Metabolomic profiles of body composition measures
Principal Investigator
Name
Erikka Loftfield
Degrees
PhD, MPH
Institution
National Cancer Institute
Position Title
Earl Stadtman Investigator
Email
About this CDAS Project
Study
IDATA
(Learn more about this study)
Project ID
IDATA-94
Initial CDAS Request Approval
Sep 15, 2025
Title
Metabolomic profiles of body composition measures
Summary
Body composition—relative proportions of body fat and fat-free mass—is used to evaluate weight status, nutritional status, and physical fitness, and is strongly linked with health, morbidity, and mortality. Measures of body composition include anthropometric measurements, such as height, weight, body circumferences (e.g., waist, hip), and body mass index (BMI), and more advanced techniques, such as deuterium dilution. Anthropometric measurements are the most frequently used measures as they tend to be practical, accessible, inexpensive, and non-invasive; however, accuracy may be confounded by measurement error (e.g., user error, use of uncalibrated tools), measurement or interpretation challenges (e.g., edema, absence of a limb), and they provide only a crude proxy of body composition.
In addition to genetics, several biological and behavioral factors affect body composition, including dietary intake, physical activity, metabolism, and inflammation. Although past studies have identified relationships among circulating or urinary metabolites and body composition measures, predominant studies have been limited by use of cross-sectional data, convenience samples, a single body composition measure, or assessment of a targeted selection of metabolites. Therefore, we propose a body composition metabolite discovery study that includes 1) multiple methods of body composition assessment, 2) measurement of an untargeted selection of metabolites, and 3) evaluation of body composition measures and metabolites at multiple time points to account for temporal variability. IDATA provides an opportunity to conduct this research in a healthy, yet vulnerable, population of individuals aged 50 to 74 years who may be at risk of chronic illnesses due to age-related shifts in body composition (e.g., increased fat mass, decreased skeletal muscle mass). Findings from this study may provide objective alternative or complementary measures of body composition that could be useful for epidemiologic studies, clinical assessment, and intervention research.
To identify candidate metabolites of body composition measures, specifically BMI, waist circumference, waist to hip ratio, and derived fat and fat-free mass (from deuterium dilution), we will conduct an untargeted metabolomics analysis of blood and urine samples within the IDATA Study. We will use the Metabolon® data created from IDATA Project ID 2020-1014, which includes data from 718 individuals who provided baseline and 6-month serum, 24-hour urine, and first morning void (FMV) urine samples. We will examine correlations between metabolites and use LASSO regression to identify metabolites in serum and urine samples that are associated with each body composition measure.
In addition to genetics, several biological and behavioral factors affect body composition, including dietary intake, physical activity, metabolism, and inflammation. Although past studies have identified relationships among circulating or urinary metabolites and body composition measures, predominant studies have been limited by use of cross-sectional data, convenience samples, a single body composition measure, or assessment of a targeted selection of metabolites. Therefore, we propose a body composition metabolite discovery study that includes 1) multiple methods of body composition assessment, 2) measurement of an untargeted selection of metabolites, and 3) evaluation of body composition measures and metabolites at multiple time points to account for temporal variability. IDATA provides an opportunity to conduct this research in a healthy, yet vulnerable, population of individuals aged 50 to 74 years who may be at risk of chronic illnesses due to age-related shifts in body composition (e.g., increased fat mass, decreased skeletal muscle mass). Findings from this study may provide objective alternative or complementary measures of body composition that could be useful for epidemiologic studies, clinical assessment, and intervention research.
To identify candidate metabolites of body composition measures, specifically BMI, waist circumference, waist to hip ratio, and derived fat and fat-free mass (from deuterium dilution), we will conduct an untargeted metabolomics analysis of blood and urine samples within the IDATA Study. We will use the Metabolon® data created from IDATA Project ID 2020-1014, which includes data from 718 individuals who provided baseline and 6-month serum, 24-hour urine, and first morning void (FMV) urine samples. We will examine correlations between metabolites and use LASSO regression to identify metabolites in serum and urine samples that are associated with each body composition measure.
Aims
• To identify metabolites associated with body composition measures—body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR), and derived fat and fat-free mass from deuterium dilution—using serum, 24-hour urine, and first morning void urine (FMV) collected six months apart from the participants in the IDATA study
• To identify a set of metabolites for each body composition measure (i.e., BMI, WC, WHR, and derived fat and fat-free mass) using LASSO regression
• To generate poly-metabolite scores corresponding to each set of body composition-associated metabolites, which may lead to advancements in body composition measurement (e.g., less measurement error than traditional methods)
Collaborators
Erikka Loftfield National Cancer Institute