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

Geidenstam, Nina LU ; Hsu, Yu Han H.; Astley, Christina M.; Mercader, Josep M.; Ridderstråle, Martin LU ; Gonzalez, Maria E.; Gonzalez, Clicerio; Hirschhorn, Joel N. and Salem, Rany M. (2019) In PLoS ONE 14(9).
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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
14
issue
9
publisher
Public Library of Science
external identifiers
  • scopus:85072709247
ISSN
1932-6203
DOI
10.1371/journal.pone.0222445
language
English
LU publication?
yes
id
aa7b18ed-5669-4fff-b8f7-984eec157811
date added to LUP
2019-10-10 12:47:51
date last changed
2019-10-10 12:47:51
@article{aa7b18ed-5669-4fff-b8f7-984eec157811,
  abstract     = {<p>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 &lt; 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/m<sup>2</sup>) 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 &lt; 5 × 10<sup>−8</sup>) and 5 suggestively (p &lt; 1 × 10<sup>−6</sup>) 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.</p>},
  articleno    = {e0222445},
  author       = {Geidenstam, Nina and Hsu, Yu Han H. and Astley, Christina M. and Mercader, Josep M. and Ridderstråle, Martin and Gonzalez, Maria E. and Gonzalez, Clicerio and Hirschhorn, Joel N. and Salem, Rany M.},
  issn         = {1932-6203},
  language     = {eng},
  number       = {9},
  publisher    = {Public Library of Science},
  series       = {PLoS ONE},
  title        = {Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain},
  url          = {http://dx.doi.org/10.1371/journal.pone.0222445},
  volume       = {14},
  year         = {2019},
}