Prevalence and Influencing Factors of Motoric Cognitive Risk Syndrome in the Elderly with Subjective Cognitive Decline in the Community
Bibliographic record
Abstract
Background Both motoric cognitive risk (MCR) syndrome and subjective cognitive decline (SCD) are early stages of cognitive decline in the elderly. MCR syndrome is currently considered as a new effective predictor of preclinical dementia. Identifying the risk factors of dementia in older people with SCD by early MCR syndrome screening is of great significance for reducing the incidence of dementia and related adverse health events in this group. Objective To investigate the prevalence and influencing factors of MCR syndrome in the elderly with SCD in the community. Methods A long-term cohort study conducted from January 2019 to January 2022 in a community in Beijing selected 459 cases of the elderly (≥60 years old) with SCD as the study population. SCD was assessed using the memory-related questions in the Geriatric Depression Scale-15 and the Chinese version of Montreal Cognitive Assessment-Basic. The the 6-metre walking test was used to assess gait status. MCR syndrome was defined as SCD in combination with gait decline. A self-designed questionnaire was used to collect the demographic data, physiological factors, psychological factors, lifestyle indicators, psychological factors, nutritional status, conditions of illness and medication history. Stepwise multinomial Logistic regression was used to explore the influencing factors of MCR syndrome. Results The prevalence of MCR syndrome was 27.2% (125/459) . The elderly in the MCR and non-MCR groups were compared for age, basic activities of daily living (BADL) , instrumental activities of daily living (IADL) , weekly exercise hours, anxiety status, nutritional status, conditions of illness〔hypertension, chronic heart failure, arrhythmia, stroke/transient ischaemic attack (TIA) , multiple lacunar infarction, diabetes mellitus, peripheral vascular disease, cancers, osteoarthritis, history of fracture in the last 2 years, oculopathy, oral disease〕, and medication history, and the difference was statistically significant (P<0.05) . Stepwise multinomial Logistic regression analysis showed that older age〔OR (95%CI) =1.083 (1.034, 1.134) 〕, lower ability to perform BADL〔OR (95%CI) =0.952 (0.914, 0.991) 〕, lower ability to perform IADL〔OR (95%CI) =0.623 (0.486, 0.798) 〕, weekly exercise hours〔OR (95%CI) =0.505 (0.295, 0.864) 〕, anxiety status〔OR (95%CI) =2.442 (1.225, 4.866) 〕, conditions of hypertension〔OR (95%CI) =1.948 (1.086, 3.497) 〕, and conditions of stroke/TIA〔OR (95%CI) =3.154 (1.745, 5.699) 〕were associated with MCR syndrome (P<0.05) . Conclusion The prevalence of MCR syndrome was high in older adults with SCD in the community. In view of this, during the MCR syndrome screening in this population, attention should be paid to risk factors such as older age, the ability to perform BADL and IADL, weekly exercise hours, anxiety status, conditions of hypertension, and conditions of stroke/TIA, and support should be provided for these people to control chronic disease and develop a healthy lifestyle, thereby improving their cognitive status.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".