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Record W4391162088 · doi:10.1093/ecco-jcc/jjad212.0648

P518 Dietary and non-dietary predictors of treatment response to adalimumab in anti-TNFα-naïve adults with Crohn’s disease

2024· article· en· W4391162088 on OpenAlexaff
Aleksandra Jatkowska, Bernadette White, Iona Campbell, E Brownson, Brenton Short, J Clowe, John Paul Seenan, Daniel R. Gaya, Shahida Din, Gwo‐Tzer Ho, E Robertson, Craig Mowat, Simon Milling, Jonathan Macdonald, Konstantinos Gerasimidis

Bibliographic record

VenueJournal of Crohn s and Colitis · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsAdalimumabMedicineCrohn's diseaseDiseaseInfliximabInternal medicineTumor necrosis factor alpha

Abstract

fetched live from OpenAlex

Abstract Background Biologics, such as anti-TNFα agents, are commonly used in the management of Crohn’s disease (CD). A significant proportion of patients do not respond to treatment, necessitating the exploration of pre-treatment predictors of treatment response. Methods Anti-TNFα-naïve adults with active CD (Crohn’s Disease Activity Index; CDAI≥150) participating in an RCT (NCT04859088) were randomised to receive adalimumab monotherapy or adalimumab combination therapy with 50% partial enteral nutrition (PEN). Treatment response (CDAI<150) was assessed after 6 weeks, baseline diet was assessed with EPIC-Norfolk FFQ, alternative Mediterranean diet scores (aMED), and principal component analysis (PCA) with orthogonal (varimax) rotation was used to identify data-derived dietary patterns. Baseline predictors evaluated included PEN use, steroid use, immunomodulator use, age, disease duration, CDAI, C-Reactive protein (CRP), albumin, haemoglobin, Scottish Index of Multiple Deprivation (SIMD) score, adherence to dietary patterns identified, aMED score, smoking status, alcohol consumption, physical activity level, body mass index (BMI), fat mass (kg/m2), fat-free mass (kg/m2), and handgrip strength. Differential analysis between responders and non-responders was carried out with general linear model or chi-square test when appropriate. Random forest model with recursive feature elimination (RF-RFE) was used to identify the most predictive factors of treatment response. Results Of 42 participants recruited to the study, 62% (26) responded to treatment. PCA revealed four dietary patterns (Figure 1A). Responders to adalimumab were younger (mean (SD): 36.0 (17.1) vs 50.8 (10.0), P=0.004), had lower baseline CDAI (mean (SD): 228 (62) vs 286 (78), P=0.018), higher CRP (14.5 (19.2) vs 4.6 (5.8) mg/L, P=0.036), were less likely to smoke (31% (5 of 16) vs 8% (2 of 26), and less likely to adhere to a dietary pattern characterised by high consumption of animal products (PC2) (P=0.030). Adherence to PC2 also correlated positively with age (r=0.327, P=0.035). The RF-RFE algorithm highlighted young age, low baseline CDAI and low PC2 adherence as key factors (Sensitivity: 77%, Specificity: 63%, PPV: 77%, NPV: 63%, OOB: 29%, P=0.012) (Figure 1B). Interestingly, exclusion of dietary factors improved diagnostic performance of the model (Sensitivity: 77%, Specificity: 75%, PPV: 83%, NPV: 67%, OOB: 24%, P=0.003) (Figure 1C), indicating potential interactions by other factors like age. Conclusion Young age, non-smoking, low baseline CDAI and elevated CRP predict adalimumab response in anti-TNFα-naïve adults. While dietary factors may also play a role, their impact seems confounded by other non-dietary factors. Further research is warranted in this area.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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
Published2024
Admission routes1
Has abstractyes

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