing weight by the squared height of a participant. Dual-energy X-ray 
absorptiometry (DXA) scans were performed on a random subset of 50% of 
participants to assess body fat and BMD at baseline, 6 mo, and 2 y. Fasting blood 
samples were collected at baseline and 6 and 24 mo. Analyses for glucose and 
insulin were done from aliquoted serum at the Pennington Laboratory 

with an immunoassay with chemiluminescent detection on an Immukite analyzer 
(Diagnostics Products Corp). Triglyceride, TC, LDL cholesterol, and HDL cholesterol 
were measured by using the Beckman Synchron CX7 analyzer (Beckman Coulter). 
Genotyping has been described in detail previously. Briefly, DNA was 
extracted by using a QIAmp Blood Kit (Qiagen). Polymorphisms were genotyped 
by using the OpenArray SNP Genotyping System (BioTrove). The genotype success 
rate and replication concordance were >99%. 
 
Outcomes included changes at 6 and 24 mo (determined as the difference from 
baseline) for anthropometric measures (weight, waist circumference, and BMI) and at 
6 mo and 2 y for DXA body fat (total body fat, lean mass, and trunk fat) and BMD 
measures. The body-composition outcomes were selected to depict various adipose 
depots (ie, abdominal or central adiposity by using waist circumference and overall 
body fat by using BMI). The other endpoints for this study were changes in 
glycemic traits including fasting glucose, fasting insulin, insulin resistance and 
insulin sensitivity over the intervention. Homeostasis model assessment (HOMA) 
models were used to estimate insulin resistance (HOMA-IR), insulin sensitivity 
(HOMA-S), and β-cell function (HOMA-B),12,13 which were calculated by the 
following equations: 
 
HOMA-IR = (fasting insulin (μU ml − 1) × fasting glucose (mmol l − 1))/22.5, 
HOMA-S = 22.5/(fasting insulin (μU ml − 1) × fasting glucose (mmol l − 1)) and 
HOMA-B = (20 × fasting insulin (μU ml − 1))/(fasting glucose (mmol l − 1) − 3.5). 
 
Outcome Measure Type and Title : 
The primary outcome is to test 
the interactions 
between the genes and diet 
interventions varying in macronutrient intake on body weight changes. 

The secondary outcome is to test the interactions between the genes and diet 
interventions varying in macronutrient intake on glycemic, waist circumference, 
visceral fat and lipids changes. 
Outcome Measure Time Frame: 
 
measured at Year 2 
 
Overall Number of Participants Analyzed: 
 
811 
 
Measure Type: 
 
Mean 
 
Measure of Dispersion/Precision: 
 
Standard Deviation 
 
Unit of Measure: Body weight: weight in kilograms 
 
glycemic changes: mg/dL for fasting glucose, uU/mL for fasting insulin 
waist circumference changes: cm 
visceral fat changes:(%) 
 
lipids changes: mg/dL for Total cholesterol, HDL cholesterol, LDL cholesterol and 
Triglycerides