ArticleJournal of health, population, and nutrition2025
Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.
Article in Journal of health, population, and nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Cardiovascular-kidney-metabolic syndrome: a comprehensive review of pathophysiology, epidemiology, diagnosis, and management.Cardiovascular diabetology · 2026Review
- The Associations of Anthropometric Indices With Stages and Mortality in Cardiovascular-Kidney-Metabolic Syndrome: Insights From NHANES.Reviews in cardiovascular medicine · 2026Article
- Comparison and validation of machine learning-based screening models for elevated depressive symptoms in peritoneal dialysis patients.Frontiers in public health · 2026Article
- Interpretable machine learning for multiclass trajectory prediction in cardiovascular-kidney-metabolic syndrome stage: development and external validation.Frontiers in endocrinology · 2026Article
- Association of cardiovascular-kidney-metabolic syndrome with depression and all-cause mortality: a population-based observational study.Frontiers in nutrition · 2025Article
- Risk factors and mediating mechanisms of restless legs syndrome in patients undergoing maintenance hemodialysis: a longitudinal cohort study combined with Mendelian randomization analysis.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
backgroundCardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM in middle-aged and elderly patients is relatively high. The current research lacks an exploration into the impact of social and environmental determinants of health on depression in CKM patients.
objectiveThis study aims to construct a depression risk prediction model for middle-aged and elderly CKM patients by social and environmental determinants of health.
methodsIn this study, 3220 participants were included and collected from three waves of the China Health and Retirement Longitudinal Study (CHARLS). A depression risk prediction model for middle-aged and elderly CKM patients was constructed by using 10 machine learning models. Additionally, the mediating effect of NO
resultsAn interpretable machine learning model framework was constructed to predict depression risk in middle-aged and elderly CKM patients using the longitudinal cohort data from CHARLS. The RF model demonstrated strong performance in predicting the training set, and the Xgboost model exhibited excellent generalization ability. The presence of arthritis showed a significant independent effect on depression outcomes, with an average direct effect of - 8.5559. The total effect of arthritis on depression outcomes was - 9.5162. The mediating effect of NO
conclusionsA depression risk prediction model for middle-aged and elderly CKM patients was developed based on the CHARLS longitudinal data from 2011 to 2015. The SHAP framework was used to provide machine learning model explanations. Intervention strategies that address social and environmental determinants of health are needed. Potential strategies include enhancing urban greening to reduce NO
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.