Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain

Area of Science:

• Metabolomics
• Obesity Research
• Genetics

Background:

• Excess weight gain in adulthood has adverse clinical outcomes.
• Identifying predictors of weight gain is challenging in free-living individuals.
• Metabolite profiling offers a systematic approach to discover novel, measurable predictors of weight gain.

Purpose of the Study:

• To identify metabolites associated with longitudinal changes in body mass index (BMI).
• To develop and validate a metabolite risk score (MRS) for predicting future weight gain.
• To explore the relationship between the MRS, insulin sensitivity, and type 2 diabetes (T2D).

Main Methods:

• Utilized baseline metabolite profiling data from the Framingham Heart Study (FHS) and Mexico City Diabetes Study (MCDS).
• Employed stepwise linear regression to construct the MRS from 8 selected metabolites.
• Performed genome-wide association study (GWAS) to identify genetic loci associated with the MRS.

Main Results:

• Identified 42 metabolites associated with BMI change; 8 were used to build the MRS.
• Validated the MRS in MCDS, showing a significant association with annual BMI increase.
• Found the MRS correlated with insulin sensitivity and negatively predicted T2D, independent of other risk factors.
• Discovered 2 genome-wide and 5 suggestively significant loci associated with the MRS.

Conclusions:

• Developed and validated a generalizable MRS for predicting future weight gain.
• The MRS is an independent predictor of weight gain, distinct from known risk factors.
• The MRS may reflect underlying biological mechanisms, potentially related to insulin sensitivity.
• Further research is needed to investigate the link between MRS-predicted weight gain and other obesity-related diseases.