Hidden malnutrition in obesity and knee osteoarthritis: Assessment, overlap with sarcopenic obesity and health outcomes
Bibliographic record
Abstract
Background & aims Malnutrition may be a hidden but relevant health condition in individuals with obesity and osteoarthritis. The Global Leadership Initiative on Malnutrition (GLIM) consensus includes muscle mass as one of the key phenotypic criteria, potentially enhancing its ability to detect malnutrition in individuals with obesity. We comprehensively profiled the nutritional status of individuals with obesity and advanced knee osteoarthritis and explored associations with health outcomes. Methods Data from the Prevention Of MusclE Loss in Osteoarthritis (POMELO) study was used, which included individuals with knee osteoarthritis and BMI≥35 kg/m 2 . Nutritional status was evaluated using the GLIM and sarcopenic obesity (SO) criteria. Low muscle mass (dual-energy x-ray absorptiometry), inflammation (C-reactive protein [CRP]), low muscle strength (handgrip/BMI), objective physical function (chair-stand test, 6-min walk test), and self-reported measures (quality of life, arthritis symptoms, and self-efficacy) were evaluated. Linear regressions were performed between GLIM-malnutrition and health outcomes, adjusted by age. Results Forty-six individuals (73.9% female, age 64.2 ± 6.7 years, BMI 42.4 ± 4.7 kg/m 2 ) were included. Regarding nutritional status, 26.1% were classified with malnutrition (i.e., defined by the combination of low muscle mass and elevated CRP concentration), 26.1% with SO, and 13% shared both conditions. Individuals with malnutrition presented with worse self-reported physical function (WOMAC function: 38.0 ± 6.6 vs. 32.0 ± 12.5, p = 0.04) and lower arthritis self-efficacy (‘other symptoms' component: 5.1 ± 1.9 vs. 6.3 ± 1.7, p = 0.04) compared to those without malnutrition. A trend was identified for lower quality of life (visual analog scale 46.8 ± 12.3 vs. 58.3 ± 20.5, p = 0.06) in those with malnutrition. Poor lipid control (R 2 = 0.15, β = 0.76, 95% CI 0.08–1.44, p = 0.030), body fat (R 2 = 0.14, β = 5.56, 95% CI 1.01–10.11, p = 0.018), and poor arthritis self-efficacy (R 2 = 0.09, β = −1.23, 95% CI -2.39–0.06, p = 0.040) were also associated with malnutrition. Conclusions Participants presented with high malnutrition rates (1 out of 4), and half of them also had SO. Malnutrition was associated with abnormal metabolic parameters, lower arthritis self-efficacy, and worse self-reported physical function. An early nutritional assessment and intervention may be imperative for individuals with osteoarthritis and obesity to mitigate health consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".