Blood multiomics reveal insights into population clusters with low prevalence of diabetes, dyslipidemia and hypertension.

• Main Authors: Ming-Wei Su, Chung-Ke Chang, Chien-Wei Lin, Shiu-Jie Ling, Chia-Ni Hsiung, Hou-Wei Chu, Pei-Ei Wu, Chen-Yang Shen
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0229922

Diabetes, dyslipidemia and hypertension are important metabolic diseases that impose a great burden on many populations worldwide. However, certain population strata have reduced prevalence for all three diseases, but the underlying mechanisms are poorly understood. We sought to identify the phenotypic, genomic and metabolomic characteristics of the low-prevalence population to gain insights into possible innate non-susceptibility against metabolic diseases. We performed k-means cluster analysis of 16,792 subjects using anthropometric and clinical biochemistry data collected by the Taiwan Biobank. Nuclear magnetic resonance spectra-based metabolome analysis was carried out for 217 subjects with normal body mass index, good exercise habits and healthy lifestyles. We found that the gene APOA5 was significantly associated with reduced prevalence of disease, and lesser associations included the genes HIF1A, LIMA1, LPL, MLXIPL, and TRPC4. Blood plasma of subjects belonging to the low disease prevalence cluster exhibited lowered levels of the GlycA inflammation marker, very low-density lipoprotein and low-density lipoprotein cholesterol, triglycerides, valine and leucine compared to controls. Literature mining revealed that these genes and metabolites are biochemically linked, with the linkage between lipoprotein metabolism and inflammation being particularly prominent. The combination of phenomic, genomic and metabolomic analysis may also be applied towards the study of metabolic disease prevalence in other populations.