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Record W4323353111 · doi:10.1093/jcag/gwac036.225

A225 QUANTIFICATION OF BONE DENSITY AND DIETARY RISK FACTORS FOR BONE FRAGILITY IN INFLAMMATORY BOWEL DISEASE

2023· article· en· W4323353111 on OpenAlexaffabout
A Macci, Rachel E. Klassen, Ryan E. Rosentreter, Jessie Szostakiwskyj, Emma O. Billington, R Panaccione, M Raman, Lauren A. Burt, C Lu

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseInternal medicineOsteoporosisVitamin D and neurologyMicronutrientGastroenterologyBone mineralBone densityLactose intoleranceDiseasePathology

Abstract

fetched live from OpenAlex

Abstract Background Nutrition and bone fracture risk are intimately related in inflammatory bowel disease (IBD). Crohn’s disease (CD) patients are at increased risk of low bone mineral density (BMD) and fractures. This may be due to chronic inflammation, corticosteroid exposure, inadequate consumption of nutrients and minerals such as calcium (ie. lactose intolerance), food aversion, bowel symptoms, and/or possible altered absorption. Fibrostenotic CD is characterized by debilitating strictures where patients are known to alter food intake to avoid obstructive symptoms. However, relationships between food intake patterns and BMD have not been well delineated according to CD phenotypes. Purpose Our study evaluated 1) BMD as measured by dual X-ray absorptiometry (DXA), and 2) energy intake, dietary components and/or micronutrients in CD patients with strictures versus inflammatory (non-stricture) behaviour. Method In this prospective pilot study, patients > 55 years old with ileal CD strictures or inflammatory behavior were recruited from the University of Calgary IBD clinic. All patients completed hip and spine DXA scans and two dietary assessment questionnaires: 1) The Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24) and 2) the Diet History Questionnaire III (DHQ III). Additional data collected included past fracture history, medication (glucocorticoid exposure), smoking, and surgical history. Standard of care laboratory investigations obtained included C-reactive protein, parathyroid hormone, calcium, albumin, and 25-hydroxyvitamin D. Patients with celiac disease, cirrhosis, heart failure, kidney disease, short gut, estrogen use, and dysphagia were excluded. Independent samples t-test and multi-variable regression analyses was conducted. Result(s) Seventeen patients had stricturing and twelve had non-stricturing CD (demographics Table 1). The mean BMD for non-stricturing CD patients was not significantly different from those with a stricturing CD phenotype (p =0.140). Non-stricture patients consumed significantly more dairy, calcium, and phosphate. For all CD patients, there was a positive correlation with BMD and intake of fat (p=0.03), carbohydrates (p=0.01), fiber (p=0.01), and alcohol (p=0.01). There was no statistically significant difference in corticosteroid exposure or smoking status. 74.7% (11/17) patients with stricturing CD had past bowel resection compared to only one patient with non-stricturing CD. Image Conclusion(s) In this pilot study, there was no difference in BMD between CD patients with and without small bowel strictures despite inflammatory behaviour patients having less surgical resections and consuming more calcium rich foods known to improve BMD. Further studies may delineate the dietary differences among CD phenotypes and provide information for interventions for nutrient supplementation, and a greater understanding of their relationships with BMD. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; Koopmans Memorial Research Fund Disclosure of Interest None Declared

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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