Using 2011-2014 National Health and Nutrition Examination Survey (NHANES) data, the research aims to explore the association of anemia and cognitive dysfunction in older patients with hypertension. The study included 2005 older participants aged ≥60 years. Cognitive performance was evaluated by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word Learning subtest, the Animal Fluency Test (AFT) and the Digit Symbol Substitution Test (DSST). Multivariate logistic regression analysis and a weighted restricted cubic spline (RCS) curve were employed to examine the potential association between hemoglobin levels and cognitive dysfunction. Anemia was associated with higher odds of cognitive dysfunction in hypertensive older adults (OR = 1.663, 95% CI: 1.082-2.557, p = 0.022). Higher level of hemoglobin was associated with reduced odds of cognitive dysfunction (OR = 0.880, 95% CI: 0.782-0.991, p = 0.036). Hemoglobin levels below 13.67 g/dL were significantly associated with increased odds of cognitive impairment. This study indicates the existence of an association between anemia and cognitive dysfunction among hypertensive older adults. Hemoglobin levels may serve as a predictor of cognitive decline, with 13.67g/dL as a potential threshold for intensive cognitive monitoring in high-risk older populations with hypertension.

: Background Existing evidence suggested a relevance between insulin resistance (IR) and stroke, but further confirmation is needed. Research priorities encompass large sample size, stratified analysis, advanced IR index, stroke subtype characterization, and analysis of potential mechanisms. Methods We applied multivariable logistic regression using data from National Health and Nutrition Examination Survey 1999-2018 to estimate the correlation between two IR indexes: metabolic score for insulin resistance (METS-IR) and homeostasis model assessment of insulin resistance (HOMA-IR), and stroke. We also performed a two-sample Mendelian randomization (MR) study to detect the causal relationship between IR phenotype, METS-IR, and HOMA-IR as exposures, and stroke as well as its ischemic subtypes as outcomes. Results A total of 15,016 participants representing 147,325,838 individuals after weighting were enrolled. Both METS-IR and HOMA-IR were not significantly correlated with stroke after strict adjustment, but METS-IR was strongly related to stroke in individuals aged 20-40 in the stratification analysis. The MR analysis showed robust causal associations between IR phenotype and any stroke (AS) as well as ischemic stroke (IS). Besides AS and IS, METS-IR also had a causal effect on large artery stroke and small vessel stroke. Conclusion IR was associated with an increased risk of stroke in young adults.

Research reports on the relationship between cardiometabolic index (CMI) and depressive symptoms are limited. This study aimed to evaluate the possible relationship between CMI and depressive symptoms. We conducted a cross-sectional analysis of adults from the National Health and Nutrition Examination Survey (NHANES). Participants aged ≥ 18years with complete data on CMI and depressive symptoms from the 2007-2018 cycles were included (n = 7965). Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) with a score ≥ 10 as the cut-off. Multivariable logistic regression models were used to assess the association. Restricted cubic splines (RCS) and logistic regression were employed to explore nonlinearity and identify inflection points. Subgroup analyses were performed to examine effect modification. Of 7965 individuals, 637 (8%) had depressive symptoms, while hypertension and stroke are present in 3039 (38.15%) and 216 (2.71%), respectively. A multivariable logistic regression model observed that When LnCMI is employed as a classification variable, no significant association between them can be observed. A dose-response fitting curve find that there is a nonlinear J-shaped association between LnCMI and depressive symptoms. A two-piecewise logistic regression model to analyze their relationship, which we observed that CMI on the left side of the cut-off point was not associated with depressive symptoms prevalence. However, among patients with LnCMI ≥ -2, there was a positive association between LnCMI and depressive symptoms prevalence (OR 1.92; 95% CI 1.40-2.64). Subgroup analysis revealed that only BMI had a moderating effect on the association between CMI and depressive symptoms (OR:2.37,95% CI 1.53-3.67; P for interaction = 0.017). Our research findings suggest a strong association between higher CMI levels and increased prevalence of depressive symptoms, particularly among American adults with BMI ≥ 25kg/m2. We further revealed a turning point by threshold effect analysis.

Atopic dermatitis (AD), allergic rhinitis (AR), and asthma are typical allergic diseases, with a common pathogenesis involving increased type 2 immunity. Despite numerous studies, the association between these diseases and metabolic syndrome (MetS) remains controversial. This study aimed to explore relationships between AD, AR, and asthma both to MetS and to each of its components. Population-based, cross-sectional data were obtained from the Korean National Health and Nutrition Examination Survey from 2019 to 2020. Multiple logistic regression analyses were conducted to evaluate the association between allergic diseases and MetS. The results were compared with those of previous studies of the association between MetS and allergic diseases, identified in a literature review following a search of PubMed and the Cochrane Library. Based on assessments of 1,329, 4,824, and 1,172 patients with AD, AR, and asthma, respectively, negative associations between both AD and AR and MetS were determined, after adjusting for confounding factors. By contrast, asthma was positively associated with MetS (odds ratio [OR] 0.58, 95% confidence interval [CI] 0.50-0.67; OR 0.73, 95% CI 0.67-0.78; OR 1.70, 95% CI 1.51-1.92, respectively). The heterogeneous findings highlight the need for further investigation of associations between the studied allergic diseases and MetS as well as each of its components.

Stroke is the second leading cause of disability and mortality worldwide. A body shape index (ABSI) is calculated as waist circumference / (body mass index 2/3 × height 1/2). The TyG-ABSI is calculated as the product of the triglyceride-glucose index (TyG) and ABSI. To date, no studies have investigated the relationship between TyG-ABSI and stroke. Therefore, the purpose of this study is to fill this gap. This multi-centre cross-sectional study included 14,394 participants from the National Health and Nutrition Examination Survey (NHANES) cycles 2007-2018 and 10,869 participants from the China Health and Retirement Longitudinal Study (CHARLS) performed from 2015-2016. The potential association between TyG-ABSI and stroke was investigated using multivariable logistic regression, supplemented by subgroup analyses, restricted cubic spline (RCS) regression and threshold effect analyses. Pearson correlation analyses and Boruta's algorithm were applied to select features. Subsequently, the discriminative ability of TyG-ABSI for stroke was assessed using seven machine learning models and the SHAP algorithm. After adjusting for sufficient covariates, the Odds Ratio (OR) for TyG-ABSI was 1.186 (95% CI: 1.027-1.370) in NHANES and 1.189 (95% CI: 1.058-1.336) in CHARLS. Subgroup analyses revealed that this association was more pronounced in non-elderly individuals without hypertension in NHANES. The RCS regression showed a linear relationship between TyG-ABSI and stroke, but no threshold effect was observed in two populations. Among the seven machine learning models, the Gradient Boosting Machine (GBM) performed the best, with an Area Under the Curve (AUC) of 0.784 (95%CI:0.751 - 0.817) in NHANES and 0.651(95%CI:0.604 - 0.699) in CHARLS. This study demonstrates a positive association between TyG-ABSI and stroke. The results suggest that TyG-ABSI is a relatively valuable indicator associated with stroke and can improve the development of public health policies.

Background/Objectives: Cardiometabolic risk factor clustering is common in older adults and is associated with substantially increased cardiometabolic morbidity and mortality. This study aimed to examine the association between serum 25-hydroxyvitamin D [25(OH)D] status and cardiometabolic risk factor clustering among rural community-dwelling older adults. Methods: This cross-sectional study analyzed data from the 2022 Korea National Health and Nutrition Examination Survey (KNHANES) for 432 adults aged ≥65 years residing in rural areas. Cardiometabolic risk factor clustering was defined as the presence of ≥2 of the following: abdominal obesity, hypertension, type 2 diabetes mellitus, and dyslipidemia. Multivariable logistic regression was performed adjusting for sociodemographic and behavioral factors. Results: Cardiometabolic risk factor clustering was more prevalent among participants with vitamin D deficiency (<15 ng/mL) than among those with sufficient levels (66.0% vs. 44.9%, p = 0.006). After adjustment, vitamin D sufficiency (≥15 ng/mL), compared with vitamin D deficiency (<15 ng/mL), was associated with lower odds of clustering (aOR = 0.422, 95% CI: 0.219–0.811, p = 0.010). Meeting the WHO physical activity guideline was also associated with lower odds of clustering (AOR = 0.450, 95% CI: 0.226–0.897, p = 0.023). Conclusions: Lower vitamin D status was associated with a higher prevalence of cardiometabolic risk factor clustering. These findings support the consideration of vitamin D assessment and lifestyle-focused strategies within community health nursing practice to reduce cardiometabolic risk in rural aging populations.

As type 2 diabetes mellitus (T2DM) becomes an increasingly urgent global health concern, interest has grown in how screen-based behaviors contribute to its risk. Excessive screen exposure is often associated with sedentary lifestyles, poor sleep quality, and circadian disruption-all potential contributors to T2DM. Yet, how screen time interacts with specific sleep characteristics in shaping diabetes risk remains underexplored. This study investigates the relationship between screen exposure and T2DM risk, with particular focus on sleep duration and diagnosed sleep disorders as potential effect modifiers. We also explored variation by age, sex, and racial/ethnic groups. We analyzed data from 23 023 US adults in the 2007 to 2016 National Health and Nutrition Examination Survey. Screen exposure was dichotomized using age-specific thresholds (≥2 vs <2 hours/day for ages 3 to 18; ≥3 vs <3 hours/day for adults). Type 2 diabetes mellitus was defined by self-reported physician diagnosis. Sleep duration and diagnosed sleep disorders were examined as modifiers. Missing data were handled using multiple imputation by chained equations, and survey-weighted multinomial logistic regression was applied. High screen exposure was associated with increased odds of T2DM in fully adjusted models (odds ratio [OR] = 3.47, 95% confidence interval [CI]: 2.74, 4.36). Sleep duration was not independently associated with T2DM, whereas sleep disorders were linked to approximately twofold higher odds (OR = 2.21, 95% CI: 1.17, 4.18). The screen-T2DM association was stronger among females than males, with variation observed across sleep and racial/ethnic subgroups. Excessive screen time is linked to elevated T2DM risk, particularly among females and individuals with sleep disorders. Longitudinal research is needed to assess causality and inform targeted interventions.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide, while depression is highly prevalent in this patient population and has long been regarded as an independent risk factor for increased mortality. However, recent evidence suggests that this association may be influenced by symptom overlap and residual confounding that has not been fully accounted for. This study aimed to evaluate the association between clinically significant depressive symptoms and the risk of all-cause, cardiovascular and non-cardiovascular mortality in adults with CVD. We conducted a secondary analysis of 2064 adults with a history of CVD using data from the National Health and Nutrition Examination Survey 2011-2018 linked with the National Death Index. Depression was defined using the Patient Health Questionnaire-9 with a cut-off score of 10 or higher. Primary outcomes were all-cause, cardiovascular and non-cardiovascular mortality. Statistical analyses were performed using R software. We employed multivariable Cox regression models as well as propensity score matching and inverse probability weighting to control for potential confounders. Over a median follow-up of 4.67 years, 403 deaths were recorded. In an adjusted multivariable Cox model controlling for age, sex and race, depression was associated with an increased risk of all-cause mortality (HR 1.33; 95% CI 1.02 to 1.75) and non-cardiovascular mortality (HR 1.46; 95% CI 1.04 to 2.05). However, after applying propensity score matching and inverse probability weighting, these associations were no longer statistically significant for any mortality outcome. After rigorous adjustment for confounders and comorbidities, depression was no longer identified as an independent risk factor for mortality among patients with CVD.

Biological aging mediates the associations between urinary metals and hypertension: Insights from cross-sectional studies on Chinese and American adults.

Benzene exposure biomarkers are associated with recently smoking tobacco and pumping gasoline in the U.S. population aged 12 and over: NHANES 2017-March 2020.

Comprehensive evaluation of appendicular lean mass and sarcopenia on human health: evidence from the NHANES program.

Associations of DNA methylation algorithm measures of aging with type 2 diabetes and mortality risk among US older adults.

The role of frailty in the association between testosterone levels and mortality risk in older men.

Association between monocyte to high-density lipoprotein cholesterol ratio and stroke prevalence in American adults: A cross-sectional NHANES Study.

Usual Nutrient Intake Adequacy and Nutritional Status of United States Children and Adolescents: National Health and Nutrition Examination Survey 2001-March 2020.

Polycyclic aromatic hydrocarbons-induced suppression of the PPARα/ACAA1 axis drives hepatic steatosis: Integrating epidemiology, network toxicology, and experimental validation.

Persistent income-related inequalities in cancer screening utilisation in South Korea: Evidence from repeated cross-sectional data, 2013-2023.

Existing studies have demonstrated a close association between sleep duration and insulin resistance (IR), diabetes, and related metabolic disorders. The estimated glucose disposal rate (eGDR) serves as a reliable marker of IR. This study examined the association between weekday sleep duration and eGDR, and the moderating role of weekend catch-up sleep (WCS). We analyzed data from the National Health and Nutrition Examination Survey (2009-2023) using a cross-sectional study design to examine the association between weekday sleep duration and eGDR, while further investigating the moderating role of WCS on this relationship. Restricted cubic splines (RCS) were employed to assess potential nonlinear associations between sleep duration and eGDR, with piecewise regression analyses conducted based on identified inflection points to evaluate threshold effects. Generalized linear models and multivariable regression models were used to analyze the associations between different weekday sleep duration categories, WCS, and eGDR. This analysis included 23 475 participants. RCS modeling revealed an inverted U-shaped relationship between sleep duration and eGDR, with an inflection point at 7.32 hours. Below this threshold, increased sleep improved eGDR (β=0.273, 95% CI 0.224 to 0.322, p<0.001), while exceeding it was inversely associated (β=-0.222, 95% CI -0.272 to -0.171, p<0.001). Multivariable regression showed that, for those with <7.32 hours of weekday sleep, 1-2 hours of WCS was associated with increased eGDR (β=0.296, 95% CI 0.107 to 0.484, p=0.002) compared with no WCS. The >2 hours WCS group negatively moderated the relationship between weekday sleep and eGDR (β=-0.568, 95% CI -0.970 to -0.167, p=0.005). This study revealed a significant inverted U-shaped relationship between weekday sleep duration and eGDR, identifying an optimal duration of approximately 7.32 hours, and that WCS is beneficial only in moderation and specifically for those with weekday sleep debt, whereas it may be detrimental for those who already sleep sufficiently. These findings underscore the importance of personalized sleep recommendations.

Association of oral contraceptive use with mortality: Findings from NHANES.

Developing explainable machine learning models from biochemical and clinical data to predict all-cause and cause-specific mortality in CVD-cancer comorbidity: A longitudinal study based on NHANES.