| Title: | Unsupervised Clustering Identified Clinically Relevant Metabolic Syndrome Endotypes in UK and Taiwan Biobanks |
| Journal: | iScience |
| Published: | 1 Apr 2024 |
| DOI: | https://doi.org/10.1016/j.isci.2024.109815 |
| URL: | http://dx.doi.org/10.1016/j.isci.2024.109815 |
Publication 9996
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Abstract
Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified; Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycaemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWAS) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.</p>
4 Authors
- Aylwin Ming Wee Lim
- Evan Unit Lim
- Pei-Lung Chen
- Cathy Shen Jang Fann
1 Application
| Application ID | Title |
|---|---|
| 46789 | How large is large enough? Is small datasets still valuable under the shadow of huge biobank data? |
Enabling scientific discoveries that improve human health