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Background The aging of the global population exacerbates the burden of gastrointestinal diseases in elderly individuals. The Developmental Origins of Health and Disease (DOHaD) hypothesis suggests that early-life malnutrition may have long-lasting effects on adult health. However, evidence regarding the link between childhood famine exposure and the risk of gastrointestinal diseases in old individuals remains limited. Methods We conducted a retrospective cohort study of 4227 participants from the China Health and Retirement Longitudinal Study (CHARLS). Childhood famine exposure was defined as experiencing hunger before age 18 during the 1959–1961 famine period. The outcomes were doctor-diagnosed gastric or other digestive diseases. Logistic regression, propensity score matching (PSM), subgroup analysis, and XGBoost machine learning with SHAP explanation were used. Results A total of 83.7% of the participants had childhood famine exposure. The famine-exposed group had a significantly higher risk of gastrointestinal diseases (OR = 1.34, 95% CI: 1.12–1.61); this association persisted after PSM (OR = 1.39, 95% CI: 1.10–1.75). Subgroup analysis yielded consistent results (all P values for interactions > 0.05). Famine exposure was identified as an important predictive factor in the XGBoost machine learning algorithm. Conclusions Childhood famine exposure is significantly associated with an increased risk of gastrointestinal diseases in old individuals. This simple indicator should be included in regular health assessments for the elderly to facilitate early identification and intervention for high-risk populations.

Sarcopenic obesity (SO) is a major public health concern linked to lipid metabolism disorders. This study investigated the association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and possible SO prevalence in China using data from the 2015 China Health and Retirement Longitudinal Study (CHARLS). Among 3,320 participants(21.02% men; median age 66.00 years [interquartile range, IQR 62.00–71.00]), multivariate logistic regression revealed that individuals in the highest NHHR tertile had a 66% increased risk of possible SO compared to the lowest tertile. Restricted cubic spline analysis identified a significant nonlinear relationship ( p  < 0.05). A two-piecewise regression model showed that for NHHR < 3.262, the adjusted odds ratio (OR) for possible SO was 2.27 (95% CI, 1.48–3.45; p  < 0.001), whereas for NHHR ≥ 3.262, the OR was 0.97 (95% CI, 0.78–1.21; p  = 0.78). This finding suggests that NHHR could serve as a novel and convenient biomarker for possible SO risk stratification; maintaining an NHHR below 3.262 may be a valuable target for preventing possible SO in the middle-aged and elderly Chinese population.

Objective To investigate the correlation between the atherogenic index of plasma (AIP), body mass index (BMI), and sarcopenia in the older adults in China, and to analyze the predictive ability of AIP and BMI for sarcopenia. Methods This study utilized data from the 2011–2015 CHARLS database (China Health and Retirement Longitudinal Study, CHARLS), focusing on participants aged 60 years and older. The cross-sectional analysis included 7,744 samples, with 2,398 in the sarcopenia group and 5,346 in the non-sarcopenia group. In the retrospective cohort study, 1,441 participants without sarcopenia at baseline were selected and followed for the development of sarcopenia. Multivariable logistic regression was employed to analyze the association between AIP, BMI, and sarcopenia risk. A restricted cubic spline regression model was used to evaluate the dose-response association, and ROC curve analysis was performed to assess the predictive ability of individual and combined indicators (AIP and BMI). Additionally, subgroup analysis was conducted to explore the association between AIP, BMI, and sarcopenia risk across different demographic groups. Results The cross-sectional analysis demonstrated that sarcopenia was significantly associated with various factors, including age, marital status, education level, residence, smoking, BMI, uric acid (UA), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), AIP, as well as hypertension, diabetes, dyslipidemia, and heart disease ( p  < 0.05). Logistic regression analysis, adjusted for potential confounders, revealed that the low AIP group was significantly associated with an increased risk of sarcopenia (OR = 1.22, 95% CI 1.03–1.44, p  = 0.02), while no significant difference was observed in the high AIP group (OR = 0.83, 95% CI 0.69–1.01, p  = 0.07). In the retrospective cohort study, the low AIP group showed a positive association with sarcopenia risk (OR = 1.79, 95% CI 1.18–2.72, p  = 0.01), and a similar trend was observed in the high AIP group (OR = 1.69, 95% CI 1.03–2.77, p  = 0.04). BMI was inversely associated with sarcopenia incidence, consistent with the cross-sectional findings. Both AIP and BMI showed a nonlinear dose-response relationship with sarcopenia risk, with AIP approximating a U-shaped curve and BMI approximating an L-shaped curve. Subgroup analysis indicated that, in the 65–69 age group, low AIP levels were significantly associated with an increased risk of sarcopenia. In participants aged 70 and above, as well as in females, both low and high AIP levels were significantly associated with higher incidence risk. ROC curve analysis showed that the combined use of AIP and BMI for predicting sarcopenia had an Area Under the Curve (AUC) of 0.8913, which was moderately better than the use of AIP (0.6499) or BMI (0.8888) alone. Conclusion The changes in AIP and BMI are associated with the risk of sarcopenia, and both provide some predictive value for sarcopenia. Taken together, the combined prediction using AIP and BMI appears to be somewhat more effective than using either indicator alone in assessing the risk of sarcopenia.

Cardiovascular disease (CVD) is a prevalent global health issue and one of the leading causes of death. Aging and air pollution are well-established risk factors for CVD. This study aims to investigate the association between air pollutants (sulfur dioxide, carbon monoxide, PM1, PM2.5, nitrogen dioxide, ozone) and the risk of heart disease. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) and the China High Air Pollutants (CHAP) database, we employed multivariable-adjusted logistic regression to analyze the relationship between pollutants and heart disease. Additionally, six binary classification machine learning algorithms—AdaBoost, Decision Tree, LightGBM, XGBoost, Random Forest, and GBDT—were used to construct predictive models. The models incorporated air pollutant concentrations (SO₂, CO, PM1, etc.) as core features, along with covariates such as gender, age, and hypertension. The data were split into an 80% training set and a 20% test set, with cross-validation applied to ensure robustness. Multivariable regression analysis revealed that after adjusting for multiple covariates (including BMI, blood glucose, and other pollutants), each 1-unit increase in SO₂ concentration was associated with an odds ratio (OR) of 1.040 for heart disease (95% confidence interval [CI]: 1.027–1.054, p  < 0.00001). Among the machine learning models, Random Forest exhibited the best performance, with an AUC of 0.794 in the training set and 0.726 in the test set. SHAP analysis confirmed that SO₂ was the most impactful pollutant. Subgroup analysis indicated a significant interaction between SO₂ and household registration type ( p  < 0.05). Future research should further explore the mechanisms underlying SO₂-induced cardiac damage and optimize the applicability of predictive models.

Background The A body shape index (ABSI) has been associated with various diseases, and existing research suggests a link with sarcopenia. However, systematic evaluations focusing on Asian populations—particularly middle-aged and older adults in China—remain insufficient, and the specific association mechanisms and predictive value have yet to be fully elucidated. This study aimed to examine this relationship utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) database. Methods A total of 1873 participants aged ≥ 45 years with complete data from the 2015 CHARLS were included. Eligibility criteria required the availability of: (1) parameters necessary for ABSI calculation, namely, waist circumference (WC), height, and weight; (2) sarcopenia assessment components (muscle mass, grip strength (GS), and physical performance); and (3) all relevant covariates. Sarcopenia was diagnosed according to AWGS2019 guidelines, defined as low muscle mass (appendicular skeletal muscle mass (ASM)/height 2  < 7.0 kg/m 2 for male, < 5.7 kg/m 2 for female) in combination with either low GS (< 28 kg for male, < 18 kg for female) or impaired physical performance (gait speed < 1 m/s or chair stand time > 12 s). Three progressively adjusted weighted logistic regression models were constructed: Model 1 (unadjusted), Model 2 (adjusted for demographic variables, including age, sex, and marital status), and Model 3 (fully adjusted for demographic, lifestyle, and clinical variables, such as physical activity, chronic diseases, and depression). Predictive performance was assessed using receiver operating characteristic (ROC) curve analysis, and a K-nearest neighbors (KNN) algorithm was applied to determine variable importance. Results The cross-sectional analysis demonstrated significant differences in ABSI between individuals with sarcopenia ( n  = 329) and those without ( n  = 1544), with mean ABSI values of 4.59 and 3.71, respectively. The prevalence of sarcopenia was approximately 17.57% in the study population. All three models consistently indicated a significant association between ABSI and sarcopenia: Model 1 (odds ratio = 5.85, 95% confidence interval: 4.73–7.33), Model 2 (odds ratio = 5.34, 95% confidence interval: 4.23–6.83), and Model 3 (odds ratio = 5.22, 95% confidence interval: 4.10–6.74). The ROC analysis revealed that the fully adjusted model yielded an area under the curve of 0.871, compared to 0.760 for the ABSI-only model. In the KNN model, ABSI ranked highest in feature importance. Conclusions ABSI is strongly associated with sarcopenia prevalence and functions as an independent indicator, suggesting its potential applicability in sarcopenia screening strategies.

Objectives This study investigates the impact of adverse childhood experiences (ACEs) and depressive symptoms on incident arthritis in middle-aged and older Chinese adults, examining the mediating role of depressive symptoms in the ACEs–arthritis relationship to uncover psychological mechanisms underlying arthritis development. Methods We used 2011–2020 data from the China Health and Retirement Longitudinal Study (CHARLS) and included 4,184 participants aged ≥ 45 years who were free of arthritis at baseline (2011). ACEs were evaluated with 11 types of childhood adversity, classified into quartiles, while depressive symptoms were measured using the CES-D scale. Multivariable logistic regression and stratified analyses assessed associations between ACEs, depressive symptoms, and incident arthritis. Restricted cubic splines and mediation analysis were applied to explore dose–response relationships and the mediating role of depression. Results Among 4184 participants, 969 developed incident arthritis. Individuals in the highest ACEs quartile (Q4) had a significantly higher arthritis risk ( OR  1.78, 95% CI 1.43–2.21, P <  0.001). This association was stronger in females and individuals aged 45–64. Depressive symptoms also increased arthritis risk, with a nonlinear dose–response relationship. Mediation analysis showed depressive symptoms partially mediated the ACEs–arthritis relationship, accounting for 12.4% of the effect. Conclusions In this national cohort, higher ACEs burden and greater depressive symptom severity were significantly associated with incident arthritis, and depressive symptoms partially mediated by the ACEs–arthritis association. Targeting childhood adversity and later-life mental health may help reduce arthritis risk in aging populations.

Objective Insulin resistance (IR) is often present in diabetes, which imposes a heavy burden on the prevention and treatment of diabetes. Triglyceride glucose index (TyG) is simple, reliable and reproducible in detecting IR, and has great advantages in predicting the risk of diabetes. The aim of this study was to analyze the potential association between TyG and the risk of diabetes in Chinese middle-aged and older adults using a prospective cohort study design. Methods This study used longitudinal data from five waves of the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2013, 2015, 2018, and 2020, involving 5886 participants. We used Cox proportional risk regression modeling to investigate the association between TyG index and the risk of new-onset diabetes, and decision tree analysis to identify high-risk groups for diabetes. Finally, ROC curves were applied in order to construct a predictive model for diabetes. Results A total of 1054 (17.9%) participants developed diabetes throughout the 9-year follow-up. Our study utilized a multivariate Cox proportional risk regression model and found a significant correlation between TyG index and diabetes risk. The analysis also revealed a nonlinear relationship between TyG index and diabetes risk.Receiver Operating Characteristic(ROC) curve analysis showed that the Area under the curve(AUC) area of TyG index in predicting the risk of new-onset diabetes was 0.652 ( P  < 0.05). Conclusions TyG index can be used as a risk factor for predicting new-onset diabetes in the middle-aged and elderly population in China. In addition, there was a nonlinear relationship between TyG index and diabetes. Improving insulin resistance by regulating glucose and lipid metabolism plays an important role in the primary prevention of diabetes.

This study aimed to examine the association between different obesity-related metabolic phenotypes, transitions in metabolic status over time, and cognitive function improvement among middle-aged and older adults in China. Data were derived from the China Health and Retirement Longitudinal Study, a nationally representative longitudinal cohort involving urban and rural residents aged 45 years and older. Baseline data were collected in 2011, with follow-up assessments extending to 2020. Participants were classified into four body mass index (BMI)-metabolic phenotypes: Metabolically Healthy Normal Weight (MHNW), Metabolically Unhealthy Normal Weight (MUNW), Metabolically Healthy Overweight/Obesity (MHOO), and Metabolically Unhealthy Overweight/Obesity (MUOO). Cognitive function was evaluated through assessments of episodic memory, mental status, and overall cognitive performance. Statistical analyses were performed using R Studio 4.3.1. Cox regression analysis models were employed to estimate associations between metabolic phenotypes, their longitudinal transitions, and changes in cognitive function. In both the 2011 and 2015 surveys, MHOO demonstrated significantly higher scores in episodic memory, mental status, and overall cognitive function compared to other groups, while MUNW demonstrated significantly lower scores across all domains. In addition, cognitive outcomes varied by BMI-metabolic phenotypes: MHOO was positively associated with cognitive improvement, MUNW was usually associated with poorer cognitive outcomes, and MUOO had no significant association with cognitive changes. Moreover, individuals with stable MUNW status exhibited a lower likelihood of cognitive improvement relative to those with stable MHNW status. In contrast, individuals with stable MHOO status demonstrated a significantly greater likelihood of cognitive improvement. Furthermore, transitioning from MHNW to MUNW was associated with decreased likelihood of favorable cognitive outcomes. Regardless of weight classification, metabolic health was associated with superior baseline cognitive function and a slower rate of cognitive decline. These findings underscore the significance of metabolic health in predicting cognitive aging trajectories among middle-aged and older individuals.

Chronic digestive system diseases (CDSD) pose a major health challenge worldwide, significantly increasing morbidity and mortality rates. The frailty index is crucial for assessing patient prognosis. To address the need for proactive healthcare, we developed a multi-timepoint frailty prediction model. This study collected data from 565 patients with CDSD, including their frailty assessments at 3 and 6 years of follow-up. Utilizing the Multi-Gate Mixture-of-Experts (MMoE) framework, we built and evaluated five models: Tab Transformer, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Extreme Gradient Boosting (XGBoost) and Random Forest (RF). We comprehensively compared the predictive capabilities of these models on both validation and test sets. The MMoE framework consistently outperforms single models in predicting both 3-year and 6-year frailty indices across most metrics. Specifically, for 3-year predictions, the single model achieves an accuracy of 0.9801 (95% CI: 0.963-0.990) on the train set and 0.5487 (95% CI: 0.457-0.637) on the test set, while the MMoE model reaches 0.956 (95% CI: 0.933-0.971) and 0.982 (95% CI: 0.938-0.995), respectively. The RF model demonstrated perfect performance, with Micro-AUC values of 1.000 in both training and test sets for both 3-year and 6-year intervals, leading other models in terms of accuracy, precision, recall, F1 score. The Tab Transformer model achieved high Micro-AUC values across all prediction intervals, with values of 0.997 and 0.995 in the training set for 3-year and 6-year predictions, respectively, and corresponding test set values of 0.999 and 0.987. This MMoE-based approach can predict frailty at key time points, offering insights into frailty progression and aiding clinical decision making. Integrating this AI model into CDSD management can promote early interventions and personalized treatment plans.

Background Falls represent the second leading cause of injury-related mortality among older adults globally. The occurrence of falls is the consequence of the interaction of numerous complex risk factors. The objective of this study was to develop a validated fall risk prediction model for the Chinese older individuals. Methods The study used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Thirty-eight indicators including biological factors, behavioral factors and health status were analyzed in this study. The study cohort was randomly divided into the training set (70%) and the validation set (30%). Variables were screened using LASSO regression analysis, the best predictive model based on 10-fold cross-validation, logistic regression model was applied to explore the correlates of fall risk in the older individuals, a nomogram was constructed to develop the predictive model, calibration curves were applied to assess the accuracy of the nomogram model, and predictive performance was assessed by area under the receiver operating characteristic curve and decision curve analysis. Result A total of 4,913 cases from the 2015 CHARLS database for people aged 60 years and older were ultimately included, and a total of 1,082 (22.02%) of the older individuals had experienced a fall within two years. Multivariate logistic regression analysis showed that Sleeping time, Hearing, Grip strength, ADL score, Cognition, Depression, Health, KD, and Pain DRUG were predictors of fall risk in the older individuals. These factors were used to construct nomogram models that showed good agreement and accuracy. The AUC value for the predictive model was 0.644 (95% CI = 0.621–0.666), with a specificity of 0.695 and a sensitivity of 0.522. For the internal validation set, the AUC value was 0.644 (95% CI = 0.611–0.678), with a specificity of 0.629 and a sensitivity of 0.577. The Hosmer-Lemeshow test value of the model for the training set is p  = 0.9368 and for the validation set is p  = 0.8545 (both > 0.05). The calibration curves show a more significant agreement between the nomogram model and the actual observations. The ROC and DCA indicate a better predictive performance of the nomogram. Conclusion The comprehensive nomogram constructed in this study is a promising and convenient tool for assessing the risk of falls in the Chinese older individuals and to help older adults understand the risk level of falls, avoid and eliminate modifiable risk factors, and reduce the incidence of falls. Clinical trial number Not applicable.