{"paper_id":"3c0b8e4b-efb3-4536-bbd1-d2999768227b","body_text":"The effect of an anti-inflammatory diet on disease activity and quality of life in patients with Rheumatoid Arthritis - Parallel controlled randomized clinical study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The effect of an anti-inflammatory diet on disease activity and quality of life in patients with Rheumatoid Arthritis - Parallel controlled randomized clinical study Ana Faria, Mariana Freitas, Maria João Almeida, Shámila Ismael, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9452089/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Rheumatoid arthritis (RA) is associated with systemic inflammation and gut microbiota dysbiosis. This study evaluated whether a structured Anti-Inflammatory Diet (AID) provides additional benefits over the Mediterranean Diet (MD) in modulating disease activity, quality of life (QoL), and gut microbiota composition. Methods In this 15-week parallel randomized controlled study (NCT05336513), patients were allocated to AID or MD group. Clinical outcomes (Disease Activity Score 28 (DAS28), Health Assessment Questionnaire (HAQ)), anthropometry, and gut microbiota (short 16S rRNA gene sequencing) were assessed at baseline and post-intervention. Results Both dietary interventions successfully reduced disease activity. A greater reduction in DAS28 was observed in the AID group, although between-group differences were not statistically significant. While the MD intervention triggered more extensive shifts in microbiota composition, diversity levels were comparable between groups. Notably, Collinsella was significantly more abundant in the AID group post-intervention. Conclusion Both dietary strategies resulted in moderate clinical improvement in RA. Greater microbiota modulation did not translate into superior clinical outcomes, highlighting the complexity of diet–microbiota–host interactions. Dietary interventions may represent a valuable adjunct therapy in RA management. Health sciences/Health care/Nutrition Health sciences/Diseases/Rheumatic diseases/Rheumatoid arthritis Anti-inflammatory diet gut microbiota rheumatoid arthritis disease activity quality of life Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease affecting roughly 0.5–1% of people worldwide ( 1 ). It primarily causes symmetric polyarthritis, progressive joint destruction ( 2 ), and reduced health-related quality of life ( 3 ). RA pathogenesis involves complex interactions between genetics, immunity and environment. Key immune players include Th17 cells, pro-inflammatory cytokines (TNFα, IL-6, IL-1β), and synovial hyperplasia driven by fibroblast-like synoviocytes ( 4 ). Despite advances in pharmacological therapies, including biologics and JAK inhibitors, many patients fail to achieve sustained remission. This has driven interest in adjunctive strategies targeting modifiable environmental factors ( 4 ). Diet is increasingly recognized as a modulator of inflammation and gut microbiota composition. Emerging evidence links gut dysbiosis and intestinal barrier dysfunction with systemic immune activation in RA ( 5 – 9 ). Moreover, patients with RA frequently exhibit poor dietary patterns, which are associated with increased inflammation, oxidative stress, and worse clinical outcomes ( 6 , 10 , 11 ). The Mediterranean Diet (MD), rich in plant-based foods, olive oil, and fish, has demonstrated anti-inflammatory and cardiometabolic benefits ( 12 ). However, evidence supporting its role in RA remains limited and inconsistent ( 7 , 13 , 14 ). This may be due to the MD being aimed at the general population and not specifically tailored to RA-related mechanisms such as intestinal permeability or targeted microbiota modulation ( 5 , 10 ). To address these limitations, structured anti-inflammatory dietary approaches incorporating specific functional components have been proposed ( 15 , 16 ). However, controlled intervention studies evaluating their combined clinical and microbiota effects remain scarce. Therefore, the present controlled clinical study (NCT05336513) aimed to compare the effects of a structured Anti-Inflammatory Diet (AID) versus the Mediterranean Diet on disease activity, quality of life, and gut microbiota composition in patients with RA. Subject and Methods Study design This 15-week parallel randomized controlled study (NCT05336513) was conducted between April and December 2021 at Hospital Particular do Algarve (HPA, Algarve, Portugal). The study was approved by the NMS Ethical Committee (44/2021/CEFCM) and HPA Saúde Group (04/2021) and conducted in accordance with the ethical principles of the Declaration of Helsinki and Good Clinical Practice guidelines. All enrolled participants provided written informed consent prior to enrollment. Participants Volunteers were recruited from autoimmune disease consultations. Eligible participants were adults, with a diagnosis of RA and active disease (DAS28 ≥ 2.6) on stable medication. Participants were excluded if they had a psychiatric illness, dementia, or eating disorder; were pregnant; had food allergies/intolerances; were vegetarian; used phytotherapy or other supplements (including pre or probiotics); or had received nutritional counseling or changed eating habits in the previous 6 months. Interventions Both dietary interventions had a duration of 15 weeks and were delivered through structured nutritional counseling every 5 weeks by a trained dietitian. Nutritional counseling sessions included individualized diet plans and standardized educational materials ( 9 ). For participants with BMI ≥ 25 kg/m 2 , the intervention also aimed at gradual weight reduction ( 6 ). Participants were encouraged to maintain habitual physical activity as advised by their physician. The AID dietary intervention was designed to target inflammatory pathways, intestinal permeability, and gut microbiota modulation. Central strategies included: Optimizing the lipid profile to achieve an omega-6/3 ratio of 2:1 by increasing extra virgin olive oil, nuts, seeds (chia and flaxseed), and fatty fish (3 servings/week; 150 g per serving), while strictly avoiding trans and saturated fats found in processed foods. High intake of prebiotic-rich foods (≥ 5 servings/day of fruits and vegetables, 3 servings/day of gluten-free whole grains), alongside daily probiotic intake in yoghurt. Restriction of gluten-containing products and cow’s milk, replaced with plant-based alternatives. Limitation of red meat to 1 weekly portion (100 g) and solanaceous vegetables. Exclusion of refined sugars, and added salt, while including functional spices such as turmeric, ginger and cinnamon. Moderate caffeine restriction (≤ 300 mg/day), favoring green tea over coffee, and allowing moderate red wine intake (≤ 125 mL/day). The dietary intervention of AID was based on principles derived from the literature ( 9 , 13 ) and is detailed and rationalized in Supplementary Table 1. Participants in the MD group received standard dietary counseling based on Mediterranean dietary principles. Adherence was assessed using the validated MEDAS questionnaire ( 17 ). Dietary intake was assessed using 24-hour dietary recalls and 5-day non-consecutive food diaries collected throughout the intervention. Data and sample collection At the first nutrition consultation, a sociodemographic and lifestyle questionnaire was administered. Blood and stool samples, Disease Activity Score 28 (DAS28) and HAQ were collected at baseline and post-intervention. Weight was assessed until the end of intervention. Clinical outcomes Disease activity was measured using DAS28, a validated tool that includes the joint count of 28 painful joints (ADO), 28 swollen joints (AE), the VSE and the pain scale (ED) (measured from 0 to 100). The calculation provides continuous measure, with the higher the value, the higher the DA at the time of assessment ( 7 ). Scores were classified as low (< 3.2) or moderate/high (≥ 3.2) ( 8 ). The functional capacity and the disease impact on daily life and QoL were assessed using the HAQ, comprising 20 items divided into eight areas of patients' activities of daily living: getting ready, getting up, eating, walking, hygiene, reaching, grasping and activities. The highest value of each domain is retained, summed, and divided by eight. Higher scores indicate greater functional disability and poorer QoL ( 18 ). Biochemical markers Blood samples were collected to determine c-reactive protein (CRP), erythrocyte sedimentation rate (ESR), rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibodies (anti-CCP). Anthropometric measures Weight was measured using a Tanita BC-601 scale. Height was self-reported, and BMI was calculated as kg/m². Gut microbiota analysis Stool samples were collected by the participants using sterile tubes (EasySampler®) containing RNAlater. Genomic DNA was extracted and purified using the NZY Tissue gDNA Isolation kit (NZYtech, Lisbon, Portugal), as previously described by Marques et al ( 19 ). Libraries were processed and sequenced following the 16S Metagenomic Sequencing Library Preparation protocol by Illumina (Illumina; San Diego, CA, USA). The primer set 515F: GTGYCAGCMGCCGCGGTAA and 806R: GGACTACNVGGGTWTCTAAT was used to capture the V3-V4 region of the bacterial 16S rRNA gene. Samples were loaded onto an Illumina MiSeq platform using 2 × 250 bp paired-end run ( 20 ). Processing of the metabarcoding data and all taxonomic analyses, were performed using mothur v.1.46.1 software ( 21 ). Briefly, all low-quality and chimeric sequences were detected and removed from the dataset with the VSEARCH software ( 22 ). Next, the remaining sequences were grouped into operational taxonomic units with 97% similarity using the OptiClust tool ( 23 ), and then classified against the ARB-Silva v.138.1 (SSU − 16S) taxonomic database ( 24 ) using the RDP classifier ( 25 ). The MicrobiomeAnalyst tool was used to perform all additional visual, comparative and statistical analyses ( 26 , 27 ). Statistical analysis Statistical analyses were performed using the software SPSS® version 27 (IBM SPSS Statistics, IBM Corporation, Armonk, NY, USA). Data normality was assessed by the Kolmogorov-Smirnov test. Comparisons between groups were performed using the t Student’s t-test or Mann-Whitney U as appropriate. Categorical variables were analyzed by using Pearson's chi-square test. A linear mixed ANCOVA model was used to analyze the variance of ESR, CRP, RF, DAS28, HAQ and BMI, with diet group as a fixed effect. Potential confounders (age, sex, baseline BMI, education, smoking status, and medication type) were tested and retained only if they altered effect estimates by ≥ 10%. Differences between beta diversity across interventions were assessed using the PERMANOVA test and correlations between DAS28 and BMI, microbiota composition (phylum and genus levels) and clinical markers after 15 weeks of intervention were analyzed by Pearson's correlation coefficient. Data are presented as n (%), mean ± SD or 95% confidence intervals. Differences were considered statistically significant when p < 0.05. Results Participant characteristics A total of 45 individuals were screened for eligibility. Of these, 16 were excluded during screening. Twenty-nine participants were randomized to the intervention groups. Three participants allocated to the MD group were excluded prior to intervention due to pre-existing adherence to a Mediterranean dietary pattern. The final analytical sample comprised 26 participants (AID n = 14; MD n = 12). Participant flow is presented in Fig. 1 . Participants were aged between 18 and 74 years, with a predominance of females (n = 22, 84.6%). Fifteen participants were receiving biological therapy and eleven were treated with conventional DMARDs, while six participants were current smokers. No significant differences were observed between the AID and MD groups at baseline regarding demographic, clinical, or lifestyle characteristics (Table 1 ). Table 1 Baseline characteristics of participants. AID MD p Age, years 52 ± 10 56 ± 15 0.385 Sex, n (%) Female 11 (78.6) 11 (91.7) 0.366 Male 3 (21.4) 1 (8.3) Marital status, n (%) Single 1 (7.1) 1 (8.3) 0.587 Married/Unmarried 10 (71.4) 7 (58.3) Divorced 2 (14.3) 2 (16.7) Widowed 1 (7.1) 2 (16.7) Level of education, n (%) Primary education 3 (21.4) 4 (33.3) Secondary education 8 (57.1) 2 (16.7) 0.113 Higher education 3 (21.4) 6 (50.0) Type of medication, n (%) BT 9 (64.3) 6 (50.0) 0.471 DMARD 5 (35.7) 6 (50.0) Anti-CCP positive, n (%) 3 (21.4) 2 (16.7) 0.840 Smokers, n (%) 3 (21.4) 3 (25.0) 0.838 Data expressed as mean ± SD for Age; all other variables are expressed as n (%). Mean differences between results were assessed using the student’s t-test or Pearson's chi-square test. Differences were considered statistically significant when p < 0.05. AID - Anti-inflammatory Diet; MD - Mediterranean Diet; DMARD - Disease Modifying Antirheumatic Drugs; BT – Biological Therapy. Clinical and anthropometric measures At baseline, DAS28 was similar between groups and corresponded, on average, to moderate disease activity ( 29 ). Similarly, no significant baseline differences were observed for inflammatory markers (ESR, CRP), serological markers (RF), functional status (HAQ), or BMI, confirming baseline homogeneity. Mean BMI values in both groups were within the overweight range (Table 2 ). After intervention, participants in the AID group showed significant reduction in DAS28 ( p = 0.001) and HAQ score ( p = 0.038), indicating a decrease in disease activity and an improvement in functional status. In the MD group, significant reductions were observed in HAQ score ( p = 0.045) and BMI ( p < 0.001), while the reduction in DAS28 did not reach statistical significance ( p = 0.219; Table 2 ). According to the EULAR response criteria, both dietary interventions resulted in a moderate clinical response, defined as a DAS28 reduction > 0.6 and ≤ 1.2 with a final DAS28 ≤ 5.1 ( 30 ). Although the mean reduction in DAS28 was numerically greater in the AID group, both groups met the criteria for a moderate response. Despite these within-group improvements, no statistically significant differences were detected between interventions when assessed using the linear mixed ANCOVA model (Table 2 ). Table 2 Demographic, clinical and anthropometric characteristics of participants, at baseline and after 15 weeks of intervention. AID MD Baseline After intervention p 1 Difference Baseline After intervention p 1 Difference p 2 p 3 ESR, mm/h 14.2 ± 12.4 14.6 ± 13.1 0.903 -0.177 (-6.831;7.185) 15.92 ± 17.4 21.5 ± 21.3 0.072 -6.207 (-13.826;1.412) 0.774 0.229 CRP, mg/dL 0.5 ± 1 1 ± 1.6 0.135 -0.466 (-1.075;0.144) 1.1 ± 1.6 1.2 ± 1.6 < 0.001 -0.054 (-0.724;0.617) 0.318 0.380 RF, IU/mL 151.9 ± 302.4 72.7 ± 115.5 0.208 126.287 (-6.496;259.068) 155.8 ± 188.6 99.9 ± 137.92 0.053 33.793 (-133.819;201.404) 0.975 0.365 DAS28, unit 5 ± 0.73 3.7 ± 0.6 0.001 1.286 (0.648;1.925) 4.8 ± 0.7 4 ± 0.7 0.219 0.841 (0.147–1.535) 0.485 0.353 HAQ, unit 0.5 ± 0.4 0.3 ± 0.4 0.038 0.208 (-0.034;0.450) 0.7 ± 0.6 0.5 ± 0.4 0.045 0.184 (-0.079;0.447) 0.236 0.895 BMI, kg/m 2 28.7 ± 6.8 28.2 ± 6.8 0.067 0.610 (0.070;1.151) 27.4 ± 5.0 26.5 ± 4.5 < 0.001 0.856 (0.280;1.443) 0.607 0.384 BMI, n (%) Low weight 1 (7.1) 1 (7.1) 1.000 0 (0.0) 0 (0.0) 1.000 1.000 1.000 Normal weight 2 (14.3) 3 (21.4) 1.000 3 (25.0) 5 (41.7) 0.667 0.635 0.401 Overweight 7 (50.0) 6 (42.9) 1.000 5 (41.7) 3 ( 25 ) 0.667 0.713 0.429 Obesity 4 (28.6) 4 (28.6) 1.000 4 (33.3) 4 (33.3) 1.000 1.000 1.000 Data expressed as n (%) or mean ± SD. Difference represents the absolute change between baseline and after intervention (Baseline – After intervention) and are expressed as mean (95% confidence intervals). Mean differences were assessed using Student’s t-test (p 1 for within-group and p 2 for between-group comparisons) and ANCOVA linear mixed analysis models (p 3 for difference comparison). Categorical variables were analyzed using Fisher’s Exact Test. Results were considered statistically significant when p < 0.05. AID – Anti-inflammatory Diet; MD – Mediterranean Diet; ESR – Erythrocyte Sedimentation Rate; CRP – C-Reactive Protein; RF - Rheumatoid Factor; anti-CCP – Anti-cyclic Citrullinated Peptide Antibodies; DAS 28 – Disease Activity Score 28; HAQ – Health Assessment Questionnaire; BMI – Body Mass Index. Regarding functional capacity, a reduction of approximately 0.2 points in HAQ score was observed in both groups over the intervention period. Reductions of 0.19 points are considered the minimal clinically important improvement in function. The proportion of participants achieving a minimal improvement in HAQ score was similar between diets (Table 3 ) ( 31 ). Table 3 – Variation in Health Assessment Questionnaire (HAQ) score variation for both diets at the end of the intervention AID MD Difference HAQ, n (%) No minimum improvement in function 8 (57.1%) 7 (58.3%) With minimum improvement in the function 6 (42.9%) 5 (41.7%) Data expressed as n (%). Mean differences between results were assessed using Pearson's chi-square test. Differences were considered statistically significant when p < 0.05. AID - Anti-inflammatory Diet; MD - Mediterranean Diet; HAQ - Health Assessment Questionnaire. Gut microbiota analysis AID group No significant changes were observed in alpha diversity following the AID intervention (Shannon index, p = 0.985; Fig. 2 A). Similarly, beta diversity analysis showed no significant differences between baseline and post-intervention microbial community structure (Fig. 2 B). At the phylum level, no significant differences were observed, although a trend towards increased Fusobacteriota abundance was observed ( p = 0.068; Fig. 2 C). At the genus level, relative abundances of Actinomyces ( p = 0.011), Blautia ( p = 0.016), Collinsella ( p = 0.015), Erysipelotrichaceae _UCG_003 ( p = 0.013) and Lachnospiraceae ( p = 0.028) decreased following the AID intervention, with a tendency towards a decrease in Coprococcus ( p = 0.064). Conversely, Lachnospiraceae _unclassified ( p = 0.039) and Sutterella ( p = 0.019) increased, with trends towards increased abundance of Roseburia ( p = 0.055) and NK4A214_group ( p = 0.050) (Fig. 2 D). MD group Following MD intervention, alpha diversity showed a non-significant increasing trend, while beta diversity differed significantly between baseline and post-intervention (p = 0.048; Fig. 3 A, B). At the phylum level, MD intervention resulted in decreases in Actinomycetota ( p = 0.005) and Bacillota ( p = 0.041), alongside increases in Bacteroidota ( p = 0.003) and Desulfobacterota ( p = 0.019) (Fig. 3 C). At the genus level, increases were detected in Bacteroides ( p = 0.004), Agathobacter ( p = 0.010), NK4A214_group ( p = 0.084), Bilophila ( p = 0.028), Roseburia ( p = 0.041) and Suterella ( p = 0.017) with trends towards increased abundance of Prevotella_9 ( p = 0.060), Parabacteroides ( p = 0.077), Lachnospira ( p = 0.060) and NK4A214_group ( p = 0.084). Moreover, significant decreases were observed in Subdoligranulum ( p = 0.004), Bifidobacterium ( p = 0.019), Enterobacteriaceae_unclassified ( p = 0.034), Actinomyces ( p = 0.019) and Collinsella ( p = 0.006). There was a trend towards a reduction of Streptococcus (p = 0.060) (Fig. 3 D). Comparison between interventions At baseline, alpha diversity (Shannon index) was similar between groups ( p = 0.439). Both interventions showed a tendency towards increased alpha diversity after 15 weeks, with a greater, though non-significant, increase observed in the MD group (Fig. 4 A). No significant differences between groups were observed in beta diversity at baseline or post-intervention (Fig. 4 B, C). At the phylum level, baseline compositions were largely comparable, except for higher Bacillota abundance in the MD group ( p = 0.041) and a trend towards higher Bacteroidota in the AID group ( p = 0.076). After intervention, no significant between-group differences were detected. Nevertheless, when comparing the variation induced by the treatments, the MD group exhibited a greater increase in Bacteroidota and a greater decrease in Bacillota compared to the AID group (both p = 0.028; Fig. 4 D). At the genus level, no differences were detected at baseline between diets. After intervention, only Collinsella was more abundant in AID compared to the MD ( p = 0.046; Fig. 4 E). Discussion This parallel randomized controlled clinical study evaluated the effects of a structured anti-inflammatory diet (AID) compared with the Mediterranean diet (MD) on disease activity, quality of life, and gut microbiota composition in patients with active RA. Both dietary interventions were associated with reduction in disease activity. This suggests that structured dietary improvement, regardless of specific pattern, may contribute to clinically relevant benefits in RA. Previous studies have shown that MD interventions can lower the disease activity and inflammatory markers such as CRP and RF levels ( 32 ). In the ADIRA study, a mean reduction of 0.42 units in DAS28 was reported, which is lower than the reduction observed in the present study, although the intervention duration was shorter (10 weeks) ( 8 ). Regarding QoL, assessed by the HAQ, both interventions resulted in an approximate reduction of 0.2 units, corresponding to a minimal clinically important improvement in function ( 30 ). The relationship between weight change and clinical outcomes ( 6 ) appeared limited in this cohort. While the MD group showed a greater reduction in BMI, this was not accompanied by superior improvement in disease activity. This observation suggests that mechanisms beyond weight loss, such as modulation of inflammatory pathways or immune responses, may contribute to the clinical benefits associated with dietary interventions. In the present study, both dietary interventions were associated with changes in gut microbiota composition, although the magnitude and nature of these changes differed between diets. While both interventions showed a tendency towards increased alpha diversity, the MD intervention induced broader shifts in microbial community structure, reflected by significant changes in beta diversity and phylum-level composition. Specifically, MD induced a higher variation in the relative abundance of Bacteroidota and Bacillota compared with AID, partially counteracting features of the microbiota profile reported in RA, which is typically characterized by increased Bacillota and Actinobacteria and reduced Bacteroidota ( 33 , 34 ). MD adherence, in combination with appropriate drug therapy, may increase species diversity, richness and evenness of the gut microbiota, as well as promoting abundance of bacteria that produce short chain fatty acids (SCFA), counteracting the dysbiotic profile characteristic in the development autoimmune diseases such as RA ( 34 , 35 ). Despite these differences, greater microbiota modulation in the MD group did not translate into superior clinical outcomes. This finding highlights the complexity of the relationship between gut microbiota and RA, suggesting that compositional changes alone may not fully explain clinical responses. It is possible that functional microbial outputs, host–microbe interactions, or direct dietary effects on immune pathways play a more relevant role than taxonomic shifts per se. At the genus level, specific taxa changes were observed in both groups. Notably, Collinsella , a genus previously associated with RA pathogenesis and intestinal permeability ( 33 , 36 , 37 ), was reduced after AID intervention. In addition, an increase in Sutterella and a tendency towards increased Roseburia were observed after AID. Interestingly, an increase in Roseburia , known for SCFA production, has also been reported in chronic patients after anti-TNF-α antibody therapy ( 38 , 39 ). Nevertheless, functional implications cannot be confirmed in the absence of metabolomic data. This study has several limitations. The sample size was relatively small, which may have limited the ability to detect between-group differences. Microbiota analysis was based on short 16S rRNA sequencing, precluding species-level resolution and functional characterization ( 40 ). Additionally, only faecal samples were analysed, which may not fully capture microbial alterations at extra-intestinal sites relevant to RA ( 7 ). Although medication was stable throughout the study, potential interactions between pharmacological treatments and diet could not be fully explored. Future studies should incorporate larger cohorts, longer follow-up, and multi-omics approaches to better characterize the functional impact of diet on host–microbiota interactions. In addition, investigating the interplay between diet and immunomodulatory therapies may also provide further insight into personalized management strategies for RA. In conclusion, in patients with moderate RA, both the tailored anti-inflammatory diet and the Mediterranean diet were associated with reductions in disease activity and improvements in quality of life over a 15-week intervention period. While MD induced more pronounced changes in gut microbiota composition, these differences did not translate into clear clinical advantage of one intervention over the other. These findings support the role of dietary modification as a complementary approach in RA management, while underscoring the need for further research to clarify the mechanisms linking diet, microbiota, and clinical outcomes. Declarations Data availability statement The datasets generated and analyzed during the current study are not publicly available but may be obtained from the corresponding author on reasonable request. Acknowledgements The authors sincerely thank all the volunteers who participated in this study for their time and commitment. We are also deeply grateful to the entire team at Hospital Particular do Algarve for their collaboration. Author contributions Conceptualization: Ana Faria, Carlos Carneiro, Conceição Calhau; Methodology: Mariana Freitas, Maria João Almeida, Shámila Ismael; Formal analysis and investigation: Mariana Freitas, Maria João Almeida, Shámila Ismael, Alexandre Alves Rodrigues, João Trovão, Joana Costa, Christophe Espírito Santo; Writing - original draft preparation: Mariana Freitas, Alexandre Alves Rodrigues; Writing - review and editing: Shámila Ismael, Alexandre Alves Rodrigues, João Trovão, Joana Costa, Christophe Espírito Santo, Ana Faria; Funding acquisition: Ana Faria, Carlos Carneiro, Conceição Calhau; Supervision: Ana Faria. All authors read and approved the final manuscript. Funding This work was supported by ERDF through the operation POCI-01-0145-ERDF-007746 funded by the Programa Operacional Competitividade e Internacionalização – COMPETE2020 and by FCT - Fundação para a Ciência e a Tecnologia, IP national support through CINTESIS, R&D Unit (UIDB/4255/2020 and UIDP/4255/2020), CHRC (UIDP/04923/2020 and UIDB/04923/2020). Ethics Approval The study was approved by the NMS Ethical Committee (44/2021/CEFCM) and HPA Saúde Group (04/2021). All procedures were performed in accordance with the relevant guidelines and regulations. Competing Interests The authors declare that they have no conflicts of interest to disclose. References Almutairi K, Nossent J, Preen D, Keen H, Inderjeeth C. The global prevalence of rheumatoid arthritis: a meta-analysis based on a systematic review. Rheumatol Int. 2021;41(5):863–77. Jahid M, Khan KU, Rehan Ul H, Ahmed RS. Overview of Rheumatoid Arthritis and Scientific Understanding of the Disease. 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The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–7. Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017;45(W1):W180–W8. Chong J, Liu P, Zhou G, Xia J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc. 2020;15(3):799–821. Hopewell S, Chan AW, Collins GS, Hrobjartsson A, Moher D, Schulz KF, et al. CONSORT 2025 statement: updated guideline for reporting randomised trials. Lancet. 2025. van Gestel AM, Haagsma CJ, van Riel PL. Validation of rheumatoid arthritis improvement criteria that include simplified joint counts. Arthritis Rheum. 1998;41(10):1845–50. Winkvist A, Barebring L, Gjertsson I, Ellegard L, Lindqvist HM. A randomized controlled cross-over trial investigating the effect of anti-inflammatory diet on disease activity and quality of life in rheumatoid arthritis: the Anti-inflammatory Diet In Rheumatoid Arthritis (ADIRA) study protocol. Nutr J. 2018;17(1):44. de Oliveira LM, Natour J, Roizenblatt S, de Araujo PM, Ferraz MB. [Monitoring the functional capacity of patients with rheumatoid arthritis for three years]. Rev Bras Reumatol. 2015;55(1):62–7. Mukherjee MS, Han CY, Sukumaran S, Delaney CL, Miller MD. Effect of anti-inflammatory diets on inflammation markers in adult human populations: a systematic review of randomized controlled trials. Nutr Rev. 2022;81(1):55–74. Wang Q, Zhang SX, Chang MJ, Qiao J, Wang CH, Li XF, et al. Characteristics of the Gut Microbiome and Its Relationship With Peripheral CD4(+) T Cell Subpopulations and Cytokines in Rheumatoid Arthritis. Front Microbiol. 2022;13:799602. Jeong Y, Kim JW, You HJ, Park SJ, Lee J, Ju JH, et al. Gut Microbial Composition and Function Are Altered in Patients with Early Rheumatoid Arthritis. J Clin Med. 2019;8(5). Chasov V, Gilyazova E, Ganeeva I, Zmievskaya E, Davletshin D, Valiullina A, et al. Gut Microbiota Modulation: A Novel Strategy for Rheumatoid Arthritis Therapy. Biomolecules. 2024;14(12). Ruiz-Limon P, Mena-Vazquez N, Moreno-Indias I, Manrique-Arija S, Lisbona-Montanez JM, Cano-Garcia L, et al. Collinsella is associated with cumulative inflammatory burden in an established rheumatoid arthritis cohort. Biomed Pharmacother. 2022;153:113518. Forbes JD, Chen CY, Knox NC, Marrie RA, El-Gabalawy H, de Kievit T, et al. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome. 2018;6(1):221. Skoldstam L, Hagfors L, Johansson G. An experimental study of a Mediterranean diet intervention for patients with rheumatoid arthritis. Ann Rheum Dis. 2003;62(3):208–14. Nikiphorou E, Norton S, Carpenter L, Dixey J, Andrew Walsh D, Kiely P, et al. Secular Changes in Clinical Features at Presentation of Rheumatoid Arthritis: Increase in Comorbidity But Improved Inflammatory States. Arthritis Care Res (Hoboken). 2017;69(1):21–7. Abebaw D, Akelew Y, Adugna A, Tegegne BA, Teffera ZH, Belayneh M, et al. Immunomodulatory properties of the gut microbiome: diagnostic and therapeutic potential for rheumatoid arthritis. Clin Exp Med. 2025;25(1):226. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Under Review Version 1 posted Review # 4 received at journal 11 May, 2026 Reviewer # 4 agreed at journal 11 May, 2026 Reviewer # 3 agreed at journal 05 May, 2026 Reviewer # 2 agreed at journal 03 May, 2026 Review # 1 received at journal 30 Apr, 2026 Reviewer # 1 agreed at journal 29 Apr, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9452089\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":631564725,\"identity\":\"3d453487-c6ea-4ccf-92d7-44507de4da0b\",\"order_by\":0,\"name\":\"Ana Faria\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3OMarCQBSF4SOB2Fxjm0J0C5MmzSvcykxjmiCCjYWFIKRT2/d4q3AHI0FtsoCRiGQDQiqxEg02gjAOViLzwy1u8cEBbLZPbQQQ6pP74xmRrCIk748L1CYmBPC5IWn+TwvI8aHV/Duul2ekfReNbaEj/n7DIDdD8vN+LyekQxee0A9TnDmlywl5HOZAKhJQoCUdFZWQF06dXRbuziaEqZhhlXBiikJFJiRQ8UCu5pyC7DaMWCQSxxO/OtJW0bKQJ95tb6thox+xqM9kqSNV8mHn7Rx6BZ57g9hsNttXdwV5cEqGNa8QkwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Faculdade de Ciências Médicas | NOVA Medical School, Universidade NOVA de Lisboa\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ana\",\"middleName\":\"\",\"lastName\":\"Faria\",\"suffix\":\"\"},{\"id\":631564726,\"identity\":\"f2b4cfe5-ac8c-4e79-886f-16a95aeb222e\",\"order_by\":1,\"name\":\"Mariana Freitas\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculdade de Ciências Médicas | NOVA Medical School, Universidade NOVA de Lisboa\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mariana\",\"middleName\":\"\",\"lastName\":\"Freitas\",\"suffix\":\"\"},{\"id\":631564727,\"identity\":\"c339b92a-7965-402f-8333-153572bbe9f4\",\"order_by\":2,\"name\":\"Maria João Almeida\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculdade de Ciências Médicas | NOVA Medical School, Universidade NOVA de Lisboa\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Maria\",\"middleName\":\"João\",\"lastName\":\"Almeida\",\"suffix\":\"\"},{\"id\":631564728,\"identity\":\"665fddf3-3576-4d62-bfb8-6c138aa13acb\",\"order_by\":3,\"name\":\"Shámila Ismael\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculdade de Ciências Médicas | NOVA Medical School, Universidade NOVA de Lisboa\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shámila\",\"middleName\":\"\",\"lastName\":\"Ismael\",\"suffix\":\"\"},{\"id\":631564729,\"identity\":\"a61aad15-e343-4ed7-99c6-a06ee654ddc5\",\"order_by\":4,\"name\":\"Alexandre Rodrigues\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculdade de Ciências Médicas | NOVA Medical School, Universidade NOVA de Lisboa\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Alexandre\",\"middleName\":\"\",\"lastName\":\"Rodrigues\",\"suffix\":\"\"},{\"id\":631564730,\"identity\":\"c8eea127-efa2-4c8e-97a7-b264e3a278fe\",\"order_by\":5,\"name\":\"João Trovão\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Coimbra\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"João\",\"middleName\":\"\",\"lastName\":\"Trovão\",\"suffix\":\"\"},{\"id\":631564731,\"identity\":\"642d72fe-14ec-4788-9b34-ec5aa4461967\",\"order_by\":6,\"name\":\"Joana Costa\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Coimbra\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Joana\",\"middleName\":\"\",\"lastName\":\"Costa\",\"suffix\":\"\"},{\"id\":631564732,\"identity\":\"0b8cddd9-87a3-4155-87f5-55d3bbb8e38d\",\"order_by\":7,\"name\":\"Christophe Espírito Santo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christophe\",\"middleName\":\"Espírito\",\"lastName\":\"Santo\",\"suffix\":\"\"},{\"id\":631564733,\"identity\":\"445d4d37-9c9c-4ddf-bd89-8121da10e9d4\",\"order_by\":8,\"name\":\"Conceição Calhau Calhau\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Conceição\",\"middleName\":\"Calhau\",\"lastName\":\"Calhau\",\"suffix\":\"\"},{\"id\":631564734,\"identity\":\"a01dd098-ed77-4b74-b69c-4612f836c845\",\"order_by\":9,\"name\":\"Carlos Carneiro\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Carlos\",\"middleName\":\"\",\"lastName\":\"Carneiro\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-17 18:40:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9452089/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9452089/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108956817,\"identity\":\"56c32b98-dd6c-48ee-a68e-92a75cdb427c\",\"added_by\":\"auto\",\"created_at\":\"2026-05-11 08:15:20\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":300428,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow diagram of participants through the study. Diagram adapted from the CONSORT 2025 Statement (28). AID: Anti-inflammatory Diet; MD: Mediterranean Diet.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9452089/v1/a8047037cdde6ffb1a8ceca5.jpg\"},{\"id\":108978025,\"identity\":\"0449be17-838b-422f-8e9d-727679b6ae6d\",\"added_by\":\"auto\",\"created_at\":\"2026-05-11 11:33:45\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":506502,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDiversity indices and taxonomic analysis of gut microbiota in AID group at baseline and after intervention. (A) Alpha diversity measured by Shannon index; (B) Principal coordinate analysis (PCoA) of Bray-Curtis dissimilarity to determine differences in gut microbiota community between groups using PERMANOVA; (C) Mean relative abundance of phyla. Each taxon is represented by a different color; (D) Mean relative abundance of genera with bars representing the top 30 taxa. Each taxon is represented by a different color. Differences were assessed using the student’s t-test. Statistical significance defined as *\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9452089/v1/9d72a50a3eefd39989fcf9fa.jpg\"},{\"id\":108956830,\"identity\":\"fe5cc05b-978f-4e08-9c85-cc2a117f4cab\",\"added_by\":\"auto\",\"created_at\":\"2026-05-11 08:15:21\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":472225,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDiversity indices and taxonomic analysis of gut microbiota in MD group at baseline and after intervention. (A) Alpha diversity measured by Shannon index; (B) Principal coordinate analysis (PCoA) of Bray-Curtis dissimilarity to determine differences in gut microbiota community between groups using PERMANOVA; (C) Mean relative abundance of phyla. Each taxon is represented by a different color; (D) Mean relative abundance of genera with bars representing the top 30 taxa. Each taxon is represented by a different color. Differences were assessed using the student’s t-test. Statistical significance defined as *\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9452089/v1/7793e7ffc993ef641de4b62a.jpg\"},{\"id\":108956683,\"identity\":\"2440e51f-8370-4c14-8ebd-991744a939e0\",\"added_by\":\"auto\",\"created_at\":\"2026-05-11 08:15:02\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":637708,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDiversity indices and taxonomic analysis of gut microbiota in AID and MD group at baseline and after intervention. (A) Alpha diversity measured by Shannon index; (B) Principal coordinate analysis (PCoA) of Bray-Curtis dissimilarity to determine differences in gut microbiota community between groups using PERMANOVA; (C) Mean relative abundance of phyla. Each taxon is represented by a different color; (D) Mean relative abundance of genera with bars representing the top 30 taxa. Each taxon is represented by a different color. Differences were assessed using the student’s t-test. Statistical Significance defined as *\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9452089/v1/72c17256ebd0d1a1d1e946fe.jpg\"},{\"id\":108979926,\"identity\":\"50d7004f-23a2-4085-807c-0760875e9f7e\",\"added_by\":\"auto\",\"created_at\":\"2026-05-11 12:02:22\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2323521,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9452089/v1/7e6b98a2-4fc1-46db-b505-6bcd035e5154.pdf\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e conflict of interest to disclose.\",\"formattedTitle\":\"The effect of an anti-inflammatory diet on disease activity and quality of life in patients with Rheumatoid Arthritis - Parallel controlled randomized clinical study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eRheumatoid arthritis (RA) is a chronic systemic autoimmune disease affecting roughly 0.5\\u0026ndash;1% of people worldwide (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). It primarily causes symmetric polyarthritis, progressive joint destruction (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e), and reduced health-related quality of life (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRA pathogenesis involves complex interactions between genetics, immunity and environment. Key immune players include Th17 cells, pro-inflammatory cytokines (TNFα, IL-6, IL-1β), and synovial hyperplasia driven by fibroblast-like synoviocytes (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite advances in pharmacological therapies, including biologics and JAK inhibitors, many patients fail to achieve sustained remission. This has driven interest in adjunctive strategies targeting modifiable environmental factors (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDiet is increasingly recognized as a modulator of inflammation and gut microbiota composition. Emerging evidence links gut dysbiosis and intestinal barrier dysfunction with systemic immune activation in RA (\\u003cspan additionalcitationids=\\\"CR6 CR7 CR8\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMoreover, patients with RA frequently exhibit poor dietary patterns, which are associated with increased inflammation, oxidative stress, and worse clinical outcomes (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe Mediterranean Diet (MD), rich in plant-based foods, olive oil, and fish, has demonstrated anti-inflammatory and cardiometabolic benefits (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHowever, evidence supporting its role in RA remains limited and inconsistent (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). This may be due to the MD being aimed at the general population and not specifically tailored to RA-related mechanisms such as intestinal permeability or targeted microbiota modulation (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo address these limitations, structured anti-inflammatory dietary approaches incorporating specific functional components have been proposed (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). However, controlled intervention studies evaluating their combined clinical and microbiota effects remain scarce.\\u003c/p\\u003e \\u003cp\\u003eTherefore, the present controlled clinical study (NCT05336513) aimed to compare the effects of a structured Anti-Inflammatory Diet (AID) versus the Mediterranean Diet on disease activity, quality of life, and gut microbiota composition in patients with RA.\\u003c/p\\u003e\"},{\"header\":\"Subject and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design\\u003c/h2\\u003e \\u003cp\\u003eThis 15-week parallel randomized controlled study (NCT05336513) was conducted between April and December 2021 at Hospital Particular do Algarve (HPA, Algarve, Portugal). The study was approved by the NMS Ethical Committee (44/2021/CEFCM) and HPA Sa\\u0026uacute;de Group (04/2021) and conducted in accordance with the ethical principles of the Declaration of Helsinki and Good Clinical Practice guidelines. All enrolled participants provided written informed consent prior to enrollment.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eParticipants\\u003c/h3\\u003e\\n\\u003cp\\u003eVolunteers were recruited from autoimmune disease consultations. Eligible participants were adults, with a diagnosis of RA and active disease (DAS28\\u0026thinsp;\\u0026ge;\\u0026thinsp;2.6) on stable medication.\\u003c/p\\u003e \\u003cp\\u003eParticipants were excluded if they had a psychiatric illness, dementia, or eating disorder; were pregnant; had food allergies/intolerances; were vegetarian; used phytotherapy or other supplements (including pre or probiotics); or had received nutritional counseling or changed eating habits in the previous 6 months.\\u003c/p\\u003e\\n\\u003ch3\\u003eInterventions\\u003c/h3\\u003e\\n\\u003cp\\u003eBoth dietary interventions had a duration of 15 weeks and were delivered through structured nutritional counseling every 5 weeks by a trained dietitian. Nutritional counseling sessions included individualized diet plans and standardized educational materials (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). For participants with BMI\\u0026thinsp;\\u0026ge;\\u0026thinsp;25 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, the intervention also aimed at gradual weight reduction (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Participants were encouraged to maintain habitual physical activity as advised by their physician.\\u003c/p\\u003e \\u003cp\\u003eThe AID dietary intervention was designed to target inflammatory pathways, intestinal permeability, and gut microbiota modulation. Central strategies included:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eOptimizing the lipid profile to achieve an omega-6/3 ratio of 2:1 by increasing extra virgin olive oil, nuts, seeds (chia and flaxseed), and fatty fish (3 servings/week; 150 g per serving), while strictly avoiding trans and saturated fats found in processed foods.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eHigh intake of prebiotic-rich foods (\\u0026ge;\\u0026thinsp;5 servings/day of fruits and vegetables, 3 servings/day of gluten-free whole grains), alongside daily probiotic intake in yoghurt.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eRestriction of gluten-containing products and cow\\u0026rsquo;s milk, replaced with plant-based alternatives.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eLimitation of red meat to 1 weekly portion (100 g) and solanaceous vegetables.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eExclusion of refined sugars, and added salt, while including functional spices such as turmeric, ginger and cinnamon.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eModerate caffeine restriction (\\u0026le;\\u0026thinsp;300 mg/day), favoring green tea over coffee, and allowing moderate red wine intake (\\u0026le;\\u0026thinsp;125 mL/day).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe dietary intervention of AID was based on principles derived from the literature (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e) and is detailed and rationalized in Supplementary Table\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e Participants in the MD group received standard dietary counseling based on Mediterranean dietary principles. Adherence was assessed using the validated MEDAS questionnaire (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDietary intake was assessed using 24-hour dietary recalls and 5-day non-consecutive food diaries collected throughout the intervention.\\u003c/p\\u003e\\n\\u003ch3\\u003eData and sample collection\\u003c/h3\\u003e\\n\\u003cp\\u003eAt the first nutrition consultation, a sociodemographic and lifestyle questionnaire was administered. Blood and stool samples, Disease Activity Score 28 (DAS28) and HAQ were collected at baseline and post-intervention. Weight was assessed until the end of intervention.\\u003c/p\\u003e\\n\\u003ch3\\u003eClinical outcomes\\u003c/h3\\u003e\\n\\u003cp\\u003eDisease activity was measured using DAS28, a validated tool that includes the joint count of 28 painful joints (ADO), 28 swollen joints (AE), the VSE and the pain scale (ED) (measured from 0 to 100). The calculation provides continuous measure, with the higher the value, the higher the DA at the time of assessment (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Scores were classified as low (\\u0026lt;\\u0026thinsp;3.2) or moderate/high (\\u0026ge;\\u0026thinsp;3.2) (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe functional capacity and the disease impact on daily life and QoL were assessed using the HAQ, comprising 20 items divided into eight areas of patients' activities of daily living: getting ready, getting up, eating, walking, hygiene, reaching, grasping and activities. The highest value of each domain is retained, summed, and divided by eight. Higher scores indicate greater functional disability and poorer QoL (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBiochemical markers\\u003c/h2\\u003e \\u003cp\\u003eBlood samples were collected to determine c-reactive protein (CRP), erythrocyte sedimentation rate (ESR), rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibodies (anti-CCP).\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eAnthropometric measures\\u003c/h3\\u003e\\n\\u003cp\\u003eWeight was measured using a Tanita BC-601 scale. Height was self-reported, and BMI was calculated as kg/m\\u0026sup2;.\\u003c/p\\u003e\\n\\u003ch3\\u003eGut microbiota analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eStool samples were collected by the participants using sterile tubes (EasySampler\\u0026reg;) containing RNAlater. Genomic DNA was extracted and purified using the NZY Tissue gDNA Isolation kit (NZYtech, Lisbon, Portugal), as previously described by Marques \\u003cem\\u003eet al\\u003c/em\\u003e (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eLibraries were processed and sequenced following the 16S Metagenomic Sequencing Library Preparation protocol by Illumina (Illumina; San Diego, CA, USA). The primer set 515F: GTGYCAGCMGCCGCGGTAA and 806R: GGACTACNVGGGTWTCTAAT was used to capture the V3-V4 region of the bacterial 16S rRNA gene. Samples were loaded onto an Illumina MiSeq platform using 2 \\u0026times; 250 bp paired-end run (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). Processing of the metabarcoding data and all taxonomic analyses, were performed using mothur v.1.46.1 software (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Briefly, all low-quality and chimeric sequences were detected and removed from the dataset with the VSEARCH software (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). Next, the remaining sequences were grouped into operational taxonomic units with 97% similarity using the OptiClust tool (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e), and then classified against the ARB-Silva v.138.1 (SSU \\u0026minus;\\u0026thinsp;16S) taxonomic database (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e) using the RDP classifier (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). The MicrobiomeAnalyst tool was used to perform all additional visual, comparative and statistical analyses (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eStatistical analyses were performed using the software SPSS\\u0026reg; version 27 (IBM SPSS Statistics, IBM Corporation, Armonk, NY, USA).\\u003c/p\\u003e \\u003cp\\u003eData normality was assessed by the Kolmogorov-Smirnov test. Comparisons between groups were performed using the t Student\\u0026rsquo;s t-test or Mann-Whitney U as appropriate. Categorical variables were analyzed by using Pearson's chi-square test.\\u003c/p\\u003e \\u003cp\\u003eA linear mixed ANCOVA model was used to analyze the variance of ESR, CRP, RF, DAS28, HAQ and BMI, with diet group as a fixed effect. Potential confounders (age, sex, baseline BMI, education, smoking status, and medication type) were tested and retained only if they altered effect estimates by \\u0026ge;\\u0026thinsp;10%.\\u003c/p\\u003e \\u003cp\\u003eDifferences between beta diversity across interventions were assessed using the PERMANOVA test and correlations between DAS28 and BMI, microbiota composition (phylum and genus levels) and clinical markers after 15 weeks of intervention were analyzed by Pearson's correlation coefficient.\\u003c/p\\u003e \\u003cp\\u003eData are presented as n (%), mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD or 95% confidence intervals. Differences were considered statistically significant when \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipant characteristics\\u003c/h2\\u003e \\u003cp\\u003eA total of 45 individuals were screened for eligibility. Of these, 16 were excluded during screening. Twenty-nine participants were randomized to the intervention groups. Three participants allocated to the MD group were excluded prior to intervention due to pre-existing adherence to a Mediterranean dietary pattern. The final analytical sample comprised 26 participants (AID n\\u0026thinsp;=\\u0026thinsp;14; MD n\\u0026thinsp;=\\u0026thinsp;12). Participant flow is presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eParticipants were aged between 18 and 74 years, with a predominance of females (n\\u0026thinsp;=\\u0026thinsp;22, 84.6%). Fifteen participants were receiving biological therapy and eleven were treated with conventional DMARDs, while six participants were current smokers. No significant differences were observed between the AID and MD groups at baseline regarding demographic, clinical, or lifestyle characteristics (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristics of participants.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAID\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.385\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (78.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11 (91.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.366\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (8.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eMarital status, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (7.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (8.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e0.587\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMarried/Unmarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (71.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (58.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDivorced\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (14.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (16.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWidowed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (7.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (16.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eLevel of education, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimary education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (33.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSecondary education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (57.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (16.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.113\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigher education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (50.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eType of medication, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (64.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (50.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.471\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDMARD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (35.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (50.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAnti-CCP positive, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (16.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.840\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSmokers, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 (25.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.838\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eData expressed as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD for Age; all other variables are expressed as n (%). Mean differences between results were assessed using the student\\u0026rsquo;s t-test or Pearson's chi-square test. Differences were considered statistically significant when \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003cp\\u003eAID - Anti-inflammatory Diet; MD - Mediterranean Diet; DMARD - Disease Modifying Antirheumatic Drugs; BT \\u0026ndash; Biological Therapy.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eClinical and anthropometric measures\\u003c/h2\\u003e \\u003cp\\u003eAt baseline, DAS28 was similar between groups and corresponded, on average, to moderate disease activity (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSimilarly, no significant baseline differences were observed for inflammatory markers (ESR, CRP), serological markers (RF), functional status (HAQ), or BMI, confirming baseline homogeneity. Mean BMI values in both groups were within the overweight range (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAfter intervention, participants in the AID group showed significant reduction in DAS28 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001) and HAQ score (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.038), indicating a decrease in disease activity and an improvement in functional status. In the MD group, significant reductions were observed in HAQ score (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.045) and BMI (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while the reduction in DAS28 did not reach statistical significance (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.219; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAccording to the EULAR response criteria, both dietary interventions resulted in a moderate clinical response, defined as a DAS28 reduction\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.6 and \\u0026le;\\u0026thinsp;1.2 with a final DAS28\\u0026thinsp;\\u0026le;\\u0026thinsp;5.1 (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). Although the mean reduction in DAS28 was numerically greater in the AID group, both groups met the criteria for a moderate response.\\u003c/p\\u003e \\u003cp\\u003eDespite these within-group improvements, no statistically significant differences were detected between interventions when assessed using the linear mixed ANCOVA model (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDemographic, clinical and anthropometric characteristics of participants, at baseline and after 15 weeks of intervention.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"12\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eAID\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c10\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eMD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAfter intervention\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ep\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDifference\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBaseline\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAfter intervention\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ep\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDifference\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ep\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ep\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eESR, mm/h\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.903\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.177 (-6.831;7.185)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e15.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e21.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;21.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.072\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-6.207 (-13.826;1.412)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.774\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.229\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.135\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.466 (-1.075;0.144)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-0.054 (-0.724;0.617)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.318\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.380\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eRF, IU/mL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e151.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;302.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;115.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.208\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e126.287 (-6.496;259.068)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e155.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;188.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e99.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;137.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.053\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e33.793 (-133.819;201.404)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.975\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.365\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eDAS28, unit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.286 (0.648;1.925)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.219\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.841 (0.147\\u0026ndash;1.535)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.485\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.353\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHAQ, unit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.208 (-0.034;0.450)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.184 (-0.079;0.447)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.895\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.067\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.610 (0.070;1.151)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e27.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e26.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.856 (0.280;1.443)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.607\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.384\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eBMI, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLow weight\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (7.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (7.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal weight\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (14.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3 (25.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e5 (41.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.635\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.401\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOverweight\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (50.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (42.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5 (41.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e3 (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.713\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.429\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eObesity\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (28.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (28.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4 (33.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4 (33.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eData expressed as n (%) or mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Difference represents the absolute change between baseline and after intervention (Baseline \\u0026ndash; After intervention) and are expressed as mean (95% confidence intervals). Mean differences were assessed using Student\\u0026rsquo;s t-test (p\\u003csup\\u003e1\\u003c/sup\\u003e for within-group and p\\u003csup\\u003e2\\u003c/sup\\u003e for between-group comparisons) and ANCOVA linear mixed analysis models (p\\u003csup\\u003e3\\u003c/sup\\u003e for difference comparison). Categorical variables were analyzed using Fisher\\u0026rsquo;s Exact Test. Results were considered statistically significant when p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003cp\\u003eAID \\u0026ndash; Anti-inflammatory Diet; MD \\u0026ndash; Mediterranean Diet; ESR \\u0026ndash; Erythrocyte Sedimentation Rate; CRP \\u0026ndash; C-Reactive Protein; RF - Rheumatoid Factor; anti-CCP \\u0026ndash; Anti-cyclic Citrullinated Peptide Antibodies; DAS 28 \\u0026ndash; Disease Activity Score 28; HAQ \\u0026ndash; Health Assessment Questionnaire; BMI \\u0026ndash; Body Mass Index.\\u003c/p\\u003e \\u003cp\\u003eRegarding functional capacity, a reduction of approximately 0.2 points in HAQ score was observed in both groups over the intervention period. Reductions of 0.19 points are considered the minimal clinically important improvement in function. The proportion of participants achieving a minimal improvement in HAQ score was similar between diets (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026ndash;\\u003c/b\\u003e Variation in Health Assessment Questionnaire (HAQ) score variation for both diets at the end of the intervention\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAID\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMD\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDifference HAQ, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo minimum improvement in function\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (57.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (58.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWith minimum improvement in the function\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (42.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (41.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eData expressed as n (%). Mean differences between results were assessed using Pearson's chi-square test. Differences were considered statistically significant when p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003cp\\u003eAID - Anti-inflammatory Diet; MD - Mediterranean Diet; HAQ - Health Assessment Questionnaire.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGut microbiota analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eAID group\\u003c/h2\\u003e \\u003cp\\u003eNo significant changes were observed in alpha diversity following the AID intervention (Shannon index, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.985; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Similarly, beta diversity analysis showed no significant differences between baseline and post-intervention microbial community structure (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt the phylum level, no significant differences were observed, although a trend towards increased \\u003cem\\u003eFusobacteriota\\u003c/em\\u003e abundance was observed (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.068; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC). At the genus level, relative abundances of \\u003cem\\u003eActinomyces\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.011), \\u003cem\\u003eBlautia\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.016), \\u003cem\\u003eCollinsella\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.015), \\u003cem\\u003eErysipelotrichaceae\\u003c/em\\u003e_UCG_003 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.013) and \\u003cem\\u003eLachnospiraceae\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.028) decreased following the AID intervention, with a tendency towards a decrease in \\u003cem\\u003eCoprococcus\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.064). Conversely, \\u003cem\\u003eLachnospiraceae\\u003c/em\\u003e_unclassified (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.039) and \\u003cem\\u003eSutterella\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.019) increased, with trends towards increased abundance of \\u003cem\\u003eRoseburia\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.055) and NK4A214_group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.050) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMD group\\u003c/h2\\u003e \\u003cp\\u003eFollowing MD intervention, alpha diversity showed a non-significant increasing trend, while beta diversity differed significantly between baseline and post-intervention (p\\u0026thinsp;=\\u0026thinsp;0.048; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, B).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt the phylum level, MD intervention resulted in decreases in \\u003cem\\u003eActinomycetota\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.005) and \\u003cem\\u003eBacillota\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.041), alongside increases in \\u003cem\\u003eBacteroidota\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.003) and \\u003cem\\u003eDesulfobacterota\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.019) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC).\\u003c/p\\u003e \\u003cp\\u003eAt the genus level, increases were detected in \\u003cem\\u003eBacteroides\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), \\u003cem\\u003eAgathobacter\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.010), \\u003cem\\u003eNK4A214_group\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.084), \\u003cem\\u003eBilophila\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.028), \\u003cem\\u003eRoseburia\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.041) and \\u003cem\\u003eSuterella\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.017) with trends towards increased abundance of \\u003cem\\u003ePrevotella_9\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.060), \\u003cem\\u003eParabacteroides\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.077), \\u003cem\\u003eLachnospira\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.060) and NK4A214_group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.084). Moreover, significant decreases were observed in \\u003cem\\u003eSubdoligranulum\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.019), \\u003cem\\u003eEnterobacteriaceae_unclassified\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.034), \\u003cem\\u003eActinomyces\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.019) and \\u003cem\\u003eCollinsella\\u003c/em\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.006). There was a trend towards a reduction of \\u003cem\\u003eStreptococcus\\u003c/em\\u003e (p\\u0026thinsp;=\\u0026thinsp;0.060) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eComparison between interventions\\u003c/h2\\u003e \\u003cp\\u003eAt baseline, alpha diversity (Shannon index) was similar between groups (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.439). Both interventions showed a tendency towards increased alpha diversity after 15 weeks, with a greater, though non-significant, increase observed in the MD group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNo significant differences between groups were observed in beta diversity at baseline or post-intervention (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB, C).\\u003c/p\\u003e \\u003cp\\u003eAt the phylum level, baseline compositions were largely comparable, except for higher \\u003cem\\u003eBacillota\\u003c/em\\u003e abundance in the MD group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.041) and a trend towards higher \\u003cem\\u003eBacteroidota\\u003c/em\\u003e in the AID group (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.076).\\u003c/p\\u003e \\u003cp\\u003eAfter intervention, no significant between-group differences were detected. Nevertheless, when comparing the variation induced by the treatments, the MD group exhibited a greater increase in \\u003cem\\u003eBacteroidota\\u003c/em\\u003e and a greater decrease in \\u003cem\\u003eBacillota\\u003c/em\\u003e compared to the AID group (both \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.028; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003cp\\u003eAt the genus level, no differences were detected at baseline between diets. After intervention, only \\u003cem\\u003eCollinsella was\\u003c/em\\u003e more abundant in AID compared to the MD (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.046; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis parallel randomized controlled clinical study evaluated the effects of a structured anti-inflammatory diet (AID) compared with the Mediterranean diet (MD) on disease activity, quality of life, and gut microbiota composition in patients with active RA.\\u003c/p\\u003e \\u003cp\\u003eBoth dietary interventions were associated with reduction in disease activity. This suggests that structured dietary improvement, regardless of specific pattern, may contribute to clinically relevant benefits in RA. Previous studies have shown that MD interventions can lower the disease activity and inflammatory markers such as CRP and RF levels (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). In the ADIRA study, a mean reduction of 0.42 units in DAS28 was reported, which is lower than the reduction observed in the present study, although the intervention duration was shorter (10 weeks) (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRegarding QoL, assessed by the HAQ, both interventions resulted in an approximate reduction of 0.2 units, corresponding to a minimal clinically important improvement in function (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe relationship between weight change and clinical outcomes (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e) appeared limited in this cohort. While the MD group showed a greater reduction in BMI, this was not accompanied by superior improvement in disease activity. This observation suggests that mechanisms beyond weight loss, such as modulation of inflammatory pathways or immune responses, may contribute to the clinical benefits associated with dietary interventions.\\u003c/p\\u003e \\u003cp\\u003eIn the present study, both dietary interventions were associated with changes in gut microbiota composition, although the magnitude and nature of these changes differed between diets. While both interventions showed a tendency towards increased alpha diversity, the MD intervention induced broader shifts in microbial community structure, reflected by significant changes in beta diversity and phylum-level composition. Specifically, MD induced a higher variation in the relative abundance of \\u003cem\\u003eBacteroidota\\u003c/em\\u003e and \\u003cem\\u003eBacillota\\u003c/em\\u003e compared with AID, partially counteracting features of the microbiota profile reported in RA, which is typically characterized by increased \\u003cem\\u003eBacillota\\u003c/em\\u003e and \\u003cem\\u003eActinobacteria\\u003c/em\\u003e and reduced \\u003cem\\u003eBacteroidota\\u003c/em\\u003e (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMD adherence, in combination with appropriate drug therapy, may increase species diversity, richness and evenness of the gut microbiota, as well as promoting abundance of bacteria that produce short chain fatty acids (SCFA), counteracting the dysbiotic profile characteristic in the development autoimmune diseases such as RA (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite these differences, greater microbiota modulation in the MD group did not translate into superior clinical outcomes. This finding highlights the complexity of the relationship between gut microbiota and RA, suggesting that compositional changes alone may not fully explain clinical responses. It is possible that functional microbial outputs, host\\u0026ndash;microbe interactions, or direct dietary effects on immune pathways play a more relevant role than taxonomic shifts per se.\\u003c/p\\u003e \\u003cp\\u003eAt the genus level, specific taxa changes were observed in both groups. Notably, \\u003cem\\u003eCollinsella\\u003c/em\\u003e, a genus previously associated with RA pathogenesis and intestinal permeability (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e), was reduced after AID intervention. In addition, an increase in \\u003cem\\u003eSutterella\\u003c/em\\u003e and a tendency towards increased \\u003cem\\u003eRoseburia\\u003c/em\\u003e were observed after AID. Interestingly, an increase in \\u003cem\\u003eRoseburia\\u003c/em\\u003e, known for SCFA production, has also been reported in chronic patients after anti-TNF-α antibody therapy (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e). Nevertheless, functional implications cannot be confirmed in the absence of metabolomic data.\\u003c/p\\u003e \\u003cp\\u003eThis study has several limitations. The sample size was relatively small, which may have limited the ability to detect between-group differences. Microbiota analysis was based on short 16S rRNA sequencing, precluding species-level resolution and functional characterization (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). Additionally, only faecal samples were analysed, which may not fully capture microbial alterations at extra-intestinal sites relevant to RA (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Although medication was stable throughout the study, potential interactions between pharmacological treatments and diet could not be fully explored.\\u003c/p\\u003e \\u003cp\\u003eFuture studies should incorporate larger cohorts, longer follow-up, and multi-omics approaches to better characterize the functional impact of diet on host\\u0026ndash;microbiota interactions. In addition, investigating the interplay between diet and immunomodulatory therapies may also provide further insight into personalized management strategies for RA.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, in patients with moderate RA, both the tailored anti-inflammatory diet and the Mediterranean diet were associated with reductions in disease activity and improvements in quality of life over a 15-week intervention period. While MD induced more pronounced changes in gut microbiota composition, these differences did not translate into clear clinical advantage of one intervention over the other. These findings support the role of dietary modification as a complementary approach in RA management, while underscoring the need for further research to clarify the mechanisms linking diet, microbiota, and clinical outcomes.\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analyzed during the current study are not publicly available but may be obtained from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors sincerely thank all the volunteers who participated in this study for their time and commitment. We are also deeply grateful to the entire team at Hospital Particular do Algarve for their collaboration.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization: Ana Faria, Carlos Carneiro, Concei\\u0026ccedil;\\u0026atilde;o Calhau; Methodology: Mariana Freitas, Maria Jo\\u0026atilde;o Almeida, Sh\\u0026aacute;mila Ismael; Formal analysis and investigation: Mariana Freitas, Maria Jo\\u0026atilde;o Almeida, Sh\\u0026aacute;mila Ismael, Alexandre Alves Rodrigues, Jo\\u0026atilde;o Trov\\u0026atilde;o, Joana Costa, Christophe Esp\\u0026iacute;rito Santo; Writing - original draft preparation: Mariana Freitas, Alexandre Alves Rodrigues; Writing - review and editing: Sh\\u0026aacute;mila Ismael, Alexandre Alves Rodrigues, Jo\\u0026atilde;o Trov\\u0026atilde;o, Joana Costa, Christophe Esp\\u0026iacute;rito Santo, Ana Faria; Funding acquisition: Ana Faria, Carlos Carneiro, Concei\\u0026ccedil;\\u0026atilde;o Calhau; Supervision: Ana Faria. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by ERDF through the operation POCI-01-0145-ERDF-007746 funded by the Programa Operacional Competitividade e Internacionaliza\\u0026ccedil;\\u0026atilde;o \\u0026ndash; COMPETE2020 and by FCT - Funda\\u0026ccedil;\\u0026atilde;o para a Ci\\u0026ecirc;ncia e a Tecnologia, IP national support through CINTESIS, R\\u0026amp;D Unit (UIDB/4255/2020 and UIDP/4255/2020), CHRC (UIDP/04923/2020 and UIDB/04923/2020).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was approved by the NMS Ethical Committee (44/2021/CEFCM) and HPA Sa\\u0026uacute;de Group (04/2021). All procedures were performed in accordance with the relevant guidelines and regulations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no conflicts of interest to disclose.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAlmutairi K, Nossent J, Preen D, Keen H, Inderjeeth C. The global prevalence of rheumatoid arthritis: a meta-analysis based on a systematic review. Rheumatol Int. 2021;41(5):863\\u0026ndash;77.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJahid M, Khan KU, Rehan Ul H, Ahmed RS. Overview of Rheumatoid Arthritis and Scientific Understanding of the Disease. Mediterr J Rheumatol. 2023;34(3):284\\u0026ndash;91.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDi Matteo A, Bathon JM, Emery P. Rheumatoid arthritis. 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Biomed Pharmacother. 2022;153:113518.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eForbes JD, Chen CY, Knox NC, Marrie RA, El-Gabalawy H, de Kievit T, et al. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome. 2018;6(1):221.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSkoldstam L, Hagfors L, Johansson G. An experimental study of a Mediterranean diet intervention for patients with rheumatoid arthritis. Ann Rheum Dis. 2003;62(3):208\\u0026ndash;14.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNikiphorou E, Norton S, Carpenter L, Dixey J, Andrew Walsh D, Kiely P, et al. Secular Changes in Clinical Features at Presentation of Rheumatoid Arthritis: Increase in Comorbidity But Improved Inflammatory States. Arthritis Care Res (Hoboken). 2017;69(1):21\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAbebaw D, Akelew Y, Adugna A, Tegegne BA, Teffera ZH, Belayneh M, et al. Immunomodulatory properties of the gut microbiome: diagnostic and therapeutic potential for rheumatoid arthritis. Clin Exp Med. 2025;25(1):226.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"european-journal-of-clinical-nutrition\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"ejcn\",\"sideBox\":\"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)\",\"snPcode\":\"41430\",\"submissionUrl\":\"https://mts-ejcn.nature.com/cgi-bin/main.plex\",\"title\":\"European Journal of Clinical Nutrition\",\"twitterHandle\":\"@ejcneditor\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Anti-inflammatory diet, gut microbiota, rheumatoid arthritis, disease activity, quality of life\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9452089/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9452089/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eRheumatoid arthritis (RA) is associated with systemic inflammation and gut microbiota dysbiosis. This study evaluated whether a structured Anti-Inflammatory Diet (AID) provides additional benefits over the Mediterranean Diet (MD) in modulating disease activity, quality of life (QoL), and gut microbiota composition.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eIn this 15-week parallel randomized controlled study (NCT05336513), patients were allocated to AID or MD group. Clinical outcomes (Disease Activity Score 28 (DAS28), Health Assessment Questionnaire (HAQ)), anthropometry, and gut microbiota (short 16S rRNA gene sequencing) were assessed at baseline and post-intervention.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eBoth dietary interventions successfully reduced disease activity. A greater reduction in DAS28 was observed in the AID group, although between-group differences were not statistically significant. While the MD intervention triggered more extensive shifts in microbiota composition, diversity levels were comparable between groups. Notably, \\u003cem\\u003eCollinsella\\u003c/em\\u003e was significantly more abundant in the AID group post-intervention.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eBoth dietary strategies resulted in moderate clinical improvement in RA. Greater microbiota modulation did not translate into superior clinical outcomes, highlighting the complexity of diet\\u0026ndash;microbiota\\u0026ndash;host interactions. Dietary interventions may represent a valuable adjunct therapy in RA management.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The effect of an anti-inflammatory diet on disease activity and quality of life in patients with Rheumatoid Arthritis - Parallel controlled randomized clinical study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-11 08:10:39\",\"doi\":\"10.21203/rs.3.rs-9452089/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2026-05-11T15:13:56+00:00\",\"index\":4,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-05-11T14:19:18+00:00\",\"index\":4,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-05-05T10:17:44+00:00\",\"index\":3,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-05-03T07:28:28+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2026-04-30T09:53:15+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-04-29T07:46:52+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-29T07:45:24+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-20T15:40:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-20T14:27:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"European Journal of Clinical Nutrition\",\"date\":\"2026-04-17T18:36:31+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"european-journal-of-clinical-nutrition\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"ejcn\",\"sideBox\":\"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)\",\"snPcode\":\"41430\",\"submissionUrl\":\"https://mts-ejcn.nature.com/cgi-bin/main.plex\",\"title\":\"European Journal of Clinical Nutrition\",\"twitterHandle\":\"@ejcneditor\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"f89df630-9544-48fc-90b0-2499d4f84bc3\",\"owner\":[],\"postedDate\":\"May 11th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2026-05-11T15:13:56+00:00\",\"index\":4,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-05-11T14:19:18+00:00\",\"index\":4,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-05-05T10:17:44+00:00\",\"index\":3,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-05-03T07:28:28+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2026-04-30T09:53:15+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":67221832,\"name\":\"Health sciences/Health care/Nutrition\"},{\"id\":67221833,\"name\":\"Health sciences/Diseases/Rheumatic diseases/Rheumatoid arthritis\"}],\"tags\":[],\"updatedAt\":\"2026-05-11T08:10:39+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-11 08:10:39\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9452089\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9452089\",\"identity\":\"rs-9452089\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}