Background: Chronic periodontitis (CP) and metabolic dysfunction-associated steatotic liver disease (MASLD) have emerged as interconnected conditions with shared mechanisms, such as systemic inflammation and metabolic dysregulation. However, the risk of CP in the newly classified subgroups of steatotic liver disease (SLD), including MASLD and metabolic alcohol-associated liver disease (MetALD), has not been extensively studied. This study investigated the association between SLD subtypes and the incidence of CP in a nationwide cohort. Methods: This retrospective cohort study used data from the Korean National Health Insurance Service database. The study included 115,619 participants aged 40 and older who underwent health screenings between 2009 and 2010. The participants were classified into four groups: normal without risk factors, normal with risk factors, MASLD, and MetALD. The primary outcome was the incidence of CP as defined by ICD-10 codes and dental treatment records. Hazard ratios (HRs) were calculated using the Cox proportional hazards model and adjusted for demographic, clinical, and lifestyle factors. Results: Over a mean follow-up of 7.4 years, individuals with MASLD and MetALD had significantly higher risks of developing CP compared with the normal group without risk factors (MASLD: adjusted HR 1.14, 95% confidence interval (CI): 1.11–1.17; MetALD: adjusted HR 1.21, 95% CI: 1.15–1.27). The risk was more pronounced for severe CP, particularly for those with MetALD (adjusted HR 1.29, 95% CI: 1.22–1.36). Subgroup and sensitivity analyses confirmed these findings across the various definitions of hepatic steatosis and metabolic risk factors. Conclusions: This study reveals that individuals with MASLD and MetALD are at an elevated risk of developing CP, highlighting the need for integrated care strategies that address both periodontal health and metabolic liver conditions. These findings underscore the importance of periodontal health management in reducing the risk of CP among SLD populations.

Insulin resistance is a major indicator of cardiovascular diseases, including hypertension. The Metabolic Score for Insulin Resistance (METS-IR) offers a simplified and cost-effective way to evaluate insulin resistance. This study aimed to identify genetic variants associated with the prevalence of hypertension stratified by METS-IR score levels. Data from the Korean Genome and Epidemiology Study (KoGES) were analyzed. The METS-IR was calculated using the following formula: ln [(2 × fasting blood glucose (FBG) + triglycerides (TG)) × body mass index (BMI)]/ ln [high-density lipoprotein cholesterol (HDL-C)]. The participants were divided into tertiles 1 (T1) and 3 (T3) based on their METS-IR scores. Genome-wide association studies (GWAS) were performed for hypertensive cases and non-hypertensive controls within these tertile groups using logistic regression adjusted for age, sex, and lifestyle factors. Among the METS-IR tertile groups, 3517 of the 19,774 participants (17.8%) at T1 had hypertension, whereas 8653 of the 20,374 participants (42.5%) at T3 had hypertension. A total of 113 single-nucleotide polymorphisms (SNPs) reached the GWAS significance threshold (p < 5 × 10−8) in at least one tertile group, mapping to six distinct genetic loci. Notably, four loci, rs11899121 (chr2p24), rs7556898 (chr2q24.3), rs17249754 (ATP2B1), and rs1980854 (chr20p12.2), were significantly associated with hypertension in the high-METS-score group (T3). rs10857147 (FGF5) was significant in both the T1 and T3 groups, whereas rs671 (ALDH2) was significant only in the T1 group. The GWASs identified six genetic loci significantly associated with hypertension, with distinct patterns across METS-IR tertiles, highlighting the role of metabolic context in genetic susceptibility. These findings underscore critical genetic factors influencing hypertension prevalence and provide insights into the metabolic–genetic interplay underlying this condition.

Background: Systemic Clinic Outcome and Routine Evaluation (SCORE-15) is a compact scale that contains the most critical family function assessment tools including assessments of the strengths, adaptability, and communication among family members. It has been translated into other languages in the United States and Europe. This study aimed to verify the reliability and validity of SCORE-15 with a small research population and justify its applicability in Korea. Methods: SCORE-15 is a self-reporting family function measurement tool for each family member over the age of 11 years. This study used the Family Communication Scale (PCS) included in the Family Adaptability and Cohesion Evaluation Scales (FACES) IV package and DACES in FACES-Ill to verify the validity of the Korean-translated SCORE-15. Cronbach's a value was calculated to check the reliability of SCORE-15. Data were analyzed using STATA ver. 15.0 (Stata Corp., College Station, TX, USA). Results: The study analyzed the correlation between FACES-Ill and SCORE-15 and FCS and SCORE-15 so that there was a significant static correlation in both comparisons (r=0.72 and r=. -0.81, respectively). Also, the research compared each subscale to analyze the correlation and the range was 0.47 to 0.95. The total SCORE-15 Cronbach's alpha value was 0.92 and those values of the subscales for family strengths, family communication, and family difficulty were 0.89, 0.73, and 0.87, respectively (P<0.001). Conclusion: Our study was the first to validate the Korean SCORE-15, which can be used as an appropriate short-form indicator for evaluating family function and changes in detecting therapeutic improvements in Korea.

Subjective life expectancy (SLE) is the predictive value of actual life expectancy. SLE has been notably associated with mortality. The 2006 Korean Longitudinal Study of Aging (KLoSA) representative sample of 10,254 Koreans aged over 45 years to assess the associations between factors of SLE. Descriptive analysis, correlations, and age-adjusted regression analyses were used to examine the relationship between SLE and demographic, socioeconomic, and health factors. We also linked the 2018 KLoSA death statistics to the 2006 data to evaluate the association between actuarial life expectancy and SLE. We found that chronic illnesses and limitations in activities of daily living affect the life expectancy of individuals. Marriage, gainful employment, and high educational qualifications increase life expectancy. People who exercise expect to live longer, while those who smoke and drink expect to live somewhat shorter lives. Better self-rated health is associated with higher SLE. People who own a house expect to live longer than non-owners, and individuals living in metropolitan cities and urban areas assume a longer life expectancy than those living in rural areas. Participants who died between 2006 and 2018 had previously predicted a lower life expectancy than those who survived until 2018. The results of the study suggest that current health status, health behaviors, socioeconomic status, and actual life expectancy showed significant associations with SLE in the expected directions. These findings imply that we could use SLE as a database of health status, health behavior, and actual life expectancy. This information could help us intervene and improve policies related to SLE.

Background: Although insulin resistance has been implicated in cancer development, its specific role in lung cancer remains unclear. The metabolic score for insulin resistance (METS-IR) is a novel surrogate marker that integrates multiple metabolic parameters and has demonstrated strong predictive value for metabolic disorders. This study aimed to investigate the association between METS-IR and lung cancer incidence in a large-scale nationwide cohort. Methods: We analyzed data from 322,624 participants of the National Health Insurance Service-National Health Screening Cohort in Republic of Korea. Participants were stratified into METS-IR quartiles, and lung cancer incidence was assessed using Kaplan–Meier survival curves and Cox proportional hazards regression models. Subgroup analyses were conducted to examine the impact of body composition, particularly sarcopenia, on the association between METS-IR and lung cancer. Results: Over a median follow-up of 9.5 years, 5912 lung cancer cases were identified. Lung cancer incidence per 1000 person-years was highest in the lowest METS-IR quartile (Q1: 2.27) and decreased across quartiles (Q2: 1.93, Q3: 1.81, Q4: 1.72). In fully adjusted Cox regression models, using Q1 as the reference group, higher METS-IR quartiles were associated with a significantly lower risk of lung cancer (Q2: HR 0.91, 95% CI 0.85–0.98; Q3: HR 0.86, 95% CI 0.79–0.92; Q4: HR 0.80, 95% CI 0.74–0.86; p for trend < 0.001). Subgroup analyses revealed that the inverse association was more pronounced in male participants and individuals with a low body mass index. Conclusions: In this nationwide cohort study, we observed a significant inverse association between METS-IR and lung cancer risk. However, METS-IR showed limitations in fully explaining lung cancer risk based on insulin resistance alone. These findings highlight the need for future studies incorporating body composition assessments to better evaluate metabolic vulnerability.

Stratifying populations based on indicators of insulin resistance and visceral adiposity may help identify genetic associations with type 2 diabetes mellitus (T2DM) that vary across metabolic environments. This study aimed to investigate metabolic context-dependent genetic associations with T2DM using the metabolic score for insulin resistance (METS-IR) and visceral fat (METS-VF). A total of 70,583 and 70,513 participants were eligible for METS-IR and METS-VF stratification, respectively. After excluding participants with missing diabetes status, the final case–control samples comprised 40,970 and 40,944 participants, respectively. Genome-wide association studies of prevalent T2DM were conducted within metabolic tertiles, with additional evaluation of lead variants in the intermediate tertile (T2). Logistic regression under an additive genetic model was performed, followed by genotype × metabolic stratum interaction analyses and Cochran’s Q heterogeneity tests. Twelve notable loci were identified, showing both shared and varying association patterns across metabolic strata. CDKAL1 showed robust associations across metabolic strata, with progressively attenuated effect sizes from T1 to T3 and significant interaction results under both METS-VF and METS-IR stratifications. PAX4, CPEB3, and HHEX also demonstrated stronger associations in metabolically favorable groups, supported by nominally significant interaction and heterogeneity tests. CDKN2A/B showed associations across metabolic strata, with evidence of differences in genetic effects. KCNQ1 exhibited broadly consistent associations, although evidence of interaction varied by metabolic index. CNNM2, HNF1B, HNF4A, SLC30A8, and DNAJC5B–TRIM55 displayed subgroup-dependent patterns of statistical significance, which did not uniformly correspond to significant interaction effects. ALDH2 showed distinct association patterns across metabolic strata and between men and women. These findings suggest that genetic associations with T2DM may vary according to metabolic context, although subgroup-specific genome-wide significance does not necessarily establish differential genetic effects. Formal interaction and heterogeneity analyses provide additional evidence for metabolic effect modification at selected loci, suggesting that metabolic stratification may help characterize variation in genetic associations with T2DM in the Korean population.