A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes
(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.
(Less)
- author
- organization
- publishing date
- 2023-02
- 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
- 2024-09-15 21:58:28
@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}}, }