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

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

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.

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.

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