Multimorbidity and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies
Bibliographic record
Abstract
Chronic diseases (e.g., hypertension, diabetes, and heart diseases) have been proposed as marked predictors of incident dementia. However, synthesised evidence on the effect of multimorbidity on dementia is still lacking. We aim to summarise the association between multimorbidity and risk of dementia in longitudinal cohorts. In this systematic review and meta-analysis, we conducted a systematic search in PubMed, Web of Science and Embase from inception to Dec 14, 2024, to identify longitudinal cohort studies reporting the association between multimorbidity or multimorbidity patterns and risk of dementia. Information of included studies were extracted by three reviewers (YaZ, YY and YuZ), and the quality assessment was conducted using the Newcastle-Ottawa Scale. The inverse-variance weighted random effects meta-analysis was performed to obtain the pooled hazard ratios (HRs) and 95 % confidence intervals (CIs) for dementia associated with multimorbidity and cardiometabolic multimorbidity (CMM). Cochran's Q test and the I 2 statistic were used to indicate heterogeneity among the studies. Meta-regression analysis, subgroup analysis and sensitivity analysis were conducted to determine any valid sources of heterogeneity. This study was registered with PROSPERO (CRD42023403684). We included 17 longitudinal cohort studies (2262,885 middle-aged and older participants) in the systematic review, of which seven were included in meta-analysis. All studies presented moderate to high methodological quality. Meta-analysis showed a positive association between multimorbidity and incident dementia (HR=1.53, 95 % CI=1.12 to 2.09), with substantial heterogeneity ( I 2 =95.2 %). Studies using health records to measure dementia tend to find a stronger positive relationship between multimorbidity and risk of dementia than those using self-report (HR health records =1.94, 95 % CI=1.35 to 2.78, I 2 =94 %; HR self-report =1.17, 95 % CI=1.07 to 1.28, I 2 =0 %). The impacts of CMM were also observed, and the HRs for dementia ranged from 2.49 (combination of heart diseases and stroke: 95 % CI=1.64 to 3.78) to 3.77 (combination of diabetes, heart diseases and stroke: 95 % CI=2.02 to 7.02). The heterogeneity was moderate, with I 2 ranging from 46.9 % (p for heterogeneity=0.152) to 84.1 % (p for heterogeneity=0.002). The impacts of number of diseases, multimorbidity clusters, and multimorbidity trajectory on risk of dementia were narratively summarised due to lacking comparable studies. Limited evidence (only one study) precluded quantitative synthesis for the association of physical and psychological multimorbidity with dementia. Multimorbidity and CMM pattern were significantly associated with risk of dementia, while the effect of physical and psychological multimorbidity remain inconclusive. Individuals affected by multimorbidity should be prioritised in risk factor modification and dementia prevention. Preventing the development of multimorbidity is also crucial—particularly those who already have one chronic disease—in order to maintain cognitive health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".