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A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Coral, Daniel E LU orcid ; Fernandez-Tajes, Juan LU ; Tsereteli, Neli LU ; Pomares-Millan, Hugo LU orcid ; Fitipaldi, Hugo LU ; Mutie, Pascal M LU ; Atabaki-Pasdar, Naeimeh LU orcid ; Kalamajski, Sebastian LU ; Poveda, Alaitz LU orcid and Miller-Fleming, Tyne W , et al. (2023) In Nature Metabolism 5(2). p.237-247
Abstract

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and... (More)

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Genetic variation, Machine learning, Metabolism, Obesity, Type 2 diabetes
in
Nature Metabolism
volume
5
issue
2
pages
11 pages
publisher
Springer Nature
external identifiers
  • pmid:36703017
  • scopus:85146842321
ISSN
2522-5812
DOI
10.1038/s42255-022-00731-5
language
English
LU publication?
yes
additional info
© 2023. The Author(s).
id
92c11d65-ef24-4552-b11e-b3519998dcae
date added to LUP
2023-02-07 10:30:13
date last changed
2024-06-09 12:55:57
@article{92c11d65-ef24-4552-b11e-b3519998dcae,
  abstract     = {{<p>Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.</p>}},
  author       = {{Coral, Daniel E and Fernandez-Tajes, Juan and Tsereteli, Neli and Pomares-Millan, Hugo and Fitipaldi, Hugo and Mutie, Pascal M and Atabaki-Pasdar, Naeimeh and Kalamajski, Sebastian and Poveda, Alaitz and Miller-Fleming, Tyne W and Zhong, Xue and Giordano, Giuseppe N and Pearson, Ewan R and Cox, Nancy J and Franks, Paul W}},
  issn         = {{2522-5812}},
  keywords     = {{Genetic variation; Machine learning; Metabolism; Obesity; Type 2 diabetes}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{237--247}},
  publisher    = {{Springer Nature}},
  series       = {{Nature Metabolism}},
  title        = {{A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes}},
  url          = {{http://dx.doi.org/10.1038/s42255-022-00731-5}},
  doi          = {{10.1038/s42255-022-00731-5}},
  volume       = {{5}},
  year         = {{2023}},
}