Association Between Midlife Obesity and Its Metabolic Consequences, Cerebrovascular Disease, and Cognitive Decline
Bibliographic record
Abstract
CONTEXT: Chronic obesity is associated with several complications, including cognitive impairment and dementia. However, we have only piecemeal knowledge of the mechanisms linking obesity to central nervous system damage. Among candidate mechanisms are other elements of obesity-associated metabolic syndrome, such as hypertension, dyslipidemia, and diabetes, but also systemic inflammation. While there have been several neuroimaging studies linking adiposity to changes in brain morphometry, a comprehensive investigation of the relationship has so far not been done. OBJECTIVE: To identify links between adiposity and cognitive dysfunction. METHODS: This observational cohort study (UK Biobank), with an 8-year follow-up, included more than 20 000 participants from the general community, with a mean age of 63 years. Only participants with data available on both baseline and follow-up timepoints were included. The main outcome measures were cognitive performance and mediator variables: hypertension, diabetes, systemic inflammation, dyslipidemia, gray matter measures, and cerebrovascular disease (volume of white matter hyperintensities on magnetic resonance imaging). RESULTS: Using structural equation modeling, we found that body mass index, waist-to-hip ratio, and body fat percentage were positively related to higher plasma C-reactive protein, dyslipidemia, hypertension, and diabetes. In turn, hypertension and diabetes were related to cerebrovascular disease. Finally, cerebrovascular disease was associated with lower cortical thickness and volume and higher subcortical volumes, but also cognitive deficits (largest significant pcorrected = 0.02). CONCLUSIONS: We show that adiposity is related to poor cognition, with metabolic consequences of obesity and cerebrovascular disease as potential mediators. The outcomes have clinical implications, supporting a role for the management of adiposity in the prevention of late-life dementia and 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".