Multimorbidity patterns and mental health in late life: a systematic review of longitudinal studies
Bibliographic record
Abstract
INTRODUCTION: Several chronic disease combinations (i.e., multimorbidity [MM] patterns) have been linked to poor mental health. This systematic review aimed to synthesize evidence on the longitudinal association between MM patterns and several mental health conditions in later life, including depression, anxiety, suicidality, cognitive decline, and dementia. METHODS: Following PRISMA guidelines (PROSPERO: CRD42024537617), we included longitudinal studies of middle-to-older individuals (45 +) that examined baseline MM patterns and the incidence or trajectories of depression, anxiety, dementia, cognitive decline, or suicidality. The search was conducted in MEDLINE and Web of Science from inception to March 2024, and involved independent screening and quality assessment using a modified Newcastle-Ottawa Scale. RESULTS: From 13,771 retrieved records, 17 studies were included, ranging from 1209 to 447,888 participants. Fourteen studies were population-based, with follow-ups between 2 and 16 years. Most studies investigated depression (n = 7) and dementia/cognitive decline (n = 9). MM pattern definitions varied, most often relying on data-driven methods (e.g., latent class analysis) and encompassing different numbers and types of diseases. Methodological quality was high across studies. MM patterns featuring cardiometabolic diseases were associated with higher risk of depression, anxiety, cognitive decline, and dementia. Patterns characterized by musculoskeletal, gastrointestinal, and pain-related conditions also showed associations with depression and anxiety. Two studies examined suicidality, with greater multimorbidity burden linked to increased suicidal ideation. CONCLUSION: MM patterns linked to higher clinical complexity present poorer mental health trajectories. Validation of MM patterns within and across populations is key for identifying older adults with complex health profiles who may benefit from targeted care strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".