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Record W4407285269 · doi:10.1093/jcag/gwae059.052

A52 OVERNUTRITION IN TREATED CELIAC DISEASE (CED) PATIENTS: THE NEED FOR PERSONALIZED NUTRITIONAL ASSESSMENT TO MANAGE THE METABOLIC SEQUELAE OF A GLUTEN-FREE DIET (GFD)

2025· article· en· W4407285269 on OpenAlexaffabout
Anil K. Verma, M Khaouli, Fatemeh Abdi, Jens Blom, F J Echagüe, FatemahM A AlEssa, Afsheen Aman, David Armstrong, María Inés Pinto-Sánchez

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOvernutritionGluten freeMedicineDiseaseGlutenIntensive care medicineFood sciencePhysiologyInternal medicineMalnutritionBiologyPathology

Abstract

fetched live from OpenAlex

Abstract Background Overnutrition, leading to overweight and obesity, is increasingly prevalent in celiac disease (CeD). A nutritionally imbalanced diet may contribute to overnutrition and the development of Metabolic Syndrome (MS) and Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD). Aims To investigate the factors contributing to the metabolic shift in treated CeD. Methods We enrolled patients with biopsy-proven CeD on a GFD and non-CeD (Inflammatory Bowel Disease and Irritable Bowel Syndrome) attending a tertiary care center adult Nutrition Assessment Clinic. We collected data on nutritional assessment (SGA), energy requirements determined by resting energy expenditure (REE: indirect calorimetry, QNRG, COSMED, US) and activity factor (IPAQ), body composition (3D scanner; Styku CA, US) and obesity risk (EOSS: Edmonton Obesity Staging System) to assess risk based on metabolic comorbidities such as diabetes, fatty liver, and cardiovascular disease. SPSS (version 22, US) was used for statistical analysis. Data are reported as median (IQR); Mann-U-Whitney was used for comparison between groups. Results From November 2021 to October 2024, 124 CeD [Female: 76%; Age: 45 (27) yr; time since diagnosis: 5 (6) yr] and 96 non-CeD [F:74%; 48 (27) yr] patients were enrolled. Nutritional assessment identified undernutrition (BMI<19) in 8% vs 20%, overweight (BMI 26-30) in 24% vs 14%, and obesity (BMI>30) in 49% vs 34% of CeD vs non-CeD (p<0.001). REE adjusted for weight was significantly lower in obese CeD compared with non-obese CeD [17 (3) vs 22 (4) kcal/kg/day; p<0.001]. Fat mass was significantly greater [19 (7) vs 40 (10) kg; p<0.001], and fat-free mass was significantly reduced [FFM:43 (11) vs 54 (10) kg; p=0.005] in CeD with overweight/obesity compared to normal BMI. FFM was positively correlated with REE (r=0.71; p<0.001). Metabolic comorbidities occurred in 26% of obese-CeD patients. MASLD was more frequent in CeD with overweight and obesity compared to normal BMI (81% vs 43%; p=0.04). Conclusions The high rate of overnutrition and reduced muscle mass in treated CeD patients is concerning, as this is associated with metabolic comorbidities. Personalized nutrition assessment with accurate measurements is crucial for nutritional guidance and will likely improve health in CeD. Funding Agencies:

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.233
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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