Mental health phenotypes of well-controlled HIV in Uganda
IntroductionThe phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accordingly, there has been a shift toward identifying MH phenotypes using data-driven methods capable of identifying novel insights into mechanisms of diverg...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1407413/full |
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| author | Leah H. Rubin Leah H. Rubin Leah H. Rubin Leah H. Rubin Kyu Cho Jacob Bolzenius Julie Mannarino Rebecca E. Easter Raha M. Dastgheyb Aggrey Anok Stephen Tomusange Deanna Saylor Maria J. Wawer Noeline Nakasujja Gertrude Nakigozi Robert Paul |
| author_facet | Leah H. Rubin Leah H. Rubin Leah H. Rubin Leah H. Rubin Kyu Cho Jacob Bolzenius Julie Mannarino Rebecca E. Easter Raha M. Dastgheyb Aggrey Anok Stephen Tomusange Deanna Saylor Maria J. Wawer Noeline Nakasujja Gertrude Nakigozi Robert Paul |
| author_sort | Leah H. Rubin |
| collection | DOAJ |
| description | IntroductionThe phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accordingly, there has been a shift toward identifying MH phenotypes using data-driven methods capable of identifying novel insights into mechanisms of divergent MH phenotypes among PWH. We leverage the analytic strengths of machine learning combined with inferential methods to identify novel MH phenotypes among PWH and the underlying explanatory features.MethodsA total of 277 PWH (46% female, median age = 44; 93% virally suppressed [<50copies/mL]) were included in the analyses. Participants completed the Patient Health Questionnaire (PHQ-9), Beck Anxiety Inventory (BAI), and the PTSD Checklist-Civilian (PCL-C). A clustering pipeline consisting of dimension reduction with UMAP followed by HBDScan was used to identify MH subtypes using total symptom scores. Inferential statistics compared select demographic (age, sex, education), viral load, and early life adversity between clusters.ResultsWe identified four MH phenotypes. Cluster 1 (n = 76; PTSD phenotype) endorsed clinically significant PTSD symptoms (average PCL-C total score > 33). Clusters 2 (n = 32; anxiety phenotype) and 3 (n = 130; mixed anxiety/depression phenotype) reported minimal PTSD symptoms, with modest BAI (Cluster 2) and PHQ-9 (Cluster 3) elevations. Cluster 4 (n = 39; minimal symptom phenotype) reported no clinical MH symptom elevations. Comparisons revealed higher rates of sexual abuse during childhood among the PTSD phenotype vs. the minimal symptom phenotype (p = 0.03).DiscussionWe identified unique MH phenotypes among PWH and confirmed the importance of early life adversity as an early risk determinant for unfavorable MH among PWH in adulthood. |
| format | Article |
| id | doaj-art-cf9bf87fc2114656be6f790faaffce7f |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-cf9bf87fc2114656be6f790faaffce7f2025-01-28T06:41:10ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.14074131407413Mental health phenotypes of well-controlled HIV in UgandaLeah H. Rubin0Leah H. Rubin1Leah H. Rubin2Leah H. Rubin3Kyu Cho4Jacob Bolzenius5Julie Mannarino6Rebecca E. Easter7Raha M. Dastgheyb8Aggrey Anok9Stephen Tomusange10Deanna Saylor11Maria J. Wawer12Noeline Nakasujja13Gertrude Nakigozi14Robert Paul15Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesMolecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United StatesMissouri Institute of Mental Health, University of Missouri - St. Louis, St. Louis, MO, United StatesMissouri Institute of Mental Health, University of Missouri - St. Louis, St. Louis, MO, United StatesMissouri Institute of Mental Health, University of Missouri - St. Louis, St. Louis, MO, United StatesDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesRakai Health Sciences Program, Kalisizo, UgandaRakai Health Sciences Program, Kalisizo, UgandaDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United StatesDepartment of Psychiatry, Makerere University, Kampala, UgandaRakai Health Sciences Program, Kalisizo, UgandaMissouri Institute of Mental Health, University of Missouri - St. Louis, St. Louis, MO, United StatesIntroductionThe phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accordingly, there has been a shift toward identifying MH phenotypes using data-driven methods capable of identifying novel insights into mechanisms of divergent MH phenotypes among PWH. We leverage the analytic strengths of machine learning combined with inferential methods to identify novel MH phenotypes among PWH and the underlying explanatory features.MethodsA total of 277 PWH (46% female, median age = 44; 93% virally suppressed [<50copies/mL]) were included in the analyses. Participants completed the Patient Health Questionnaire (PHQ-9), Beck Anxiety Inventory (BAI), and the PTSD Checklist-Civilian (PCL-C). A clustering pipeline consisting of dimension reduction with UMAP followed by HBDScan was used to identify MH subtypes using total symptom scores. Inferential statistics compared select demographic (age, sex, education), viral load, and early life adversity between clusters.ResultsWe identified four MH phenotypes. Cluster 1 (n = 76; PTSD phenotype) endorsed clinically significant PTSD symptoms (average PCL-C total score > 33). Clusters 2 (n = 32; anxiety phenotype) and 3 (n = 130; mixed anxiety/depression phenotype) reported minimal PTSD symptoms, with modest BAI (Cluster 2) and PHQ-9 (Cluster 3) elevations. Cluster 4 (n = 39; minimal symptom phenotype) reported no clinical MH symptom elevations. Comparisons revealed higher rates of sexual abuse during childhood among the PTSD phenotype vs. the minimal symptom phenotype (p = 0.03).DiscussionWe identified unique MH phenotypes among PWH and confirmed the importance of early life adversity as an early risk determinant for unfavorable MH among PWH in adulthood.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1407413/fullmental healthglobalUgandaHIVcognitiondepression |
| spellingShingle | Leah H. Rubin Leah H. Rubin Leah H. Rubin Leah H. Rubin Kyu Cho Jacob Bolzenius Julie Mannarino Rebecca E. Easter Raha M. Dastgheyb Aggrey Anok Stephen Tomusange Deanna Saylor Maria J. Wawer Noeline Nakasujja Gertrude Nakigozi Robert Paul Mental health phenotypes of well-controlled HIV in Uganda Frontiers in Public Health mental health global Uganda HIV cognition depression |
| title | Mental health phenotypes of well-controlled HIV in Uganda |
| title_full | Mental health phenotypes of well-controlled HIV in Uganda |
| title_fullStr | Mental health phenotypes of well-controlled HIV in Uganda |
| title_full_unstemmed | Mental health phenotypes of well-controlled HIV in Uganda |
| title_short | Mental health phenotypes of well-controlled HIV in Uganda |
| title_sort | mental health phenotypes of well controlled hiv in uganda |
| topic | mental health global Uganda HIV cognition depression |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1407413/full |
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