Search Results (967)

Background: Mechanistic evidence increasingly implicates fructose exposures as contributors to the development and exacerbation of asthma and other atopic diseases. Proposed mechanisms include gut dysbiosis, impaired epithelial barrier integrity in the gut and airways, metabolic endotoxemia, and amplification of type 2 immune responses. However, epidemiologic findings linking fructose intake with asthma and atopic disorders remain heterogeneous. Objective: To conduct a review of epidemiologic studies evaluating associations between dietary fructose-containing exposures and atopic outcomes in pediatric populations. Methods: A systematic search of PubMed and Embase identified cohort, case-control, cross-sectional, and randomized feeding studies assessing fructose exposure in relation to asthma and atopic outcomes in pediatric populations. Eligibility screening, data extraction, and risk-of-bias assessment were conducted by one reviewer and confirmed by the other. Results: Seventeen epidemiologic studies met criteria. Multiple cohorts (e.g., BRISA, PIAMA) reported modest to moderate associations between higher sugar-sweetened beverage (SSB) intake and pediatric asthma or “asthma traits.” Cross-sectional analyses from NHANES and the National Children’s Study showed stronger associations, with greater fructose exposures linked to two- to five-fold higher odds of asthma. High fructose beverage consumption demonstrated the most consistent positive associations. Large ISAAC-based studies reported largely null findings, reflecting broad dietary exposure categories and limited specificity for fructose-rich beverages. Evidence for rhinitis, eczema, and sensitization was directionally consistent. Conclusions: Despite heterogeneity, the convergence of mechanistic plausibility with epidemiologic signals supports a potential contributory role of high fructose exposure in pediatric atopic disease. More rigorous longitudinal studies with biomarker-based exposure assessment are needed to refine causal inference. Full article

Background and Objectives: Pro-inflammatory diet and high-fat diet (HFD) often coexist in real-world, but their combined impact on the gut–liver axis and potential nutritional countermeasures remain insufficiently studied. This study aimed to evaluate a pro-inflammatory–high-fat composite dietary pattern on the intestine and liver in the population, and to further evaluate the protective potential of astaxanthin (ATX) in complementary experimental systems. Methods: Data from the NHANES 2005–2010 were used to construct four composite exposure groups based on the dietary inflammation index (DII) and energy from fat. Survey-weighted regression analyses were performed to examine associations with systemic inflammation and liver injury. Interaction and C-reactive protein (CRP)-mediated effect analyses were conducted. Fifty SD rats were randomly divided into control group, model group induced by HFD combined with inflammatory factors, and low-, medium-, and high-dose Haematococcus pluvialis (HP) intervention groups. Serum lipids, liver enzymes, liver and colon pathology, and inflammatory and oxidative markers were measured in rats. In an in vitro organ-on-chip barrier model, the effect of ATX was observed when colonic barrier damage was induced using palmitic acid and lipopolysaccharides. Results: The high DII combined with HFD showed the largest increases in CRP, liver enzymes, and fatty liver index. A synergistic interaction was observed between DII and HFD, with CRP mediating approximately 20% of the effect. In rat model, HP-derived ATX improved the lipid profile, attenuated hepatic steatosis and oxidative damage, and reduced colonic pro-inflammatory cytokines, while restoration of tight junction proteins was limited. In colon organoid model, ATX showed limited efficacy in improving inflammation and barrier function. Conclusions: The pro-inflammatory–high-fat dietary pattern synergistically exacerbates gut–liver dysfunction. HP-derived ATX alleviates metabolic and inflammation-induced enterohepatic comorbidity, but its effect on repairing barrier structure is limited. Full article

Background: We (and others) have previously identified five clinically distinct diabetes subtypes. Currently, few models to identify diabetes subtypes are readily accessible. Further, while COVID-19 has been associated with increased risk of new-onset diabetes, it remains unknown whether the pandemic is also associated with changes in diabetes subtype distribution. Methods: We used the electronic health records of patients diagnosed with diabetes from 2010 to 2019 at the Kirklin Clinic of the University of Alabama at Birmingham (UAB) to train models to assign diabetes subtypes previously identified by hierarchical clustering. We then applied the trained model to conduct a retrospective cluster analysis of electronic health records of patients diagnosed with diabetes from 2020 to 2024 at UAB. We further validated our findings using data from the 2015–2023 National Health and Nutrition Examination Surveys (NHANES). Results: The trained classification model had an average specificity of 98% and an average sensitivity of 93%. Using the model, we identified a significant difference in the distribution of type 2 diabetes subtypes in patients at UAB and in participants in NHANES. In particular, the proportion of patients with severe insulin-dependent diabetes or severe insulin-resistant diabetes subtypes increased from 42% to 61% and 31% to 40% at the UAB and in NHANES, respectively. Conclusions: The model presented here can facilitate the identification of diabetes subtypes. The proportions of patients with severe subtypes of diabetes have seemed to increase in the more recent years following the pandemic. Further studies are required to determine the potential causes of this phenomenon. Full article

Background/Objectives: Dietary fiber has been associated with lower levels of inflammatory biomarkers, but nationally representative evidence using recent U.S. data remains limited. We evaluated the association between dietary fiber intake and inflammatory biomarkers in U.S. adults using the National Health and Nutrition Examination Survey (NHANES) 2017–2018, the last fully completed cycle before the COVID-19 pandemic, providing a pre-pandemic benchmark for future comparisons. Methods: We analyzed 3570 adults (≥20 years) from NHANES 2017–2018 with complete dietary and biomarker data. Fiber intake was averaged from two 24 h recalls. Outcomes included serum high-sensitivity C-reactive protein (hs-CRP; primary outcome), white blood cell count (WBC), and neutrophil count. Survey-weighted regression models adjusted for demographic, socioeconomic, lifestyle, clinical, and dietary covariates. Associations were examined continuously (per 5 g/day fiber), by quartiles, and with restricted cubic splines. Sensitivity analyses excluded participants with cardiometabolic conditions or modified covariate sets. Results: Each 5 g/day higher fiber intake was associated with 4–7% lower hs-CRP (p < 0.001). Participants in the highest versus lowest fiber quartile had 20.7% lower hs-CRP (95% CI −27.1, −14.4) and 47% lower odds of elevated hs-CRP (OR 0.53, 95% CI 0.32–0.85). Secondary outcomes showed significant inverse associations: each +5 g/day was associated with −0.98% WBC (95% CI −1.84, −0.13; p = 0.024) and −1.44% neutrophils (95% CI −2.62, −0.26; p = 0.017) in fully adjusted models. Spline analyses showed no nonlinearity for WBC (p = 0.227) but nonlinear inverse associations for neutrophils (p = 0.0017). Sensitivity analyses confirmed robustness to exclusion of individuals with diabetes, hypertension, or hyperlipidemia, and to alternative covariate specifications. Conclusions: Higher dietary fiber intake was independently associated with a more favorable inflammatory biomarker profile (hs-CRP, WBC, and neutrophils) in U.S. adults, providing a pre-pandemic benchmark for future comparisons. Longitudinal and interventional studies are needed to clarify temporality and causality. Full article

Hearing loss affects approximately 1.5 billion people globally, yet access to comprehensive audiometric datasets for research remains limited due to privacy constraints. Synthetic data generation offers a promising solution, enabling broader data sharing while preserving privacy. This study developed and validated two complementary approaches for synthesising audiometric data: Kernel Density Estimation (KDE) and Variational Autoencoders (VAE). Using the National Health and Nutrition Examination Survey (NHANES) dataset comprising 36,676 participants with comprehensive hearing assessments, we trained both generative models and evaluated synthetic data quality through a rigorous Train-on-Synthetic-Test-on-Real (TSTR) machine learning validation framework and blinded expert clinical assessment by two independent audiologists. The VAE approach achieved 86.3% utility for hearing loss prediction, as compared to the benchmark real data (Train-on-Real-Test-on-Real). Both methods demonstrated strong privacy preservation, with zero exact record matches and robust membership inference attack resistance. Statistical validation confirmed equivalence within clinically negligible margins (<1 dB HL) across all audiometric frequencies. Blinded assessment of 85 patient profiles by two independent expert audiologists revealed that VAE synthetic data achieved high clinical plausibility ratings, with 96.7% of VAE profiles rated as plausible, compared to 13.3% for KDE. Inter-rater reliability was moderate (Cohen’s weighted $\kappa =0.553$, ICC $=0.556$), with 84.7% of ratings within one point, and both raters independently ranking VAE above real data above KDE. These findings establish validated methodologies for generating privacy-preserving synthetic audiometric data suitable for machine learning applications and clinical education, addressing a critical gap in hearing health research infrastructure. Full article

Background: Hearing loss affects over 466 million individuals globally and is recognized as a major risk factor for Alzheimer’s disease, yet treatment personalization remains limited due to the complexity and diversity of underlying causes. Current diagnostic and therapeutic approaches lack standardized methods to accurately predict the most appropriate intervention for individual patients. The integration of medical ontologies with machine learning offers a promising solution for enhancing diagnostic accuracy and treatment personalization. Aim: Our study aimed to (i) develop a Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT)-aligned clinical ontology for hearing loss using Semantic Web Rule Language for automated reasoning; (ii) implement a Random Forest classifier trained on ontology-enriched patient data to classify hearing loss types (conductive, sensorineural, mixed, or normal); and (iii) predict optimal personalized treatments based on laterality, severity, audiometric thresholds, and medical history using real-world patient data. Methods: We developed a task ontology using Protégé 5.6.3 with Web Ontology Language (OWL), integrated SNOMED CT terminology alignment, and implemented Semantic Web Rule Language rules executed by the Pellet 2.2.0 reasoner. The framework was trained and evaluated on 3723 adult patients from the 2015–2016 National Health and Nutrition Examination Survey (NHANES) dataset with complete audiometric and clinical data. Random Forest models were developed using an 80–20 train-test split with stratified sampling and five-fold cross-validation. Performance was compared between K-Means clustering-based labeling and ontology-based semantic inference using accuracy, precision, recall, F1-score, and log loss metrics. Results: The ontology successfully generated semantic labels for all 3723 patients, enabling precise classification of hearing loss types, severity levels, and laterality. The Random Forest model with K-Means clustering achieved a test accuracy of 90.2% with a log loss of 0.2766 and a cross-validation mean accuracy of 91.22% (standard deviation 1.2%). Integration of ontology-based semantic enrichment significantly improved performance, achieving a test accuracy of 92.48% with a cross-validation mean accuracy of 92.80% (standard deviation 0.9%). F1-scores improved across all classes, with mixed hearing loss showing a notable increase from 0.86 to 0.92. Feature importance analysis identified audiometric thresholds, ontology-derived severity labels, and medical history as top predictors, enhancing clinical interpretability. Conclusions: This study demonstrates that combining SNOMED CT-aligned ontology with Random Forest classification achieves superior diagnostic accuracy and enables personalized treatment recommendations for hearing loss. The hybrid framework provides clinically interpretable decision support while ensuring semantic interoperability with electronic health records. Multi-institutional validation studies are necessary to assess generalizability across diverse populations before clinical deployment. Full article

Background/Objectives: Tinnitus has been increasingly associated with cardiovascular disease, and recent phenome-wide analyses have identified angina pectoris as a condition linked to tinnitus. This study aimed to replicate and quantify the association between tinnitus and angina pectoris in a nationally representative U.S. adult sample using NHANES, while adjusting for key demographic, cardiovascular, and tinnitus-related risk factors. Methods: Using data from four NHANES cycles 2009–2018, a cross-sectional analysis was conducted, which included 9185 participants, and used multivariate logistic regression analyses to investigate the association between tinnitus and angina pectoris. Results: Among 9185 adults, angina was associated with higher odds of tinnitus in all models. In the crude model, OR = 3.30 (95% CI: 2.18–4.91, p < 0.001); partially adjusted, OR = 1.92 (95% CI: 1.27–2.89, p = 0.002); fully adjusted, OR = 1.65 (95% CI: 1.07–2.55, p = 0.026). In the fully adjusted model, hearing loss (OR = 4.11), noise exposure (OR = 1.63), current smoking (OR = 1.29), older age (OR = 1.01 per year), and total cholesterol (OR = 1.003 per mg/dL) were additional significant predictors for tinnitus. Conclusions: In this nationally representative sample of U.S. adults, tinnitus was more frequently reported among individuals with a history of angina pectoris, and this association persisted after adjustment for demographic factors, socioeconomic status, hearing loss, noise exposure, smoking, and cardiometabolic comorbidities. These findings support emerging evidence that cardiovascular conditions may be associated with tinnitus, potentially reflecting shared vascular or systemic mechanisms. Given the cross-sectional design, causal inferences cannot be drawn, and the temporal relationship between angina and tinnitus remains unclear. Future longitudinal studies are needed to clarify underlying mechanisms, assess directionality, and determine whether cardiovascular risk modification may have implications for tinnitus prevention or management. Full article

Background: Measuring glycated hemoglobin (HbA1c) is essential for assessing long-term glycemic control, yet direct testing remains expensive and underutilized in many large-scale health surveys and resource-constrained settings. This study aims to (i) deliver a highly accurate and interpretable ML model for predicting HbA1c from routinely collected clinical, biochemical, and demographic data, (ii) reduce dependency on extensive laboratory panels by identifying a compact, cost-efficient subset of key predictors, and (iii) establish a transferable, explainable modeling framework applicable across chronic disease biomarkers. Unlike prior HbA1c prediction studies that focus primarily on classification or accuracy-driven models, this work introduces a unified framework for continuous HbA1c regression that jointly integrates cost-oriented feature parsimony, stratified regression validation, and explainability by design. Methods: We aggregated data from the National Health and Nutrition Examination Survey (NHANES) cycles 2007–2020, encompassing 66,148 records and 224 candidate features. We implemented a two-stage feature selection pipeline: Incremental Correlation Selection (ICS) to narrow the variable space, followed by Recursive Feature Elimination with Cross-Validation (RFECV) to isolate the most informative features. Model interpretability was assessed using partial dependence plots and feature importance analysis. Results: The optimal model, LightGBMRegressor with most-frequent imputation, achieved R² = 0.7161, MAE = 0.334, MSE = 0.304, and MAPE = 5.56%, while using only 40 selected features. Interpretability analysis revealed clinically coherent relationships that align with physiological expectations. Discussion: The proposed framework maintains robust predictive performance while substantially reducing the number of required input features, enabling cost-efficient HbA1c estimation together with transparent, physiologically coherent model insights. By consolidating continuous HbA1c prediction, cost-aware feature selection, stratified evaluation, and explainability within a single pipeline are enhanced. Conclusions: This study advances beyond existing approaches and offers a practical blueprint for scalable biomarker estimation in population health and clinical decision-support applications. Its explainable, efficient, and generalizable design positions it as a strong candidate for clinical decision-support and population-health applications. Full article

Trouble sleeping has become a global public health challenge. However, the relationship between organophosphate flame retardant (OPFR) exposure and trouble sleeping remains unclear. This study integrated epidemiological analysis, network toxicology, molecular docking, molecular dynamics simulations, and adverse outcome pathway (AOP) construction to identify OPFRs linked to trouble sleeping and attempted to elucidate underlying molecular mechanisms. We analyzed cross-sectional data from the U.S. National Health and Nutrition Examination Survey (NHANES 2013–2018) involving 4585 eligible adults. Logistic regression confirmed dibutyl phosphate (DBuP) as significantly correlated with trouble sleeping. Restricted cubic splines (RCSs) revealed a significant non-linear, J-shaped relationship between dibutyl phosphate (DBuP) levels and trouble sleeping. Weighted quantile sum (WQS) analysis determined that DBuP accounted for the majority contribution (58.23%) to the observed effects within exposure mixtures. These findings indicated that DBuP, a metabolite of tributyl phosphate (TnBP), was closely related to trouble sleeping, suggesting that the environmental health risks of TnBP may be jointly contributed to by itself and DBuP. We used network analysis to identify five core target genes (PPARG, MMP9, PTGS2, APP, EGFR) that interact with DBuP and its parent compound TnBP. Molecular docking predicted binding poses of TnBP and DBuP toward these five core targets; all showed moderate binding affinity (ΔG ≤ −5.0 kcal/mol) except MMP9, which exhibited weak binding. Molecular dynamics simulations further supported this putative binding. Enrichment analysis highlighted inflammatory response pathways. Ultimately, we elucidated the process from molecular exposure to trouble sleeping by constructing an AOP framework. In conclusion, we proposed that TnBP and DBuP may contribute to trouble sleeping through multi-target interactions, primarily through PPARG-driven inflammatory dysregulation. These findings suggest a potential link between OPFR exposure and trouble sleeping, providing insights that warrant further mechanistic investigation. Full article

Background/Objectives: Socioeconomic status (SES) and health insurance are critical determinants of chronic disease outcomes. This study evaluates their impact on the hypertension and diabetes “care cascade” (diagnosis, treatment, and control) across three distinct health systems: China, the United States (US), and the United Kingdom (UK). Methods: We analyzed cross-sectional data from pooled survey waves of the China Health and Retirement Longitudinal Study (CHARLS), the US National Health and Nutrition Examination Survey (NHANES), and the English Longitudinal Study of Ageing (ELSA). The final analytic sample comprised a total of 46,054 participants with hypertension and 11,805 with diabetes. Logistic regression model was employed to estimate the associations of education, wealth, and health insurance with disease management outcomes. Results: Significant cross-national heterogeneity was observed. China exhibited the steepest attrition in the care cascade, with disparities strongly linked to insurance fragmentation; notably, Urban Employee Insurance was associated with significantly better outcomes compared to the Rural Cooperative Medical Scheme. In the US, health insurance was strongly associated with diagnosis and treatment initiation but showed attenuated associations with disease control, suggesting that financial barriers (“underinsurance”) may persist. The UK demonstrated the highest equity in access due to universal National Health Service coverage, though education remained a predictor for diabetes identification; moreover, a persistent wealth-based gradient in disease control remained despite universal access. Conclusions: Universal health coverage effectively mitigates access barriers but does not eliminate inequalities driven by cumulative socioeconomic disadvantage. Achieving equity requires context-specific strategies: reducing insurance fragmentation in China, minimizing out-of-pocket costs in the US, and addressing upstream social determinants in the UK. Full article

Background/Objectives: Although body mass index (BMI) is a conventional screening tool for type 2 diabetes mellitus (T2D), its reliability as a sole indicator of metabolic health is controversial, and the metabolic profile of a subset of individuals with normal BMI is indicative of obesity-related complications. This study aimed to estimate the prevalence and predictors of undiagnosed diabetes among Metabolically Unhealthy Normal Weight (MUNW) adults. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2017–March 2020 were analyzed. Normal weight adults (BMI 18.5–24.9 kg/m²) were categorized into Metabolically Healthy (MHNW) and Unhealthy (MUNW) phenotypes based on the presence of ≥2 metabolic risk factors, including elevated blood pressure, triglycerides, waist circumference, or low HDL cholesterol. The primary outcome was undiagnosed diabetes, defined as HbA1c ≥ 6.5% or Fasting Plasma Glucose ≥ 126 mg/dL. Results: The study population represented approximately 60 million US adults. The prevalence of undiagnosed diabetes was nearly four times higher in the MUNW group (4.84%) compared to the MHNW group (1.28%). In multivariable logistic regression analysis, age and race emerged as significant predictors. Notably, Asian adults exhibited a significantly higher risk of undiagnosed diabetes (OR 6.10; 95% CI: 1.32–28.2) compared to White adults, independent of metabolic phenotype. Conclusions: Reliance solely on BMI may overlook undiagnosed diabetes in normal-weight adults, particularly those with metabolic clustering or of Asian descent. These findings underscore the importance of multidimensional risk assessment integration into preventive care, optimizing clinical management. Full article

Depression is a major public health concern, and evidence continues to show that environmental toxicants may contribute to its development. This study evaluated the association between depressive symptoms and per- and polyfluoroalkyl substances (PFAS), heavy metals, phthalates, and organophosphate metabolites using data from NHANES 2017–2018. Depressive symptoms were measured with the Patient Health Questionnaire-9 (PHQ-9). Environmental exposure variables were analyzed using multivariable linear regression and Bayesian Kernel Machine Regression (BKMR). All models adjusted for demographic, socioeconomic, behavioral, and clinical covariates. In multivariable linear regression models adjusted for demographic, socioeconomic, behavioral, and clinical covariates, higher urinary dimethylphosphate concentrations were significantly associated with increased depressive symptom scores (β = 0.15; 95% CI: 0.04, 0.27; p = 0.0098). Mono-(2-ethylhexyl) phthalate (MEHP) was also positively associated with PHQ-9 scores (β = 0.001; 95% CI: 0.0003, 0.0019; p = 0.0043). Because environmental mixtures tend to follow non-linear patterns, BKMR analysis was run. BKMR analyses indicated that organophosphate metabolites exhibited the greatest overall contribution to depressive symptoms (group posterior inclusion probability = 0.7875), with diethylphosphate emerging as the most influential individual exposure within the group (conditional PIP = 0.7211). Exposure–response functions suggested non-linear and threshold relationships for several metabolites. These findings identify specific organophosphate and phthalate metabolites as potential contributors to depressive symptoms and support the importance of evaluating chemical mixtures rather than single exposures. Additional longitudinal studies are needed to clarify temporal relationships and to inform public health efforts aimed at reducing exposure to organophosphate pesticides and endocrine-disrupting chemicals. Full article

Background/Objectives: Contemporary food systems face dual imperatives of ensuring nutritional adequacy while minimizing environmental resource consumption, yet conventional dietary assessment methodologies inadequately integrate these competing objectives. This simulation-based proof-of-concept study developed an artificial intelligence-driven computational framework synthesizing nutritional evaluation, environmental footprint quantification, and economic accessibility assessment. Methods: The analytical architecture integrated random forest classification, dimensionality reduction, and scenario-based optimization across a simulated population cohort of 1500 individuals. Food composition data encompassed 55 representative foods across eight categories linked with greenhouse gas emissions, water use, and price parameters. Four dietary patterns (Mediterranean, Western, Plant-based, Mixed) were characterized across nutrient adequacy, greenhouse gas emissions, water consumption, and economic cost. Results: Random forest classification achieved 39.1% accuracy, with cost, greenhouse gas emissions, and water consumption emerging as the most discriminating features. Dietary patterns exhibited convergent macronutrient profiles (protein 108.8–112.8 g per day, 4% variation) despite categorical distinctions, while calcium inadequacy pervaded all patterns (867–927.5 mg per day, 7–13% below requirements). Environmental footprints demonstrated limited differentiation (greenhouse gas 3.73–3.96 kg CO₂e per day, 6% range). Bootstrap resampling (n = 1000) confirmed narrow confidence intervals, with NHANES validation revealing substantial energy intake deviations (38–58% above observed means) attributable to adequacy-prioritized design rather than observed consumption patterns. Scenario modeling identified seasonally flexible dietary configurations maintaining micronutrient and protein adequacy while reducing water use to 87% of baseline at modest cost increases. Conclusions: This framework establishes a validated computational infrastructure for integrated dietary assessment benchmarked against sustainability thresholds and epidemiological reference data, demonstrating the feasibility of AI-driven evaluation of dietary patterns across nutritional, environmental, and economic dimensions. Full article

Background/Objectives: Chronic kidney disease (CKD) in older adults is frequently accompanied by substantial medication burden, increasing risks of adverse drug events and poor adherence. The Planetary Health Diet Index (PHDI), emphasizing plant-based foods and sustainable dietary patterns, may improve cardiometabolic health and reduce medication requirements. This study examined the association between PHD adherence as measured by the PHDI and medication burden among older adults with CKD. Methods: We analyzed cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) 2003–2018 cycles. Older individuals aged ≥ 65 years with CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m² or albumin-to-creatinine ratio > 30 mg/g) at the baseline visit were included (n = 3161). PHDI scores (0–150) were calculated from two consecutive 24 h dietary recalls. Medication burden was assessed as the total prescription medication count and frequency of individual classes. Multivariable Poisson regression models evaluated associations between PHDI score and number of prescribed medications, adjusting for sociodemographic, lifestyle, and clinical covariates; logistic regression models were used to evaluate the association between PHDI score and specific medication classes. Results: Mean (SD) age was 75.0 (5.5) years; mean PHDI score was 62.4 (18.7). Participants in the highest PHDI tertile had significantly lower medication burden compared to the lowest tertile. In fully adjusted Poisson regression models, each 10-point increase in PHDI score was associated with 3% fewer medications (RR: 0.97, 95% CI: 0.96–0.99, p = 0.011). Participants in the highest PHDI tertile had 8% fewer medications compared to the lowest tertile (RR: 0.92, 95% CI: 0.87–0.98, p = 0.013). Higher PHDI scores were significantly associated with lower odds of proton pump inhibitor use (OR: 0.86, 95% CI: 0.79–0.94 per 10-point increase) and nonsteroidal anti-inflammatory drug prescription (OR: 0.86, 95% CI: 0.76–0.97 per 10-point increase). Participants in the highest PHDI tertile had 34% lower odds of PPI use (OR: 0.66, 95% CI: 0.49–0.89) and nonsignificant lower odds of NSAID use (OR: 0.67, 95% CI: 0.40–1.11) compared to those in the lowest tertile. Conclusions: Higher PHDI adherence was independently associated with lower medication burden in older adults with CKD. These findings suggest that plant-forward, sustainable dietary patterns may reduce pharmacological complexity in this vulnerable population. Prospective studies are needed to assess causality and clinical implementation strategies. Full article

Background/Objectives: The Metabolic Score for Insulin Resistance (METS-IR), a non-insulin-based index of insulin resistance (IR), and subclinical myocardial injury (SCMI), identified by electrocardiogram (ECG), are each associated with cardiovascular disease (CVD). However, their joint impact on mortality remains unclear. We examined the association of the METS-IR with SCMI and evaluated the individual and combined associations of SCMI and IR with cardiovascular mortality. Methods: We analyzed adults without baseline CVD from the Third National Health and Nutrition Examination Survey (1988–1994) with mortality follow-up through 31 December 2019. The METS-IR was calculated from fasting glucose, triglycerides, high-density lipoprotein cholesterol, and body mass index and categorized as low (<75th percentile) or high (≥75th percentile). SCMI was defined as a cardiac infarction injury score ≥ 10 on ECG. Multivariable logistic regression assessed associations between the METS-IR and SCMI, and Cox regression estimated cardiovascular mortality risk across SCMI-IR combinations. Results: Among 6079 participants, 14.1% had SCMI. Higher METS-IR values were associated with greater SCMI odds (OR (95% CI): 1.58 (1.31–1.90)). Over a median of 18.8 years, 563 (9.1%) cardiovascular deaths occurred. Both SCMI and high IR were individually associated with increased cardiovascular mortality ((HR (95% CI): 1.41 (1.19–1.69) and 1.32 (1.09–1.59), respectively). Participants with both SCMI and high IR had the highest risk (HR 1.92; 95% CI 1.49–2.50) compared with those with neither condition. Conclusions: In adults without prior CVD, the METS-IR was positively associated with SCMI. The coexistence of SCMI and high IR identified a subgroup at nearly twofold higher risk of cardiovascular mortality, supporting the combined use of ECG-based injury markers and metabolic indices for cardiovascular risk stratification. Full article