Insulin-like Growth Factor 1 and Adiponectin and Associations with Muscle Deficits, Disease Characteristics, and Treatments in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is associated with low muscle mass and density. The objective of our study was to evaluate associations between 2 serum biomarkers [insulin-like growth factor 1 (IGF-1) and adiponectin] and skeletal muscle in RA. METHODS: Whole-body dual energy X-ray absorptiometry measures of the appendicular lean mass index (ALMI; kg/m(2)) and total fat mass index (kg/m(2)), as well as the peripheral quantitative computed tomography measures of the lower leg muscle and fat cross-sectional area (CSA; cm(2)) and muscle density (an index of fat infiltration) were obtained from 50 participants with RA, ages 18-70 years. Multivariable linear regression analyses evaluated associations between body composition and levels of adiponectin and IGF-1, adjusted for age, sex, and adiposity. RESULTS: Greater age was associated with higher adiponectin (p = 0.06) and lower IGF-1 (p = 0.004). Eight subjects had IGF-1 levels below the reference range for their age and sex. These subjects had significantly lower ALMI and muscle CSA in multivariable models. Lower IGF-1 levels were associated with greater clinical disease activity and severity, as well as low ALMI, muscle CSA, and muscle density (defined as 1 SD below normative mean). After adjusting for age and sex, greater adiponectin levels were associated with lower BMI (p = 0.02) as well as lower ALMI, and lower muscle CSA, independent of adiposity (p < 0.05). Only greater Health Assessment Questionnaire scores were significantly associated with lower adiponectin levels. CONCLUSION: Low IGF-1 and greater adiponectin levels are associated with lower muscle mass in RA. Lower IGF-1 levels were seen in subjects with greater disease activity and severity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".