Considerable interpersonal variability in weight loss exists among participants in diet interventions. Traditional one-size-fits-all intervention strategy generally neglects such variation, leading to lower effectiveness; however our currently knowledge on the intrinsic factors affecting interpersonal variability is extremely poor. The proposed systems study aims to fill this significant research gap, to identify novel in-built factors that determine interpersonal variability in diet interventions. In two of the largest and most comprehensive comparator trials on the popular weight-loss diets - POUNDS LOST (N=811) and DIRECT (N=322), we propose to identify novel circulating microRNAs (miRNAs) on whole genome scale associated with 2-year weight loss in response to diet interventions. We will also prospectively examine effects of weight-loss diet interventions varying in macronutrient compositions on dynamic changes in serum miRNAs; and examine the relations between miRNAs and markers regulating energy intake and expenditure. In addition, we will apply newly-developed multi-omics algorithm to predict weight loss, by integrating miRNAs with genomics, metabolomics, biochemical and clinical measures. We have assembled a solid group of experienced collaborators with expertise in Nutrigenetics and Nutrigenomics, bioinformatics, diet interventions and biostatistics. We believe that our study will provide novel insights into the roles of miRNAs in determining weight loss in response to diet interventions, and contribute significantly to improve efficiency of precision obesity management.

Public Health Relevance

In two of the largest and most comprehensive comparator trials on the popular weight-loss diets - POUNDS LOST and DIRECT, the study proposes to detect novel circulating microRNAs (miRNAs) on whole genome scale associated with 2-year weight loss in response to diet interventions. We will also assess the dietary effects on dynamic changes in miRNAs, the relations between miRNAs and energy balance, and develop multi-omics algorithm to predict weight loss by integrating miRNAs with genomics, metabolomics, biochemical and clinical measures. Our study would lead to identification of novel modifiable targets for diet interventions, and improve efficiency of precision obesity management.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
1R01DK115679-01
Application #
9425969
Study Section
Kidney, Nutrition, Obesity and Diabetes (KNOD)
Program Officer
Maruvada, Padma
Project Start
2018-01-01
Project End
2021-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Tulane University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
053785812
City
New Orleans
State
LA
Country
United States
Zip Code
70118