ABS0546 MEDITERRANEAN DIET ADHERENCE IN IDIOPATHIC INFLAMMATORY MYOPATHIES AND OTHER RMDS: A COVAD GROUP STUDY
Notice bibliographique
Résumé
Background: The Mediterranean diet (MeD) is associated with improved health outcomes [1], though its potential impact on patients with Idiopathic Inflammatory Myopathies (IIM) and other rheumatic diseases remains understudied. Objectives: Using data from the 3rd Collating the Voice of People in Autoimmune Diseases (CoVAD) study, we explored MeD adherence patterns and their association with clinical outcomes in patients with autoimmune diseases, particularly IIM. Secondary objectives included identifying predictors of dietary adherence and assessing its association with physical function and disease parameters. Methods: CoVAD is an international e-survey which collected patient-reported data from participants with IIM, other rheumatic diseases (RMD), non-rheumatic autoimmune diseases (nrAID) and healthy controls (HC). Data included patient demographics and a holistic assessment of biopsychological determinants of health using various validated measures spanning key domains: physical & Mental health (PROMIS Global-10), Physical Function (PROMIS Physical Function(PF)-4a), Disease severity (Patient Global Disease Activity Score, Patient Global Damage Assessment), Mental wellbeing (Brief Resilience Scale, Loneliness Scale, Self Efficacy in Managing Chronic Diseases), Social factors (Family APGAR scale, Short Index of Job Satisfaction), and lifestyle behaviors (Mediterranean Diet Adherence Screener (MEDAS) (Figure 1A). Statistical analysis included Pearson's correlations, t-tests/Mann-Whitney U tests and chi-squared tests as appropriate for data distribution. Multivariable regression identified predictors of MeD adherence in IIM patients. Results: The study included 207 patients with Idiopathic inflammatory myopathies (IIM), 761 with other rheumatic diseases (RMD), 125 with non-rheumatic autoimmune diseases (nrAID) and 312 healthy controls. Mediterranean Diet Adherence Screener (MEDAS) scores were nearly similar across groups IIM: 5.34 (2.16), RMD: 5.53 (2.11), nrAID: 5.24 (1.91) (p<0.001). Among IIM patients, 55.07% demonstrated low Med adherence (MEDAS ≤5), 40.09% moderate adherence (MEDAS 6–9), and 4.83% high adherence. (MEDAS≥10) [Figure 2A]. Notably, Inclusion Body Myositis (IBM) and Necrotizing Autoimmune Myopathy patients showed better MeD adherence, compared to other IIM subtypes. Most consumed foods groups included sweet beverages and poultry, while olive oil, wine, and seafood were least consumed in IIM patients [Figure 1B]. IBM patients demonstrated a significant inverse relationship between MEDAS adherence and pain. Dermatomyositis (DM) patients show inverse relationship with fatigue and positive in self-efficacy [Figure 1E]. Multivariate analysis identified basic multimorbidity (B=1.128, p=0.047) and marital status (B=0.712, p=0.044) as positive predictors of dietary adherence, whilst active disease (B=-1.901, p=0.017), pain (B=-0.168, p=0.045), and caregiving responsibilities (B=-0.898, p=0.018) emerged as negative predictors [Figure 1C]. MEDAS scores positively correlated with physical health and exercise, whilst showing negative associations with pain and fatigue. High self-efficacy in DM was associated with high MEDAS [Figure 1E]. In cluster analysis, the distribution of IIM was categorized into three identified clusters. Cluster 1 had lower comorbidities, higher PROMIS PF4a, subjective well-being, and physical health. Cluster 2 showed higher pain, fatigue, and loneliness, with lower physical and mental health. Cluster 3 had the longest disease duration and moderate physical health, but higher caregiver roles and comorbidity burden [Figure 2C]. In other RMDs, MeD adherence showed positive correlations with physical and mental health metrics, including physical function, subjective well-being, resilience and exercise habits, self-efficacy, job satisfaction whilst negatively correlating with fatigue, loneliness and disease activity. Similarly, in nrAIDs, dietary adherence positively associated with physical health and negatively with fatigue levels [Figure 1D]. Conclusion: This study reveals a complex interplay between mediterranean diet adherence and health outcomes in autoimmune conditions. The findings suggest a syndemic cluster where dietary behaviour interweaves with both psychosocial and clinical variables. Notably, adverse disease parameters and social stressors showed outsized negative impacts on dietary adherence, whilst positive health metrics clustered together, suggesting a bidirectional relationship. The consistent associations across IIM, rheumatic and non-rheumatic autoimmune groups point towards a broader construct where dietary behaviour forms part of an intricate wellness pattern, rather than functioning as an isolated health determinant. This understanding emphasizes the need for holistic interventional approaches that address both clinical and psychosocial barriers to dietary adherence, and potentially affecting patient well-being. REFERENCES: [1] Martinez-Lacoba R. Eur J Public Health. 2018 Oct 1;28(5):955-961. Figure 1A) Variables Evaluated to Assess Holistic Well-Being B) Percentage of Participants in the high intake per food group C) Association from Multivariate Analysis of IIM patients D) Correlation Coefficients between MEDAS and variables across all groups E) Correlation Coefficients between MEDAS and variables in IIM subtypes. Figure 2A) Baseline characteristics of all groups. B) Inclusion Criteria Flow Chart for IIM Responses. C) Cluster analysis in IIM patients. Acknowledgements: Patient research partners: Peter Boyd, Linda Kobert, Paula Jordan, Kirtida Oza, Dr. Ingrid De Groot, Allison Foss, Celia Meyer, Karin Blomkvist Sporre, Annika Broberg Lavén, Veronica Fatura, Ailsa Bosworth, Malak Aburas, Silvia Aguilera, Rachel bromley; Patient Support Groups: Cure JM, JCR, CYPLER, EULAR PARE, Myositis Support and Understanding, Myositis UK, The Myositis Association, ARCH Network, ArLAR, Young GRAPPA, APLAR myositis SIG, Myasthenia Gravis Association, Wolverhampton PSG, Patients Alliance for Rheumatic Diseases (PARD), SSc UK, Conquer Myasthenia Gravis, Myasthenia Gravis Association of Western PA, The MG Holistic Society, MG Ohio, Myasthenia Gravis Foundation of Michigan,MIHRA, EULAR Rehfap, Myositis Canada, AAAA, NRAS, National Association for SLE, TMA Michigan Support Group Co-leader, TMA Adelante Affinity Group Co-leader, Myasthenia Gravis Foundation of America, MIHRA, EULAR Reproductive Health and Family Planning (ReHFaP), Rodney Jansen (Myositis Canada), Asociacion Miastenia de Espana, Conquer MG, Associazione Italiana Miastenia, Associazione Miastenia, EU-MGA, Hellenic Myasthenia Association, MG Holistic Society, MG Japan, MG Ohio, MG Society of Canada, Myasthenia Gravis Association, Myasthenia Gravis Association of Western PA, Myasthenia Gravis Foundation of America, Myasthenia Gravis Foundation of Bulgaria, Myasthenia Gravis Foundation of Michigan, MyAware, Netherlands MG Association, Stowarzyszenie Miastenia Gravis Face to Face, Mission Arthritis India (MAI), Ankylosing Spondylitis Welfare Society (ASWS), StandForAS, Scleroderma India. Disclosure of Interests: Sreoshy Saha: None declared, Manali Sarkar: None declared, Maria Rosaria Pellico: None declared, Elena Philippou: None declared, Praggya Yaadav: None declared, Tsvetelina Velikova received speaker honoraria from Pfizer and AstraZeneca, non-related to the current abstract, Abraham Edgar Gracia-Ramos: None declared, Aviya Lanis: None declared, Karen Cheng employed by Sobi working on projects unrelated to this abstract, Dzifa Dey: None declared, Elena Nikiphorou received speaker honoraria/participated in advisory boards for Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly, holds research grants from Pfizer and Lilly, Jasmine Parihar: None declared, Vikas Agarwal: None declared, Vincenzo Venerito: None declared, Latika Gupta: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».