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.

Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease.

BACKGROUND: "Brain-predicted age" estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. METHODS: We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-ß-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE Îµ4 carrier status, sex, and education. RESULTS: Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. CONCLUSIONS: We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.

Precipitating Mechanisms of Falls in Preclinical Alzheimer's Disease.

Background: Individuals with Alzheimer's disease (AD) are more than twice as likely to incur a serious fall as the general population of older adults. Although AD is commonly associated with cognitive changes, impairments in other clinical measures such as strength or functional mobility (i.e., gait and balance) may precede symptomatic cognitive impairment in preclinical AD and lead to increased fall risk. Objective: To examine mechanisms (i.e., functional mobility, cognition, AD biomarkers) associated with increased falls in cognitively normal older adults. Methods: This 1-year study was part of an ongoing longitudinal cohort study. We examined the relationships among falls, clinical measures of functional mobility and cognition, and neuroimaging AD biomarkers in cognitively normal older adults. We also investigated which domain(s) best predicted fall propensity and severity through multiple regression models. Results: A total of 182 older adults were included (mean age 75 years, 53% female). A total of 227 falls were reported over the year; falls per person ranged from 0-16 with a median of 1. Measures of functional mobility were the best predictors of fall propensity and severity. Cognition and AD biomarkers were associated with each other but not with the fall outcome measures. Conclusion: These results suggest that, although subtle changes in cognition may be more closely associated with AD neuropathology, functional mobility indicators better predict falls in cognitively normal older adults. This study adds to our understanding of the mechanisms underlying falls in older adults and could lead to the development of targeted fall prevention strategies.

Increased Peripheral Inflammation Is Associated With Structural Brain Changes and Reduced Blood Flow in People With Virologically Controlled HIV.

BACKGROUND: While antiretroviral therapy (ART) has improved outcomes for people with HIV (PWH), brain dysfunction is still evident. Immune activation and inflammation remain elevated in PWH receiving ART, thereby contributing to morbidity and mortality. Previous studies demonstrated reduced functional and structural changes in PWH; however, underlying mechanisms remain elusive. METHODS: Our cohort consisted of PWH with ART adherence and viral suppression ( < 50 copies/mL; N = 173). Measurements included immune cell markers of overall immune health (CD4/CD8 T-cell ratio) and myeloid inflammation (CD16+ monocytes), plasma markers of inflammatory status (soluble CD163 and CD14), and structural and functional neuroimaging (volume and cerebral blood flow [CBF], respectively). RESULTS: Decreased CD4/CD8 ratios correlated with reduced brain volume, and higher levels of inflammatory CD16+ monocytes were associated with reduced brain volume in total cortex and gray matter. An increase in plasma soluble CD14-a marker of acute peripheral inflammation attributed to circulating microbial products-was associated with reduced CBF within the frontal, parietal, temporal, and occipital cortices and total gray matter. CONCLUSIONS: CD4/CD8 ratio and number of CD16+ monocytes, which are chronic immune cell markers, are associated with volumetric loss in the brain. Additionally, this study shows a potential new association between plasma soluble CD14 and CBF.

Comparison of Resting State Functional Connectivity in Persons With and Without HIV: A Cross-sectional Study.

BACKGROUND: This study examined the effects of human immunodeficiency virus (HIV) on resting state functional connectivity (RSFC) in a large cohort of people with HIV (PWH) and healthy controls without HIV (PWoH). Within PWH analyses focused on the effects of viral suppression and cognitive impairment on RSFC. METHODS: A total of 316 PWH on stable combination antiretroviral therapy and 209 demographically matched PWoH were scanned at a single institution. Effects of the virus were examined by grouping PWH by detectable (viral load > 20 copies/mL; VLD) and undetectable (VLU) viral loads and as being cognitively impaired (CI) (Global Deficit Score ≥ 0.5) or cognitively normal (CN). Regression analysis, object oriented data analysis, and spring embedded graph models were applied to RSFC measures from 298 established brain regions of interest comprising 13 brain networks to examine group differences. RESULTS: No significant RSFC differences were observed between PWH and PWoH. Within PWH, there were no significant differences in RSFC between VLD and VLU subgroups and CI and CN subgroups. CONCLUSIONS: There were no significant effects of HIV on RSFC in our relatively large cohort of PWH and PWoH. Future studies could increase the sample size and combine with other imaging modalities.

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.

Effects of clinical, comorbid, and social determinants of health on brain ageing in people with and without HIV: a retrospective case-control study.

BACKGROUND: Neuroimaging reveals structural brain changes linked with HIV infection and related neurocognitive disorders; however, group-level comparisons between people with HIV and people without HIV do not account for within-group heterogeneity. The aim of this study was to quantify the effects of comorbidities such as cardiovascular disease and adverse social determinants of health on brain ageing in people with HIV and people without HIV. METHODS: In this retrospective case-control study, people with HIV from Washington University in St Louis, MO, USA, and people without HIV identified through community organisations or the Research Participant Registry were clinically characterised and underwent 3-Tesla T1-weighted MRI between Dec 3, 2008, and Oct 4, 2022. Exclusion criteria were established by a combination of self-reports and medical records. DeepBrainNet, a publicly available machine learning algorithm, was applied to estimate brain-predicted age from MRI for people with HIV and people without HIV. The brain-age gap, defined as the difference between brain-predicted age and true chronological age, was modelled as a function of clinical, comorbid, and social factors by use of linear regression. Variables were first examined singly for associations with brain-age gap, then combined into multivariate models with best-subsets variable selection. FINDINGS: In people with HIV (mean age 44·8 years [SD 15·5]; 78% [296 of 379] male; 69% [260] Black; 78% [295] undetectable viral load), brain-age gap was associated with Framingham cardiovascular risk score (p=0·0034), detectable viral load (>50 copies per mL; p=0·0023), and hepatitis C co-infection (p=0·0065). After variable selection, the final model for people with HIV retained Framingham score, hepatitis C, and added unemployment (p=0·0015). Educational achievement assayed by reading proficiency was linked with reduced brain-age gap (p=0·016) for people without HIV but not for people with HIV, indicating a potential resilience factor. When people with HIV and people without HIV were modelled jointly, selection resulted in a model containing cardiovascular risk (p=0·0039), hepatitis C (p=0·037), Area Deprivation Index (p=0·033), and unemployment (p=0·00010). Male sex (p=0·078) and alcohol use history (p=0·090) were also included in the model but were not individually significant. INTERPRETATION: Our findings indicate that comorbid and social determinants of health are associated with brain ageing in people with HIV, alongside traditional HIV metrics such as viral load and CD4 cell count, suggesting the need for a broadened clinical perspective on healthy ageing with HIV, with additional focus on comorbidities, lifestyle changes, and social factors. FUNDING: National Institute of Mental Health, National Institute of Nursing Research, and National Institute of Drug Abuse.

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.

Adverse driving behaviors increase over time as a function of preclinical Alzheimer's disease biomarkers.

INTRODUCTION: We investigated the relationship between preclinical Alzheimer's disease (AD) biomarkers and adverse driving behaviors in a longitudinal analysis of naturalistic driving data. METHODS: Naturalistic driving data collected using in-vehicle dataloggers from 137 community-dwelling older adults (65+) were used to model driving behavior over time. Cerebrospinal fluid (CSF) biomarkers were used to identify individuals with preclinical AD. Additionally, hippocampal volume and cognitive biomarkers for AD were investigated in exploratory analyses. RESULTS: CSF biomarkers predicted the longitudinal trajectory of the incidence of adverse driving behavior. Abnormal amyloid beta (Aß42 /Aß40 ) ratio was associated with an increase in adverse driving behaviors over time compared to ratios in the normal/lower range. DISCUSSION: Preclinical AD is associated with increased adverse driving behavior over time that cannot be explained by cognitive changes. Driving behavior as a functional, neurobehavioral marker may serve as an early detection for decline in preclinical AD. Screening may also help prolong safe driving as older drivers age.

Biomarker clustering in autosomal dominant Alzheimer's disease.

INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

Assessment of Pretreatment and Posttreatment Evolution of Neurofilament Light Chain Levels in Patients Who Develop Immune Effector Cell-Associated Neurotoxicity Syndrome.

Importance: Determining whether neurofilament light chain (NfL) elevations in patients who develop immune effector cell-associated neurotoxicity syndrome (ICANS) occur before or after infusion of cellular product is important to identify high-risk patients and inform whether neuroaxonal injury is latent or a consequence of treatment. Objective: To quantify serial NfL levels in patients undergoing cellular therapy. Design, Setting, and Participants: This retrospective 2-center study examined plasma NfL levels in 30 patients with detailed medical and treatment history, including all major pretreatment and posttreatment risk factors. Exclusion criteria included dementia and severe, symptomatic central nervous system (CNS) involvement. Main Outcomes and Measures: Patients' NfL levels were measured at 7 time points: baseline (prelymphodepletion), during lymphodepletion, postinfusion day (D) 1, D3, D7, D14, and D30. Prediction accuracy for the development of ICANS was next modeled using receiver operating characteristic (ROC) classification. Finally, univariate and multivariate modeling examined the association between NfL levels, ICANS, and potential risk factors including demographic (age, sex), oncologic (tumor burden, history of CNS involvement), neurologic (history of nononcologic CNS disease or neuropathy), and neurotoxic exposure histories (vincristine, cytarabine, methotrexate, or CNS radiotherapy). Results: A total of 30 patients (median [range] age, 64 [22-80] years; 12 women [40%] and 18 men [60%]) were included. Individuals who developed ICANS had elevations in NfL prior to lymphodepletion and chimeric antigen receptor T-cell infusion compared with those who did not develop ICANS (no ICANS: 29.4 pg/mL, vs any ICANS: 87.6 pg/mL; P < .001). Baseline NfL levels further predicted ICANS development with high accuracy (area under the ROC curve, 0.96), sensitivity (0.91), and specificity (0.95). Levels of NfL remained elevated across all time points, up to 30 days postinfusion. Baseline NfL levels correlated with ICANS severity but not demographic factors, oncologic history, nononcologic neurologic history, or history of exposure to neurotoxic therapies. Conclusions and Relevance: In a subset of patients in this cross-sectional study, the risk of developing ICANS was associated with preexisting neuroaxonal injury that was quantifiable with plasma NfL level. This latent neuroaxonal injury was present prior to drug administration but was not associated with historic neurotoxic therapies or nononcologic neurologic disease. Preinfusion NfL may further permit early screening and identification of patients most at risk for ICANS. Additional studies are needed to determine NfL's utility as a predictive biomarker for early (preemptive or prophylactic) intervention and to delineate the origin of this underlying neural injury.

Socioeconomic status largely explains integrase inhibitors-related body composition differences in chronically infected men living with HIV.

BACKGROUND: Substantial body composition alterations have been reported after starting combined antiretroviral therapy (cART). We characterized a cohort of chronically infected and virologically suppressed (VL < 50 copies/ml) men (≥50 years old) living with HIV (MLWH) who were switched to integrase inhibitors (INSTI), and compared their body composition parameters and proinflammatory/endocrine profiles to age-matched MLWH on integrase inhibitor free (non-INSTI) regimens, taking into account neighborhood-level measures of socioeconomic status (SES). In addition, we used previously published HIV-seronegative men of the same age as controls. METHODS: We used dual energy X-ray absorptiometry to quantify body composition parameters, and measured plasma proinflammatory/endocrine markers in 56 MLWH. We compared body composition to a publicly available dataset of 450 HIV-seronegative men of similar age. Within the MLWH group, body composition and plasma proinflammatory/endocrine markers were compared between individuals on INSTI and non-INSTI regimens, accounting for SES. RESULTS: Men living with HIV tended to have a greater android/gynoid ratio compared to HIV-seronegative men (p < 0.001). INSTI usage in MLWH was associated with lower adiposity measures when compared to non-INSTI, although these differences largely disappeared after controlling for SES. Proinflammatory/endocrine markers were similar for INSTI and non-INSTI MLWH. CONCLUSIONS: Among cART-experienced MLWH, those receiving INSTI-containing regimens had modestly lower adiposity compared to non-INSTI MLWH, although these differences were explained by SES. Future studies examining the relationship between INSTI use and body composition should consider the impact of SES.

Assessment of Instrumental Activities of Daily Living in Preclinical Alzheimer Disease.

Questionnaires are used to assess instrumental activities of daily living (IADL) among individuals with preclinical Alzheimer disease (AD). They have indicated no functional impairment among this population. We aim to determine among cognitively normal (CN) older adults with and without preclinical AD whether: (a) performance-based IADL assessment measures a wider range of function than an IADL questionnaire and (b) biomarkers of AD are associated with IADL performance. In this cross-sectional analysis of 161 older adults, participants in studies of AD completed an IADL questionnaire, performance-based IADL assessment, cognitive assessments, and had biomarkers of AD (amyloid, hippocampal volume, brain network strength) assessed within 2 to 3 years. Performance-based IADL scores were more widely distributed compared with the IADL questionnaire. Smaller hippocampal volumes and reduced brain network connections were associated with worse IADL performance. A performance-based IADL assessment demonstrates functional impairment associated with neurodegeneration among CN older adults.

Naturalistic driving measures of route selection associate with resting state networks in older adults.

Our objective was to identify functional brain changes that associate with driving behaviors in older adults. Within a cohort of 64 cognitively normal adults (age 60+), we compared naturalistic driving behavior with resting state functional connectivity using machine learning. Functional networks associated with the ability to interpret and respond to external sensory stimuli and the ability to multi-task were associated with measures of route selection. Maintenance of these networks may be important for continued preservation of driving abilities.

The Structural and Functional Correlates of Frailty in Persons With Human Immunodeficiency Virus.

BACKGROUND: Persons with HIV (PWH) are at increased risk of frailty, a clinically recognizable state of increased vulnerability resulting from aging-associated decline in multiple physiologic systems. Frailty is often defined by the Fried criteria, which includes subjective and objective standards concerning health resiliency. However, these frailty metrics do not incorporate cognitive performance or neuroimaging measures. METHODS: We compared structural (diffusion tensor imaging [DTI]) and functional (cerebral blood flow [CBF]) neuroimaging markers in PWH with frailty and cognitive performance. Virologically controlled PWH were dichotomized as either frail (≥3) or nonfrail (<3) using the Fried criteria. Cognitive Z-scores, both domain (executive, psychomotor speed, language, and memory) and global, were derived from a battery of tests. We identified three regions of reduced CBF, based on a voxel-wise comparison of frail PWH compared with nonfrail PWH. These clusters (bilateral frontal and posterior cingulate) were subsequently used as seed regions of interest (ROIs) for DTI probabilistic white matter tractography. RESULTS: White matter integrity connecting the ROIs was significantly decreased in frail compared with nonfrail PWH. No differences in cognition were observed between frail and nonfrail PWH. However, reductions in white matter integrity among these ROIs was significantly associated with worse psychomotor speed and executive function across the entire cohort. CONCLUSIONS: We conclude that frailty in PWH can lead to structural and functional brain changes, including subtle changes that are not detectable by standard neuropsychological tests. Multimodal neuroimaging in conjunction with frailty assessment could identify pathological brain changes observed in PWH.

Network dysfunction in cognitively normal APOE ε4 carriers is related to subclinical tau.

INTRODUCTION: Apolipoprotein E (APOE) ε4 allele status is associated with amyloid and tau-related pathological changes related to Alzheimer's disease (AD). However, it is unknown whether brain network changes are related to amyloid beta (Aß) and/or tau-related pathology in cognitively normal APOE ε4 carriers with subthreshold Aß accumulation. METHODS: Resting state functional connectivity measures of network integrity were evaluated in cognitively normal individuals (n = 121, mean age 76.6 ± 7.8 years, 15% APOE ε4 carriers, 65% female) with minimal Aß per cerebrospinal fluid (CSF) or amyloid positron emission tomography. RESULTS: APOE ε4 carriers had increased lateralized connections relative to callosal connections within the default-mode, memory, and salience networks (P = .02), with significant weighting on linear regression toward CSF total tau (P = .03) and CSF phosphorylated tau at codon 181 (P = .03), but not CSF Aß42 . DISCUSSION: Cognitively normal APOE ε4 carriers with subthreshold amyloid accumulation may have network reorganization associated with tau.

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.

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.