Muscle Deficits in Rheumatoid Arthritis Contribute to Inferior Cortical Bone Structure and Trabecular Bone Mineral Density
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is associated with muscle loss, osteoporosis, and fracture. We examined associations between skeletal muscle mass, strength, and quality and trabecular and cortical bone deficits in patients with RA and healthy controls. METHODS: Participants, ages 18-75 years, completed whole-body dual-energy x-ray absorptiometry and peripheral quantitative computed tomography (pQCT) of the tibia to quantify appendicular lean mass and fat mass indices (ALMI, FMI), muscle density at the lower leg, trabecular bone density, and cortical bone thickness. Age-, sex-, and race-specific Z scores were calculated based on distributions in controls. Associations between body composition and pQCT bone outcomes were assessed in patients with RA and controls. Linear regression analyses assessed differences in bone outcomes after considering differences in body mass index (BMI) and body composition. RESULTS: The sample consisted of 112 patients with RA (55 men) and 412 controls (194 men). Compared to controls, patients with RA had greater BMI Z score (p < 0.001), lower ALMI Z score after adjustment for FMI (p = 0.02), lower muscle strength Z score (p = 0.01), and lower muscle density Z score (p < 0.001). Among RA, ALMI Z scores were positively associated with trabecular density [β: 0.29 (0.062-0.52); p = 0.01] and cortical thickness [β: 0.33 (0.13-0.53; p = 0.002]. Associations were similar in controls. Bone outcomes were inferior in patients with RA after adjusting for BMI, but similar to controls when adjusting for body composition. Radiographic damage and higher adiponectin levels were independently associated with inferior bone outcomes. CONCLUSION: Patients with RA exhibit deficits in cortical bone structure and trabecular density at the tibia and a preserved functional muscle-bone unit. A loss of mechanical loading may contribute to bone deficits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".