{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:03:07Z","timestamp":1787022187516,"version":"build-2736575974"},"reference-count":16,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Excessive weight gain after initiation of antiretroviral therapy (ART) has become a recognized concern among people living with HIV. Individual weight trajectories remain highly heterogeneous and challenging to predict using conventional methods. We leveraged the French Dat\u2019AIDS national cohort to assess whether machine learning (ML) could enhance the prediction of individual body weight at 6, 12, and 24 months after ART initiation. Using 112 baseline variables encompassing demographic, clinical, laboratory, and treatment-related data, we trained XGBoost models and evaluated performance using root mean square error (RMSE), R\u00b2, and mean prediction error. A simple benchmark model based on baseline weight was used for comparison. Among 24,014 eligible ART-na\u00efve adults, the ML models achieved RMSEs of approximately 4.6\u2009kg, 5.3\u2009kg, and 6.4\u2009kg at 6, 12, and 24 months respectively, with declining predictive power over time. Baseline weight (Weight_M0) consistently emerged as the strongest predictor, while other factors contributed minimally. Although ML marginally outperformed the benchmark (Weight_M0), accuracy remained insufficient for clinical decision-making. Sensitivity analyses excluding individuals with implausibly large monthly weight changes modestly improved RMSE (3.9\u20136.0\u2009kg), underscoring the impact of data quality. Our results demonstrate that, despite large sample size and rich clinical variables, ML lacks the precision necessary for individual weight forecasting in this context. These findings highlight the limitations of applying artificial intelligence to heterogeneous real-world cohorts and underscore the need to incorporate behavioral and lifestyle factors to improve predictive modeling.<\/jats:p>","DOI":"10.1371\/journal.pone.0344570","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:48:25Z","timestamp":1772822905000},"page":"e0344570","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning prediction of weight gain after antiretroviral therapy initiation in people with HIV: Insights from a large french real-world cohort"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3831-1904","authenticated-orcid":true,"given":"Cyrielle","family":"Codde","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6335-7474","authenticated-orcid":true,"given":"Cl\u00e9ment","family":"Benoist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2264-4822","authenticated-orcid":true,"given":"Laurent","family":"Hocqueloux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyrille","family":"Delpierre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1010-1270","authenticated-orcid":true,"given":"Clotilde","family":"Allevena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Am\u00e9lie","family":"M\u00e9nard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Ch\u00e9ret","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2332-2507","authenticated-orcid":true,"given":"C\u00e9dric","family":"Arvieux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Fran\u00e7ois","family":"Faucher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1695-0695","authenticated-orcid":true,"given":"Jean-Baptiste","family":"Woillard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"on behalf of the Dat\u2019AIDS Study Group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"issue":"8","key":"pone.0344570.ref001","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1093\/cid\/cir510","article-title":"Long-term treatment with raltegravir or efavirenz combined with tenofovir\/emtricitabine for treatment-naive human immunodeficiency virus-1-infected patients: 156-week results from STARTMRK","volume":"53","author":"JK Rockstroh","year":"2011","journal-title":"Clin Infect Dis"},{"issue":"7","key":"pone.0344570.ref002","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1093\/cid\/ciw017","article-title":"Body composition changes after initiation of raltegravir or protease inhibitors: ACTG A5260s","volume":"62","author":"GA McComsey","year":"2016","journal-title":"Clin Infect 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