Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study.

BACKGROUND: In people with genetic forms of Alzheimer's disease, such as in Down syndrome and autosomal-dominant Alzheimer's disease, pathological changes specific to Alzheimer's disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer's disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer's disease. METHODS: In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer's Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer's disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium-Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (18F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid. FINDINGS: We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer's disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer's disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer's disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer's disease. Temporally, increases in tau were more strongly associated with increases in amyloid for people with Down syndrome compared with autosomal-dominant Alzheimer's disease. INTERPRETATION: Although the general progression of amyloid followed by tau is similar for people Down syndrome and people with autosomal-dominant Alzheimer's disease, we found subtle differences in the spatial distribution, timing, and magnitude of the tau burden between these two cohorts. These differences might have important implications; differences in the temporal pattern of tau accumulation might influence the timing of drug administration in clinical trials, whereas differences in the spatial pattern and magnitude of tau burden might affect disease progression. FUNDING: None.

Predicting continuous amyloid PET values with CSF and plasma Aß42/Aß40.

Introduction: Continuous measures of amyloid burden as measured by positron emission tomography (PET) are being used increasingly to stage Alzheimer's disease (AD). This study examined whether cerebrospinal fluid (CSF) and plasma amyloid beta (Aß)42/Aß40 could predict continuous values for amyloid PET. Methods: CSF Aß42 and Aß40 were measured with automated immunoassays. Plasma Aß42 and Aß40 were measured with an immunoprecipitation-mass spectrometry assay. Amyloid PET was performed with Pittsburgh compound B (PiB). The continuous relationships of CSF and plasma Aß42/Aß40 with amyloid PET burden were modeled. Results: Most participants were cognitively normal (427 of 491 [87%]) and the mean age was 69.0 ± 8.8 years. CSF Aß42/Aß40 predicted amyloid PET burden until a relatively high level of amyloid accumulation (69.8 Centiloids), whereas plasma Aß42/Aß40 predicted amyloid PET burden until a lower level (33.4 Centiloids). Discussion: CSF Aß42/Aß40 predicts the continuous level of amyloid plaque burden over a wider range than plasma Aß42/Aß40 and may be useful in AD staging. Highlights: Cerebrospinal fluid (CSF) amyloid beta (Aß)42/Aß40 predicts continuous amyloid positron emission tomography (PET) values up to a relatively high burden.Plasma Aß42/Aß40 is a comparatively dichotomous measure of brain amyloidosis.Models can predict regional amyloid PET burden based on CSF Aß42/Aß40.CSF Aß42/Aß40 may be useful in staging AD.

A practitioner's guide to geospatial analysis in a neuroimaging context.

Introduction: Health disparities arise from biological-environmental interactions. Neuroimaging cohorts are reaching sufficiently large sample sizes such that analyses could evaluate how the environment affects the brain. We present a practical guide for applying geospatial methods to a neuroimaging cohort. Methods: We estimated brain age gap (BAG) from structural magnetic resonance imaging (MRI) from 239 city-dwelling participants in St. Louis, Missouri. We compared these participants to population-level estimates from the American Community Survey (ACS). We used geospatial analysis to identify neighborhoods associated with patterns of altered brain structure. We also evaluated the relationship between Area Deprivation Index (ADI) and BAG. Results: We identify areas in St. Louis, Missouri that were significantly associated with higher BAG from a spatially representative cohort. We provide replication code. Conclusion: We observe a relationship between neighborhoods and brain health, which suggests that neighborhood-based interventions could be appropriate. We encourage other studies to geocode participant information to evaluate biological-environmental interaction.

Comparison of amyloid burden in individuals with Down syndrome versus autosomal dominant Alzheimer's disease: a cross-sectional study.

BACKGROUND: Important insights into the early pathogenesis of Alzheimer's disease can be provided by studies of autosomal dominant Alzheimer's disease and Down syndrome. However, it is unclear whether the timing and spatial distribution of amyloid accumulation differs between people with autosomal dominant Alzheimer's disease and those with Down syndrome. We aimed to directly compare amyloid changes between these two groups of people. METHODS: In this cross-sectional study, we included participants (aged ≥25 years) with Down syndrome and sibling controls who had MRI and amyloid PET scans in the first data release (January, 2020) of the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. We also included carriers of autosomal dominant Alzheimer's disease genetic mutations and non-carrier familial controls who were within a similar age range to ABC-DS participants (25-73 years) and had MRI and amyloid PET scans at the time of a data freeze (December, 2020) of the Dominantly Inherited Alzheimer Network (DIAN) study. Controls from the two studies were combined into a single group. All DIAN study participants had genetic testing to determine PSEN1, PSEN2, or APP mutation status. APOE genotype was determined from blood samples. CSF samples were collected in a subset of ABC-DS and DIAN participants and the ratio of amyloid ß42 (Aß42) to Aß40 (Aß42/40) was measured to evaluate its Spearman's correlation with amyloid PET. Global PET amyloid burden was compared with regards to cognitive status, APOE ɛ4 status, sex, age, and estimated years to symptom onset. We further analysed amyloid PET deposition by autosomal dominant mutation type. We also assessed regional patterns of amyloid accumulation by estimated number of years to symptom onset. Within a subset of participants the relationship between amyloid PET and CSF Aß42/40 was evaluated. FINDINGS: 192 individuals with Down syndrome and 33 sibling controls from the ABC-DS study and 265 carriers of autosomal dominant Alzheimer's disease mutations and 169 non-carrier familial controls from the DIAN study were included in our analyses. PET amyloid centiloid and CSF Aß42/40 were negatively correlated in carriers of autosomal dominant Alzheimer's disease mutations (n=216; r=-0·565; p<0·0001) and in people with Down syndrome (n=32; r=-0·801; p<0·0001). There was no difference in global PET amyloid burden between asymptomatic people with Down syndrome (mean 18·80 centiloids [SD 28·33]) versus asymptomatic mutation carriers (24·61 centiloids [30·27]; p=0·11) and between symptomatic people with Down syndrome (77·25 centiloids [41·76]) versus symptomatic mutation carriers (69·15 centiloids [51·10]; p=0·34). APOE ɛ4 status and sex had no effect on global amyloid PET deposition. Amyloid deposition was elevated significantly earlier in mutation carriers than in participants with Down syndrome (estimated years to symptom onset -23·0 vs -17·5; p=0·0002). PSEN1 mutations primarily drove this difference. Early amyloid accumulation occurred in striatal and cortical regions for both mutation carriers (n=265) and people with Down syndrome (n=128). Although mutation carriers had widespread amyloid accumulation in all cortical regions, the medial occipital regions were spared in people with Down syndrome. INTERPRETATION: Despite minor differences, amyloid PET changes were similar between people with autosomal dominant Alzheimer's disease versus Down syndrome and strongly supported early amyloid dysregulation in individuals with Down syndrome. Individuals with Down syndrome aged at least 35 years might benefit from early intervention and warrant future inclusion in clinical trials, particularly given the relatively high incidence of Down syndrome. FUNDING: The National Institute on Aging, Riney and Brennan Funds, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the German Center for Neurodegenerative Diseases, and the Japan Agency for Medical Research and Development.

Proteomic clusters underlie heterogeneity in preclinical Alzheimer's disease progression.

Heterogeneity in progression to Alzheimer's disease (AD) poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical AD biomarkers, assessed patterns for developing preclinical AD across the amyloid-tau-(neurodegeneration) [AT(N)] framework, and considered potential sources of difference by analysing the CSF proteome. Participants (n = 10) enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of amyloid-ß (Aß)42, phosphorylated tau (pTau181), and neurofilament light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including PET amyloid and tau, and structural MRI, were repeatedly obtained when available. Individuals were staged according to the amyloid-tau-(neurodegeneration) framework. Growth mixture modelling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and Aß42. Two groups (AD Biomarker Positive and Intermediate AD Biomarker) showed distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive versus AD Biomarker Negative = 0.857, AUCAD Biomarker Positive versus Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers versus AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before the age of 50 years. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing versus other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood-brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.

Sleep and longitudinal cognitive performance in preclinical and early symptomatic Alzheimer's disease.

Sleep monitoring may provide markers for future Alzheimer's disease; however, the relationship between sleep and cognitive function in preclinical and early symptomatic Alzheimer's disease is not well understood. Multiple studies have associated short and long sleep times with future cognitive impairment. Since sleep and the risk of Alzheimer's disease change with age, a greater understanding of how the relationship between sleep and cognition changes over time is needed. In this study, we hypothesized that longitudinal changes in cognitive function will have a non-linear relationship with total sleep time, time spent in non-REM and REM sleep, sleep efficiency and non-REM slow wave activity. To test this hypothesis, we monitored sleep-wake activity over 4-6 nights in 100 participants who underwent standardized cognitive testing longitudinally, APOE genotyping, and measurement of Alzheimer's disease biomarkers, total tau and amyloid-ß42 in the CSF. To assess cognitive function, individuals completed a neuropsychological testing battery at each clinical visit that included the Free and Cued Selective Reminding test, the Logical Memory Delayed Recall assessment, the Digit Symbol Substitution test and the Mini-Mental State Examination. Performance on each of these four tests was Z-scored within the cohort and averaged to calculate a preclinical Alzheimer cognitive composite score. We estimated the effect of cross-sectional sleep parameters on longitudinal cognitive performance using generalized additive mixed effects models. Generalized additive models allow for non-parametric and non-linear model fitting and are simply generalized linear mixed effects models; however, the linear predictors are not constant values but rather a sum of spline fits. We found that longitudinal changes in cognitive function measured by the cognitive composite decreased at low and high values of total sleep time (P < 0.001), time in non-REM (P < 0.001) and REM sleep (P < 0.001), sleep efficiency (P < 0.01) and <1 Hz and 1-4.5 Hz non-REM slow wave activity (P < 0.001) even after adjusting for age, CSF total tau/amyloid-ß42 ratio, APOE ε4 carrier status, years of education and sex. Cognitive function was stable over time within a middle range of total sleep time, time in non-REM and REM sleep and <1 Hz slow wave activity, suggesting that certain levels of sleep are important for maintaining cognitive function. Although longitudinal and interventional studies are needed, diagnosing and treating sleep disturbances to optimize sleep time and slow wave activity may have a stabilizing effect on cognition in preclinical or early symptomatic Alzheimer's disease.

Modeling the Effects of HIV and Aging on Resting-State Networks Using Machine Learning.

BACKGROUND: The relationship between HIV infection, the functional organization of the brain, cognitive impairment, and aging remains poorly understood. Understanding disease progression over the life span is vital for the care of people living with HIV (PLWH). SETTING: Virologically suppressed PLWH (n = 297) on combination antiretroviral therapy and 1509 HIV-uninfected healthy controls were evaluated. PLWH were further classified as cognitively normal (CN) or cognitively impaired (CI) based on neuropsychological testing. METHODS: Feature selection identified resting-state networks (RSNs) that predicted HIV status and cognitive status within specific age bins (younger than 35 years, 35-55 years, and older than 55 years). Deep learning models generated voxelwise maps of RSNs to identify regional differences. RESULTS: Salience (SAL) and parietal memory networks (PMNs) differentiated individuals by HIV status. When comparing controls with PLWH CN, the PMN and SAL had the strongest predictive strength across all ages. When comparing controls with PLWH CI, the SAL, PMN, and frontal parietal network (FPN) were the best predictors. When comparing PLWH CN with PLWH CI, the SAL, FPN, basal ganglia, and ventral attention were the strongest predictors. Only minor variability in predictive strength was observed with aging. Anatomically, differences in RSN topology occurred primarily in the dorsal and rostral lateral prefrontal cortex, cingulate, and caudate. CONCLUSION: Machine learning identified RSNs that classified individuals by HIV status and cognitive status. The PMN and SAL were sensitive for discriminating HIV status, with involvement of FPN occurring with cognitive impairment. Minor differences in RSN predictive strength were observed by age. These results suggest that specific RSNs are affected by HIV, aging, and HIV-associated cognitive impairment.

Comparison of CSF biomarkers in Down syndrome and autosomal dominant Alzheimer's disease: a cross-sectional study.

BACKGROUND: Due to trisomy of chromosome 21 and the resultant extra copy of the amyloid precursor protein gene, nearly all adults with Down syndrome develop Alzheimer's disease pathology by the age of 40 years and are at high risk for dementia given their increased life expectancy compared with adults with Down syndrome in the past. We aimed to compare CSF biomarker patterns in Down syndrome with those of carriers of autosomal dominant Alzheimer's disease mutations to enhance our understanding of disease mechanisms in these two genetic groups at high risk for Alzheimer's disease. METHODS: We did a cross-sectional study using data from adults enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study, a multisite longitudinal study of Alzheimer's disease in Down syndrome, as well as a cohort of carriers of autosomal dominant Alzheimer's disease mutations and non-carrier sibling controls enrolled in the Dominantly Inherited Alzheimer Network (DIAN) study. For ABC-DS, participants with baseline CSF, available clinical diagnosis, and apolipoprotein E genotype as of Jan 31, 2019, were included in the analysis. DIAN participants with baseline CSF, available clinical diagnosis, and apolipoprotein E genotype as of June 30, 2018, were evaluated as comparator groups. CSF samples obtained from adults with Down syndrome, similarly aged carriers of autosomal dominant Alzheimer's disease mutations, and non-carrier siblings (aged 30-61 years) were analysed for markers of amyloid ß (Aß1-40, Aß1-42); tau phosphorylated at threonine 181-related processes; neuronal, axonal, or synaptic injury (total tau, visinin-like protein 1, neurofilament light chain [NfL], synaptosomal-associated protein 25); and astrogliosis and neuroinflammation (chitinase-3-like protein 1 [YKL-40]) via immunoassay. Biomarker concentrations were compared as a function of dementia status (asymptomatic or symptomatic), and linear regression was used to evaluate and compare the relationship between biomarker concentrations and age among groups. FINDINGS: We assessed CSF samples from 341 individuals (178 [52%] women, 163 [48%] men, aged 30-61 years). Participants were adults with Down syndrome (n=41), similarly aged carriers of autosomal dominant Alzheimer's disease mutations (n=192), and non-carrier siblings (n=108). Individuals with Down syndrome had patterns of Alzheimer's disease-related CSF biomarkers remarkably similar to carriers of autosomal dominant Alzheimer's disease mutations, including reductions (all p<0·0080) in Aß1-42 to Aß1-40 ratio and increases in markers of phosphorylated tau-related processes; neuronal, axonal, and synaptic injury (p<0·080); and astrogliosis and neuroinflammation, with greater degrees of abnormality in individuals with dementia. Differences included overall higher concentrations of Aß and YKL-40 (both p<0·0008) in Down syndrome and potential elevations in CSF tau (p<0·010) and NfL (p<0·0001) in the asymptomatic stage (ie, no dementia symptoms). FUNDING: National Institute on Aging, Eunice Kennedy Shriver National Institute of Child Health and Human Development, German Center for Neurodegenerative Diseases, and Japan Agency for Medical Research and Development.

Temporal Correlation of CSF and Neuroimaging in the Amyloid-Tau-Neurodegeneration Model of Alzheimer Disease.

OBJECTIVE: To evaluate temporal correlations between CSF and neuroimaging (PET and MRI) measures of amyloid, tau, and neurodegeneration in relation to Alzheimer disease (AD) progression. METHODS: A total of 371 cognitively unimpaired and impaired participants enrolled in longitudinal studies of AD had both CSF (ß-amyloid [Aß]42, phosphorylated tau181, total tau, and neurofilament light chain) and neuroimaging (Pittsburgh compound B [PiB] PET, flortaucipir PET, and structural MRI) measures. The pairwise time interval between CSF and neuroimaging measures was binned into 2-year periods. Spearman correlations identified the time bin when CSF and neuroimaging measures most strongly correlated. CSF and neuroimaging measures were then binarized as biomarker-positive or biomarker-negative using Gaussian mixture modeling. Cohen kappa coefficient identified the time bin when CSF measures best agreed with corresponding neuroimaging measures when determining amyloid, tau, and neurodegeneration biomarker positivity. RESULTS: CSF Aß42 and PiB PET showed maximal correlation when collected within 6 years of each other (R ≈ -0.5). CSF phosphorylated tau181 and flortaucipir PET showed maximal correlation when CSF was collected 4 to 8 years prior to PET (R ≈ 0.4). CSF neurofilament light chain and cortical thickness showed low correlation, regardless of time interval (R avg ≈ -0.3). Similarly, CSF total tau and cortical thickness had low correlation, regardless of time interval (R avg < -0.2). CONCLUSIONS: CSF Aß42 and PiB PET best agree when acquired in close temporal proximity, whereas CSF phosphorylated tau precedes flortaucipir PET by 4 to 8 years. CSF and neuroimaging measures of neurodegeneration have low correspondence and are not interchangeable at any time interval.

Neuroimaging the Neuropathogenesis of HIV.

PURPOSE OF REVIEW: This review highlights neuroimaging studies of HIV conducted over the last 2 years and discusses how relevant findings further our knowledge of the neuropathology of HIV. Three major avenues of neuroimaging research are covered with a particular emphasis on inflammation, aging, and substance use in persons living with HIV (PLWH). RECENT FINDINGS: Neuroimaging has been a critical tool for understanding the neuropathological underpinnings observed in HIV. Recent studies comparing levels of neuroinflammation in PLWH and HIV-negative controls show inconsistent results but report an association between elevated neuroinflammation and poorer cognition in PLWH. Other recent neuroimaging studies suggest that older PLWH are at increased risk for brain and cognitive compromise compared to their younger counterparts. Finally, recent findings also suggest that the effects of HIV may be exacerbated by alcohol and drug abuse. These neuroimaging studies provide insight into the structural, functional, and molecular changes occurring in the brain due to HIV. HIV triggers a strong neuroimmune response and may lead to a cascade of events including increased chronic inflammation and cognitive decline. These outcomes are further exacerbated by age and age-related comorbidities, as well as lifestyle factors such as drug use/abuse.

Comparison of [11C]-PBR28 Binding Between Persons Living With HIV and HIV-Uninfected Individuals.

OBJECTIVE: Despite combined antiretroviral therapy, neuroinflammation may persist in persons living with HIV (PLWH) and contribute to cognitive impairment in this population. Positron emission tomography (PET) imaging targeting 18 kDa translocator protein (TSPO) has been used to localize neuroinflammation. We aimed to use TSPO-PET imaging to evaluate neuroinflammation in PLWH. DESIGN: Twenty-four virologically suppressed PLWH on combined antiretroviral therapy and 13 HIV-negative (HIV-) controls completed TSPO-PET imaging using the radiotracer [C]PBR28. Because of tracer complexity and differing procedures used in previous studies, we employed an expansive methodological approach, using binding potential (BP) and standard uptake value ratio and multiple different reference regions to estimate [C]PBR28 binding. METHODS: [C]PBR28 binding was measured in 30 cortical and subcortical regions and compared between PLWH and HIV- controls. Pearson correlation evaluated the association between [C]PBR28 binding and cognition and clinical measures of HIV. RESULTS: Analyses conducted using multiple reference regions and measures of tracer uptake revealed no significant differences between [C]PBR28 binding in PLWH compared with HIV- controls. In addition, [C]PBR28 binding in PLWH was not significantly associated with clinical measures of HIV or plasma biomarkers of inflammation. [C]PBR28 binding was not significantly elevated in cognitively impaired PLWH compared with unimpaired PLWH, but there were inverse relationships between cognitive performance (executive and global function) and [C]PBR28 binding in PLWH. CONCLUSIONS: Our results suggest that neuroinflammation may play a role in cognitive deficits, but overall neuroinflammatory levels as measured by TSPO-PET imaging in PLWH are not significantly different from those seen in HIV- controls.

Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

BACKGROUND: Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals. SETTING: Machine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH. METHODS: Participants included 105 older (average age = 55.6) PLWH, with at least a 3-month history of combination antiretroviral therapy (median CD4 = 546). Predictors included demographics, HIV clinical markers, comorbid health conditions, cognition, and neuroimaging (ie, volumetrics, resting-state functional connectivity, and cerebral blood flow). Gradient-boosted multivariate regressions were implemented to establish linear and interactive classification models. Model performance was determined by sensitivity/specificity (F1 score) with 5-fold cross validation. RESULTS: The linear gradient-boosted multivariate regression classifier included lower current CD4 count, lower psychomotor performance, and multiple neuroimaging indices (volumes, network connectivity, and blood flow) in visual and motor brain systems (F1 score = 71%; precision = 84%; and sensitivity = 66%). The interactive model identified novel synergies between neuroimaging features, female sex, symptoms of depression, and current CD4 count. CONCLUSIONS: Data-driven algorithms built from highly dimensional clinical and brain imaging features implicate disruption to the visuomotor system in older PLWH designated as frail individuals. Interactions between lower CD4 count, female sex, depressive symptoms, and neuroimaging features suggest potentiation of risk mechanisms. Longitudinal data-driven studies are needed to guide clinical strategies capable of preventing the development of frailty as PLWH reach advanced age.

Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.

BACKGROUND: Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors. SETTING: Virologically suppressed (<50 copies/mL) PLWH (n = 125) on combination antiretroviral therapy were enrolled. Participants averaged 51.4 (11.4) years of age and 13.7 (2.8) years of education. Participants were administered a neuropsychological battery, assessed for frailty, and completed structural neuroimaging. METHODS: Deep neural network (DNN) models were trained to classify PLWH as cognitively unimpaired or impaired based on neuropsychological tests (Hopkins Verbal Learning Test-Revised and Brief Visuospatial Memory Test-Revised, Trail making, Letter-Number Sequencing, Verbal Fluency, and Color Word Interference), as well as frail, prefrail, or nonfrail based on the Fried phenotype criteria (at least 3 of the following 5: weight loss, physical inactivity, exhaustion, grip strength, walking time). RESULTS: DNNs classified individuals with cognitive impairment in the learning, memory, and executive domains with 82%-86% accuracy (0.81-0.87 AUC). Our model classified nonfrail, prefrail, and frail PLWH with 75% accuracy. The strongest predictors of cognitive impairment were cortical (parietal, occipital, and temporal) and subcortical (amygdala, caudate, and hippocampus) regions, whereas the strongest predictors of frailty were subcortical (amygdala, caudate, hippocampus, thalamus, pallidum, and cerebellum). CONCLUSIONS: DNN models achieved high accuracy in classifying cognitive impairment and frailty status in PLWH. Feature selection algorithms identified predictive regions in each domain and identified overlapping regions between cognitive impairment and frailty. Our results suggest frailty in HIV is primarily subcortical, whereas cognitive impairment in HIV involves subcortical and cortical brain regions.

Tau Positron Emission Tomography Binding Is Not Elevated in HIV-Infected Individuals.

Regional standardized uptake value ratios (SUVRs) for tau positron emission tomography (PET) were compared among 19 cognitively normal human immunodeficiency virus (HIV)-negative control individuals, 20 HIV-negative patients with symptomatic Alzheimer disease, 15 cognitively normal HIV-positive individuals, and 17 cognitively impaired HIV-positive individuals. Among the HIV-positive participants, the correlation between tau PET SUVRs and both HIV loads and CD4+ T-cell counts (recent and nadir). Tau PET SUVRs were similar for HIV-positive individuals and HIV-negative control individuals. Individuals with symptomatic Alzheimer disease had elevated tau PET SUVRs. Tau PET SUVRs did not correlate with impairment or clinical markers in HIV-positive participants. Older HIV-positive individuals are not at increased risk of tau-mediated neurodegeneration.