Does depression in mid-life predispose to greater cognitive decline in later life in the Whitehall II cohort?
Bibliographic record
Abstract
BACKGROUND: Later-life depression appears to have different symptomatology and possibly underlying pathology to younger adults. Depression is linked to dementia but whether it is a risk factor or an early sign of dementia remains unclear. Neuroinflammation is increasingly recognised in both conditions. AIMS: To investigate the link between depression, inflammation and dementia. We hypothesised that recurrent depression increases the rate of cognitive decline in older adults and that this effect is modified by anti-inflammatory medication. METHODS: We used data from Whitehall II including cognitive test results and reliable measures to assess depression. Depression was defined as a self-reported diagnosis or a score of ≥20 on the CESD. The presence/absence of inflammatory illness was assessed using a standardised list of inflammatory conditions. Individuals with dementia, chronic neurological and psychotic conditions were excluded. Logistic and linear regression was used to examine the effect of depression on cognitive test performance and the effect of chronic inflammation. LIMITATIONS: Lack of clinical diagnoses of depression. RESULTS: There were 1063 individuals with and 2572 without depression. Depression did not affect deterioration in episodic memory, verbal fluency or the AH4 test at 15-year follow up. We found no evidence of an effect of anti-inflammatory medication. Depressed individuals had worse cross-sectional performance on the Mill Hill test and tests of abstract reasoning and verbal fluency at both baseline and 15-year follow-up. CONCLUSIONS: Using a UK based study with a long follow-up interval we have shown that depression in individuals aged >50 is not associated with increased cognitive decline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".