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.

The aim of this study was to investigate the longitudinal change in muscle mass over 10 years according to serum calcium levels and calcium intake. A total of 1497 men and 1845 women aged 50 years and older were included. Significant muscle loss (SML) was defined as a 5% or greater loss from baseline, while time-dependent development of SML was assessed according to quartiles for corrected calcium level and daily calcium intake using Cox regression models. The incidence of SML was 6.7 and 7.7 per 100-person-years among men and women, respectively. Groups with the lowest corrected calcium levels had more prominent SML than those with higher calcium levels, regardless of sex. The relationship between SML and calcium intake was significant only among women. The hazard ratio for SML per 1 mmol/L increase in corrected calcium level was 0.236 and 0.237 for men and women, respectively. In conclusion, low serum calcium levels may predict SML among adults aged ≥ 50 years, while low calcium intake may be a predictor for muscle loss among women. Therefore, encouraging dietary calcium intake among middle-aged and older adults for preservation of muscle mass should be considered.

Genome-wide association studies (GWAS) and candidate gene approaches have identified single nucleotide polymorphisms (SNPs) associated with new-onset diabetes after renal transplantation (NODAT). We evaluated associations between NODAT and SNPs identified in previous studies. We genotyped 1102 renal transplant recipients from the Korean Organ Transplantation Registry (KOTRY) database; 13 SNPs were assessed for associations with NODAT (occurring in 254 patients; 23.0%), within one year after transplantation. The frequency of the T allele at KCNQ1 rs2237892 was significantly lower in patients with NODAT compared to control patients (0.30 vs. 0.39; p = 8.5 × 10⁻⁵). The T allele at rs2237892 was significantly associated with decreased risk of NODAT after adjusting for multiple variables, compared to the C allele (OR 0.63, 95% CI 0.51–0.79; p = 5.5 × 10⁻⁵). Dominant inheritance modeling showed that CT/TT genotypes were associated with a lower risk for development of NODAT (OR 0.56, 95% CI 0.42–0.76; p = 2.0 × 10⁻⁴) compared to the CC genotype. No other SNPs were associated with NODAT. Our study validated the protective effect of T allele at KCNQ1 rs2237892 on the development of NODAT in a large cohort of renal transplant recipients. Our findings on susceptibility variants might be a useful tool to predict NODAT development after renal transplantation.