The cardiovascular–kidney–metabolic (CKM) syndrome framework integrates cardiometabolic risks. However, longitudinal evidence on the association between atherosclerotic cardiovascular disease 10-year risk (ASCVD10 year) scores and cardiovascular events across CKM syndrome stages remains limited in large aging populations. We analyzed data from 4,565 adults aged ≥ 45 years in the China Health and Retirement Longitudinal Study (CHARLS). ASCVD10 year scores were calculated using the China-PAR model at baseline (2012) and follow-up (2015), with cumulative risk scores derived accordingly. Participants were stratified into four trajectory clusters via k-means clustering based on ASCVD score dynamics. Logistic regression, restricted cubic spline models, subgroup analysis, and mediation analyses were conducted to assess associations and underlying pathways. Higher baseline and cumulative ASCVD10 year scores were significantly associated with increased risks of cardiovascular disease (CVD), heart disease, and stroke across CKM stages 0–3 (all P < 0.0001). Compared to the low-risk cluster, the highest-risk cluster exhibited a > 4-fold increased CVD risk (OR = 4.12, 95% CI: 2.81–6.02). Nonlinear associations were observed for CVD and heart disease, with inflection points indicating stronger effects at lower ASCVD ranges. Subgroup analyses revealed stronger associations in individuals ≤ 65 years, females, and those with hypertension or diabetes. Mediation analyses identified HbA1c, fasting glucose, and the TyG index as partial mediators, accounting for 3–6% of the total effect. ASCVD10 year scores, particularly cumulative exposure, are robust predictors of cardiovascular events across early CKM stages in middle-aged and older adults. These findings support the utility of longitudinal ASCVD10 year scores in preventive cardiometabolic strategies.

Objective: The cardiometabolic index (CMI) serves as a comprehensive indicator of metabolic risk. Recent findings indicate a potential association between CMI and the likelihood of diabetes onset. The objective of this research is to explore the correlation between initial CMI values and the occurrence of newly diagnosed diabetes mellitus in individuals aged 45 years and above in China. Methods: In this research, data were sourced from the China Health and Retirement Longitudinal Study (CHARLS). To evaluate the association between baseline CMI and the incidence of newly diagnosed diabetes, multivariate logistic regression models were employed, with adjustments made for various potential confounding factors. Additionally, stratified analyses evaluated subgroup variations, and mediation analysis explored pathways through which CMI influences diabetes risk. Results: A cohort of 4,944 participants was followed, with 786 diagnosed with new‐onset diabetes. The occurrence of diabetes escalated with increasing CMI quartiles, with Q4 showing a 141% increased risk (OR 2.41, 95% CI, 1.94–3.02) in unadjusted models. This association remained significant after adjustment. Stratified analyses found that dyslipidemia modified the CMI–diabetes association. CMI, age, BMI, HbA1c, and hypertension were identified as independent predictors of new‐onset diabetes. Mediation analysis revealed that HbA1c mediated 9.70% of the CMI–diabetes relationship. Conclusion: In summary, our research establishes a connection between increased levels of CMI and the onset of diabetes, suggesting its potential as a predictive tool. The findings highlight CMI’s role in assessing diabetes risk and call for targeted interventions. Future research should validate these associations and explore underlying mechanisms for better prevention strategies.

Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an elevated risk of developing cardiovascular diseases, including carotid atherosclerosis. While machine learning models have been widely used in predicting cardiovascular risks in Type 1 and Type 2 diabetes, research on LADA remains limited. Early prediction of carotid atherosclerosis using machine learning models could help in timely intervention and improved patient outcomes for this specific population. We conducted a retrospective cross-sectional analysis involving 142 LADA patients diagnosed within the endocrinology department at Shanxi Bethune Hospital, China. Various clinical, demographic, and laboratory variables were analyzed using univariate and multivariate logistic regression, complemented by LASSO regression for feature selection. Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis. Significant risk factors for carotid atherosclerosis were identified, including age, smoking history, BMI, ALB, HDL-C, and ALT. Among the various machine learning models evaluated, the LR model exhibited the highest performance, achieving an area under the curve (AUC) of 0.936, alongside an accuracy of 86%. NNET and SVM models also demonstrated robust predictive capacities with AUC values of 0.919 and 0.918, respectively. This study highlights the critical role of identifying risk factors for carotid atherosclerosis in LADA patients. Our use of ML models builds on the growing body of work in diabetes-related cardiovascular risk prediction, and it offers a novel approach by specifically targeting the LADA population. Incorporating ML models into clinical practice can improve risk stratification and patient management in LADA. Future research should validate these models across diverse populations and investigate the underlying mechanisms linking LADA to cardiovascular risk. Not applicable.

Cardio-kidney-metabolic (CKM) syndrome is a complex, multi-system health condition that affects aging populations, potentially contributing to frailty. Thi

Although Ubc9‐mediated SUMOylation are recognized to regulate the multiple aspects of hepatic biological processes, its impact on hepatic senescence and metabolic dysfunction‐associated steatotic liver disease (MASLD), however, is yet to be fully addressed. Herein noted an age‐dependent decrease of hepatic Ubc9 expression is first noted along with an escalated decrease of protein SUMOylation, which is coupled with enhanced senescent marker expressions both in humans and mice. Interestingly, Ubc9 is dispensable for liver development at the embryonic stage. However, Ubc9 deficiency in hepatocytes rendered mice with an exacerbated hepatic aging phenotype and more susceptible to fatty liver disease and steatohepatitis following the challenge of a methionine‐ and choline‐deficient (MCD)‐diet. Ii is further demonstrated that nuclear ribosomal protein L3 (RPL3) interacts with DExD/H‐box (DDX/DHX) helicases (DHX9), which then recruits RNA polymerase II to the p16 promoter to transcribe its expression, thereby exacerbating the hepatocyte aging process. However, Ubc9‐mediated SUMOylation prevents RPL3 nuclear translocation, by which it represses the expression of senescent markers such as p16 to attenuate the hepatic aging process. Together, the study highlights that Ubc9‐mediated SUMOylation of RPL3 could be an unappreciated mechanism against hepatic aging in clinical settings.

Aim: Cardiometabolic multimorbidity (CMM) is increasingly prevalent in China’s aging population, posing major public health challenges. Developing machine learning (ML) models for early prediction is essential to inform prevention. Methods: We used data from 16,970 adults aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS) across four waves (2011-2018). We used 42 predictors from 2013/2015 (demographics, lifestyle factors, physical measures, and blood biomarkers) to train five ML models: generalized linear model (GLM), gradient boosting machine (GBM), distributed random forest (DRF), deep learning (DL), and a Stacked Ensemble model. The primary outcome was CMM in 2018, defined as self-reported diagnoses of ≥ 2 conditions among hypertension, diabetes, heart disease, and stroke. Models were evaluated using the area under the curve (AUC), Brier score, and calibration curves. Synthetic Minority Over-sampling Technique (SMOTE) and 5-fold cross-validation were used to optimize performance. Results: The Stacked Ensemble model achieved the best internally validated predictive performance (AUC = 0.755), significantly outperforming GLM and DL (both DeLong’s P = 0.03), with comparable performance to GBM and DRF. Calibration analysis confirmed reliable prediction (Brier score = 0.153). Variable importance analysis based on GBM and DRF identified dyslipidemia, age, triglycerides, high-density lipoprotein cholesterol, waist circumference, self-rated health expectations, pain, cooking fuel type, weight change, and cystatin C as the top-ranked predictors common to both algorithms. Conclusion: ML algorithms, particularly ensemble models, can effectively predict CMM risk in China’s aging population. The integration of diverse health indicators and self-perceived health measures enhances predictive power.