Abstract 5083: Body Composition, Cardiorespiratory Fitness and Low-Grade Inflammation in Middle-Aged Men and Women
Bibliographic record
Abstract
Background: Although low levels of cardiorespiratory fitness (CRF) and obesity are often associated with a deteriorated cardiometabolic risk profile including low-grade inflammation, the respective contributions of specific indices of body fat distribution and CRF to variation of inflammatory markers remains uncertain. We therefore sought to determine the respective contributions of visceral adipose tissue (VAT) accumulation and CRF to variation of inflammatory markers in middle-aged men and women. Methods and Results: A complete lipoprotein-lipid profile and circulating levels of inflammatory markers such as C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and adiponectin were obtained and in a sample of healthy men (n=120) and women (n=152) covering a wide range of obesity values. VAT accumulation was measured by computed tomography and CRF levels were measured by a progressive submaximal physical working capacity test. In both men and women, VAT was positively associated with CRP and IL-6 levels (r≥0.35, p<0.0001), negatively associated with adiponectin (r≤ −0.29, p≤0.0003), after adjusting for CRF. After adjusting for VAT, CRF was not associated with variation in inflammatory markers in women and only with adiponectin in men (r= −0.20, p=0.03). An inflammation score was developed based on the sex-specific 50 th percentile values of each inflammatory marker (0 or 1) which yielded a score ranging from 0 (low) to 4 (high). Participants who scored 0, 1 or 2 were considered as having a low score and participants who scored 3 or 4 had an elevated inflammation score. In participants with low VAT (<130cm 2 for men and <100cm 2 for women), the prevalence of participants with an elevated inflammation score was of 23.9% and of 28.0%, respectively for participants with high and low CRF, whereas in participants with an elevated VAT accumulation, the prevalence of an elevated inflammation score was of 60.0% and of 61.7%, respectively for high and low CRF. Conclusions: These results suggest that the inflammatory state associated with low CRF is largely attributable to the increased VAT accumulation often observed in poorly fit individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".