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Using data\u2010driven approaches that enhance generalizability can help elucidate relationships between treatment outcomes and alcohol consumption, aiding in the discovery of novel treatment targets for AUD subtypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We merged data (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092045) across four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial (NIAAA Sponsored). Participants were clustered based on self\u2010reported drinking during treatment maintenance. A gradient boosted machine learning model with end\u2010of\u2010treatment clinical features was used to predict the clusters we identified.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified a three\u2010cluster solution corresponding to low (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>Standard Drinking Units (SDU)<\/jats:sub>\n                      \u2009=\u20091.68,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091677), moderate (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>SDU<\/jats:sub>\n                      \u2009=\u20096.70,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009253), and high (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>SDU<\/jats:sub>\n                      \u2009=\u200912.92,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009115) clusters of alcohol consumption during treatment maintenance. We achieved modest prediction of the clusters (Accuracy\n                      <jats:sub>Train\u2009<\/jats:sub>\n                      =\u200971.0%; AUC\n                      <jats:sub>Train<\/jats:sub>\n                      \u2009=\u20090.79) using demographics and end\u2010of\u2010treatment clinical and biological assessments. Between\u2010cluster differences were observed between low and high clusters on measures of depression and anxiety (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>Difference\u2009<\/jats:sub>\n                      =\u20090.49, SE\u2009=\u20090.13,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.004), drinking consequences (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>Difference\u2009<\/jats:sub>\n                      =\u20091.02, SE\u2009=\u20090.13,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.001) and liver functioning (0.39\u2009\u2264\u2009\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>Difference<\/jats:sub>\n                      \u2009\u2264\u20090.52, 0.12\u2009\u2264\u2009SE\u2009\u2264\u20090.13, 0.001\u2009\u2264\u2009\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u2009.005).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusions<\/jats:title>\n                    <jats:p>These findings suggest that generalizable clusters of alcohol consumption exist across these clinical trials characterized by core demographics, clinical, and biological phenotypes, irrespective of the treatment received. We further show that some assessments may not be useful in distinguishing between higher levels of consumption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Scientific Significance<\/jats:title>\n                    <jats:p>Identifying predictive features of AUD subtypes, across different phases of treatment, can assist clinicians in identifying individuals who require additional support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ajad.70132","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:39:30Z","timestamp":1769585970000},"page":"471-479","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying clinical correlates of drinking clusters during treatment for alcohol use 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