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-β42 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-β42 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.

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The development of combined antiretroviral therapy (cART) has increased the lifespan of persons living with HIV (PLWH), with most PLWH having a normal life expectancy. While significant progress has occurred, PLWH continue to have multiple health complications, including HIV associated neurocognitive disorders (HAND). While the exact cause of HAND is not known, persistent neuroinflammation is hypothesized to be an important potential contributor. Molecular imaging using positron emission tomography (PET) can non-invasively evaluate neuroinflammation. PET radiotracers specific for increased expression of the translocator protein18kDa (TSPO) on activated microglia can detect the presence of neuroinflammation in PLWH. However, results from these studies have been inconsistent and inconclusive. Future studies are needed to address key limitations that continue to persist with these techniques before accurate conclusions can be drawn regarding the role of persistent neuroinflammation in PLWH.

Objective: This study examined relationships between anticholinergic medication burden and brain integrity in people living with HIV (PLWH) and people without HIV (HIV−). Methods: Neuropsychological performance z-scores (learning, retention, executive function, motor/psychomotor speed, language domains, and global cognition), and neuroimaging measures (brain volumetrics and white matter fractional anisotropy) were analyzed in PLWH (n = 209) and HIV− (n = 95) grouped according to the Anticholinergic Cognitive Burden (ACB) scale (0 = no burden, 1–3 = low burden, >3 = high burden). Neuropsychological performance and neuroimaging outcomes were compared between HIV− and PLWH with high anticholinergic burden. Within a cohort of PLWH (n = 90), longitudinal change in ACB score over ∼2 years was correlated to the rate of change per month of study interval in neuropsychological performance and neuroimaging measures. Results: A higher number of anticholinergic medications and ACB was observed in PLWH compared with HIV− (P < 0.05). A higher ACB was associated with worse motor/psychomotor performance, smaller occipital lobe, putamen, subcortical gray matter and total gray matter volumes in HIV−; and poorer executive function, retention and global cognition, smaller brain volumes (frontal, parietal and temporal lobes, hippocampus, amygdala, cortex, subcortical gray matter and total gray matter), and reduced fractional anisotropy (posterior corpus callosum, perforant pathway) in PLWH. PLWH with high anticholinergic burden performed worse on tests of learning and executive function compared with HIV− with high anticholinergic burden. Longitudinally, PLWH who reduced their ACB over time had better neuropsychological performance and neuroimaging measures. Conclusion: Anticholinergic medications were associated with worse neuropsychological performance and reduced structural brain integrity, and these effects were more widespread in PLWH. Use of anticholinergic medications should be carefully monitored in older adults with deprescription considered whenever possible.

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.

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Objective:Temporal correlations between CSF and neuroimaging (PET and MRI) measures of amyloid, tau, and neurodegeneration were evaluated in relation to Alzheimer disease (AD) progression.Methods:Three hundred seventy-one cognitively unimpaired and impaired participants enrolled in longitudinal studies of AD had both CSF (amyloid-β42, phosphorylated tau181, total tau, and neurofilament light chain) and neuroimaging (PiB PET, flortaucipir PET, and structural MRI) measures. The pairwise time interval between CSF and neuroimaging measures was binned into two 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 modelling. Cohen’s 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 amyloid-β42 and PiB PET showed maximal correlation when collected within six years of each other (R ≈ -0.5). CSF phosphorylated tau181 and flortaucipir PET showed maximal correlation when CSF was collected four to eight years prior to PET (R ≈ 0.4). CSF neurofilament light chain and cortical thickness showed low correlation, regardless of time interval (Ravg ≈ -0.3). Similarly, CSF total tau and cortical thickness had low correlation, regardless of time interval (Ravg < -0.2).Conclusions:CSF amyloid-β42 and PiB PET best agree when acquired in close temporal proximity, whereas CSF phosphorylated tau precedes flortaucipir PET by four to eight years. CSF and neuroimaging measures of neurodegeneration have low correspondence and are not interchangeable at any time interval.

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