Fasting-driven suppression of disease activity in rheumatoid arthritis

Fasting-driven suppression of disease activity in rheumatoid arthritis | 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 Fasting-driven suppression of disease activity in rheumatoid arthritis Bérénice Hansen, Rémy Villette, Viacheslav Petrov, Cédric C Laczny, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6904467/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prolonged fasting (PF) and time-restricted eating (TRE) have emerged as widely used dietary regimens gaining attention for potential health benefits, but evidence is sparse. We investigated the combined effects of one week of PF followed by 12 months of TRE in patients suffering from the chronic autoimmune disease rheumatoid arthritis (RA). Participants experienced sustained reductions in the RA Clinical Disease Activity Index (CDAI) from 31.81 to 8.16 ( P -value < 0.001) and improved well-being and functional ability. These effects were accompanied by a significant and sustained decrease in BMI from 25.02 (day 1) to 24.08 (week 52). TRE was also associated with a tendency for an adherence to a Mediterranean diet (MD). Statistical analysis revealed that reductions in BMI, adherence to TRE and MD significantly impact CDAI improvements. TRE might therefore represent a valuable tool to sustain PF-induced health benefits over time. Biological sciences/Immunology/Autoimmunity Health sciences/Health care/Nutrition Health sciences/Diseases/Rheumatic diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Fasting is defined as the voluntary abstinence from caloric ingestion for a limited time [ 1 ]. Fasting practices date back to ancient civilizations, where periods of food abstinence were primarily a result of food scarcity and the hunter-gatherer lifestyle. Despite advancements in food availability, fasting has persisted through various cultural, religious and health practices until today. Popular fasting methods practiced nowadays include prolonged fasting (PF), typically lasting from four to 21 days with an energy intake below 350 kcal/d, and time-restricted eating (TRE), whereby food intake is restricted to a daily eating window [ 1 ]. Recent research has yielded promising results associating several underlying mechanisms to the health benefits of fasting. Some have been attributed to enhanced mitochondrial function, increased autophagy, gut microbiome changes and an overall reduced inflammation [ 2 ] , [ 3 ]. Thereby, fasting has been proposed to be efficacious in alleviating clinical symptoms in human chronic diseases with inflammatory signatures such as rheumatoid arthritis (RA). RA is a multifactorial chronic and systemic auto-immune disease, affecting 1% of the global population with women being at a higher risk than men [ 4 , 5 ]. The disease typically affects the synovial lining of the joints but also entails various comorbidities including the vasculature, the metabolism and the bones of the patients, significantly reducing their quality of life [ 6 ]. RA pathogenesis is associated with both genetic and environmental risk factors, interacting on various levels several years before the onset of clinical symptoms [ 7 , 8 ]. Specific HLA-DRB1 alleles are known as the “shared epitope (SE)” and are associated with an increased incidence in RA [ 7 , 9 ]. Various environmental factors such as cigarette smoking, air pollution, dust exposure, infections, lifestyle and nutrition can trigger the onset of systemic autoimmunity and the production of RA-specific autoantibodies [ 7 , 8 ]. Also, the bidirectional connection between the immune system and the gut microbiome has been associated with the onset of autoimmune disease such as RA [ 10 ]. It has been suggested that a gut microbiome dysbiosis might lead to systemic inflammation in genetically predisposed patients [ 10 ]. Typical treatment options for patients with RA include non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids for the management of acute flares, and disease modifying anti-rheumatic drugs (DMARDs) and/or corticosteroids to stabilize the disease [ 11 ]. However, these treatments are accompanied by several side effects and mixed efficacy [ 11 ]. Dietary interventions, including fasting, have gained significant attention for their potential to influence the onset and progression of RA [ 7 , 12 ]. It has previously been shown that fasting combined with a lactovegetarian diet can improve disease activity, including reduced pain and stiffness [ 13 ]. In addition, it was found that specifically the Mediterranean diet (MD), characterized by a high consumption of fruits, vegetables, whole grains, legumes, nuts and olive oil has promising benefits for individuals with RA [ 14 ]. Understanding the changes induced by PF in patients with RA, and, subsequently, the different dietary strategies sustaining the beneficial effects of PF are crucial. Within the framework of the ExpoBiome study, a clinical cross-sectional and longitudinal intervention trial, we investigated the combined effects and underlying mechanisms of one week of PF followed by 12 months of TRE in patients diagnosed with RA [ 15 ]. RESULTS Baseline characteristics and treatment regimens A total of 30 patients with RA was included in this longitudinal study and underwent one week of PF followed by 12 months of TRE. The patients were mostly female (90%), corresponding to the higher incidence of RA in women. Most patients did receive RA-specific pharmaceutical treatment (70%). The detailed baseline characteristics are shown in Table 1 . Table 1 Baseline characteristics of the study cohort, n = 30 N = 30 Median** IQR Age(y), mean, SD 55.88 10.16 Female [%] 90.00 NA Disease duration [years] 4.75 8.56 Weight [kg] 69.50 20.6 BMI [kg/m2] 25.02 7.22 WHR 0.81 0.07 Systolic blood pressure [mmHg] 127.57 16.67 CDAI 31.81 22.25 HAQ 0.50 0.875 Omnivore [%] 67 NA CRP [mg/L] 1.52 3.03 Medical Treatment [%] * 70 NA Abbreviations: BMI, body mass index; WHR, waist-hip ratio; CDAI, clinical disease activity index; HAQ, health assessment questionnaire; CRP, C-reactive protein; IQR, interquartile range. *Either bDMARD, DMARD, NSAID, corticosteroid or combined treatment **Median and IQR, except if indicated differently PF and TRE induced a sustained decrease of disease activity. Our main objective was to compare disease metrics during the different time points, after PF and during TRE, with the baseline. The duration of overnight fasting was significantly increased at all stages of the intervention ( P -value < 0.001, Figure 1A). We assessed changes in clinical disease activity index (CDAI) and clinico-anthropometric parameters over time compared to the baseline. In this context, a statistically significant decrease was observed for the CDAI, with the lowest being recorded immediately after the PF, but with a significant sustained decrease remaining for the whole duration of the intervention ( P -value < 0.05, Figure 1B-D), i.e. during TRE. At baseline, 62% of patients showed a high disease activity which decreased to 24% after PF and was still as low as 37% after 12 months of TRE (Figure 1D). Three patients remained at a high CDAI for all timepoints (Figure 1D). Crucially, we observed a statistically significant shift from the high activity to the low activity category, especially at early stages of the intervention (Q-value < 0.0001, Figure 1D). Furthermore, a statistically significant difference in the health assessment questionnaire (HAQ) and Non-Motor Syndrome Questionnaire (NMSQ) were noted ( Q -value < 0.05 Friedman test, Figure 1B-C). The Hannover Functional Ability Questionnaire (FFbH-R) was also found to be statistically different when comparing before correction ( P -value = 0.052, Q -value = 0.09, Friedman test, Figure 1B). Subsequently, we performed Wilcoxon signed-rank tests for each time point relative to the baseline, for which all results are presented in supplemental table 1. Using a post-hoc analysis, we found a significant decrease CDAI and HAQ at all time points compared to baseline (q < 0.05, Wilcoxon test, Figure 1C). We detected a significant increase in FFbH-R at Day 8-12 and a statistically significant decrease of Hospital Anxiety and Depression Scale (HADS) at Week 3 and Week 52, respectively (q < 0.01, q < 0.05 and q < 0.05, respectively, Wilcoxon test, Figure 1C). The Profile Moods of States (POMS), Bristol Stool Score and the Quality-of-Life Questionnaire (WHO-5) were not statistically different at any timepoint (q > 0.5 and p > 0.05, for all comparisons, Wilcoxon test, Supplemental fig. 1A-B). Finally, we found a significant decrease for NMSQ at W3, W26 and W52 compared to baseline (q < 0.05, Wilcoxon test, Supplemental fig. 1B). Prolonged fasting and time-restricted eating induce sustained reductions in body mass index, blood pressure and biological markers. We next assessed anthropometric changes and routine blood test changes during the intervention. For this purpose, we used repeated measures ANOVA followed by a post-hoc T-test, both corrected using FDR, complete results are presented in supplemental table 2. We noted several sustained anthropometric changes. More specifically, a significant decrease was recorded for BMI and hip circumference until week 52 (q < 0.05, Figure 2A), while a significant reduction in diastolic and systolic blood pressure (BP), as well as waist circumference was sustained until week 26 (q < 0.01, Figure 2A). An increased heart rate was observed during the PF period (q < 0.01, Day 6, Figure 2A). At the final timepoint, marking 12 months of TRE after the PF (Week 52), no other significant anthropometrical changes were recorded after correction for multiple testing with FDR. Moreover, we noted a decrease in fasting blood glucose, albumin, bilirubin, creatinine, HbA1C, HDL cholesterol, insulin and uric acid directly after the PF (q < 0.05, Figure 2B). In addition, we found a decrease for LDL cholesterol, total cholesterol and cholinesterase at D8-12 (q < 0.01, T-test, Figure 2B). We also found a reduced blood count for erythrocytes, leukocytes, lymphocytes, neutrophils and reticulocytes at multiple time points (q< 0.05, T-test, Figure 2C). We observed an increase in alanine transaminase (ALT) on Day 6 and Day 8-12, which was then followed by a decrease at week 26, both of which were not retained by FDR correction (q > 0.05, p 0.05, T-test, Figure 2B). Finally, we found a significant increase of basophils at W52 (q < 0.01, T-test, Figure 2C). PF and TRE are accompanied by dietary shifts In addition to adhering to the 16:8 TRE pattern, some changes in dietary habits were recorded. The number of meals was significantly decreased until week 26 (q = 0.0390, Wilcoxon test), the time at which patients took their breakfast was significantly altered to a later timepoint for up to 52 weeks (q = 0.0110, Wilcoxon test), as well as their type of breakfast for up to 26 weeks (p = 0.0240, Wilcoxon test). Alongside these findings, we could also see an increase in consistency of the timing of food intake until at least week 26 into TRE (q = 0.0140, Wilcoxon test), and the speed at which patients consumed their meals was decreased after the PF (q = 0.0170, Wilcoxon test). The patients also reduced overeating, e.g. continuing to eat after they were full, until week 52 (q = 0.0100, Wilcoxon test). The 24FR showed a significant decrease in caloric intake (q = 0.0127) only immediately after the PF. No further significant changes in kcal, carbohydrate or fat intake occurred during the 52 weeks of TRE (p > 0.05). For protein intake a significant decrease was recorded from week 3 (p = 0.0001) until week 26 (p = 0.0063) during TRE. These observations aligned with the findings of the FFQ, which show a significant decrease in consumption of meat, fish and dairy products during the 12 months of the study (p < 0.00001, Friedman test, Figure 3A-B). In addition, we noted a decrease in the consumption of sweets over the course of the intervention (p = 0.012, Friedman test, Figure 3A). Simultaneously, a continuously increased intake in legumes was reported for the whole duration of the study as well as a higher consumption of fruits for the first six months of the study, although not being statistically significant (p < 0.0001 and p = 0.06, respectively, Friedman test, Figure 3A-B and Supplemental fig. 2). These changes translated into a spontaneous and unsupervised dietary shift towards an increase in ovo-lacto-vegetarian- and vegan-like dietary pattern during the study (p < 0.0001, Figure 3C). To resolve the overall patterns, we calculated two different Mediterranean diet (MD) indices, namely the MDScale and MD score (MedDiet score), based on the food questionnaire data. Both scores indicated a shift from a Western diet towards a higher adherence of a MD diet over the course of the intervention (p < 0.01 Figure 3D). Finally, we also calculated the dietary inflammatory index (DII) [16, 17]. The DII indicated that the patients neither followed a pronounced pro- nor anti-inflammatory dietary pattern at baseline and despite the changes we could observe for specific food items, no significant shifts were observable in the DII (p > 0.1, Friedman test). Overall, the dietary pattern of the patients shifted towards a shorter eating window with more consistent timing, a decrease in animal product consumption, a higher adherence to a MD diet, without significant changes in kcal intake or a higher adherence to DII. Increased overnight fast duration and adherence to a Mediterranean diet leads to sustained health benefits As the result of a linear mixed-effects model analysis on several health questionnaires as response variables, we found that BMI had a significant effect on CDAI, FFbH-R and HAQ (p = 0.003, p= 0.003 and p= 0.004, respectively, mixed model effects test) while TRE (overnight fast) impacted CDAI and HADS significantly (p= 0.03 and p= 0.01, mixed model effects test). Moreover, CDAI was also affected by the composition of the diet. In addition, physical activity, while not changing over the time of the intervention, was a significant predictor for the quality of life of patients (WHO-5). Consistency of eating patterns, dietary regimen and hours of sleep did not impact the health outcomes significantly (Figure 4). We ran an additional mixed-effects model focusing on the BMI changes. Interestingly, we found a significant association between BMI, overnight fasting (p = 0.02) and MedDietScore (p = 0.001), but not with the frequency of physical activity (gardening, window cleaning, etc., see Material and Methods), sport activities or number of meals (p > 0.2, for all factors). DISCUSSION Our data shows the profound impacts of fasting and diet on RA CDAI over time, highlighting the combined effect of weight loss, dietary adaptations, and fasting on improving RA disease severity. Contrary to previous findings [14], we found in our present study that the decrease in CDAI induced via PF could be sustained by TRE for as long as 12 months. This improvement in RA severity was accompanied by distinct biochemical changes reflecting reduced systemic inflammation. While general dietary guidelines for RA primarily focus on specific nutrients and food items, they underappreciate overall dietary composition and meal timing [18]. Our findings highlight the significance of meal timing in relation to the body's circadian rhythm. This can be affected through strategies such as TRE. The observed beneficial outcomes may be the results of various mechanisms at play, including enhanced ketogenesis, improved mitochondrial function, increased autophagy, modulated gene expression, weight reduction, reduced apoptosis, and decreased inflammation [1, 19, 20]. These are reflected in the improved metabolic profile of the patients observed after fasting, including improved blood glucose and cholesterol levels alongside a reduction in CRP. Fasting-induced gut microbiome modulation might also be a key mediator of the impact of TRE and PF on RA [21]. Also, having overweight or obesity has been linked to NCDs. In particular, obesity has been suggested to be a key risk factor for RA leading to a more severe pathogenesis when compared to patients with a BMI below 25. Although BMI does play an important role in our findings and probably partially mediates the associated health benefits observed after PF and TRE, the composition of the food items with resprect to the ingested nutrients should not be neglected. Despite the DII remaining unchanged, we can observe a significantly higher adherence to an MD and an overall shift to vegetarian eating patterns during the intervention. This aligns partially with dietary recommendations for patients with RA from organizations such as the German Nutrition Society (DGE) [22]. The increase in the consumption of legumes has been reported by several studies to result in beneficial health effects mediated partially by an increased fiber intake, and a resulting beneficial effect on the gut microbiome. As mentioned above, RA is associated with an apparent shift in the gut microbiome composition. Dietary adaptations supporting a diverse and beneficial gut microbiome composition are therefore crucial and nutritional composition remains a vital dimension for RA disease management. The observed higher adherence to a MD has previously been reported as potentially beneficial in patients with RA, leading to a reduction in pain and improved physical function [23, 24]. However, evidence to generally recommend a MD for patients with RA has been assessed insufficient in 2018 [24-29]. In this context, the present study is highly relevant, and it underscores the importance of integrating both quality and timing of diet into RA management. By combining and implementing various dietary strategies, namely PF followed by a maintenance diet consisting of TRE and a higher MD adherence, patients with RA could optimize health outcomes and benefit from personalized nutrition interventions tailored to their individual needs. Methods Trial registration number and date at clinicaltrials.gov: NCT04847011, April 14, 2021 Ethical approval and consent Ethical approval for this study was from the institutional review board of the Charité-Universitätsmedizin Berlin (EA1/204/19), the ethics committee of the state medical association (Landesärztekammer) of Hessen (2021-2230-zvBO) and the Ethics Review Panel (ERP) of the University of Luxembourg (ERP 21-001-A ExpoBiome). Participants were included into the study only after written informed consent [15]. Sample and data collection The study intervention consisted of 30 patients with RA undergoing one week of PF which was followed by 12 months of TRE. Patients attended a total of 11 study visits, and at each timepoint sample and data collection took place. The samples for routine blood chemistry were collected at the Charité-Universitätsmedizin Berlin and measurements were performed immediately after each visit. Blood samples were taken at a baseline visit (Day 1), on day six of the PF and on nine additional visits during the TRE over 12 months [15]. Several questionnaires assessing clinical and nutritional data were answered either on site during the visit or prior to the visit at home and captured in REDCap (Table 2). The CDAI was calculated based on the swelling and pain of the joints of the patients and divided into different severity categories, defined as: Remission [0,2.8], Low [>2.8,10], Medium [>10,22] and High [>22]. The dietary behaviour of the patients was assessed by 24-hour food recalls (24HFR), food frequency questionnaires (FFQ) and dietary habits (DH) questionnaires. Some questionnaires and data scuh as anthropometric parameters, e.g. BMI, WHR, medication and blood pressure were captured on each visit, while more extensive questionnaires concerning the health assessment and nutritional data, were limited to a selected number of visits, namely day 1, day 8-12, week 3, week 26 and week 52. Most patients (70%) underwent a typical RA treatment over the total course of the study, consisting of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs, 50%), or biological DMARDs (bDMARDs, 27%), either alone or in combination with csDMARDs or glucocorticoids. No significant changes in the treatment regimens were observed during the 12 months of the study. Table 2: Health assessment questionnaires Disease specific Hannover Functional Ability Questionnaire (FFbH-R)[30] Clinical Disease Activity Index (CDAI)[31] Dietary behaviour and lifestyle Fasting experience, expectation, and intervention Lifestyle 24H-Food-recall Food Frequency Questionnaire (FFQ) General health and well-being Health Assessment Questionnaire (HAQ)[32] Bristol Stool Scale[33] Quality of Life questionnaire (WHO-5)[34] Hospital Anxiety and Depression Scale (HADS)[35] Profile of Mood States (POMS)[36] Non-Motor Symptoms Questionnaire (NMSQ)[37] Dietary Inflammatory Index The DII was calculated based on the following 23 food components or nutrients: Alcohol, coffee, carbohydrate, cholesterol, energy, total fat, folic acid, garlic, ginger, iron, magnesium, multi-unsaturated fatty acids (MUFA), saturated fat, niacin, protein, riboflavin, selenium, thiamine, vitamin A, C, D, E and green or black tea [16, 17]. The data was taken from both 24FR and FFQ. Mediterranean diet scores For measuring adherence to a MD diet, we selected two MD indices, namely the MDScale and the MedDiet score [38, 39], was based on an extensive literature review comparing five MD indices [40]. The first score (MDScale), is the oldest MD adherence assessment index and was developed in 1995 and revised in 2003, using sex-specific median calculations and a total of 9 food groups [38, 40]. The second score (MedDiet index) was developed in Greece in 2005 as an alternative to the gender-specific MDScale and has been widely used ever since [39, 40]. The calculation of the indices was slightly adapted as follows. For the MDScale, nine items were included and calculated based on a gender-specific median amount, namely vegetables, legumes, fruits, nuts, cereals and fish as beneficial items and dairy products, meat including poultry and alcohol as negative items. This resulted in a score between zero and nine, with nine indicating the highest adherence to an MD diet. Deviating from the original MDScale, the mono-unsaturated fatty acid to saturated fatty acid ratio was not considered as this data was not collected during our study. For the MedDiet score a total of eight items was included and the score was calculated based on their frequency of consumption per week. Items leading to a higher adherence were cereals, fruits, vegetables, legumes, and fish, while items negatively impacting the MDScale score were dairy products, meat and alcohol. In this index, potatoes, olive oil and poultry were not included for our study. The adapted MedDiet score calculated in this study ranges from 0 to 40, with 40 indicating a high MD adherence. Statistical analyses The clinical and nutritional data collected in REDCap and the molecular data provided by SGS were analysed and integrated using R (R 4.3.2, RRID:SCR_001905) and R studio (2023.09.1+494, RRID:SCR_000432). The 24FR were digitalised using the Nutrilog software and the OSAV food data bank which allowed an overview of the overall energy intake as well the macro- and micronutrient consumption of the patients [41]. A repeated measures ANOVA test or Friedman test, depending on whether the data followed a normal distribution or not, was performed to identify significantly divergent values over the different timepoints and corrected with false-discovery rate (FDR). For the parameters for which the repeated measures ANOVA/Friedman tests showed a significant value, post-hoc Student/Wilcoxon tests for paired values were applied. The post hoc test p- values were not corrected as correction was already applied on the repeated measures tests. A Cochran test was applied for the CDAI. Wilcoxon, Friedman, Student and Cochran tests were performed using the rstatix package (v0.7.2, RRID:SCR_021240). Chi-square tests were performed using the stats R package (v4.4.2, RRID:SCR_025968). Mixed linear modelling was done using the package lme4 (v1.1.35.5, RRID:SCR_015654) and lmerTest (v3.1.3, RRID:SCR_015656) in R [42, 43]. Plotting was performed using ggalluvial A first mixed-effect model was applied for the primary outcome parameters captured in the health questionnaires. Mixed effect models contain both fixed effects and random effects and are particularly useful when dealing with data that have multiple levels of variability such as the data analysed in the present study [44]. The fixed effects are constant across individuals and the primary variables of interest in the study, while the random effects account for variation across different levels of the data and are not of primary interest [44]. The model used to analyse the effects of various effects on the different health outcomes was as follows: Health outcome ~ MDScale + MedDietScore + BMI + regimen + overnight_fast + sleep_hour + gluten_free + consistency of eating pattern + Physical activity`+ (1 | Record_id). A second model was built with BMI as response, e.g. BMI ~ Physical activity + Sports + MedDietScore + number_of_meals + overnight_fast + walking + (1 | Record_id). The model as was also applied to the MD indices. The p-values were corrected for multiple testing with FDR correction. Plotting was performed using ggalluvial (v0.12.5, RRID:SCR_021253) , ggrepel (v0.9.6, RRID:SCR_017393) , patchwork (v1.3.0, RRID:SCR_000072) , ImmuMicrobiome (v1.0.1, RRID:SCR_026073) and ggplot2 (v 3.5.2, RRID:SCR_014601) [42, 43]. Study limitations The unexpected dietary adaptations in relation to MD might have impacted the observed benefits of PF and TRE. Investigating whether combining TRE with a MD or a specific anti-inflammatory diet yields greater improvements requires further investigation. Declarations Data availability The clinical data used in this study is held by the clinical partner and is not shared publicly due to privacy and confidentiality agreements. Access to this clinical data may be granted upon direct request to the clinical partner, subject to their approval and ethical considerations. Code availability All code used for the data analysis can be found in the following repository: gitlab.lcsb.uni.lu/TBD. Acknowledgements We thank Audrey Frachet-Bour, Janine Habier, Jordan Caussin, Léa Grandmougin, Dr. Catharina Delebinski, Melanie Dell’Oro, Grit Langhans, Ursula Reuß, Maik Schröder and Nadine Sylvester for their support during the study. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 863664). This work was supported by the Luxembourg National Research Fund (FNR) under grant PRIDE/11823097. Author contributions Study design and protocol: B.H, C.C.L, J.G. S, P.W; the conceptualisation of the intervention: E.H, D.A.K, A.M, A.R.K, B.M, S.S, N.S, J.G.S, P.W; clinical trial and sample collection design and administration by B.H, E.H, D.A.K, A.M, A.R.K, B.M, S.S; funding acquisition: P.W, C.C.L; statistical analysis, calculation of the DII, MD indices was done by B.H, F.V, V.P, R.V; data visualisation: R.V, B.H; sample size calculation: C.C.L, J.G. S, P.W, K.R; initial draft writing: B.H, R.V, editing process coordination: by B.H; sample protocol preparation: B.H; . Review and editing: J.G.S, P.W; all authors contributed, read and approved the final manuscript. Competing interests Authors declared no competing interests. 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Wolfe, F., A brief clinical health assessment instrument: CLINHAQ. Arthritis Rheum, 1989. 32 (suppl 4): p. S99. Lewis, S.J. and K.W. Heaton, Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol, 1997. 32 (9): p. 920-4. Topp, C.W., et al., The WHO-5 Well-Being Index: a systematic review of the literature. Psychother Psychosom, 2015. 84 (3): p. 167-76. Zigmond, A.S. and R.P. Snaith, The hospital anxiety and depression scale. Acta Psychiatr Scand, 1983. 67 (6): p. 361-70. McNair, D.M., M. Lorr, and L.F. Droppleman, EdITS Manual for the Profile of Mood States (POMS) . 1992: Educational and industrial testing service. Chaudhuri, K.R., et al., International multicenter pilot study of the first comprehensive self-completed nonmotor symptoms questionnaire for Parkinson's disease: the NMSQuest study. Mov Disord, 2006. 21 (7): p. 916-23. Trichopoulou, A., et al., Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med, 2003. 348 (26): p. 2599-608. Panagiotakos, D.B., C. Pitsavos, and C. Stefanadis, Dietary patterns: a Mediterranean diet score and its relation to clinical and biological markers of cardiovascular disease risk. Nutr Metab Cardiovasc Dis, 2006. 16 (8): p. 559-68. Aoun, C., et al., Comparison of five international indices of adherence to the Mediterranean diet among healthy adults: similarities and differences. Nutr Res Pract, 2019. 13 (4): p. 333-343. https://naehrwertdaten.ch/en/downloads/. [cited 2024 11.09.2024]; OSAV food data bank download]. Kuznetsova, A., P.B. Brockhoff, and R.H.B. Christensen, lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 2017. 82 (13): p. 1 - 26. Bates, D., et al., Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 2015. 67 (1): p. 1 - 48. McNeish, D. and K. Kelley, Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychol Methods, 2019. 24 (1): p. 20-35. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles0625.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6904467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476251687,"identity":"c4473d4f-c086-4834-9587-ce50cb124277","order_by":0,"name":"Bérénice 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Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Rémy","middleName":"","lastName":"Villette","suffix":""},{"id":476251689,"identity":"332f745e-4363-43dd-8717-10319c637d37","order_by":2,"name":"Viacheslav Petrov","email":"","orcid":"","institution":"University of Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Viacheslav","middleName":"","lastName":"Petrov","suffix":""},{"id":476251690,"identity":"402e1339-4d7f-4077-921f-a4aa4cd3c476","order_by":3,"name":"Cédric C Laczny","email":"","orcid":"","institution":"University of Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Cédric","middleName":"C","lastName":"Laczny","suffix":""},{"id":476251691,"identity":"450d694b-b662-4d4a-8755-5226e04cb12c","order_by":4,"name":"Farhad Vahid","email":"","orcid":"","institution":"Luxembourg Institute of Health","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Vahid","suffix":""},{"id":476251692,"identity":"8217a20b-cbda-4544-97f3-2161418054fc","order_by":5,"name":"Kirsten Roomp","email":"","orcid":"","institution":"University of Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Kirsten","middleName":"","lastName":"Roomp","suffix":""},{"id":476251693,"identity":"50908608-3e41-48c3-a0a8-72ab98bf449d","order_by":6,"name":"Etienne Hanslian","email":"","orcid":"","institution":"Charité Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Etienne","middleName":"","lastName":"Hanslian","suffix":""},{"id":476251694,"identity":"7ebafefa-e1d2-44fb-a32e-9c6c0b94f54a","order_by":7,"name":"Daniela A Koppold","email":"","orcid":"","institution":"Charité Universitätsmedizin 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Kassel","correspondingAuthor":false,"prefix":"","firstName":"Brit","middleName":"","lastName":"Mollenhauer","suffix":""},{"id":476251701,"identity":"ee6e87fd-f87b-44fd-8f43-1ef8d61967c0","order_by":14,"name":"Andreas Michalsen","email":"","orcid":"","institution":"Charité Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Michalsen","suffix":""},{"id":476251702,"identity":"922c5ff1-99e9-43cd-8c80-fb8ae3d76cb1","order_by":15,"name":"Jochen G Schneider","email":"","orcid":"","institution":"University of Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Jochen","middleName":"G","lastName":"Schneider","suffix":""},{"id":476251703,"identity":"1f0254bc-e683-497b-98a3-2786de69247e","order_by":16,"name":"Paul Wilmes","email":"","orcid":"","institution":"University of Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Wilmes","suffix":""}],"badges":[],"createdAt":"2025-06-16 10:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6904467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6904467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85754055,"identity":"34995e56-3b02-4165-9dd2-74caa1c42730","added_by":"auto","created_at":"2025-07-01 10:37:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":646887,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of prolonged fasting (PF) followed by time-restricted eating (TRE) on several health outcome in patients with RA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Boxplot showing the increased overnight fast over the 12 months of the study.\u003cstrong\u003e B. \u003c/strong\u003eLollipop plot showing the results of Friedman tests corrected with FDR for the different health scores. Quality-of-Life Questionnaire (WHO-5), Profile moods of states (POMS), Hospital Anxiety and Depression Scale (HADS), Hannover Functional Ability Questionnaire (FFbH-R), Non-motor symptoms questionnaire (NMSQ), Health Assessment Questionnaire (HAQ) and clinical disease activity index (CDAI). \u003cstrong\u003eC\u003c/strong\u003e. Boxplots representing time series for the different scores measured during the intervention. From left to right: CDAI, FFbH-R, HADS, and HAQ. Each dot represents a different patient, with the dots connected by a dashed line indicating the longitudinal changes. \u003cem\u003eP\u003c/em\u003e-values are derived from paired Wilcoxon tests for all timepoints against the baseline (Day 1). \u003cstrong\u003eD.\u003c/strong\u003eAlluvial plot showing the evolution of CDAI score after PF and over 12 months of TRE. Categories are defined as: Remission [0,2.8], Low [\u0026gt;2.8,10], Medium [\u0026gt;10,22] and High [\u0026gt;22]. \u003cem\u003eP\u003c/em\u003e-values are derived from Chi-square tests performed relative to Day 1. \u0026nbsp;D, Day; W, Week\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6904467/v1/4a32b90d5bf44201189930ed.png"},{"id":85752191,"identity":"0257f858-4042-4bc8-bb33-ded63908ba70","added_by":"auto","created_at":"2025-07-01 10:21:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273572,"visible":true,"origin":"","legend":"\u003cp\u003eProlonged fasting and time-restricted eating induces sustained changes in BMI, blood pressure, blood parameters and blood cells.\u003c/p\u003e\n\u003cp\u003eFigure 2 shows heatmaps representing the mean differences compared to the baseline (Day 1). All markers depicted were statistically significant using a repeated measures ANOVA test and FDR correction. In a subsequent analysis, a paired T-test was used as a post-hoc test without applying additional multiple test corrections. Heatmaps show anthropomorphic evolution (\u003cstrong\u003eA\u003c/strong\u003e), blood biochemical parameters (\u003cstrong\u003eB\u003c/strong\u003e), and blood cell counts (\u003cstrong\u003eC\u003c/strong\u003e). D, Day; W, Week; BMI, Body mass index; BP, Blood pressure; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; CRP, C-reactive protein; HbA1c, Hemoglobin A1c; HDL, High-density lipoprotein; LDL, Low density lipoprotein; TSH, Thyroid stimulating hormone; BUN, Blood urea nitrogen; MCHC, Mean corpuscular hemoglobin concentration; RDW, Red cell distribution width.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6904467/v1/9e3be485a8eaf646e9180b09.png"},{"id":85752182,"identity":"e8c77876-ad44-475b-8bd9-2ce9238d2718","added_by":"auto","created_at":"2025-07-01 10:21:33","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":715721,"visible":true,"origin":"","legend":"\u003cp\u003ePF and TRE were linked to dietary changes during 12 months of intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eLollipop plot showing the change of consumption of different food items over time, based on Friedman tests. Significant changes over the study intervention can be observed for cereals, alcohol, sweets, legumes, fish, dairy products and meat. All \u003cem\u003eP\u003c/em\u003e-values are corrected with FDR correction. \u003cstrong\u003eB.\u003c/strong\u003e Boxplots representing the changes for the significant dietary categories over time (\u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05 from the Friedman test). A significant decrease in the consumption of animal products, namely dairy products, fish and meat, can be observed, while we see an increase of legume consumption until week 52 compared to day 1. \u003cem\u003eP\u003c/em\u003e-values are Wilcoxon test corrected with FDR. \u003cstrong\u003eC.\u003c/strong\u003e This Barplot represents the dietary pattern changes over the intervention period. A shift from a predominantly omnivore dietary pattern to an increased adherence to a vegetarian or vegan diet can be observed in week 3 compared to day 1. This shift stays apparent until the end of the study (week 52). \u003cstrong\u003eD. \u003c/strong\u003eBoxplots showing the increased adherence to an MDscale and MedDietscore over the intervention period. \u003cem\u003eP\u003c/em\u003e-values are Wilcoxon tests corrected with FDR. D, Day; W, Week.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6904467/v1/bd2df28466f9a71911d78b25.jpeg"},{"id":85754056,"identity":"16939813-805b-4616-80d7-d1f448cbc8bf","added_by":"auto","created_at":"2025-07-01 10:37:33","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209736,"visible":true,"origin":"","legend":"\u003cp\u003eA linear mixed-effects model for assessing the impact of different effects on specific outcomes.\u003c/p\u003e\n\u003cp\u003eThe fixed effects include the consistency of eating pattern, the level of general physical activity, body-mass index (BMI), dietary composition such as a gluten-free diet or an adherence to a Mediterranean diet (MDScale and MedDietScore), and the overall composition of the regimen (omnivore, pescetarian, vegetarian, and vegan), as well as the duration of the overnight fast and the hours of sleep. The response variables include a quality-of-life questionnaire (WHO-5), profile of mood states (POMS), health assessment questionnaire (HAQ), Hospital Anxiety and Depression Scale (HADS), functional ability questionnaire (FFbH-R) and clinical disease activity index (CDAI). D, Day; W, Week.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6904467/v1/194e60d2be55f22fb067de8e.jpeg"},{"id":90097928,"identity":"85140aa9-df54-4009-837a-167d66202f65","added_by":"auto","created_at":"2025-08-28 12:47:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2606742,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6904467/v1/a4d2b3fe-2014-4b06-a64f-88a99d20babd.pdf"},{"id":85753019,"identity":"a701acba-399c-43f7-8255-6f188fd5677b","added_by":"auto","created_at":"2025-07-01 10:29:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":900762,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles0625.docx","url":"https://assets-eu.researchsquare.com/files/rs-6904467/v1/bacd896b09821812c7c5f9d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fasting-driven suppression of disease activity in rheumatoid arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFasting is defined as the voluntary abstinence from caloric ingestion for a limited time [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Fasting practices date back to ancient civilizations, where periods of food abstinence were primarily a result of food scarcity and the hunter-gatherer lifestyle. Despite advancements in food availability, fasting has persisted through various cultural, religious and health practices until today. Popular fasting methods practiced nowadays include prolonged fasting (PF), typically lasting from four to 21 days with an energy intake below 350 kcal/d, and time-restricted eating (TRE), whereby food intake is restricted to a daily eating window [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent research has yielded promising results associating several underlying mechanisms to the health benefits of fasting. Some have been attributed to enhanced mitochondrial function, increased autophagy, gut microbiome changes and an overall reduced inflammation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thereby, fasting has been proposed to be efficacious in alleviating clinical symptoms in human chronic diseases with inflammatory signatures such as rheumatoid arthritis (RA). RA is a multifactorial chronic and systemic auto-immune disease, affecting 1% of the global population with women being at a higher risk than men [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The disease typically affects the synovial lining of the joints but also entails various comorbidities including the vasculature, the metabolism and the bones of the patients, significantly reducing their quality of life [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. RA pathogenesis is associated with both genetic and environmental risk factors, interacting on various levels several years before the onset of clinical symptoms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Specific HLA-DRB1 alleles are known as the \u0026ldquo;shared epitope (SE)\u0026rdquo; and are associated with an increased incidence in RA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Various environmental factors such as cigarette smoking, air pollution, dust exposure, infections, lifestyle and nutrition can trigger the onset of systemic autoimmunity and the production of RA-specific autoantibodies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Also, the bidirectional connection between the immune system and the gut microbiome has been associated with the onset of autoimmune disease such as RA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It has been suggested that a gut microbiome dysbiosis might lead to systemic inflammation in genetically predisposed patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Typical treatment options for patients with RA include non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids for the management of acute flares, and disease modifying anti-rheumatic drugs (DMARDs) and/or corticosteroids to stabilize the disease [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, these treatments are accompanied by several side effects and mixed efficacy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Dietary interventions, including fasting, have gained significant attention for their potential to influence the onset and progression of RA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has previously been shown that fasting combined with a lactovegetarian diet can improve disease activity, including reduced pain and stiffness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, it was found that specifically the Mediterranean diet (MD), characterized by a high consumption of fruits, vegetables, whole grains, legumes, nuts and olive oil has promising benefits for individuals with RA [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Understanding the changes induced by PF in patients with RA, and, subsequently, the different dietary strategies sustaining the beneficial effects of PF are crucial. Within the framework of the ExpoBiome study, a clinical cross-sectional and longitudinal intervention trial, we investigated the combined effects and underlying mechanisms of one week of PF followed by 12 months of TRE in patients diagnosed with RA [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics and treatment regimens\u003c/h2\u003e \u003cp\u003eA total of 30 patients with RA was included in this longitudinal study and underwent one week of PF followed by 12 months of TRE. The patients were mostly female (90%), corresponding to the higher incidence of RA in women. Most patients did receive RA-specific pharmaceutical treatment (70%). The detailed baseline characteristics are shown in 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 the study cohort, n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y), mean, SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration [years]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight [kg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI [kg/m2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure [mmHg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmnivore [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP [mg/L]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Treatment [%] *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eAbbreviations: BMI, body mass index; WHR, waist-hip ratio; CDAI, clinical disease activity index; HAQ, health assessment questionnaire; CRP, C-reactive protein; IQR, interquartile range.\u003c/p\u003e\n\u003cp\u003e*Either bDMARD, DMARD, NSAID, corticosteroid or combined treatment\u003c/p\u003e\n\u003cp\u003e**Median and IQR, except if indicated differently\u003c/p\u003e\n\u003ch2\u003ePF and TRE induced a sustained decrease of disease activity.\u003c/h2\u003e\n\u003cp\u003eOur main objective was to compare disease metrics during the different time points, after PF and during TRE, with the baseline.\u0026nbsp;The duration of overnight fasting was significantly increased at all stages of the intervention (\u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001, Figure 1A). We assessed\u0026nbsp;changes in clinical disease activity index (CDAI) and clinico-anthropometric parameters over time compared to the baseline.\u0026nbsp;In this context, a\u0026nbsp;statistically significant decrease was observed for the CDAI, with the lowest being recorded immediately after the PF, but with a significant sustained decrease remaining for the whole duration of the intervention (\u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05, Figure 1B-D), i.e. during TRE. At baseline, 62% of patients showed a high disease activity which decreased to 24% after PF and was still as low as 37% after 12 months of TRE (Figure 1D). Three patients remained at a high CDAI for all timepoints (Figure 1D). Crucially, we observed a statistically significant shift from the high activity to the low activity category, especially at early stages of the intervention (Q-value \u0026lt; 0.0001, Figure 1D). Furthermore, a statistically significant difference in the health assessment questionnaire (HAQ) and Non-Motor Syndrome Questionnaire (NMSQ) were noted (\u003cem\u003eQ\u003c/em\u003e-value \u0026lt; 0.05 Friedman test, Figure 1B-C). The Hannover Functional Ability Questionnaire (FFbH-R) was also found to be statistically different when comparing\u0026nbsp;before correction (\u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;= 0.052, \u003cem\u003eQ\u003c/em\u003e-value = 0.09, Friedman test, Figure 1B).\u0026nbsp;Subsequently, we performed Wilcoxon signed-rank tests for each time point relative to the baseline, for which all results are presented in supplemental table 1. Using a post-hoc analysis, we found a significant decrease CDAI and HAQ at all time points compared to baseline (q \u0026lt; 0.05, Wilcoxon test, Figure 1C). We detected a significant increase in FFbH-R at Day 8-12 and a statistically significant decrease of Hospital Anxiety and Depression Scale (HADS)\u0026nbsp;at Week 3 and Week 52, respectively (q \u0026lt; 0.01, q \u0026lt; 0.05 and q \u0026lt; 0.05, respectively, Wilcoxon test, Figure 1C). The Profile Moods of States (POMS), Bristol Stool Score and the Quality-of-Life Questionnaire (WHO-5) were not statistically different at any timepoint (q \u0026gt; 0.5 and p \u0026gt; 0.05, for all comparisons, Wilcoxon test, Supplemental fig. 1A-B). Finally, we found a significant decrease for NMSQ at W3, W26 and W52 compared to baseline (q \u0026lt; 0.05, Wilcoxon test, Supplemental fig. 1B). \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProlonged fasting and time-restricted eating induce sustained reductions in body mass index, blood pressure and biological markers.\u003c/h2\u003e\n\u003cp\u003eWe next assessed anthropometric changes and routine blood test changes during the intervention. For this purpose, we used repeated measures ANOVA followed by a post-hoc T-test, both corrected using FDR, complete results are presented in supplemental table 2. We noted several sustained anthropometric changes. More specifically, a significant decrease was recorded for BMI and hip circumference until week 52 (q\u0026nbsp;\u0026lt; 0.05, Figure 2A), while a significant reduction in diastolic and systolic blood pressure (BP), as well as waist circumference was sustained until week 26 (q\u0026nbsp;\u0026lt; 0.01, Figure 2A). An increased heart rate was observed during the PF period (q \u0026lt; 0.01, Day 6, Figure 2A). At the final timepoint, marking 12 months of TRE after the PF (Week 52), no other significant anthropometrical changes were recorded after correction for multiple testing with FDR.\u003c/p\u003e\n\u003cp\u003eMoreover, we noted a decrease\u0026nbsp;in fasting blood glucose, albumin, bilirubin, creatinine, HbA1C, HDL cholesterol, insulin\u0026nbsp;and uric acid directly after the PF (q \u0026lt; 0.05, Figure 2B). In addition, we found a decrease for LDL cholesterol, total cholesterol and cholinesterase at D8-12 (q \u0026lt; 0.01, T-test, Figure 2B). We also found a reduced blood\u0026nbsp;count for erythrocytes, leukocytes, lymphocytes, neutrophils and reticulocytes at multiple time points (q\u0026lt; 0.05, T-test, Figure 2C). We observed an increase in alanine transaminase (ALT) on Day 6 and Day 8-12, which was then followed by a decrease at week 26, both of which were not retained by FDR correction (q \u0026gt; 0.05, p \u0026lt; 0.05, Figure 2B). We found an increase for ALT, AST and CRP on Day 6 but not statistically significant (q \u0026gt; 0.05, T-test, Figure 2B). Finally, we found a significant increase of basophils at W52 (q \u0026lt; 0.01, T-test, Figure 2C).\u003c/p\u003e\n\u003ch2\u003ePF and TRE are accompanied by dietary shifts\u003c/h2\u003e\n\u003cp\u003eIn addition to adhering to the 16:8 TRE pattern, some changes in dietary habits were recorded. The number of meals was significantly decreased until week 26 (q\u0026nbsp;\u0026nbsp;= 0.0390, Wilcoxon test), the time at which patients took their breakfast was significantly altered to a later timepoint for up to 52 weeks (q\u0026nbsp;=\u0026nbsp;0.0110, Wilcoxon test), as well as their type of breakfast for up to 26 weeks (p = 0.0240, Wilcoxon test). \u0026nbsp;Alongside these findings, we could also see an increase in consistency of the timing of food intake until at least week 26 into TRE (q\u0026nbsp;=\u0026nbsp;0.0140, Wilcoxon test), and the speed at which patients consumed their meals was decreased after the PF (q\u0026nbsp;=\u0026nbsp;0.0170, Wilcoxon test). The patients also reduced overeating, e.g. continuing to eat after they were full, \u0026nbsp;until week 52 (q = 0.0100, Wilcoxon test). The 24FR showed a significant decrease in caloric intake (q = 0.0127) only immediately after the PF. No further significant changes in kcal, carbohydrate or fat intake occurred during the 52 weeks of TRE (p \u0026gt; 0.05). For protein intake a significant decrease was recorded from week 3 (p = 0.0001) until week 26 (p = 0.0063) during TRE. These observations aligned with the findings of the FFQ, which show a significant decrease in consumption of meat, fish and dairy products during the 12 months of the study (p \u0026lt; 0.00001, Friedman test, Figure 3A-B). In addition, we noted a decrease in the consumption of sweets over the course of the intervention (p = 0.012, Friedman test, Figure 3A). Simultaneously, a continuously increased intake in legumes was reported for the whole duration of the study as well as a higher consumption of fruits for the first six months of the study, although not being statistically significant (p \u0026lt; 0.0001 and p = 0.06, respectively, Friedman test, Figure 3A-B and Supplemental fig. 2). These changes translated into a spontaneous and unsupervised dietary shift towards an increase in ovo-lacto-vegetarian- and vegan-like dietary pattern during the study (p \u0026lt; 0.0001, Figure 3C). To resolve the overall patterns, we calculated two different Mediterranean diet (MD) indices, namely the MDScale and MD score (MedDiet score), based on the food questionnaire data. Both scores indicated a shift from a Western diet towards a higher adherence of a MD diet over the course of the intervention (p \u0026lt; 0.01 Figure 3D). Finally, we also calculated the dietary inflammatory index (DII) [16, 17]. The DII indicated that the patients neither followed a pronounced pro- nor anti-inflammatory dietary pattern at baseline and despite the changes we could observe for specific food items, no significant shifts were observable in the DII (p \u0026gt; 0.1, Friedman test).\u003c/p\u003e\n\u003cp\u003eOverall, the dietary pattern of the patients shifted towards a shorter eating window with more consistent timing, a decrease in animal product consumption, a higher adherence to a MD diet, without significant changes in kcal intake or a higher adherence to DII.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eIncreased overnight fast duration and adherence to a Mediterranean diet leads to sustained health benefits\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAs the result of a linear mixed-effects model analysis on several health questionnaires as response variables, we found that BMI had a significant effect on CDAI, FFbH-R and HAQ (p = 0.003, p= 0.003 and p= 0.004, respectively, mixed model effects test) while TRE (overnight fast) impacted CDAI and HADS significantly (p= 0.03 and p= 0.01, mixed model effects test). Moreover, CDAI was also affected by the composition of the diet. In addition, physical activity, while not changing over the time of the intervention, was a significant predictor for the quality of life of patients (WHO-5). Consistency of eating patterns, dietary regimen and hours of sleep did not impact the health outcomes significantly (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe ran an additional mixed-effects model focusing on the BMI changes. Interestingly, we found a significant association between BMI, overnight fasting (p = 0.02) and MedDietScore (p = 0.001), but not with the frequency of physical activity (gardening, window cleaning, etc., see Material and Methods), sport activities or number of meals (p \u0026gt; 0.2, for all factors).\u003c/p\u003e"},{"header":"DISCUSSION ","content":"\u003cp\u003eOur data shows the profound impacts of fasting and diet on RA CDAI over time, highlighting the combined effect of weight loss, dietary adaptations, and fasting on improving RA disease severity. Contrary to previous findings [14], we found in our present study that the decrease in CDAI induced via PF could be sustained by TRE for as long as 12 months. This improvement in RA severity was accompanied by distinct biochemical changes reflecting reduced systemic inflammation. While general dietary guidelines for RA primarily focus on specific nutrients and food items, they underappreciate overall dietary composition and meal timing [18]. Our findings highlight the significance of meal timing in relation to the body\u0026apos;s circadian rhythm. This can be affected through strategies such as TRE.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observed beneficial outcomes may be the results of various mechanisms at play, including enhanced ketogenesis, improved mitochondrial function, increased autophagy, modulated gene expression, weight reduction, reduced apoptosis, and decreased inflammation [1, 19, 20]. These are reflected in the improved metabolic profile of the patients observed after fasting, including improved blood glucose and cholesterol levels alongside a reduction in CRP. Fasting-induced gut microbiome modulation might also be a key mediator of the impact of TRE and PF on RA [21]. Also, having overweight or obesity has been linked to NCDs. In particular, obesity has been suggested to be a key risk factor for RA leading to a more severe pathogenesis when compared to patients with a BMI below 25. \u0026nbsp; Although BMI does play an important role in our findings and probably partially mediates the associated health benefits observed after PF and TRE, the composition of the food items with resprect to the ingested nutrients should not be neglected. Despite the DII remaining unchanged, we can observe a significantly higher adherence to an MD and an overall shift to vegetarian eating patterns during the intervention. This aligns partially with dietary recommendations for patients with RA from organizations such as the German Nutrition Society (DGE) [22]. \u0026nbsp;The increase in the consumption of legumes has been reported by several studies to result in beneficial health effects mediated partially by an increased fiber intake, and a resulting beneficial effect on the gut microbiome. As mentioned above, RA is associated with an apparent shift in the gut microbiome composition. Dietary adaptations supporting a diverse and beneficial gut microbiome composition are therefore crucial and nutritional composition remains a vital dimension for RA disease management. The observed higher adherence to a MD has previously been reported as potentially beneficial in patients with RA, leading to a reduction in pain and improved physical function [23, 24]. However, evidence to generally recommend a MD for patients with RA has been assessed insufficient in 2018 [24-29]. \u0026nbsp;In this context, the present study is highly relevant, and it underscores the importance of integrating both quality and timing of diet into RA management. By combining and implementing various dietary strategies, namely PF followed by a maintenance diet consisting of TRE and a higher MD adherence, patients with RA could optimize health outcomes and benefit from personalized nutrition interventions tailored to their individual needs.\u003c/p\u003e"},{"header":"Methods ","content":"\u003ch2\u003eTrial registration number and date at clinicaltrials.gov:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNCT04847011, April 14, 2021\u003c/p\u003e\n\u003ch2\u003eEthical approval and consent\u003c/h2\u003e\n\u003cp\u003eEthical approval for this study was from the institutional review board of the Charité-Universitätsmedizin Berlin (EA1/204/19), the ethics committee of the state medical association (Landesärztekammer) of Hessen (2021-2230-zvBO) and the Ethics Review Panel (ERP) of the University of Luxembourg (ERP 21-001-A ExpoBiome). Participants were included into the study only after written informed consent [15].\u003c/p\u003e\n\u003ch2\u003eSample and data collection\u003c/h2\u003e\n\u003cp\u003eThe study intervention consisted of 30 patients with RA undergoing one week of PF which was followed by 12 months of TRE. Patients attended a total of 11 study visits, and at each timepoint sample and data collection took place. The samples for routine blood chemistry were collected at the Charité-Universitätsmedizin Berlin and measurements were performed immediately after each visit. Blood samples were taken at a baseline visit (Day 1), on day six of the PF and on nine additional visits during the TRE over 12 months [15]. Several questionnaires assessing clinical and nutritional data were answered either on site during the visit or prior to the visit at home and captured in REDCap (Table 2). The CDAI was calculated based on the swelling and pain of the joints of the patients and divided into different severity categories, defined as: Remission [0,2.8], Low [\u0026gt;2.8,10], Medium [\u0026gt;10,22] and High [\u0026gt;22]. The dietary behaviour of the patients was assessed by 24-hour food recalls (24HFR), food frequency questionnaires (FFQ) and dietary habits (DH) questionnaires. Some questionnaires and data scuh as anthropometric parameters, e.g. BMI, WHR, medication and blood pressure were captured on each visit, while more extensive questionnaires concerning the health assessment and nutritional data, were limited to a selected number of visits, namely day 1, day 8-12, week 3, week 26 and week 52. Most patients (70%) underwent a typical RA treatment over the total course of the study, consisting of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs, 50%), or biological DMARDs (bDMARDs, 27%), either alone or in combination with csDMARDs or glucocorticoids. No significant changes in the treatment regimens were observed during the 12 months of the study.\u003c/p\u003e\n\u003cp\u003eTable 2: Health assessment questionnaires\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease specific\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eHannover Functional Ability Questionnaire (FFbH-R)[30]\u003c/li\u003e\n \u003cli\u003eClinical Disease Activity Index (CDAI)[31]\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDietary behaviour and lifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eFasting experience, expectation, and intervention\u003c/li\u003e\n \u003cli\u003eLifestyle\u003c/li\u003e\n \u003cli\u003e24H-Food-recall\u003c/li\u003e\n \u003cli\u003eFood Frequency Questionnaire (FFQ)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral health and well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth Assessment Questionnaire (HAQ)[32]\u003c/li\u003e\n \u003cli\u003eBristol Stool Scale[33]\u003c/li\u003e\n \u003cli\u003eQuality of Life questionnaire (WHO-5)[34]\u003c/li\u003e\n \u003cli\u003eHospital Anxiety and Depression Scale (HADS)[35]\u003c/li\u003e\n \u003cli\u003eProfile of Mood States (POMS)[36]\u003c/li\u003e\n \u003cli\u003eNon-Motor Symptoms Questionnaire (NMSQ)[37]\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eDietary Inflammatory Index\u003c/h2\u003e\n\u003cp\u003eThe DII was calculated based on the following 23 food components or nutrients: Alcohol, coffee, carbohydrate, cholesterol, energy, total fat, folic acid, garlic, ginger, iron, magnesium, multi-unsaturated fatty acids (MUFA), saturated fat, niacin, protein, riboflavin, selenium, thiamine, vitamin A, C, D, E and green or black tea [16, 17]. The data was taken from both 24FR and FFQ.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMediterranean diet scores\u003c/h2\u003e\n\u003cp\u003eFor measuring adherence to a MD diet, we selected two MD indices, namely the MDScale and the MedDiet score [38, 39], was based on an extensive literature review comparing five MD indices [40]. The first score (MDScale), is the oldest MD adherence assessment index and was developed in 1995 and revised in 2003, using sex-specific median calculations and a total of 9 food groups [38, 40]. The second score (MedDiet index) was developed in Greece in 2005 as an alternative to the gender-specific MDScale and has been widely used ever since [39, 40]. The calculation of the indices was slightly adapted as follows. For the MDScale, nine items were included and calculated based on a gender-specific median amount, namely vegetables, legumes, fruits, nuts, cereals and fish as beneficial items and dairy products, meat including poultry and alcohol as negative items. This resulted in a score between zero and nine, with nine indicating the highest adherence to an MD diet. Deviating from the original MDScale, the mono-unsaturated fatty acid to saturated fatty acid ratio was not considered as this data was not collected during our study. For the MedDiet score a total of eight items was included and the score was calculated based on their frequency of consumption per week. Items leading to a higher adherence were cereals, fruits, vegetables, legumes, and fish, while items negatively impacting the MDScale score were dairy products, meat and alcohol. In this index, potatoes, olive oil and poultry were not included for our study. The adapted MedDiet score calculated in this study ranges from 0 to 40, with 40 indicating a high MD adherence.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analyses\u003c/h2\u003e\n\u003cp\u003eThe clinical and nutritional data collected in REDCap and the molecular data provided by SGS were analysed and integrated using R (R 4.3.2, RRID:SCR_001905) and R studio (2023.09.1+494, RRID:SCR_000432). The 24FR were digitalised using the Nutrilog software and the OSAV food data bank which allowed an overview of the overall energy intake as well the macro- and micronutrient consumption of the patients [41]. \u0026nbsp;A repeated measures ANOVA test or Friedman test, depending on whether the data followed a normal distribution or not, was performed to identify significantly divergent values over the different timepoints and corrected with false-discovery rate (FDR). For the parameters for which the repeated measures ANOVA/Friedman tests showed a significant value, post-hoc Student/Wilcoxon tests for paired values were applied. The post hoc test p- values were not corrected as correction was already applied on the repeated measures tests. A Cochran test was applied for the CDAI. Wilcoxon, Friedman, Student and Cochran tests were performed using the \u003cem\u003erstatix\u003c/em\u003e package (v0.7.2, RRID:SCR_021240). Chi-square tests were performed using the stats R package (v4.4.2, RRID:SCR_025968). Mixed linear modelling was done using the package lme4 (v1.1.35.5, RRID:SCR_015654) and lmerTest (v3.1.3, RRID:SCR_015656) in R\u0026nbsp;[42, 43].\u0026nbsp;Plotting was performed using \u003cem\u003eggalluvial\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA first mixed-effect model was applied for the primary outcome parameters captured in the health questionnaires. Mixed effect models contain both fixed effects and random effects and are particularly useful when dealing with data that have multiple levels of variability such as the data analysed in the present study [44]. The fixed effects are constant across individuals and the primary variables of interest in the study, while the random effects account for variation across different levels of the data and are not of primary interest [44]. The model used to analyse the effects of various effects on the different health outcomes was as follows: Health outcome ~ MDScale + MedDietScore + BMI + regimen + overnight_fast \u0026nbsp;+ sleep_hour + gluten_free + consistency of eating pattern + Physical activity`+ (1 | Record_id). A second model was built with BMI as response, e.g. BMI ~ Physical activity + Sports + MedDietScore + number_of_meals + overnight_fast + walking + (1 | Record_id). The model as was also applied to the MD indices. \u0026nbsp;The p-values were corrected for multiple testing with FDR correction. Plotting was performed using \u003cem\u003eggalluvial\u0026nbsp;\u003c/em\u003e(v0.12.5, RRID:SCR_021253)\u003cem\u003e, ggrepel\u0026nbsp;\u003c/em\u003e(v0.9.6, RRID:SCR_017393)\u003cem\u003e, patchwork\u0026nbsp;\u003c/em\u003e(v1.3.0, RRID:SCR_000072)\u003cem\u003e, ImmuMicrobiome\u0026nbsp;\u003c/em\u003e(v1.0.1, RRID:SCR_026073) \u003cem\u003eand ggplot2\u0026nbsp;\u003c/em\u003e(v\u0026nbsp;3.5.2, RRID:SCR_014601)\u0026nbsp;[42, 43].\u003c/p\u003e\n\u003ch2\u003eStudy limitations\u003c/h2\u003e\n\u003cp\u003eThe unexpected dietary adaptations in relation to MD might have impacted the observed benefits of PF and TRE. Investigating whether combining TRE with a MD or a specific anti-inflammatory diet yields greater improvements requires further investigation.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe clinical data used in this study is held by the clinical partner and is not shared publicly due to privacy and confidentiality agreements. Access to this clinical data may be granted upon direct request to the clinical partner, subject to their approval and ethical considerations.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eAll code used for the data analysis can be found in the following repository: gitlab.lcsb.uni.lu/TBD.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank Audrey Frachet-Bour, Janine Habier, Jordan Caussin, L\u0026eacute;a Grandmougin, Dr. Catharina Delebinski, Melanie Dell\u0026rsquo;Oro, Grit Langhans, Ursula Reu\u0026szlig;, Maik Schr\u0026ouml;der and Nadine Sylvester for their support during the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis project has received funding from the European Research Council (ERC) under the European Union\u0026rsquo;s Horizon 2020 research and innovation program (grant agreement number 863664). This work was supported by the Luxembourg National Research Fund (FNR) under grant PRIDE/11823097.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eStudy design and protocol: B.H, C.C.L, J.G. S, P.W; the conceptualisation of the intervention: E.H, D.A.K, A.M, A.R.K, B.M, S.S, N.S, J.G.S, P.W; clinical trial and sample collection design and administration by B.H, E.H, D.A.K, A.M, A.R.K, B.M, S.S; funding acquisition: P.W, C.C.L; statistical analysis, calculation of the DII, MD indices was done by B.H, F.V, V.P, R.V; data visualisation: R.V, B.H; sample size calculation: C.C.L, J.G. S, P.W, K.R; initial draft writing: B.H, R.V, editing process coordination: by B.H; sample protocol preparation: B.H;\u0026nbsp;. Review and editing: J.G.S, P.W; all authors contributed, read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAuthors declared no competing interests.\u003c/p\u003e\n\u003cp\u003eThis project has received funding from the European Research Council (ERC) under the European Union\u0026rsquo;s Horizon 2020 research and innovation program (grant agreement number 863664). This work was supported by the Luxembourg National Research Fund (FNR) under grant PRIDE/11823097.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eB\u0026eacute;r\u0026eacute;nice Hansen, K.R., Hebah Ebid, Jochen G Schneider, \u003cem\u003ePerspective: The Impact of Fasting and Caloric Restriction on Neurodegenerative Diseases in Humans.\u003c/em\u003e Advances in Nutrition 2024. \u003cstrong\u003e15\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003ePaoli, A., et al., \u003cem\u003eCommon and divergent molecular mechanisms of fasting and ketogenic diets.\u003c/em\u003e Trends in Endocrinology \u0026amp; Metabolism, 2024. \u003cstrong\u003e35\u003c/strong\u003e(2): p. 125-141.\u003c/li\u003e\n\u003cli\u003eHartmann, A.M., et al., \u003cem\u003ePost Hoc Analysis of a Randomized Controlled Trial on Fasting and Plant-Based Diet in Rheumatoid Arthritis (NutriFast): Nutritional Supply and Impact on Dietary Behavior.\u003c/em\u003e Nutrients, 2023. \u003cstrong\u003e15\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eHealthline, V.L. \u003cem\u003eRheumatoid Arthritis by the Numbers: Facts, Statistics, and You\u003c/em\u003e. 2021.\u003c/li\u003e\n\u003cli\u003eGuo, Q., et al., \u003cem\u003eRheumatoid arthritis: pathological mechanisms and modern pharmacologic therapies.\u003c/em\u003e Bone Res, 2018. \u003cstrong\u003e6\u003c/strong\u003e: p. 15.\u003c/li\u003e\n\u003cli\u003eScherer, H.U., T. 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Kelley, \u003cem\u003eFixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations.\u003c/em\u003e Psychol Methods, 2019. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 20-35.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6904467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6904467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProlonged fasting (PF) and time-restricted eating (TRE) have emerged as widely used dietary regimens gaining attention for potential health benefits, but evidence is sparse. We investigated the combined effects of one week of PF followed by 12 months of TRE in patients suffering from the chronic autoimmune disease rheumatoid arthritis (RA). Participants experienced sustained reductions in the RA Clinical Disease Activity Index (CDAI) from 31.81 to 8.16 (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and improved well-being and functional ability. These effects were accompanied by a significant and sustained decrease in BMI from 25.02 (day 1) to 24.08 (week 52). TRE was also associated with a tendency for an adherence to a Mediterranean diet (MD). Statistical analysis revealed that reductions in BMI, adherence to TRE and MD significantly impact CDAI improvements. TRE might therefore represent a valuable tool to sustain PF-induced health benefits over time.\u003c/p\u003e","manuscriptTitle":"Fasting-driven suppression of disease activity in rheumatoid arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 10:21:29","doi":"10.21203/rs.3.rs-6904467/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6912e568-99bd-4601-8cbb-16be7fdb8b7a","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50572574,"name":"Biological sciences/Immunology/Autoimmunity"},{"id":50572575,"name":"Health sciences/Health care/Nutrition"},{"id":50572576,"name":"Health sciences/Diseases/Rheumatic diseases"}],"tags":[],"updatedAt":"2025-08-28T12:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 10:21:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6904467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6904467","identity":"rs-6904467","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}