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PLoS One. 2019 Sep 27;14(9):e0222445. doi: 10.1371/journal.pone.0222445. eCollection 2019.

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

Author information

1
Clinical Obesity, Institution of Clinical Sciences, Malmö, Lund University, Sweden.
2
Department of Genetics, Harvard Medical School, Boston, MA, United States of America.
3
Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, United States of America.
4
Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, United States of America.
5
Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America.
6
Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico.
7
Centro de Estudios en Diabetes, Mexico City, Mexico.
8
Department of Family Medicine and Public Health, UC San Diego, San Diego, CA, United States of America.

Abstract

BACKGROUND:

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches.

METHODS AND RESULTS:

Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m2) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10-8) and 5 suggestively (p < 1 × 10-6) significant loci, several of which have been previously linked to obesity-related phenotypes.

CONCLUSIONS:

We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

PMID:
31560688
PMCID:
PMC6764659
DOI:
10.1371/journal.pone.0222445
[Indexed for MEDLINE]
Free PMC Article

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