Gender-Specific Moderating Role of Physical Fitness and Cardiovascular Risk Factors in the Relationship Between BMI and C-Reactive Protein: Cross-Sectional Study
Bibliographic record
Abstract
Background: Physical fitness and cardiovascular risk factors may affect C-reactive protein (CRP) levels by influencing BMI. However, the extent to which these factors influence CRP through BMI remains unclear. Objective: This study aims to explore how indicators related to physical fitness and cardiovascular risk factors affect the relationship between BMI and CRP levels. Methods: This study selected 453 participants from 518 individuals at a sports hospital in Jiangsu Province, including 231 (51%) males and 222 (49%) females. Using interaction effect analyses, CRP served as the dependent variable and BMI as the independent variable. Physical fitness and cardiovascular risk factors (body morphology, blood pressure, blood lipids, and blood glucose) were categorized into quartiles from the lowest (Q1) to the highest (Q4) and treated as effect modifiers for interaction effect analyses. Sex-specific analyses were conducted to explore the influence of BMI on CRP across different genders, based on physical fitness and cardiovascular risk factor-related indicators. Results: BMI was positively associated with CRP (β=0.203, 95% CI 0.163-0.243; P<.001). After adjusting for confounding factors, this association remained significant (β=0.206, 95% CI 0.164-0.248; P<.001). In the stratified gender analysis, interaction effect analyses showed that in females, waist circumference (median 110.5, IQR 93-128; β=0.342, 95% CI 0.232-0.452; P<.001), hip circumference (median 120, IQR 104-136; β=0.361, 95% CI 0.260-0.462; P<.001), waist-hip ratio (median 1.08, IQR 0.9-1.25; β=0.291, 95% CI 0.204-0.378; P<.001), diastolic blood pressure (median 101, IQR 85-117; β=0.344, 95% CI 0.252-0.436; P<.001), triglycerides (median 1.41, IQR 1.13-1.68; β=0.313, 95% CI 0.216-0.409; P<.001), triglyceride-rich lipoprotein cholesterol (median 0.64, IQR 0.51-0.76; β=0.365, 95% CI 0.249-0.481; P<.001), 20-49 years one-leg standing time with closed eyes (median 3, IQR 1-5; β=0.371, 95% CI 0.276-0.466; P<.001), and 20-49 years grip strength (median 6.5, IQR 6-7; β=0.369, 95% CI 0.270-0.467; P<.001) significantly influenced the effect of BMI on CRP. In males, etotal cholesterol, glucose, one-leg standing time with closed eyes, sit-and-reach, and grip strength demonstrated trends in the effect of BMI on CRP (P>.05). Conclusions: This study demonstrates a positive correlation between BMI and CRP with gender-specific characteristics. In females, multiple body morphology, physiological, and physical fitness indicators significantly influence the effect of BMI on CRP, while some indicators in males also exhibit trends. These findings suggest the necessity of developing health management strategies tailored to different genders, particularly for female populations. Such strategies should consider body morphology indicators such as waist circumference and hip circumference, physiological indicators including blood pressure and blood lipids, and physical fitness metrics such as one-leg standing time with closed eyes. This comprehensive approach can better regulate the impact of BMI on CRP and promote overall health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".