Muscle Mass and Inflammation in Older Adults: Impact of the Metabolic Syndrome
Bibliographic record
Abstract
BACKGROUND: Inflammatory processes are a cause of accelerated loss of muscle mass. Metabolic syndrome (MetS) is a highly prevalent age-related condition, which may promote and be promoted by inflammation. However, whether inflammation in MetS (metaflammation) is associated with lower muscle mass is still unclear. METHODS: Complete cross-sectional data on body composition, MetS, and the inflammatory markers interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF), and C-reactive protein (CRP) were available for 1,377 BASE-II participants (51.1% women; 68 ± 4 years old). Appendicular lean mass (ALM) was assessed by dual-energy X-ray absorptiometry. Low muscle mass (low ALM-to-BMI ratio [ALMBMI]) was defined according to the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project. Regression models, adjusted for an increasing number of confounders (sex, age, physical activity, morbidities, diabetes mellitus type II, TSH, albumin, HbA1c, smoking habits, alcohol intake, education, and energy intake/day), were used to calculate the association between low ALMBMI and high inflammation (tertile 3) according to MetS. RESULTS: MetS was present in 36.2% of the study population, and 9% had low ALMBMI. In the whole study population, high CRP (odds ratio [OR]: 2.7 [95% CI: 1.6-4.7; p = 0.001]) and high IL-6 (OR: 2.1 [95% CI: 1.2-1.9; p = 0.005]) were associated with low ALMBMI. In contrast, no significant association was found between TNF, IL-10, or IL-1β with low ALMBMI. When participants were stratified by MetS, results for IL-6 remained significant only in participants with MetS. CONCLUSIONS: Among BASE-II participants, low ALMBMI was associated with inflammation. Low-grade inflammation triggered by disease state, especially in the context of MetS, might favor loss of muscle mass, so a better control of MetS might help to prevent sarcopenia. Intervention studies to test whether strategies to prevent MetS might also prevent loss of muscle mass seem to be promising.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".