Understanding the genetic architecture of the metabolically unhealthy normal weight and metabolically healthy obese phenotypes in a Korean population - PubMed (original) (raw)

Understanding the genetic architecture of the metabolically unhealthy normal weight and metabolically healthy obese phenotypes in a Korean population

Jae-Min Park et al. Sci Rep. 2021.

Abstract

Understanding the mechanisms underlying the metabolically unhealthy normal weight (MUHNW) and metabolically healthy obese (MHO) phenotypes is important for developing strategies to prevent cardiometabolic diseases. Here, we conducted genome-wide association studies (GWASs) to identify the MUHNW and MHO genetic indices. The study dataset comprised genome-wide single-nucleotide polymorphism genotypes and epidemiological data from 49,915 subjects categorised into four phenotypes-metabolically healthy normal weight (MHNW), MUHNW, MHO, and metabolically unhealthy obese (MUHO). We conducted two GWASs using logistic regression analyses and adjustments for confounding variables (model 1: MHNW versus MUHNW and model 2: MHO versus MUHO). GCKR, ABCB11, CDKAL1, LPL, CDKN2B, NT5C2, APOA5, CETP, and APOC1 were associated with metabolically unhealthy phenotypes among normal weight individuals (model 1). LPL, APOA5, and CETP were associated with metabolically unhealthy phenotypes among obese individuals (model 2). The genes common to both models are related to lipid metabolism (LPL, APOA5, and CETP), and those associated with model 1 are related to insulin or glucose metabolism (GCKR, CDKAL1, and CDKN2B). This study reveals the genetic architecture of the MUHNW and MHO phenotypes in a Korean population-based cohort. These findings could help identify individuals at a high metabolic risk in normal weight and obese populations and provide potential novel targets for the management of metabolically unhealthy phenotypes.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1

Miami plot of the GWASs for model 1 (MHNW versus MUHNW) and model 2 (MHO versus MUHO). SNP locations are plotted on the x-axis according to their chromosomal position. The −log10(_p_-values) derived from the logistic regression analysis are plotted on the y-axis. The _p_-values were adjusted for age, sex, exercise status, smoking status, alcohol intake, body mass index, and PC1 and PC2. The horizontal red line indicates the formal threshold for genome-wide significance at p = 5.00 × 10−8. GWASs, genome-wide association studies; MHNW, metabolically healthy normal weight; MUHNW, metabolically unhealthy normal weight; MHO, metabolically healthy obese; MUHO, metabolically unhealthy obese; PC, principle component; SNP, single-nucleotide polymorphism. The figure was generated using EasyStrata version 8.6 (

http://www.genepi-regensburg.de/easystrata

References

1. Lazar MA. How obesity causes diabetes: not a tall tale. Science. 2005;307:373–375. doi: 10.1126/science.1104342. - DOI - PubMed
1. Klop B, Elte JW, Cabezas MC. Dyslipidemia in obesity: mechanisms and potential targets. Nutrients. 2013;5:1218–1240. doi: 10.3390/nu5041218. - DOI - PMC - PubMed
1. Seravalle G, Grassi G. Obesity and hypertension. Pharmacol. Res. 2017;122:1–7. doi: 10.1016/j.phrs.2017.05.013. - DOI - PubMed
1. Kachur S, Lavie CJ, de Schutter A, Milani RV, Ventura HO. Obesity and cardiovascular diseases. Minerva Med. 2017;108:212–228. - PubMed
1. De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. J. Obes. 2013;2013:291546. doi: 10.1155/2013/291546. - DOI - PMC - PubMed

Understanding the genetic architecture of the metabolically unhealthy normal weight and metabolically healthy obese phenotypes in a Korean population - PubMed (original) (raw)