Obesity and Lowered Cognitive Performance in a Canadian First Nations Population
Bibliographic record
Abstract
The association between obesity, other cardiovascular risk factors, and cognitive function in a Canadian First Nations population was investigated using a cross-sectional design. Eligible individuals were aged >/=18 years, without a history of stroke, nonpregnant, with First Nations status, and who had undergone cognitive function assessment by the Clock Drawing Test (CDT) and Trail Making Test Parts A and B. Parts A and B were combined into an Executive Function Score (TMT-exec). Hypertension, a previous history of cardiovascular disease, dyslipidemia, metabolic syndrome, insulin resistance, and the presence and duration of diabetes were examined in addition to obesity. In the case of TMT-exec only, obese individuals were at an approximately fourfold increased risk for lowered cognitive performance compared to those who were not obese in multivariable models (odds ratio (OR): 3.77, 95% confidence interval (CI): 1.46-9.72) whereas there was no effect for overweight individuals compared to those with a normal weight in unadjusted analysis. Those having an increased waist circumference also had 5 times the risk compared to those without an increased waist circumference (OR: 5.41, 95% CI: 1.83-15.99). Adjusted for age, sex, and insulin resistance, individuals having the metabolic syndrome were at an approximately fourfold increased risk compared to those without the metabolic syndrome (OR: 3.67, 95% CI: 1.34-10.07). No other cardiovascular risk factors were associated. Obesity and metabolic syndrome were associated with lowered cognitive performance. These results highlight the importance of studying the health effects of obesity beyond traditional disease endpoints, even in a relatively youthful population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".