Interaction of obesity and systemic inflammation with knee extensor strength in knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis is accompanied by reduced muscle strength and a greater body mass index (BMI). Large BMI from accumulated fat mass is linked with greater systemic inflammation. Separate studies show inflammation is associated with reduced muscle strength in healthy adults. The purpose of this study was to investigate whether systemic inflammatory biomarkers interact with BMI to relate with knee extensor strength, and if the relationship differs based on sex. METHODS: 93 participants with clinical knee osteoarthritis (n = 59 females, n = 34 males) were included. Knee extensor strength (Nm) was an average of 5 maximum voluntary isometric knee extension efforts. BMI was measured while barefoot, wearing a t-shirt and shorts. Serum cytokines were obtained from venous blood samples from the antecubital fossae following a 30-min rest period. Linear regressions tested the interaction effect of each CRP and IL-10 on the relationship between knee extensor strength (dependent variable) and BMI (independent variable), for both males and females. FINDINGS: Among females with a large BMI, higher levels of IL-10 related to greater knee extensor strength (t = 2.18, p = 0.03). For males, the interaction of IL-10 (t = 0.61, p = 0.55) with CRP (t = -1.75, p = 0.09) with BMI was not significant. INTERPRETATION: Anti-inflammatory properties of IL-10 may serve to balance the effect of an increased BMI in females by counteracting inflammation-induced muscle damage. Understanding this association provides insight into biological factors that may contribute to disease progression, serving as a potential target for therapeutic interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".