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Record W4408902593 · doi:10.1016/j.clnu.2025.03.019

Hidden malnutrition in obesity and knee osteoarthritis: Assessment, overlap with sarcopenic obesity and health outcomes

2025· article· en· W4408902593 on OpenAlexafffund
Flávio Teixeira Vieira, Kristine Godziuk, Rocco Barazzoni, John A. Batsis, Tommy Cederholm, Lorenzo M. Donini, Marı́a Cristina González, Gordon L. Jensen, Mary Forhan, Carla M. Prado

Bibliographic record

VenueClinical Nutrition · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersObesity CanadaNational Institutes of HealthAlberta InnovatesSapienza Università di RomaChildren's Health Research InstituteNovo NordiskCanada Foundation for InnovationUniversity of AlbertaArthritis SocietyRegeneron PharmaceuticalsWomen and Children's Health Research InstituteMinistero dell’Istruzione, dell’Università e della RicercaEuropean CommissionPfizer
KeywordsMedicineSarcopenic obesityObesityMalnutritionSarcopeniaOsteoarthritisPhysical therapyPhysical medicine and rehabilitationInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.437
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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