Discovering Determinants of Food Intake by Application of Artificial Intelligence to Complex, High-Dimensional Data

Study Description: The study will be an outpatient study with 6-7 total visits, 3-4 completed at baseline and 3 follow-up visits, occurring over 2 years. We will collect broad, multiscale data (e.g., clinical, genetic, microbiome, energy expenditure, physical activity, psychosocial). Then, we will apply artificial intelligence algorithms with the goal of identifying novel determinants of food intake. Objectives: Primary Objective: To identify novel determinants of objectively measured ad libitum energy intake using broad, multiscale data. Secondary Objectives: 1. To investigate whether determinants of ad libitum energy intake predict changes in weight, adiposity, and markers of metabolic health. 2. To determine associations of metabolic measures with psychosocial, hormonal, or other metabolic markers 3. To identify novel determinants of ad libitum self-reported energy and nutrient intake Endpoints: Primary Endpoint: Energy intake during ad libitum meal test, including total energy and macronutrient specific energy intake (e.g., carbohydrate, fat, protein) measured during the initial baseline period (visit 3). The potential determinants (broad multiscale data) will be collected during visits 1, 2, and 3. Secondary Endpoints: 1. Measures at 0.5, 1, and 2 years of body weight, BMI, fat mass, body fat percentage, HbA1c, and fasting glucose, lipids, insulin, and c-peptide. 2. Psychosocial measures (visits 1-6), metabolic measures (e.g., fasting glucose, HbA1c, lipids, insulin, and c-peptide), resting metabolic rate, total energy expenditure, and physical activity 3. Energy and nutrient intake from ASA-24. The potential determinants (broad multiscale data) will be collected during visits 1, 2, and 3.