Cognitive Phenotypes of HIV Defined Using a Novel Data-driven Approach

被引:7
|
作者
Paul, Robert H. [1 ,2 ]
Cho, Kyu [2 ]
Belden, Andrew [2 ]
Carrico, Adam W. [3 ]
Martin, Eileen [4 ]
Bolzenius, Jacob [2 ]
Luckett, Patrick [5 ]
Cooley, Sarah A. [5 ]
Mannarino, Julie [2 ]
Gilman, Jodi M. [6 ]
Miano, Mariah [7 ]
Ances, Beau M. [5 ]
机构
[1] Univ Missouri, Dept Psychol Sci, St Louis, MO 63121 USA
[2] Univ Missouri, Missouri Inst Mental Hlth, St Louis, MO 63121 USA
[3] Univ Miami, Dept Publ Hlth, Sch Med, Coral Gables, FL USA
[4] Rush Univ, Dept Psychiat, Sch Med, Chicago, IL USA
[5] Washington Univ, Dept Neurol, St Louis, MO USA
[6] Harvard Med Sch, Ctr Addict Med, Massachusetts Gen Hosp, Boston, MA USA
[7] No Arizona Univ, Dept Commun Sci & Disorders, Flagstaff, AZ USA
关键词
HIV; Cognition; Substance use; Machine learning; NEUROPSYCHOLOGICAL TEST-PERFORMANCE; ACTION VERB FLUENCY; NORMATIVE DATA; AFRICAN-AMERICANS; CATEGORY FLUENCY; INCREASES RISK; METHAMPHETAMINE; IMPAIRMENT; NORMS; ACCULTURATION;
D O I
10.1007/s11481-021-10045-0
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The current study applied data-driven methods to identify and explain novel cognitive phenotypes of HIV. Methods: 388 people with HIV (PWH) with an average age of 46 (15.8) and median plasma CD4+ T-cell count of 555 copies/mL (79% virally suppressed) underwent cognitive testing and 3T neuroimaging. Demographics, HIV disease variables, and health comorbidities were recorded within three months of cognitive testing/neuroimaging. Hierarchical clustering was employed to identify cognitive phenotypes followed by ensemble machine learning to delineate the features that determined membership in the cognitive phenotypes. Hierarchical clustering identified five cognitive phenotypes. Cluster 1 (n=97) was comprised of individuals with normative performance on all cognitive tests. The remaining clusters were defined by impairment on action fluency (Cluster 2; n=46); verbal learning/memory (Cluster 3; n=73); action fluency and verbal learning/memory (Cluster 4; n=56); and action fluency, verbal learning/memory, and tests of executive function (Cluster 5; n=114). HIV detectability was most common in Cluster 5. Machine learning revealed that polysubstance use, race, educational attainment, and volumes of the precuneus, cingulate, nucleus accumbens, and thalamus differentiated membership in the normal vs. impaired clusters. The determinants of persistent cognitive impairment among PWH receiving suppressive treatment are multifactorial nature. Viral replication after ART plays a role in the causal pathway, but psychosocial factors (race inequities, substance use) merit increased attention as critical determinants of cognitive impairment in the context of ART. Results underscore the need for comprehensive person-centered interventions that go beyond adherence to patient care to achieve optimal cognitive health among PWH.
引用
收藏
页码:515 / 525
页数:11
相关论文
共 50 条
  • [11] A Novel Data-Driven Approach to Autonomous Fuzzy Clustering
    Gu, Xiaowei
    Ni, Qiang
    Tang, Guolin
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (06) : 2073 - 2085
  • [12] Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach
    Wang, Xiwu
    Ye, Teng
    Zhou, Wenjun
    Zhang, Jie
    Alzheimer's Disease Neuroimaging Initiative, Alzheimer's Disease Neuroimaging Initiative
    ALZHEIMERS RESEARCH & THERAPY, 2023, 15 (01)
  • [13] Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach
    Xiwu Wang
    Teng Ye
    Wenjun Zhou
    Jie Zhang
    Alzheimer's Research & Therapy, 15
  • [14] Data-driven clustering approach to identify novel phenotypes using multiple biomarkers in acute ischaemic stroke: A retrospective, multicentre cohort study
    Ding, Lingling
    Mane, Ravikiran
    Wu, Zhenzhou
    Jiang, Yong
    Meng, Xia
    Jing, Jing
    Ou, Weike
    Wang, Xueyun
    Liu, Yu
    Lin, Jinxi
    Zhao, Xingquan
    Li, Hao
    Wang, Yongjun
    Li, Zixiao
    ECLINICALMEDICINE, 2022, 53
  • [15] Data-driven identification of complex disease phenotypes
    Strauss, Markus J.
    Niederkrotenthaler, Thomas
    Thurner, Stefan
    Kautzky-Willer, Alexandra
    Klimek, Peter
    JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2021, 18 (180)
  • [16] Mapping the Literature on Nutritional Interventions in Cognitive Health: A Data-Driven Approach
    Walsh, Erin I.
    Cherbuin, Nicolas
    NUTRIENTS, 2019, 11 (01):
  • [17] Mild cognitive impairment understanding: an empirical study by data-driven approach
    Liu, Liyuan
    Yu, Bingchen
    Han, Meng
    Yuan, Shanshan
    Wang, Na
    BMC BIOINFORMATICS, 2019, 20 (Suppl 15)
  • [18] Mild cognitive impairment understanding: an empirical study by data-driven approach
    Liyuan Liu
    Bingchen Yu
    Meng Han
    Shanshan Yuan
    Na Wang
    BMC Bioinformatics, 20
  • [19] The spectrum of cognitive disorders in Parkinson's disease: A data-driven approach
    Dujardin, Kathy
    Leentjens, Albert F. G.
    Langlois, Carole
    Moonen, Anja J. H.
    Duits, Annelien A.
    Carette, Anne-Sophie
    Duhamel, Alain
    MOVEMENT DISORDERS, 2013, 28 (02) : 183 - 189
  • [20] Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach
    Ziemer, Jonas
    Stein, Gideon
    Wicker, Carolin
    Jaenichen, Jannik
    Kloepper, Daniel
    Last, Katja
    Denzler, Joachim
    Schmullius, Christiane
    Shadaydeh, Maha
    Dubois, Clemence
    REMOTE SENSING, 2025, 17 (06)