Gender differences in the association of C‐reactive protein with coronary artery calcium in Type‐2 diabetes
Bibliographic record
Abstract
OBJECTIVES: Plasma C-reactive protein (CRP) is associated with cardiovascular disease (CVD), but effects may vary by gender and degree of CVD risk. Whether CRP has value as a CVD risk marker in type-2 diabetes (T2DM) is unclear. We examined whether CRP has gender differences in association with coronary artery calcium (CAC) in diabetic and nondiabetic samples without clinical CVD. METHODS: We performed cross-sectional analyses of gender influence on CRP association with CAC in the Penn Diabetes Heart Study (N = 1299 with T2DM), the Study of Inherited Risk of Coronary Atherosclerosis (N = 860 nondiabetic subjects) and a combined sample. RESULTS: Female gender was associated with higher plasma CRP in diabetic and nondiabetic samples after adjustment for covariates. There was a strong interaction by gender in the association of CRP with CAC (interaction P < 0·001). In diabetic women, CRP was associated with higher CAC even after further adjustment for age, race, medications, metabolic syndrome, Framingham risk score and body mass index [Tobit ratio 1·60, 95% CI (1·03-2·47)]. Although this relationship was attenuated in nondiabetic women, the combined sample maintained this association in fully adjusted models [1·44, 95% CI (1·13-1·83)]. There was no association of CRP with CAC in either diabetic or nondiabetic men. CONCLUSIONS: CRP may be a useful marker of cardiovascular risk in women, particularly in diabetic women who otherwise have no known CVD. Prospective studies are needed to better assess the gender differences in CRP utility and the use of CRP in T2DM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".