Cardiometabolic risk, biological sex, and age do not share an interactive relationship with cognitive function: a cross-sectional analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
It is unclear whether cardiometabolic risk shares an interactive relationship with age-associated differences in cognition, and whether this relationship varies by biological sex. We conducted a cross-sectional analysis using baseline data from the Canadian Longitudinal Study on Aging (CLSA; 2010–2015) to examine whether 1) cardiometabolic risk has an interactive relationship with age-associated cognition; and 2) interactive effects are sex-dependent. We measured memory, executive function, and verbal fluency in the Comprehensive cohort (N = 25 830; 45–86 years). Each cognitive domain was modeled using restricted cubic splines for age and each cardiometabolic risk factor (HbA1c, HSCRP, TG, and LDL and HDL cholesterol). Sex was included as a predictor in all models. Wald χ2 statistics were used to determine the relative importance of age, cardiometabolic risk, sex, and their interactive effects on cognition. Age was the most important variable in each model (proportion χ2 = 34%–48%). Biological sex was the second most important variable for memory (proportion χ2 = 26%) but was unimportant for executive function and verbal fluency (proportion χ2 = 3%–5%). Cardiometabolic risk factors were unimportant predictors in each model (proportion χ2 = 1%–3%). Two- and 3-way interactions between cardiometabolic risk, age, and sex were also unimportant (proportion χ2 = 0%–2%). Thus, cardiometabolic risk factors did not meaningfully account for age-associated differences in cognition, and these associations (or lack thereof) did not vary by sex. Novelty: Males have poorer age-associated cognitive performance than females. Females and males differ in cardiometabolic risk across middle and older adulthood. Cardiometabolic risk has a small association with age-associated cognition, and there are no sex differences in this relationship.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".