{"doi":"10.1002/ctm2.959","title":"Baseline microbiome and metabolome are associated with response to ITIS diet in an exploratory trial in patients with rheumatoid arthritis","abstract":"Changes in diet might modify the faecal microbiome and metabolomic profile, affecting pain in rheumatoid arthritis (RA). We examined the effect of an anti-inflammatory “ITIS” diet1 on clinical outcomes, gut microbiome, and metabolome in RA patients, and found that baseline faecal microbiome and metabolome composition were associated with the pain response. A prospective, open-label pilot trial was conducted to evaluate a 2-week isocaloric ITIS diet (Figure 1A) in patients with active RA. The study was approved by the Institutional Board Review. Change in pain (assessed on a visual analogue scale from 0 to 10) was the primary outcome. Patients were classified as responders (N = 7) or non-responders (N = 13), based on the achievement of a 50% improvement in pain. Amplicon sequencing was used for microbiome profiling and untargeted metabolomics for metabolite analysis. Additional methods are included in the supporting information. Twenty patients finalized the trial. Demographics and disease characteristics are summarized in Figure 1B. A diet score (212 = gold-standard) was designed to characterize patients’ baseline diet (Figure 1C and Tables S1 and 2). Patients with higher baseline disease activity had lower anti-inflammatory food scores, specifically fruit, probiotics, and anti-inflammatory spices (Figure 1D). Dietary intervention was well tolerated, and overall adherence based on the self-reported diaries was approximately 70%, except for plant protein and probiotics, with final average scores less than 60% of the gold standard (Figure 1C,E and Table S3). We also assessed adherence using a reference data-driven metabolomics approach. In large, the foods recommended increased while forbidden foods decreased post-intervention (https://assets.researchsquare.com/files/rs-654519/v1/bc74ec0e-1d08-4c67-ad53-98a73e03e3ff.pdf?c=1631886103 and Figure 1F). Outcomes significantly improved post-2-weeks of the ITIS diet (Figure 1G and Tables S4 and 5). Pain improved from 3.89 ± 1.9 before versus 2.45 ± 2.4 after diet, p < .01 (Figure 1G). No significant change in BMI was observed (Figure S1A). Although obese patients (BMI ≥ 30) had higher disease activity, outcomes scores decreased in all patients (Figure S1B,C). There were no significant BMI changes in responders and non-responders (Figure S1D). Baseline pain was similar in both groups (Figure S1E). Yet, patients that reached remission had lower DAS28CRP (Figure S1F). Total diet scores before and after intervention were not different between responders and non-responders (Figure S2A). Yet, responders had a higher baseline anti-inflammatory score than non-responders (Figure S2B). Responders also had a less negative proinflammatory score than non-responders after diet (meaning responders ate less forbidden ingredients than non-responders) (Figure S2C). Additionally, patients with a higher baseline intake of whole grains, berries, enzymatic fruits, and unsaturated fat responded better to diet (Figure S2D–F). Yet, these scores were similar in responders and non-responders post-diet. We next evaluated changes in microbiome and metabolome post-intervention. Faecal microbiome and plasma and faecal metabolome alpha-diversity didn't change over time (Figure 2A–C and Figure S3B). Changes in microbiome trajectories were very discrete, while they were more pronounced in both faecal and plasma metabolome (Figure S4). Different microbial and metabolic features increased or decreased post-diet (Figure 2D–F). Interestingly, some metabolites are microbial metabolism products, including phenylacetylglutamine, bile acids (BA), and tryptophan/kynurenine. We also evaluated if baseline microbiome or metabolome were associated with response. Baseline microbiome but not metabolome alpha-diversity was significantly higher in responders (Figure 3A–C and Figure S3A), possibly reflecting the baseline dietary differences between the two groups (Figure S2D–F). Diet explains over 25% of the microbial structural variati","journal":"Clinical and Translational Medicine","year":2022,"id":264268,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.958,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":106555,"name":"Cameron Martino","orcid":"0000-0001-9334-1258","position":1,"is_corresponding":false},{"id":105861,"name":"Julia M. Gauglitz","orcid":"0000-0003-3146-2489","position":2,"is_corresponding":false},{"id":922330,"name":"Francesca Cedola","orcid":null,"position":3,"is_corresponding":false},{"id":105887,"name":"Anupriya Tripathi","orcid":"0000-0001-8912-9684","position":4,"is_corresponding":false},{"id":105863,"name":"Alan K. Jarmusch","orcid":"0000-0002-2228-6308","position":5,"is_corresponding":false},{"id":922331,"name":"Maram Alharthi","orcid":null,"position":6,"is_corresponding":false},{"id":922332,"name":"Marta Fernandez‐Bustamante","orcid":null,"position":7,"is_corresponding":false},{"id":922333,"name":"Meritxell Agustin‐Perez","orcid":null,"position":8,"is_corresponding":false},{"id":694582,"name":"Abha G. Singh","orcid":null,"position":9,"is_corresponding":false},{"id":922334,"name":"Soo‐In Choi","orcid":null,"position":10,"is_corresponding":false},{"id":921881,"name":"Tania Rivera","orcid":"0000-0002-2951-0798","position":11,"is_corresponding":false},{"id":921882,"name":"Katherine Nguyen","orcid":"0000-0002-8620-8616","position":12,"is_corresponding":false},{"id":285201,"name":"Tatyana Shekhtman","orcid":"0000-0002-2424-2202","position":13,"is_corresponding":false},{"id":922335,"name":"Tiffany Holt","orcid":null,"position":14,"is_corresponding":false},{"id":454995,"name":"Susan Lee","orcid":"0000-0003-4288-7394","position":15,"is_corresponding":false},{"id":922336,"name":"Shahrokh Golshan","orcid":null,"position":16,"is_corresponding":false},{"id":3553,"name":"Pieter C. Dorrestein","orcid":"0000-0002-3003-1030","position":17,"is_corresponding":false},{"id":3554,"name":"Rob Knight","orcid":"0000-0002-0975-9019","position":18,"is_corresponding":false},{"id":255038,"name":"Mónica Gumá","orcid":"0000-0003-1951-9411","position":19,"is_corresponding":false},{"id":255037,"name":"Roxana Coras","orcid":"0000-0001-9547-218X","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T00:26:37.567338Z","pmid":"35802808","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}