Background: Waist circumference (WC) has become an essential diagnostic marker of metabolic syndrome; however, its accuracy in reflecting insulin resistance remains uncertain, particularly among Asian people who develop metabolic complications at lower levels of adiposity. Methods: We analyzed 20,202 adults (8886 men and 11,316 women) from the Korean National Health and Nutrition Examination Survey (KNHANES) 2015 and 2019–2021. All analyses accounted for the survey sampling weights and the complex survey design. WC, insulin resistance, and β-cell function were assessed using fasting insulin level, HOMA-IR, and HOMA-β. Associations between WC and insulin indices were evaluated using correlation analyses, stratification by WC deciles, and multivariable regression models adjusted for demographic and metabolic covariates. Results: WC was positively associated with HOMA-IR in both sexes (all p < 0.001). In the fully adjusted model, HOMA-IR remained significantly associated with WC [men, β = 0.014 (95% CI, 0.011–0.016); women, β = 0.009 (95% CI, 0.008–0.011)]. Subgroup analyses stratified by age revealed significant associations across all age groups. The association was stronger in older adults, particularly among men, whereas women exhibited significant associations across all ages with a larger age-related increase. Conclusions: WC is independently associated with insulin resistance in Korean adults, with stronger associations in older adults and consistent associations in women. These findings support WC as a simple yet pathophysiologically meaningful marker of metabolic risk and highlight the necessity of refining WC cutoff values for clinical screening of Asian populations and an age-specific approach for diagnosing metabolic syndrome.

Endometriosis is a complex disease with diverse etiologies, including hormonal, immunological, and environmental factors; however, its exact pathogenesis remains unknown. While surgical approaches are the diagnostic and therapeutic gold standard, identifying endometriosis-associated genes is a crucial first step. Five endometriosis-related gene expression studies were selected from the available datasets. Approximately, 14,167 genes common to these 5 datasets were analyzed for differential expression. Meta-analyses utilized fold-change values and standard errors obtained from each analysis, with the binomial and continuous datasets contributing to endometriosis presence and endometriosis severity meta-analysis, respectively. Approximately 160 genes showed significant results in both meta-analyses. For Bayesian analysis, endometriosis-related single nucleotide polymorphisms (SNPs), the human transcription factor catalog, uterine SNP-related gene expression, disease–gene databases, and interactome databases were utilized. Twenty-four genes, present in at least three or more databases, were identified. Network analysis based on Pearson’s correlation coefficients revealed the HLA-DQB1 gene with both a high score in the Bayesian analysis and a central position in the network. Although ZNF24 had a lower score, it occupied a central position in the network, followed by other ZNF family members. Bayesian analysis identified genes with high confidence that could support discovering key diagnostic biomarkers and therapeutic targets for endometriosis.

Endometriosis is a complex disease with diverse etiologies, including hormonal, immunological, and environmental factors; however, its exact pathogenesis remains unknown. While surgical approaches are the diagnostic and therapeutic gold standard, identifying endometriosis-associated genes is a crucial first step. Endometriosis-related five gene expression studies were selected from the available datasets. Approximately, 14,167 genes common to these five datasets were analyzed for differential expression. Meta-analyses utilized fold-change values and standard errors obtained from each analysis, with the binomial and continuous datasets contributing to the endometriosis presence and endometriosis severity meta-analysis, respectively. Approximately, 160 genes showed significant results in both meta-analyses. For Bayesian analysis, endometriosis-related single nucleotide polymorphisms (SNPs), the human transcription factor catalog, uterine SNP-related gene expression, disease-gene databases, and interactome databases were utilized. Twenty-four genes, present in at least three or more databases, were identified. Network analysis based on Pearson's correlation coefficients revealed HLA-DQB1 gene with both a high score in the Bayesian analysis and a central position in the network. Although ZNF24 had a lower score, it occupied a central position in the network, followed by other ZNF family members. Bayesian analysis identified genes with high confidence that could support discovering key diagnostic biomarkers and therapeutic targets for endometriosis.