Cardiovascular Risk Profile and Osteoarthritis—Considering Sex and Multisite Joint Involvement: A Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate a profile of cardiovascular disease (CVD) risk factors by sex among individuals with and without osteoarthritis (OA) and to consider single-site and multisite joint OA. METHODS: Data were sourced from Cycle 1, Comprehensive Cohort, Canadian Longitudinal Study on Aging, a national sample of individuals ages 45 to 85 years. Systemic inflammatory/metabolic CVD risk factors collected were high-sensitivity C-reactive protein (hsCRP) level, high-density lipoprotein, triglycerides, total cholesterol, body mass index (BMI), systolic blood pressure, and hemoglobin A1c. Smoking history was also collected. Respondents indicated doctor-diagnosed OA in the knees, hips, and/or hands and were characterized as yes/no OA and single site/multisite OA. Individuals with OA were age- and sex-matched to non-OA controls. Covariates were age, sex, education, income, physical activity, timed up and go test findings, and comorbidities. A latent CVD risk variable was derived in women and men; standardized scores were categorized as follows: lowest, mid-low, mid-high, and highest risk. Associations with OA were quantified using ordinal logistic regressions. RESULTS: A total of 6,098 respondents (3,049 with OA) had a median age of 63 years, and 55.8% were women. One-third of OA respondents were in the highest risk category versus one-fifth of non-OA respondents. Apart from BMI (the largest contributor in both sexes), hsCRP level (an inflammation marker) was predominant in women, and metabolic factors and smoking were predominant in men. Overall, OA was associated with worse CVD risk quartiles compared with non-OA. OA was increasingly associated with worse CVD risk quartiles with increasing risk thresholds among women with multisite OA, but not men. CONCLUSION: Findings suggest unique CVD risks by sex/multisite subgroups and point to a potentially important role for inflammation in OA over and above traditional CVD risk factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".