ArticlePsychiatry investigation2026
Multimorbidity Clusters and Depressive Symptom Networks in Old Age: Evidence From China.
Article in Psychiatry investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveTo identify patterns of chronic disease multimorbidity in older Chinese adults, examine variables associated with the link between multimorbidity and depression, and characterize depressive symptom network features.
methodsWe analyzed cross-sectional data from the China Health and Retirement Longitudinal Study including 2,117 adults aged ≥60 years. Fourteen physician-diagnosed chronic conditions were subjected to exploratory factor analysis to identify latent multimorbidity clusters. Logistic regression was used to examine correlates of depression. For each cluster, Mixed Graphical Models estimated partial correlations among depressive symptoms assessed by the 10-item Center for Epidemiologic Studies Depression Scale and selected correlates, with centrality and bridge indices computed to characterize network structure.
resultsTetrachoric factor analysis identified two stable clusters-a metabolic-vascular cluster (hypertension, dyslipidemia, diabetes, stroke) and a visceral-neurodegenerative cluster (liver disease, kidney disease, arthritis, memory-related disorders). Ten variables were significantly associated with depression (area under the curve=0.734, 95% confidence interval 0.708-0.793), with cognitive impairment showing the strongest association. Network analyses indicated that effort-related and motivational symptoms were central across clusters, whereas cognitive impairment consistently exhibited the highest bridge strength linking multimorbidity-related factors and depressive symptoms.
conclusionMultimorbidity in older adults forms clinically meaningful clusters associated with distinct depressive symptom network characteristics. Cognitive impairment emerged as the most prominent bridge-related correlate. Findings are associative and hypothesisgenerating. Longitudinal and interventional studies are warranted.
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.