DIETFITS Study (Diet Intervention Examining the Factors Interacting With Treatment Success

Obesity Clinical Trial

— DIETFITS  

Official title:

Do Insulin Secretion or Genotype Pattern Predict Low Fat vs Low Carb Weight Loss Success?

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01826591
• Other study ID #: 22305
• Secondary ID: 1R01DK091831T32H
• Status: Completed
• Phase: N/A
• Start date: January 2013
• Est. completion date: May 2016

Study information

• Verified date: February 2023
• Source: Stanford University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Genomics research is advancing rapidly, and links between genes and obesity continue to be discovered and better defined. A growing number of single nucleotide polymorphisms (SNPs) in multiple genes have been shown to alter an individual's response to dietary macronutrient composition. Based on prior genetic studies evaluating the body's physiological responses to dietary carbohydrates or fats, the investigators identified multi-locus genotype patterns with SNPs from three genes (FABP2, PPARG, and ADRB2): a low carbohydrate-responsive genotype (LCG) and a low fat-responsive genotype (LFG). In a preliminary, retrospective study (using the A TO Z weight loss study data), the investigators observed a 3-fold difference in 12-month weight loss for initially overweight women who were determined to have been appropriately matched vs. mismatched to a low carbohydrate (Low Carb) or low fat (Low Fat) diet based on their multi-locus genotype pattern. The primary objective of this study is to confirm and expand on the preliminary results and determine if weight loss success can be increased if the dietary approach (Low Carb vs. Low Fat) is appropriately matched to an individual' s genetic predisposition (Low Carb Genotype vs. Low Fat Genotype) toward those diets.

Description:

If the intriguing preliminary retrospective results are confirmed in this full scale study, the results will demonstrate that inexpensive DNA testing could help dieters predict whether they will have greater weight loss success on a Low Carb or a Low Fat diet. Commensurate with increasing scientific interest in personalized medicine approaches to intervention development, this would provide an example of the potentially substantial health impacts that could be obtained through understanding specific gene-environment interactions that have been anticipated from the unraveling of the human genome.

Mobile App Sub-Study-For the purpose of augmenting adherence to high vegetable consumption in both diet groups, we will develop a theory-based mobile app to increase vegetable consumption through goal-setting, self-monitoring, and social comparison. Participants from both diet groups with iPhones will be re-randomized to receive the app at either months 4-5 or months 7-8. The first phase during months 4-7 will be used to compare the effect of a mobile app (intervention) vs. no mobile app (waiting-list control). The a priori hypothesis is that vegetable consumption will increase among those who receive the app in both diet arms. The investigator and outcomes assessor will be blinded to group assignment. Intention-to-treat analysis will be used.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 609
• Est. completion date: May 2016
• Est. primary completion date: May 2016
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 50 Years

Eligibility

Inclusion Criteria:

• Age: > 18 years of age

• Women: Pre-menopausal (self-report) and <50 years of age

• Men: <50 years of age

• BMI (body mass index): 27-40 kg/m2 (need to lose >10% body weight to achieve healthy BMI)

• Body weight stable for the last two months, and not actively on a weight loss plan

• No plans to move from the area over the next two years

• Available and able to participate in the evaluations and intervention for the study period

• Willing to accept random assignment

• To enhance study generalizability, people on medications not noted below as specific exclusions can

• participate if they have been stable on such medications for at least three months

• Ability and willingness to give written informed

• No known active psychiatric illness

Exclusion Criteria:

Subjects with the following conditions will be excluded (determined by self-report):

• Pregnant, lactating, within 6 months post-partum, or planning to become pregnant in the next 2 years

• Diabetes (type 1 and 2) or history of gestational diabetes or on hypoglycemic medications for any other indication

• Prevalent diseases: Malabsorption, renal or liver disease, active neoplasms, recent myocardial infarction (<6 months)(patient self-report and, if available, review of labs from primary care provider)

• Smokers (because of effect on weight and lipids)

• History of serious arrhythmias, or cerebrovascular disease

• Uncontrolled hyper- or hypothyroidism (TSH not within normal limits)

• Medications: Lipid lowering, antihypertensive medications, and those known to affect weight/energy expenditure

• Excessive alcohol intake (self-reported, >3 drinks/day)

• Musculoskeletal disorders precluding regular physical activity

• Unable to follow either of the two study diets for reasons of food allergies or other (e.g., vegan)

• Currently under psychiatric care, or taking psychiatric medications

• Inability to communicate effectively with study personnel

Related Conditions & MeSH terms

• Insulin Resistance
• Obesity

Intervention

Behavioral:
• Low-Carbohydrate Diet
Counseling/instruction on how to follow a low-carbohydrate diet.
• Low-Fat Diet
Counseling/instruction on how to follow a low-fat diet.
• Mobile App
Mobile app to increase vegetable consumption. Participants with iPhones will be re-randomized to receive a mobile app beginning at either months 4-5 or months 7-8. The first phase during months 4-7 will be used to compare the effect of a mobile app (intervention) vs. no mobile app (waiting-list control). The a priori hypothesis is that vegetable consumption will increase among those who receive the app in both diet groups.

Locations

Country	Name	City	State
United States	Stanford University School of Medicine	Stanford	California

Sponsors (5)

Lead Sponsor	Collaborator
Stanford University	National Center for Advancing Translational Sciences (NCATS), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Nutrition Science Initiative

Country where clinical trial is conducted

United States, 

References & Publications (9)

Fielding-Singh P, Patel ML, King AC, Gardner CD. Baseline Psychosocial and Demographic Factors Associated with Study Attrition and 12-Month Weight Gain in the DIETFITS Trial. Obesity (Silver Spring). 2019 Dec;27(12):1997-2004. doi: 10.1002/oby.22650. Epub 2019 Oct 21. — View Citation

Figarska SM, Rigdon J, Ganna A, Elmstahl S, Lind L, Gardner CD, Ingelsson E. Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention. Sci Rep. 2020 May 13;10(1):7913. doi: 10.1038/s41598-020-64636-7. — View Citation

Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JPA, Desai M, King AC. Effect of Low-Fat vs Low-Carbohydrate Diet on 12-Month Weight Loss in Overweight Adults and the Association With Genotype Pattern or Insulin Secretion: The DIETFITS Randomized Clinical Trial. JAMA. 2018 Feb 20;319(7):667-679. doi: 10.1001/jama.2018.0245. Erratum In: JAMA. 2018 Apr 3;319(13):1386. JAMA. 2018 Apr 24;319(16):1728. — View Citation

Grembi JA, Nguyen LH, Haggerty TD, Gardner CD, Holmes SP, Parsonnet J. Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention. Sci Rep. 2020 Jan 29;10(1):1405. doi: 10.1038/s41598-020-58000-y. Erratum In: Sci Rep. 2020 Jul 1;10(1):11095. — View Citation

Mummah SA, King AC, Gardner CD, Sutton S. Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption. Int J Behav Nutr Phys Act. 2016 Aug 8;13:90. doi: 10.1186/s12966-016-0400-z. — View Citation

Mummah SA, Mathur M, King AC, Gardner CD, Sutton S. Mobile Technology for Vegetable Consumption: A Randomized Controlled Pilot Study in Overweight Adults. JMIR Mhealth Uhealth. 2016 May 18;4(2):e51. doi: 10.2196/mhealth.5146. — View Citation

Mummah SA, Robinson TN, King AC, Gardner CD, Sutton S. IDEAS (Integrate, Design, Assess, and Share): A Framework and Toolkit of Strategies for the Development of More Effective Digital Interventions to Change Health Behavior. J Med Internet Res. 2016 Dec 16;18(12):e317. doi: 10.2196/jmir.5927. — View Citation

Shih CW, Hauser ME, Aronica L, Rigdon J, Gardner CD. Changes in blood lipid concentrations associated with changes in intake of dietary saturated fat in the context of a healthy low-carbohydrate weight-loss diet: a secondary analysis of the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial. Am J Clin Nutr. 2019 Feb 1;109(2):433-441. doi: 10.1093/ajcn/nqy305. Erratum In: Am J Clin Nutr. 2020 Feb 1;111(2):490. — View Citation

Stanton MV, Robinson JL, Kirkpatrick SM, Farzinkhou S, Avery EC, Rigdon J, Offringa LC, Trepanowski JF, Hauser ME, Hartle JC, Cherin RJ, King AC, Ioannidis JP, Desai M, Gardner CD. DIETFITS study (diet intervention examining the factors interacting with treatment success) - Study design and methods. Contemp Clin Trials. 2017 Feb;53:151-161. doi: 10.1016/j.cct.2016.12.021. Epub 2016 Dec 24. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change from baseline in weight at 12 months	Weight change was calculated as the 12 month value minus the baseline value. The study was designed to determine if either insulin secretion or genotype pattern (low-fat genotype pattern vs .low-carb genotype pattern) were significant effect modifiers of 12-month weight loss for the two diet arms (e.g., 2X2 analyses).	Baseline and 12 months
Secondary	Change from baseline in LDL cholesterol at 12 months	LDL-cholesterol change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in HDL cholesterol at 12 months	HDL-cholesterol change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in triglycerides at 12 months	Triglycerides change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in fasting insulin at 12 months	Fasting insulin change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in fasting glucose at 12 months	Fasting glucose change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in insulin after an oral-glucose tolerance test (OGTT) at 12 months	Post-OGTT insulin change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in glucose after an oral-glucose tolerance test (OGTT) at 12 months	Post-OGTT glucose change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in body fat percentage at 12 months.	Body fat percentage was assessed by dual-energy x-ray absorptiometry (DXA) and the change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in body mass index (BMI) at 12 months.	BMI change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months
Secondary	Change from baseline in resting energy expenditure (REE) at 12 months.	REE was assessed by indirect calorimetry and the change was calculated as the 12 month value minus the baseline value.	Baseline and 12 months

External Links

•   Study description and results summary