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Identifying clinical correlates of drinking clusters during treatment for alcohol use disorder.

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Title: Identifying clinical correlates of drinking clusters during treatment for alcohol use disorder.
Authors: Kohler, Robert J. (AUTHOR), Zhou, Hang (AUTHOR), Zakiniaeiz, Yasmin (AUTHOR), Verplaetse, Terril L. (AUTHOR), Garcia‐Rivas, Vernon (AUTHOR), Peltier, MacKenzie R. (AUTHOR), Banini, Bubu A. (AUTHOR), McKee, Sherry A. (AUTHOR), Roberts, Walter (AUTHOR)
Source: American Journal on Addictions. Jul2026, Vol. 35 Issue 4, p471-479. 9p.
Subjects: Drinking behavior, Clinical trials, Machine learning, Mental depression, Liver function tests, Anxiety, Alcoholism, Treatment effectiveness
Abstract: Background and Objectives: Despite the availability of treatments for alcohol use disorder (AUD), relapse prevalence and health‐related consequences associated with AUD remains high. Using data‐driven 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. Methods: We merged data (n = 2045) 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‐reported drinking during treatment maintenance. A gradient boosted machine learning model with end‐of‐treatment clinical features was used to predict the clusters we identified. Results: We identified a three‐cluster solution corresponding to low (MStandard Drinking Units (SDU) = 1.68, n = 1677), moderate (MSDU = 6.70, n = 253), and high (MSDU = 12.92, n = 115) clusters of alcohol consumption during treatment maintenance. We achieved modest prediction of the clusters (AccuracyTrain = 71.0%; AUCTrain = 0.79) using demographics and end‐of‐treatment clinical and biological assessments. Between‐cluster differences were observed between low and high clusters on measures of depression and anxiety (MDifference = 0.49, SE = 0.13, p =.004), drinking consequences (MDifference = 1.02, SE = 0.13, p <.001) and liver functioning (0.39 ≤ MDifference ≤ 0.52, 0.12 ≤ SE ≤ 0.13, 0.001 ≤ p ≤.005). Discussion and Conclusions: 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. Scientific Significance: Identifying predictive features of AUD subtypes, across different phases of treatment, can assist clinicians in identifying individuals who require additional support. [ABSTRACT FROM AUTHOR]
Copyright of American Journal on Addictions is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Identifying clinical correlates of drinking clusters during treatment for alcohol use disorder.
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Kohler%2C+Robert+J%2E%22&quot;&gt;Kohler, Robert J.&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zhou%2C+Hang%22&quot;&gt;Zhou, Hang&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zakiniaeiz%2C+Yasmin%22&quot;&gt;Zakiniaeiz, Yasmin&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Verplaetse%2C+Terril+L%2E%22&quot;&gt;Verplaetse, Terril L.&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Garcia‐Rivas%2C+Vernon%22&quot;&gt;Garcia‐Rivas, Vernon&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Peltier%2C+MacKenzie+R%2E%22&quot;&gt;Peltier, MacKenzie R.&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Banini%2C+Bubu+A%2E%22&quot;&gt;Banini, Bubu A.&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22McKee%2C+Sherry+A%2E%22&quot;&gt;McKee, Sherry A.&lt;/searchLink&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Roberts%2C+Walter%22&quot;&gt;Roberts, Walter&lt;/searchLink&gt; (AUTHOR)
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22American+Journal+on+Addictions%22&quot;&gt;American Journal on Addictions&lt;/searchLink&gt;. Jul2026, Vol. 35 Issue 4, p471-479. 9p.
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Drinking+behavior%22&quot;&gt;Drinking behavior&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Clinical+trials%22&quot;&gt;Clinical trials&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Machine+learning%22&quot;&gt;Machine learning&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Mental+depression%22&quot;&gt;Mental depression&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Liver+function+tests%22&quot;&gt;Liver function tests&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Anxiety%22&quot;&gt;Anxiety&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Alcoholism%22&quot;&gt;Alcoholism&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Treatment+effectiveness%22&quot;&gt;Treatment effectiveness&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background and Objectives: Despite the availability of treatments for alcohol use disorder (AUD), relapse prevalence and health‐related consequences associated with AUD remains high. Using data‐driven 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. Methods: We merged data (n = 2045) 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‐reported drinking during treatment maintenance. A gradient boosted machine learning model with end‐of‐treatment clinical features was used to predict the clusters we identified. Results: We identified a three‐cluster solution corresponding to low (MStandard Drinking Units (SDU) = 1.68, n = 1677), moderate (MSDU = 6.70, n = 253), and high (MSDU = 12.92, n = 115) clusters of alcohol consumption during treatment maintenance. We achieved modest prediction of the clusters (AccuracyTrain = 71.0%; AUCTrain = 0.79) using demographics and end‐of‐treatment clinical and biological assessments. Between‐cluster differences were observed between low and high clusters on measures of depression and anxiety (MDifference = 0.49, SE = 0.13, p =.004), drinking consequences (MDifference = 1.02, SE = 0.13, p &lt;.001) and liver functioning (0.39 ≤ MDifference ≤ 0.52, 0.12 ≤ SE ≤ 0.13, 0.001 ≤ p ≤.005). Discussion and Conclusions: 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. Scientific Significance: Identifying predictive features of AUD subtypes, across different phases of treatment, can assist clinicians in identifying individuals who require additional support. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: &lt;i&gt;Copyright of American Journal on Addictions is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=194642047
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1111/ajad.70132
    Languages:
      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 471
    Subjects:
      – SubjectFull: Drinking behavior
        Type: general
      – SubjectFull: Clinical trials
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Liver function tests
        Type: general
      – SubjectFull: Anxiety
        Type: general
      – SubjectFull: Alcoholism
        Type: general
      – SubjectFull: Treatment effectiveness
        Type: general
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      – TitleFull: Identifying clinical correlates of drinking clusters during treatment for alcohol use disorder.
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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