Increased brain-predicted aging in treated HIV disease

Abstract

Objective:To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters.Methods:A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out.Results:HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures.Conclusion:Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging.

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

“…They stated a mean BAS of 4.66 and 5.97 years for GM and WM modalities, respectively, among TBL patients. In another study (Cole et al., 2017), the authors conducted a brain‐age model by combining the GM and WM modalities from 2,001 healthy controls to investigate the brain age on HIV‐positive and HIV‐negative subjects. According to this research, they realized that HIV‐positive subjects have a significantly greater BAS in comparison with HIV‐negative subjects.…”

Clinical round-up

“…The CO-morBidity in Relation to AIDS (COBRA) collaboration looked in this paper4 to determine whether HIV is associated with abnormal levels of age-related brain atrophy. They estimated apparent brain age using neuroimaging and explored whether these estimates were related to age, cognitive performance, HIV status and HIV-related clinical parameters.…”

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

“…One-year bias was associated to 10% higher risk conversion. In relation to other pathologies, and also using morphological descriptors to estimate the brain age, the differences between brain age and ChA explained for instance brain deterioration from attenuated psychosis to chronic schizophrenia 54 , brain deterioration in patients with human immunodeficiency virus 41 , accelerated atrophy after traumatic brain injury 39 (suggesting that the chronic effects after the insult can resemble normal ageing), but also brain rejuvenation after meditation 58 .…”

“…Deviations of brain-predicted ageing from healthy brain ageing have been described for several brain diseases, including traumatic brain injury 39 , mild cognitive impairment and Alzheimer's disease 40 , HIV infection 41 and schizophrenia 42 . A critical issue in the study of brain biological ageing by computational neuroimaging is the selection of an adequate approach with the highest robustness and precision for the quantification of the mismatch between chronological and brain biological ages.…”

“…To test these hypotheses, following previous work [39][40][41][51][52][53][54][55][56][57][58][59] , we built an ageing data-driven model to estimate the ChA of participants based on SC and FC biomarkers and investigated the extent to which the level of PA mediates the participant's brain biological age. Lastly, we discuss the general implications and applications of the described methodology.…”