Body weight trajectories from midlife are associated with cognitive decline in advanced age
Bibliographic record
Abstract
Fluctuations in body weight may impact cognitive decline, but current evidence is inconclusive. The aim of this study is to investigate associations between body weight trajectories from midlife to later life and cognitive decline. This retrospective study analyzed harmonized data from two population-based longitudinal studies, the Progetto Veneto Anziani and the Italian Longitudinal Study of Aging, encompassing baseline and two follow-up assessments over 9 years. Weight changes were recorded from baseline to the last available follow-up or from 50 years (self-reported data) to the last available follow-up. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and cognitive decline was defined as experiencing a MMSE change from baseline to the follow-up within the lowest quartile of the change distribution in the total sample. In a sample of 3852 individuals (46% females, age 65-96 years at baseline), we investigated the impact of weight change on cognitive decline with two sets of analyses. First, using weight measurements obtained during old age, growth mixture modelling identified three weight trajectories: decreasing, stable, and increasing. None of these trajectories was significantly associated with cognitive decline. Second, we considered weight at age 50 as the baseline assessment to capture weight changes from midlife. Among the three trajectories detected (increasing, stable, and decreasing), the decreasing trajectory was significantly associated with a higher likelihood of cognitive decline in males (HR 1.44, 95% CI 1.06-1.94) and females (HR = 1.37, 95%CI 1.23-1.67), whereas the increasing trajectory was associated with cognitive decline only in females (HR = 1.33, 95%CI 1.01-1.76). These results suggest that changes in body weight from middle to older age are associated with cognitive decline in advanced age. Since body weight is influenced by multiple factors, a broader assessment of health-including metabolic, vascular, behavioral, and social dimensions-should be considered in both research and clinical settings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".