P0300 Ultra-processed food consumption affects body composition in a cohort of italian patients with inflammatory bowel disease
Notice bibliographique
Résumé
Abstract Background Diet is a key factor in the development of IBD, which are showing rising incidence in industrialized countries, where the “Western” diet, rich in ultra-processed foods (UPF), is prevalent. Consumption of UPFs has been linked to an increased risk of both IBD developing and relapse. However, there is limited data on the relationship between UPF intake and body composition in IBD patients. We aimed to evaluate the impact of UPF consumption on body composition in individuals with IBD. Methods In this single-center observational study, we enrolled adult IBD patients. Exclusion criteria were active disease, defined as Harvey-Bradshaw Index > 5 for Crohn’s disease (CD) and Partial Mayo Score > 2 for Ulcerative Colitis (UC), pregnant or breastfeeding women, use of corticosteroids, high physical activity as assessed by the International Physical Activity Questionnaire (IPAQ), and previous bowel surgery. UPF consumption was assessed using country-specific validated food frequency questionnaires (FFQ), whose evaluated foods were categorized based on the NOVA classification into 4 classes according to their degree of processing. The percentage of UPF consumption was divided into 3 tertiles (T1=low; T3=high). Body composition was assessed through bioimpedance analysis. Continuous variables were expressed as mean±standard deviation (SD) or as median and interquartile range (IQR) according to their distribution assessed by the Kolmogorov-Smirnov test. Frequencies were calculated for the categorical variables. The Mann-Whitney rank-sum test, t-test, or ANOVA test was used to compare continuous variables; the Chi-square and Fisher’s exact test were used to compare categorical variables. Multiple linear regression analysis was used for fatty-free mass (FFM) predicted by UPF consumption. Results We enrolled 40 IBD patients (12 UC and 28 CD). The mean age and BMI were 42.5±16.2 years and 25±4.4 kg/m², respectively. UPF consumption was higher in CD than UC patients, both in terms of g/day (823±312 vs 609±248, p=0.04) and % weight (20.3±6.9 vs 15.1±6.1, p=0.028). Patients with T3 UPF consumption compared to T1 had a lower age (37.69±10.7 vs 55.9±17, p=0.001) and lower FFM (0.69±0.06 vs 0.75±0.09, p=0.012). When adjusting for age, sex and disease type the percentage of FFM was lower in T3 compared with T1 (β= -0.56; p=0.002). Higher UPF consumption was associated with FFM reduction in younger IBD patients. Conclusion Our findings emphasize the need for nutrition counseling in IBD patients, focusing on reducing the intake of UPFs, which can exacerbate the disease either by directly inflaming the bowel or by negatively impacting body composition. Further large-scale prospective studies are needed to validate our results. References 1.Elly Mertens, Chiara Colizzi, Josè L. Penalvo. Ultra-processed food consumption in adults across Europe. 61(3):1521-1539. 2.Narula N, Wong ECL, Dehghan M, et al. Association of ultra-processed food intake with risk of inflammatory bowel disease: Prospective cohort study. BMJ 374:n1554 3.Kathy Vagianos, Casandra Dolovich, KelcieWitges, Lesley A.Graff, Charles N. Bernstei. Ultra-Processed Food, Disease Activity, andInflammation in Ulcerative Colitis: The Manitoba Living With IBD Study. The American College of Gastroenterology 2024. 119(6):1102-1109. 4.Carlos A Monteiro, Geoffrey Cannon, Renata B Levy; Jean-Claude Moubarac, Maria LC Louzada, Fernanda Rauber. Ultra-processed foods: what they are and how to identify them. Public Health Nutrition 2018. 936-941 5.Eva Visser, Judith Wellens, Joao Sabino. Ultra-processed foods as a possible culprit for the rising prevalence of inflammatory bowel diseases. Frontiers in Medicine 2022.9:1058373 6.Dinu M, Bonaccio M, Martini D, Madarena MP, Vitale M, Pagliai G, Esposito S, Ferraris C, Guglielmetti M, Rosi A, Angelino D. Reproducibility and validity of a food-frequency questionnaire (NFFQ) to assess food consumption based on the NOVA classification in adults. Int J Food Sci Nutr. 2021 Sep;72(6):861-869
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».