Role of age and exposure duration in the association between metabolic syndrome and risk of incident dementia: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome could be a modifiable risk factor for dementia. However, the effects of age and duration of exposure to metabolic syndrome on dementia risk remains underexplored. The aim of this study was to determine whether the association between metabolic syndrome and risk of dementia differs across mid-life versus late-life, and to explore how duration of metabolic syndrome affects this risk. METHODS: We conducted a population-based prospective study using data from the European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) cohort. Metabolic syndrome was defined as having at least three of the following: elevated waist circumference, triglycerides, blood pressure, or glycated haemoglobin, or reduced HDL cholesterol. Incident all-cause dementia was ascertained through hospital inpatient, death, and mental health-care records. In full-cohort analyses, we studied 20 150 adults without dementia aged 50-79 years who attended baseline assessments. Cox proportional hazards models were used to estimate the association between metabolic syndrome and dementia in the full sample, and in mid-life (50-59 years and 60-69 years) and late-life (70-79 years). To assess duration of metabolic syndrome, group-based trajectory analysis was performed on 12 756 participants who attended at least two health assessments over 20 years. FINDINGS: =0·0040), but not in other age groups. In trajectory analysis, a prolonged duration of metabolic syndrome was associated with a significantly increased risk of developing dementia (1·26, 1·13-1·40) when compared to those with consistently low metabolic syndrome. No association was found for increasing metabolic syndrome (1·01, 0·88-1·17). INTERPRETATION: These findings provide insights into how certain age windows and time periods might differentially affect dementia risk in the context of metabolic syndrome, and highlight the importance of considering age and duration of exposure to metabolic syndrome when devising dementia prevention strategies. FUNDING: Canadian Institutes of Health Research-Institute of Aging, Oxford Population Health, and the Nicolaus and Margrit Langbehn Foundation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".