The Association between Multimorbidity and Cognitive Decline: A Systematic Review
Bibliographic record
Abstract
Abstract Background The worldwide population ageing is a major driver for the increasing prevalence of multimorbidity, which is the co‐occurrence/ coexistence of multiple chronic conditions in the same individual. Simultaneously, cognitive decline has become a worldwide burden. Previous research has shown that there might be an association between multimorbidity as a risk for cognitive decline but no systematic review has evaluated this association. Objective To quantify the association between multimorbidity and cognitive decline through a systematic review, defining cognitive decline as a change in neuropsychological test scores or clinical diagnosis of mild cognitive impairment or dementia. Method We followed the PRISMA guidelines. We searched Medline/Pubmed, EMBASE, PsychINFO, CINAHL, LILACS, Scopus and ProQuest for studies published between January 1996 and May 2021. We evaluated study references for additional studies. We selected studies with a clear definition of multimorbidity, and cognitive decline, defined as cognitive impairment, dementia, or change in cognitive test scores. With one exception, all data were from high‐income countries and largely Caucasian populations. We employed the critical appraisal tools from the Joanna Briggs Institute to assess the risk of bias. We present a narrative data synthesis from each study in conjunction with key design information. PROSPERO register (CRD42020167253). Result We initially identified 1433 references, of which 16 studies fulfilled inclusion criteria, and were included; we found three additional studies through studies references. These studies encompassed a total of 90606 participants. Thirteen studies were cohorts, four cross‐sectional and two case‐control designs. Ten studies were population registers, five memory clinics cohorts, two population‐based surveys and one from administrative data. We found a high degree of heterogeneity among studies, driven by the use of multiple definitions of multimorbidity and cognitive decline that precluded a meta‐analysis. In general, all studies noticed an increased risk of cognitive decline or diminishing test scores with increasing accumulation of chronic disease. Future research will need to address a standardized definition of multimorbidity and cognitive decline/scores as well as obtain and evaluate data from low‐ and middle‐income countries. Conclusion We found an association between suffering multimorbidity and increasing risk of cognitive decline measured as cognitive test scores or diagnosis of MCI or dementia.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".