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The proportion of aging populations affected by dementia is increasing. There is an urgent need to identify biological aging markers in mid-life before symptoms of age-related dementia present for early intervention to delay the cognitive decline and the onset of dementia. In this cohort study involving 1,676 healthy participants (mean age 40) with up to 15 years of follow up, we evaluated the associations between cognitive function and two classes of novel biological aging markers: blood-based epigenetic aging and neuroimaging-based brain aging. Both accelerated epigenetic aging and brain aging were prospectively associated with worse cognitive outcomes. Specifically, every year faster epigenetic or brain aging was on average associated with 0.19-0.28 higher (worse) Stroop score, 0.04-0.05 lower (worse) RAVLT score, and 0.23-0.45 lower (worse) DSST (all false-discovery-rate-adjusted p

IMPORTANCE: Meta-analyses of randomized clinical trials have indicated that improved hypertension control reduces the risk for cognitive impairment and dementia. However, it is unclear to what extent pathways reflective of Alzheimer disease (AD) pathology are affected by hypertension control. OBJECTIVE: To evaluate the association of intensive blood pressure control on AD-related brain biomarkers. DESIGN, SETTING, AND PARTICIPANTS: This is a substudy of the Systolic Blood Pressure Intervention Trial (SPRINT MIND), a multicenter randomized clinical trial that compared the efficacy of 2 different blood pressure-lowering strategies. Potential participants (n = 1267) 50 years or older with hypertension and without a history of diabetes or stroke were approached for a brain magnetic resonance imaging (MRI) study. Of these, 205 participants were deemed ineligible and 269 did not agree to participate; 673 and 454 participants completed brain MRI at baseline and at 4-year follow-up, respectively; the final follow-up date was July 1, 2016. Analysis began September 2019 and ended November 2020. INTERVENTIONS: Participants were randomized to either a systolic blood pressure goal of less than 120 mm Hg (intensive treatment: n = 356) or less than 140 mm Hg (standard treatment: n = 317). MAIN OUTCOMES AND MEASURES: Changes in hippocampal volume, measures of AD regional atrophy, posterior cingulate cerebral blood flow, and mean fractional anisotropy in the cingulum bundle. RESULTS: Among 673 recruited patients who had baseline MRI (mean [SD] age, 67.3 [8.2] years; 271 women [40.3%]), 454 completed the follow-up MRI at a median (interquartile range) of 3.98 (3.7-4.1) years after randomization. In the intensive treatment group, mean hippocampal volume decreased from 7.45 cm3 to 7.39 cm3 (difference, -0.06 cm3; 95% CI, -0.08 to -0.04) vs a decrease from 7.48 cm3 to 7.46 cm3 (difference, -0.02 cm3; 95% CI, -0.05 to -0.003) in the standard treatment group (between-group difference in change, -0.033 cm3; 95% CI, -0.062 to -0.003; P = .03). There were no significant treatment group differences for measures of AD regional atrophy, cerebral blood flow, or mean fractional anisotropy. CONCLUSIONS AND RELEVANCE: Intensive treatment was associated with a small but statistically significant greater decrease in hippocampal volume compared with standard treatment, consistent with the observation that intensive treatment is associated with greater decreases in total brain volume. However, intensive treatment was not associated with changes in any of the other MRI biomarkers of AD compared with standard treatment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01206062.

IntroductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).MethodsThree brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.ResultsWMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.DiscussionA Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Background: As β‐amyloid (Aβ) targeted clinical trials have had questionable benefit in treating patients with early symptomatic Alzheimer's disease, treatment would presumably be most effective if administered during the disease's preclinical phase. PET scans, while highly sensitive to AD pathology, are less practical for screening a large, cognitively normal population for evidence of preclinical AD. Recruitment methods that produce an Aβ enriched sample before PET data collection would decrease participant burden and potentially lower costs. Here, we develop a simple predictive model using subject demographics, structural MRI, and APOE4 status. Using the model's estimates of one's probability of Aβ positivity, we significantly increase the proportion of Aβ+ subjects in a cognitively normal sample. Method: Data from 875 participants from the Baltimore Longitudinal Study of Aging, Penn Memory Center and the Alzheimer's Disease Neuroimaging Initiative database were separated into a discovery set including AD, MCI, and cognitively normal participants (n = 673) and a cognitively normal replication set (n = 202). Aβ prediction performance of a logistic regression model was compared across multiple predictor sets. Regression‐based probability values were used to threshold the data, i.e., participants whose values fell below a cutoff were excluded from the thresholded sample. The proportion of Aβ+ individuals between the restricted and complete samples was compared across multiple cutoffs. Result: Applying a probability cutoff of 0.4 yielded samples with significantly higher proportions of Aβ+ subjects in 3 of the 5 tested models (Table 1). The Lesions model achieved the highest sample enrichment; 40.54% of the thresholded sample were Aβ+, significantly higher than the base rate of 28.22%, χ2 (1) = 8.32, p =.004 (Figure 1). However, there were no significant differences between the enriched sample produced by the Lesions model and those produced by the other significant models, indicating sample enrichment is possible even with different predictors. Conclusion: Aβ+ probability metrics produced by predictive models provide a novel method of sample enrichment and can be used as an individualized measure of Aβ classification certainty. Fewer PET scans will be necessary to obtain a sufficient Aβ+ sample, reducing burden on participants and researchers and potentially lowering costs. [ABSTRACT FROM AUTHOR]

Background: Clinical trials in patients with COVID-19 have exclusively used self- or proxy-reporting to characterize alcohol consumption. The aim of this study was to measure an objective biomarker of recent alcohol use in patients hospitalized with severe COVID-19-associated respiratory failure who were enrolled in an investigational clinical trial to determine the prevalence of alcohol misuse, and to explore the relationship of alcohol use with outcomes., Methods: We conducted a substudy of patients enrolled in the multicenter, phase 2, adaptive platform design, Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And molecular Analysis in COVID-19 trial (ClinicalTrials.gov: NCT04488081), conducted at 20 hospital systems across the United States. Three hundred and fifty-five patients with available red blood cell (RBC) samples and 60-day follow-up assessments were included. RBCs were utilized to measure phosphatidylethanol (PEth). Prespecified thresholds of PEth were utilized to stratify patients into groups: low/no alcohol use (PEth < 20 ng/mL), significant alcohol use (PEth 20-200 ng/mL), and heavy alcohol use (PEth ≥ 200 ng/mL)., Results: In this cohort, 17% of patients met criteria for significant alcohol use, while 4% met criteria for heavy alcohol use. Alcohol misuse was associated with diminished odds for home discharge, though this finding did not achieve statistical significance., Conclusions: In a cohort of patients with severe COVID-19 enrolled in a clinical trial, alcohol consumption of two or more standard drinks per day was present among 21%, approximating the proportion of patients with diabetes, and raising the possibility that alcohol consumption alters risk for severe viral pneumonia. Undetected alcohol misuse among clinical trial participants has the potential to influence study outcomes or contribute to adverse events., (© 2024 Research Society on Alcohol.)

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T 1 -weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients ( n  = 718) and age- and sex-matched CN adults ( n  = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group ( n  = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group ( n  = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling ( r  = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)