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A cohort of 294 RA patients was analyzed using hierarchical clustering techniques. Data collected included body mass index (BMI), adherence to physical activity, prevalence of comorbidities, seropositivity, Health Assessment Questionnaire (HAQ) scores, and genetic polymorphisms in TNF pathway. Responders to TNFi demonstrated a more favorable clinical profile, with a lower BMI (25.6 vs. 27.0;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.03), higher physical activity adherence (56.6% vs. 33.3%;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01), reduced prevalence of comorbidities (69.8% vs. 82.4%;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.02), and improved functionality (HAQ: 0.8 vs. 1.7;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01) compared with non-responders. The C allele of rs767455 was significantly more frequent among responders (74.4% vs. 62.4%;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.04). Clustering analysis identified three distinct subgroups: a better prognosis group characterized by a 73.5% response rate, minimal use of combination therapies, high adherence to physical activity, and male predominance; a poorer therapeutic response group with 82.9% therapeutic failure, intensive medication use, and low exercise adherence; and an intermediate group. TNFi response in RA is multifactorial. Integrating clinical, demographic, and genetic data enables the identification of distinct patient profiles, supporting the development of personalized treatment strategies and the advancement of precision medicine in RA management.\n                  <\/jats:p>","DOI":"10.1007\/s12026-026-09822-x","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T06:46:38Z","timestamp":1786949198000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering of rheumatoid arthritis patients: an unsupervised machine learning approach for characterizing the TNFi response"],"prefix":"10.1007","volume":"74","author":[{"given":"Pedro Augusto Silva","family":"dos Santos Rodrigues","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarina Mattos","family":"Brand\u00e3o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lilian","family":"de S\u00e1 Garcia Landeiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laryssa Cardoso","family":"Calmon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Almirane Lima","family":"de Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thamara Barbosa","family":"Miranda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Locke Ferreira","family":"de Ara\u00fajo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael Valente","family":"Veiga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camila Alexandrina Viana","family":"de Figueiredo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"de Moura Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3075-0729","authenticated-orcid":false,"given":"Ryan","family":"dos Santos Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"9822_CR1","volume-title":"Handbook for guideline development","author":"World Health Organization","year":"2018","unstructured":"World Health Organization. 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