Diabetes, dyslipidemia and hypertension have global prevalence rates of 8.5%, 38%, and 40%, respectively. Disease prevalence may be used as a proxy for disease incidence so it follows that population clusters with reduced disease prevalence may potentially represent individuals with reduced disease susceptibility, under normal circumstances.
A study led by Chen-Yang Shen of Academia Sinica, Taipei, Taiwan, used this principle of phenotypic cluster analysis to subset of the Taiwan Biobank cohort and identified a cluster of subjects with low prevalence rates for diabetes, dyslipidemia and hypertension based on self-reported disease status. They then used a multi-omics approach to infer metabolites and genes associated with lower incidence. The results were published in the journal PLoS One.
Only high-density lipoprotein cholesterol (HDL) was inversely associated with the three diseases, consistent with its moniker of “good cholesterol”. Risk factors identified included blood triglyceride concentration for dyslipidemia; fasting blood glucose for diabetes and systolic blood pressure for hypertension.
The authors used genome-wide association (GWA) to identify single nucleotide polymorphisms (SNPs) associated with lower disease prevalence. Three SNPs were identified one of which, rs651821, is located in a known gene, apolipoprotein A5 (APOA5) (p < 5 × 10−8).
When the significance threshold was reduced to p < 10−5, SNPs in lipoprotein lipase (LPL), MLX interacting protein-like (MLXIPL), hypoxia inducible factor 1 subunit α (HIF1A), transient receptor potential cation channel subfamily C member 4 (TRPC4), and LIM domain and actin-binding 1 (LIMA1) were identified.
Metabolomic analysis suggested that blood lipid composition was altered and inflammation reduced in the lower disease prevalence group.
Limitations of the study include that disease status was self-reported. The correlations do not imply causation.
“In this study, we used a combination of phenomics, genomics and metabolomics to characterize human subjects belonging to a population cluster having a low prevalence of diabetes, dyslipidemia and hypertension. The approach allowed us to identify probable links between the genes, metabolites and phenotypes involved in the three metabolic diseases. Our findings show that individuals belonging to the low-disease-prevalence cluster share a common genetic background, which may translate to better blood plasma lipid profiles and reduced levels of inflammation-inducing metabolites,” stated the authors.