Correlation of Subjective Cognitive Decline with Multimorbidity among Elderly People
Bibliographic record
Abstract
Background Subjective cognitive decline (SCD) is a target for early prevention of Alzheimer's disease (AD). AD is closely related to multimorbidity, but the correlation of SCD with multimorbidity has not been well defined. Objective To investigate the correlation between SCD and multimorbidity in the elderly, providing a theoretical basis for early prevention and intervention of AD. Methods From January 2021 to June 2022, 612 elderly people aged≥60 years were sampled by convenience sampling method in urban communities and elderly care institutions in Guangzhou. The objective cognitive function was assessed using the Chinese version of Montreal Cognitive Assessment-Basic (MoCA-BC), Chinese version of Clinical Dementia Rating Scale (CDR-C), and Chinese version of Hachinski Ischemic Scale (HIS-C). SCD was diagnosed using the conceptual framework proposed by the working group of the Subjective Cognitive Decline Initiative and Chinese version of Subjective Cognitive Decline-Questionnaire 9 (SCD-Q9-C). Then according to the assessment results, participants were divided into SCD group (having normal overall objective cognitive function, SCD and SCD-Q9-C score≥5) and normal cognitive (NC) group (having normal overall objective cognitive function, and SCD-Q9-C score<5). A general information questionnaire to collect socio-demographics〔gender, age, place of residence (community or elderly care institution), years of education, marital status, type of occupation before retirement, monthly income〕and health-related information〔body mass index, waist circumference, habits of smoking, alcohol consumption and drinking tea, exercise frequency, habit and average duration of siesta, sleep quality assessed using the Chinese version of Pittsburgh Sleep Quality Index (PSQI-C), depressive symptoms assessed using the Chinese version of Patient Health Questionnaire (PHQ-9-C), anxiety symptoms assessed using the Chinese version of Generalized Anxiety Disorder Scale-7 (GAD-7-C), and activities of daily living (ADLs) assessed using the ADL Scale for Chinese Adults〕. Besides, another questionnaire to collect the history of chronic illness. The level of multimorbidity was classified into three categories〔no multimorbidity (0-1), low multimorbidity (2-4) and high multimorbidity (≥5) 〕by the number of chronic conditions. A binary Logistic regression analysis was used to explore the effect of multimorbidity on the SCD. Results The mean SCD-Q9-C score was (4.20±1.95) in 612 elderly people in this survey. Two hundred and fifty cases (40.8%) and 362 cases (59.2%) were assigned to the SCD group, and NC group, respectively. Univariate analysis showed statistically significant differences in gender, age, years of education, type of occupation before retirement, monthly income, tea drinking habits, sleep quality, depressive symptoms, anxiety symptoms and ADL scores between SCD and NC groups (P<0.05). Five hundred and seventy-four cases (93.8%) had chronic diseases, and 475 (77.6%) of them had multimorbidity, including 352 (57.5%) with low multimorbidity level and 123 (20.1%) with high multimorbidity level. The differences in multimorbidity prevalence, multimorbidity level, diabetes, arthritis and osteoporosis between SCD and NC groups were statistically significant (P<0.05). Binary Logistic regression analysis showed that older age, poor sleep quality, presence of anxiety symptoms, poor ADLs, and high level of multimorbidity were statistically significant risk factors for SCD (P<0.05), with the risk of SCD being 1.826〔95%CI (1.037, 3.216) 〕times higher for high multimorbidity level than for no multimorbidity (P<0.05). Longer years of education was a protective factor for SCD (P<0.05) . Conclusion High multimorbidity level is associated with increased risk of SCD. Community and elderly care providers can use multimorbidity as an assessment indicator of cognitive decline, and collaboratively implement management of multimorbidity and related factors to actively identify and intervene in SCD in order to delay the development of AD in older adults and promote healthy ageing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".