AUTHOR=Dastgheyb Raha M. , Buchholz Alison S. , Fitzgerald Kathryn C. , Xu Yanxun , Williams Dionna W. , Springer Gayle , Anastos Kathryn , Gustafson Deborah R. , Spence Amanda B. , Adimora Adaora A. , Waldrop Drenna , Vance David E. , Milam Joel , Bolivar Hector , Weber Kathleen M. , Haughey Norman J. , Maki Pauline M. , Rubin Leah H. 

TITLE=Patterns and Predictors of Cognitive Function Among Virally Suppressed Women With HIV

JOURNAL=Frontiers in Neurology

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2021.604984

DOI=10.3389/fneur.2021.604984

ISSN=1664-2295

ABSTRACT=<p>Cognitive impairment remains frequent and heterogeneous in presentation and severity among virally suppressed (VS) women with HIV (WWH). We identified cognitive profiles among 929 VS-WWH and 717 HIV-uninfected women from 11 Women's Interagency HIV Study sites at their first neuropsychological (NP) test battery completion comprised of: Hopkins Verbal Learning Test-Revised, Trail Making, Symbol Digit Modalities, Grooved Pegboard, Stroop, Letter/Animal Fluency, and Letter-Number Sequencing. Using 17 NP performance metrics (T-scores), we used Kohonen self-organizing maps to identify patterns of high-dimensional data by mapping participants to similar nodes based on T-scores and clustering those nodes. Among VS-WWH, nine clusters were identified (entropy = 0.990) with four having average T-scores ≥45 for all metrics and thus combined into an “unimpaired” profile (<italic>n</italic> = 311). Impaired profiles consisted of weaknesses in: (1) sequencing (<italic>Profile-1</italic>; <italic>n</italic> = 129), (2) speed (<italic>Profile-2</italic>; <italic>n</italic> = 144), (3) learning + recognition (<italic>Profile-3</italic>; <italic>n</italic> = 137), (4) learning + memory (<italic>Profile-4</italic>; <italic>n</italic> = 86), and (5) learning + processing speed + attention + executive function (<italic>Profile-5</italic>; <italic>n</italic> = 122). Sociodemographic, behavioral, and clinical variables differentiated profile membership using Random Forest models. The top 10 variables distinguishing the combined impaired vs. unimpaired profiles were: clinic site, age, education, race, illicit substance use, current and nadir CD4 count, duration of effective antiretrovirals, and protease inhibitor use. Additional variables differentiating each impaired from unimpaired profile included: depression, stress-symptoms, income (<italic>Profile-1</italic>); depression, employment (<italic>Profile 2</italic>); depression, integrase inhibitor (INSTI) use (<italic>Profile-3</italic>); employment, INSTI use, income, atazanavir use, non-ART medications with anticholinergic properties (<italic>Profile-4</italic>); and marijuana use (<italic>Profile-5</italic>). Findings highlight consideration of NP profile heterogeneity and potential modifiable factors contributing to impaired profiles.</p>