Association between depressive symptoms and mild cognitive impairment among the elderly in China: a community-based study
Bibliographic record
Abstract
INTRODUCTION: Elderly individuals with depressive symptoms often show increased susceptibility to mild cognitive impairment (MCI). This study explores the association between depressive symptoms and MCI among older adults in China. METHODS: Data from the Shanghai Brain Aging Study (SBAS) were used in this cross-sectional study. MCI was diagnosed through clinical assessments and Montreal Cognitive Assessment (MoCA) scores (≤23). Depressive symptoms were defined as a Geriatric Depression Scale (GDS) score of >10. Binary logistic regression and restricted cubic spline (RCS) analyses were conducted to evaluate the associations between depressive symptoms and MCI, adjusting for potential covariates. RESULTS: The study included 1,506 participants, with 43.6% diagnosed with MCI. Logistic regression analysis revealed a significant association between depressive symptoms and MCI. In the fully adjusted model, depressive symptoms were associated with a 65% higher likelihood of MCI (odds ratio: 1.65, 95% confidence interval: 1.17-2.34). RCS analysis indicated a significant non-linear relationship between depressive symptoms and MCI (p for non-linear = 0.029). Participants with depressive symptoms scored significantly lower on the MoCA subscores for visuospatial and executive function, as well as language abilities (all p < 0.05). CONCLUSION: Our findings demonstrate a significant association between depressive symptoms and MCI, with depressive symptoms being linked to a higher prevalence of MCI. Early identification and intervention of depressive symptoms, including community screening, psychological therapies, or pharmacological treatments for older adults, may potentially mitigate cognitive decline. However, the cross-sectional design limits causal conclusions, and generalizability may be affected by self-reported depression measures and regional sampling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".