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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".