Machine Learning Quantifies Accelerated White-Matter Aging in Persons With HIV.
Machine Learning Quantifies Accelerated White-Matter Aging in Persons With HIV.
复制标题
机器学习量化艾滋病毒感染者白质加速老化。
DOI：
10.1093/infdis/jiac156
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ances,BeauM
中科院分区：
文献类型：
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作者：
Petersen,KalenJ;Strain,Jeremy;Cooley,Sarah;Vaida,Florin;Ances,BeauM
BackgroundPersons with HIV (PWH) undergo white matter changes, which can be quantified using the brain-age gap (BAG), the difference between chronological age and neuroimaging-based brain-predicted age. Accumulation of microstructural damage may be accelerated in PWH, especially with detectable viral load (VL).MethodsIn total, 290 PWH (85% with undetectable VL) and 165 HIV-negative controls participated in neuroimaging and cognitive testing. BAG was measured using a Gaussian process regression model trained to predict age from diffusion magnetic resonance imaging in publicly available normative controls. To test for accelerated aging, BAG was modeled as an age × VL interaction. The relationship between BAG and global neuropsychological performance was examined. Other potential predictors of pathological aging were investigated in an exploratory analysis.ResultsAge and detectable VL had a significant interactive effect: PWH with detectable VL accumulated +1.5 years BAG/decade versus HIV-negative controls (P= .018). PWH with undetectable VL accumulated +0.86 years BAG/decade, although this did not reach statistical significance (P= .052). BAG was associated with poorer global cognition only in PWH with detectable VL (P< .001). Exploratory analysis identified Framingham cardiovascular risk as an additional predictor of pathological aging (P= .027).ConclusionsAging with detectable HIV and cardiovascular disease may lead to white matter pathology and contribute to cognitive impairment.
