Cognitive resilience in preclinical Alzheimer's disease: Higher NPTX2 and VGF levels are associated with reduced cognitive decline.

Introduction The aging global population underscores the need to understand brain aging and its links to neurodegenerative diseases. While most brain aging studies use cognitive impairment as exclusion criteria, preclinical biomarkers may influence results, potentially masking early pathological effects. This study evaluates how preclinical AD and vascular biomarkers impact brain aging models in cognitively normal subjects. Methods Using baseline data from the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS), we analyzed 1,380 cognitively unimpaired participants (50+ years) stratified into five groups based on cerebrospinal fluid biomarkers (Aβ42, t-tau, p-tau) and vascular pathology (Fazekas scale, microbleeds). Structural MRI volumes of cortical/subcortical regions were normalized and compared using Nadaraya-Watson kernel regression. Bootstrapping and Bonferroni-corrected statistical tests assessed differences in the relationship between age and brain volume between groups. Results Significant differences emerged in the relationship between age and brain volume in biomarker-negative and biomarker-positive groups, particularly in the entorhinal cortex, amygdalas, and basal forebrain ( p < 0.01). The AD and mixed AD/vascular groups showed the largest deviations. Gender-specific analyses revealed stronger effects in males. Vascular pathology alone affected distinct regions (e.g., left entorhinal cortex) without amygdala involvement, suggesting disease-specific atrophy patterns. Discussion Preclinical AD and vascular biomarkers significantly alter brain aging in cognitively normal individuals. These findings highlight the importance of biomarker stratification in brain age studies to avoid biased estimates. Entorhinal cortex and amygdala volumes may serve as sensitive early indicators of neurodegeneration, supporting their use in targeted interventions and personalized monitoring.

Alzheimer’s Disease (AD) is a neurodegenerative condition affecting millions worldwide. Defining early pathobiological events remains challenging, in part due to inaccessibility of neural tissue. Because olfactory neurons are accessible, and olfactory loss is prevalent in AD, we evaluated olfactory brush biopsies from controls, individuals with cerebrospinal fluid (CSF) biomarker-confirmed AD, and cognitively typical individuals whose positive biomarkers signal a pre-clinical AD stage. We define via single cell RNA-sequencing (n=22 subjects) conserved neuroinflammatory T cell, myeloid, and olfactory neuron changes detectable even in pre-clinical AD subjects. Activated memory T cell states were a hallmark of pre-clinical AD, paralleling CSF T cell phenotypes seen in advanced disease, accompanied by both microglia-like inflammatory programs and olfactory neuron inflammatory injury. Together, our findings establish a novel platform permitting analysis of neural tissue in AD at its earliest stages.

Addressing the association between early-changing Alzheimer disease (AD) biomarkers and cognition is essential for characterizing preclinical AD. However, few studies have explored this relationship longitudinally, especially across multiple biomarker modalities. The aim of this study was to evaluate longitudinal associations of multimodal core 1 AD biomarkers with cognition in a cognitively unimpaired (CU) population at increased risk of AD dementia. This prospective observational study included CU individuals from the Alzheimer's and Families+ cohort, based in Barcelona, Spain. Individuals had available baseline CSF biomarker measurements (normal or AD continuum profiles) and longitudinal neuropsychological assessments (2 time points, 3-year follow-up). The primary study outcome was the modified Preclinical Alzheimer's Cognitive Composite (mPACC) score. Study measurements included plasma phosphorylated tau (p-tau) 217, CSF p-tau181/β-amyloid (Aβ) 42, and Aβ PET ([¹⁸F]flutemetamol). Mixed-effects models were used to evaluate the longitudinal associations between fluid biomarkers and mPACC score; voxel-wise models were used to evaluate the longitudinal association between neuroimaging and mPACC score. Sensitivity analyses evaluated these associations stratifying by Aβ status. In total, 350 individuals were included (mean age: 61 years; 60% female; mean education: 14 years). In the full sample, increases in plasma p-tau217 concentration (βSTD = -0.124, 95% CI -0.172 to -0.076; p < 0.001), CSF p-tau181/Aβ42 ratio (βSTD = -0.059, 95% CI -0.100 to -0.019; p = 0.005), and Aβ PET load in the prefrontal cortex, cingulate cortex, precuneus, and anterior temporal regions were associated with mPACC decline. In the Aβ-positive group (35%), increases in plasma p-tau217 concentration (βSTD = -0.121, 95% CI -0.194 to -0.049; p < 0.001) and Aβ PET load in anterior temporal regions were associated with mPACC decline. In the Aβ-negative group (65%), increases in plasma p-tau217 concentration (βSTD = -0.084, 95% CI -0.157 to -0.011; p = 0.024) and Aβ PET load in frontoparietal regions were associated with mPACC decline. This study demonstrated significant longitudinal associations between multimodal core 1 AD biomarkers and cognitive function over 3 years in CU individuals, including those at the Aβ-negative stage. Distinct biomarker modalities provided complementary insights, emphasizing the potential of blood-based biomarkers for tracking subtle cognitive decline and neuroimaging for delineating vulnerable brain regions in aging and preclinical AD. These findings are crucial for identifying and monitoring high-risk CU individuals, further informing preventive intervention strategies for AD.

Amyloid-β (Aβ) and APOE4 represent two of the strongest pathological and genetic risk factors for Alzheimer’s disease (AD), but how these co-pathogens interact during preclinical stages remains undefined. We addressed this question by developing a humanized knock-in model expressing physiological, endogenously regulated human Aβ and APOE4. AgedAppNLF:APOE4 mice displayed incipient amyloidosis with subtle memory-related changes, consistent with preclinical AD. We found largely distinct, non-overlapping APOE4- and Aβ-driven functional synaptic, sleep, and behavioral alterations. However, at the transcriptomic level, APOE4xAβ had a pronounced detrimental interaction in neuronal populations, whereas glial populations were primarily affected by either genotype. We found APOE4xAβ molecular interactions in neuronal populations, including excitatory and inhibitory cells, converged on a core lysosomal-ER proteostasis axis. We propose that APOE4xAβ interaction produces an early neuronal pathogenic signature, involving the lysosomal-ER proteostasis axis, preceding functional decline and driving disease progression. APOE4xAβ-KI models provide a physiologically relevant platform to study early pathogenesis.HighlightsEarly synergistic APOE4xAβ interaction emerges predominantly at the transcriptomic level in neurons, but not in glial cells.APOE4 and Aβ drive largely non-overlapping physiological changes in preclinical stages of disease, but converge at the level of network hyperexcitability.APOE4xAβ neuronal synergy converges on a conserved lysosomal-ER proteostasis axis.Humanized APOE4xAβ KI mice provide a physiologically relevant model to dissect early AD pathogenesis in preclinical stages

Down syndrome is characterized by triplication of chromosome 21, leading to early-onset Alzheimer disease pathology, with nearly all individuals with Down syndrome developing amyloid and tau pathology. In the new era of amyloid modifying therapies, it is vital to identify early biomarkers for Alzheimer disease (AD) pathology in Down syndrome. Striatal amyloid may begin to accumulate sooner than cortical amyloid in Down syndrome. Tau phosphorylation at specific sites, including 217, can be quantified in plasma and may represent an important mechanistic step in the development of tau pathology. This study had two aims: 1. To compare the relative age at increase of multiple biomarkers (cortical amyloid, striatal amyloid, plasma pTau217 and summary tau pathology) 2. To test whether plasma pTau217 can identify both the current presence and likely future accumulation of amyloid and tau pathology. To identify optimal biomarkers for early intervention, we examined longitudinal cortical and striatal amyloid PET, plasma pTau217, and tau PET in 328 individuals with Down syndrome enrolled in the Alzheimer Biomarker Consortium - Down Syndrome study. To compare the timing of biomarker changes, we modeled longitudinal biomarkers using generalized additive mixed models relative to age. We used receiver operating characteristic curve analysis to identify thresholds for both current and likely future accumulation of amyloid and tau pathology. For all comparisons, we used age as the null model, performing Delong tests to evaluate the performance of age relative to biomarker-based prediction. Imaging biomarkers increased around 40 years old, with plasma pTau217 increasing somewhat later than the three PET biomarkers. Striatal amyloid increased before cortical amyloid in some participants; however, this was not uniform across individuals. If an individual was classified as a reliable accumulator with one biomarker, he or she was likely to be a reliable accumulator in other biomarkers. Age was as sensitive as plasma pTau217 in its ability to both detect preclinical Alzheimer disease pathology and predict near future accumulation of both amyloid and tau. These results suggest that all adults with Down syndrome should be screened for Alzheimer disease pathology starting shortly before age 40 and considered for clinical trials. Age alone was as effective at detecting both current pathology and likely future accumulation as plasma pTau217. Because this disease is so closely concurrent with age in individuals with Down Syndrome, plasma pTau217 may not provide more diagnostic benefits than age.

BackgroundThe shift toward a biological definition of Alzheimer's disease (AD) has led to increased use of biomarkers (e.g., amyloid, tau, APOE genotype) in research settings. As return of biomarker results becomes more common in AD research, understanding how participants perceive and make decisions about receiving individual research results is essential for ethical trial design and informed consent practices.Objective(1) To map and appraise the landscape of qualitative and mixed-methods research focused on perspectives of AD research community members related to biomarker result sharing in AD research settings; and (2) to synthesize findings on decisional needs of research participants considering whether to receive individual biomarker or genetic risk information.MethodsWe searched PubMed and EMBASE for qualitative and mixed-methods studies published between January 2012 and January 2025. Eligibility criteria included empirical studies reporting qualitative data on experiences, attitudes, or decision-making processes related to receiving AD biomarker or genetic results in research contexts.ResultsOf 3269 records screened, 42 records were assessed for eligibility and 8 met inclusion criteria and were included in our final analysis. Participants emphasized empowerment through research participation and desire for knowledge related to research results. Across studies, decisional needs, characterized as "aspects of decision-making" clustered around four domains: (1) clarity of biomarker meaning and limitations, (2) emotional readiness and support, (3) social consequences of disclosure, and (4) alignment with personal values and future planning.ConclusionsFindings can inform the development of ethically grounded protocols for returning individual research results in preclinical AD trials.

Physical inactivity is a recognized modifiable risk factor for Alzheimer's disease (AD), yet its relationship with progression of AD pathology in humans remains unclear, limiting the effective translation into prevention trials. Using pedometer-measured step counts in cognitively unimpaired older adults, we demonstrated an association between higher physical activity and slower cognitive and functional decline in individuals with elevated baseline amyloid. Importantly, this beneficial association was not related to lower amyloid burden at baseline or longitudinally. Instead, higher physical activity was associated with slower amyloid-related inferior temporal tau accumulation, which significantly mediated the association with slower cognitive decline. Dose-response analyses further revealed a curvilinear relationship, where the associations with slower tau accumulation and cognitive decline reached a plateau at a moderate level of physical activity (5,001-7,500 steps per day), potentially offering a more approachable goal for older sedentary individuals. Collectively, our findings support targeting physical inactivity as an intervention to modify the trajectory of preclinical AD in future prevention trials, and further suggest that preferentially enrolling sedentary individuals with elevated amyloid may maximize the likelihood of demonstrating a protective effect of physical activity on tau accumulation and cognitive and functional decline in early AD.

ABSTRACTStructural changes in the cerebellum contribute to cognitive decline due to aging and Alzheimer's disease (AD). However, it is unclear whether age and AD pathology are associated with structural alterations in the cerebellum among cognitively unimpaired individuals and how these alterations relate to cognition. This study examined the association of age and cerebrospinal fluid (CSF) AD biomarkers (amyloid beta [Aβ42/Aβ40], phosphorylated tau [p‐tau181]) with cerebellar gray matter (GM) and white matter (WM) volumes and cerebellar WM microstructure, measured via magnetic resonance imaging (MRI) among 176 cognitively unimpaired middle‐aged and older adults (mean age = 66.70, range = 34–89). Cognition was measured with executive function and visuospatial composite scores. Older age was associated with lower cerebellar GM and WM volumes (ps < 0.01) and greater mean diffusivity (MD) in the cerebellar peduncles (p < 0.01). In contrast, more abnormal Aβ levels were associated with lower MD in three regions of interest, including the middle cerebellar peduncle (MCP, p < 0.01), a composite of superior, middle, and inferior peduncles (p < 0.05), and within‐cerebellar WM (p < 0.05). Patterns were similar when comparing biomarker positive versus negative groups, particularly for the MCP. Further, lower MD in the peduncles and cerebellar WM was associated with better executive function and visuospatial composite scores (ps < 0.05), whereas cerebellar volumetric measures were not related to cognition. Results suggest that older age is associated with microstructural and volumetric cerebellar GM and WM alterations. In contrast, Aβ levels are associated with WM microstructural properties in cognitively unimpaired individuals. These findings highlight the importance of cerebellar WM microstructure to cognition and are consistent with, and expand on, previous reports that have linked more abnormal amyloid levels to WM microstructure in cerebral tracts. They also suggest that cerebellar WM alterations may be markers of preclinical AD.

Phytosterols as modulators of gut-brain axis and neuroinflammation in Alzheimer's disease: A novel therapeutic avenue in aging research.

Traditional clinical subtype classifications (such as amnestic and non-amnestic mild cognitive impairment) rely on subjective interpretations of overlapping patterns of performance on cognitive tests, which may lead to unreliable categorization. A more precise and objective classification of mild cognitive impairment subtypes can be achieved through data-driven clustering techniques. However, because previous studies have not restricted their cohorts to patients who have mild cognitive impairment with the pathology of Alzheimer's disease, the nature of cognitive variability and its impact on disease progression in a strictly defined biomarker-positive preclinical Alzheimer's disease cohort remains unknown. We examined cognitive heterogeneity among participants with mild cognitive impairment due to Alzheimer's disease and evaluated its prognostic utility. Neuropsychological test data from 389 patients with mild cognitive impairment in whom the cerebrospinal fluid biomarker confirmed Alzheimer's disease were obtained from the Alzheimer's Disease Neuroimaging Initiative cohorts. Principal component analysis and model-based clustering were used to identify cognitive profiles, which were then validated through a 100-time bootstrap analysis. Pairwise comparisons tested for differences between the identified subgroups in participant characteristics, scores on cognitive and clinical outcomes, levels of cerebrospinal fluid biomarkers, and magnetic resonance imaging-derived brain volumes. Longitudinal analyses evaluated differences in rate of change of magnetic resonance imaging volumetric measurements and clinical outcomes over 48 months. Survival analysis assessed risk for conversion to dementia. Alpha-synuclein levels and white matter hyperintensity volumes were considered for sensitivity analysis. Two distinct cognitive profiles were identified: a "typical" group (56.04% of the sample) that demonstrated relatively poorer scores on memory testing than non-memory tests, and an "atypical" group (43.96% of the sample) with smaller differences between memory and non-memory measures, indicating a more uniform pattern of impairment across cognitive domains. While the groups had comparable levels of overall cognitive impairment and cerebrospinal fluid biomarkers of Alzheimer's disease, the typical group displayed accelerated atrophy rates every 6 months across multiple brain regions (hippocampus: 29.02 mm3, standard error [SE] = 10.13, P = 0.005; whole brain: 1799.85 mm3, SE = 781.57, P = 0.023; entorhinal cortex: 22.26 mm3, SE = 11.15, P = 0.048; fusiform gyrus: 66.24 mm3, SE = 28.53, P = 0.021). Survival analysis revealed markedly higher dementia conversion risk (hazard ratio: 1.70, 95% confidence interval: 1.27, 2.27, P < 0.001) and shorter progression time in the typical group. These findings persisted after controlling for comorbid pathologies. In conclusion, this data-driven approach identified two distinct cognitive subtypes of mild cognitive impairment due to Alzheimer's disease that differed in their rates of clinical decline and neurodegeneration. These findings could be used to improve prognostic models and inform clinical trial stratification.

Women are at higher risk for Alzheimer’s disease (AD) than men and hormonal changes during perimenopause are considered a risk factor. The relationship between ovarian hormones and AD has been explored using AD animal models, especially through ovariectomy (OVX) in established AD models. The link between early-stage AD and ovarian hormones, however, remains unclear, largely due to the lack of suitable animal models. Appropriate models for studying early-stage AD pathology, treatment, and prevention are critically needed. The App knock-in mouse model, which carries a single amyloid precursor protein (App) gene mutation, effectively reproduces early amyloid AD pathology. To elucidate the relationship between ovarian hormone deficiency and the behavioral phenotypes of a preclinical AD model, we applied a comprehensive behavioral test battery to this mouse model with bilateral OVX. The App mutation reduced anxiety-like behavior and impaired performance in the fear memory task. OVX restored the anxiety-like behavior of the App mutation mice to a level comparable to that in wild-type (WT) mice. Furthermore, OVX enhanced performance in a fear memory task in both genotypes and reduced amyloid-β staining in WT mice. Together, these findings suggest that OVX attenuates AD-related phenotypes in a preclinical AD model and in C57BL/6 J WT mice.

BackgroundMultianalyte plasma proteomic panels that can accurately detect initial AD pathology in preclinical populations and simultaneously measure related biological processes relevant for disease risk are critical for advancing early detection and prognosis.MethodsUsing the NULISAseq CNS panel, we measured plasma from 193 clinically unimpaired (CU) older adults enrolled in the Stanford Aging and Memory Study (SAMS). We evaluated correspondence of core AD-relevant biomarkers Aβ42, Aβ40, pTau217, pTau181, GFAP, NfL, Aβ42/Aβ40, and pTau217/Aβ42 measured using NULISAseq and established Lumipulse immunoassays. ROC curve analyses compared the accuracy of these biomarkers for detecting Lumipulse CSF Aβ-positivity across platforms. Linear models were applied across 124 NULISAseq proteins to examine associations with common AD risk factors, including age, female sex, andAPOE-ε4, as well as with biomarkers CSF Aβ42/Aβ40 and pTau181, 18F-PI2620 Tau PET, and memory. Fold change differences in NULISAseq proteins as a function of CSF Aβ (A+) and pTau181 (T+) status were examined using Wilcoxon rank-sum tests.ResultsModerate to high correlations were observed between NULISAseq and Lumipulse AD plasma biomarkers. Across platforms, plasma pTau217/Aβ42 exhibited the highest performance in discriminating CSF A+ (NULISAseq AUC: 0.940, 95%CI: 0.885-0.995; Lumipulse AUC: 0.907, 95%CI: 0.849-0.966). Age and sex were associated with differential expression of NULISAseq targets linked to neurodegeneration, microglial activation, and inflammation. CSF A+ was associated with fold change differences in Aβ42, pTau217, pTau231, pTau181, and GFAP, while CSF T+ was additionally associated with increases in TREM1, TIMP3, SAA1, and S100A12. When stratified by AT groups, A+T-exhibited lower Aβ42 and elevated pTau217 compared to A-T-, whereas A+T+ exhibited elevated pTau231 and pTau181 compared to A+T-. Temporal cortex tau was positively associated with NULISAseq pTau217, pTau231, pTau181, and pTau217/Aβ42. Memory function was negatively associated with pTau isoforms and PRDX6, YWHAZ, ENO2, ARSA, CHI3L1, CXCL8, and FCN2. These associations remained when controlling for pTau217 and restricting to A-CU, suggesting these targets may represent AD-independent biological pathways relevant for memory function.ConclusionsNULISAseq immunoassay-based multiplexing accurately detects AD pathology among CU older adults and identifies multiple biological pathways related to early biomarker abnormality and memory function that may become dysregulated in preclinical AD.

Predicting the likelihood of developing Alzheimer’s disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal brain amyloidosis but remain cognitively unimpaired. Here, we trained and evaluated ML classifiers to predict whether individuals with preclinical AD will progress to mild cognitive impairment or dementia within multiple fixed time windows, ranging from one to five years. Models were trained on regional imaging features extracted from amyloid positron emission tomography and magnetic resonance imaging pooled across seven independent sites and from two amyloid radiotracers ([18F]-florbetapir and [11C]-Pittsburgh-compound-B). Out-of-sample generalizability was evaluated via a leave-one-site-out and leave-one-tracer-out cross-validation. Classifiers achieved an out-of-sample receiver operating characteristic area-under-the-curve of 0.66 or greater when applied to all except one hold-out sites and 0.72 or greater when applied to each hold-out radiotracer. Additionally, when applying our models in a retroactive cohort enrichment analysis on A4 clinical trial data, we observed increased statistical power of detecting differences in amyloid accumulation between placebo and treatment arms after enrichment by ML stratifications. As emerging investigations of new disease-modifying therapies for AD increasingly focus on asymptomatic, preclinical populations, our findings underscore the potential applicability of ML-based patient stratification for recruiting more homogeneous cohorts and improving statistical power for detecting treatment effects for future clinical trials.HighlightsMachine learning can predict future cognitive impairment in preclinical Alzheimer’sModels achieved high out-of-sample ROC-AUC on external sites and PET tracersModels were able to distinguish cognitively stable from decliners in the A4 cohortML cohort enrichment enhanced secondary treatment effect detection in the A4 cohort

INTRODUCTIONAmyloid beta (Aβ), a hallmark of early Alzheimer's disease (AD), disrupts white matter (WM) microstructure, but its spatial patterns and transcriptomic links in cognitively normal individuals remain underexplored.METHODSWe compared the WM microstructure between Aβ‐positive (Aβ+) and Aβ‐negative (Aβ−) individuals at the cognitively normal stage. We investigated the relationship between the fibers and the cortical and subcortical regions to which they are connected, as well as the underlying gene expression.RESULTSWM damage observed in Aβ+ individuals was characterized across eight fiber tracts, even prior to the evidence of atrophy and during the cognitive normal stage. This damage is primarily associated with cortical Aβ accumulation and may be linked to genes that regulate oligodendrocyte function and myelination.DISCUSSIONCortical Aβ‐related WM changes precede gray matter atrophy in preclinical AD, highlighting their potential as early biomarkers. Oligodendrocyte dysfunction and myelination pathways may underlie Aβ‐driven WM vulnerability, offering targets for intervention.HighlightsWM microstructural changes precede gray matter atrophy in preclinical AD.Aβ‐driven WM damage persists even after adjusting for age.WM microstructural damage is primarily linked to cortical Aβ burden in cognitively normal individuals.Oligodendrocytes and myelin underlie the vulnerability of WM‐related to Aβ.

BackgroundAlzheimer’s disease (AD) clinical trials enroll participants at various site types including research-focused academic institutions and independent non-academic sites. Limited research has examined the impact of site type on recruitment and retention outcomes.MethodsTo evaluate potential differences between site types, we used data from the Anti-Amyloid Treatment for Asymptomatic AD (A4) trial, the largest completed preclinical AD trial to date. We first compared the frequency of varying recruitment sources between site types. We then examined potential differences in participant- and site-level characteristics. To assess potential site type differences in retention, we fit a multivariable logistic regression model adjusting for variables associated with site type. For participants who prematurely discontinued, we examined potential differences by site type in reasons for dropout.ResultsOne thousand and fifty-eight participants were randomized at 50 academic (N = 835) and 15 non-academic (N = 223) sites in North America. Academic sites had higher proportions of participants recruited through earned media and organizational referrals and lower proportions recruited through internal referrals and advertisements, compared to non-academic sites. Participant-level characteristics differed between site types. Compared to non-academic sites, academic sites had higher proportions of participants with a family history of dementia and a professional degree (highest education category), but lower proportions of individuals with a history of diabetes, a CDR-SB score above 0, and belonging to a racial and ethnic underrepresented group. Though the results were not statistically significant, non-academic sites had a higher screening rate (number of participants screened/site/month), but a lower randomization rate (randomized/screened) compared to academic sites. No site type differences in completion rates were observed. When examining reasons for discontinuation, we found that among the 72 participants who discontinued the trial at non-academic sites, 56 (77.8%) withdrew consent or were lost to follow up. In contrast, 140 out of 243 (57.6%) participants who discontinued the trial in academic sites withdrew consent or were lost to follow up.ConclusionOur findings shed light on important site type differences that investigators should consider when making choices around site, design, and conduct in multisite preclinical AD trials.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13195-025-01867-8.

INTRODUCTIONBlood‐based biomarkers (BBMs) are promising tools for Alzheimer's disease (AD) diagnosis, but their accuracy may be affected by body mass index (BMI) and blood volume (BV) through dilution. We investigated how BMI and BV influence BBM concentrations and PET prediction.METHODSData from 241 cognitively unimpaired participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were examined to evaluate the influence of BMI/BV on BBMs (Aβ42/40, p‐Tau181, p‐Tau217, glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL]) and BBM‐based PET predictions.RESULTSElevated BMI/BV associated with lower BBM concentrations, especially for p‐Tau217 and NfL, independent of brain amyloid burden. BMI‐stratified thresholds improved amyloid PET prediction, with higher BBM thresholds and area under the curve (AUC) values seen in normal weight compared to overweight or obese participants. Drastic BMI/BV declines due to weight loss increased BBM variability and systematic PET misclassification.DISCUSSIONAdjusting for BMI/BV in BBM‐based diagnostics appears to improve accuracy and reliable detection of AD pathology, especially in preclinical stages.HighlightsBody mass index (BMI) and blood volume (BV) significantly influenced plasma BBM concentrations in cognitively unimpaired (CU) individuals.Blood‐based biomarkers (BBMs) associated more strongly with BV than with BMI.Dilution effects were independent of brain amyloid burden.BMI‐stratified BBM thresholds improved amyloid positron emission tomography (PET) classification accuracy.Declines in BMI/BV resulted in PET prediction bias and systematic errors.

Elevated peripheral inflammatory markers among older adults are associated with cognitive decline, particularly for women, for whom cognitive decline is more prevalent and rapid. Few prospective studies have examined gender-specific associations between inflammation and cognitive decline or examined stable, mean differences (i.e., between-person effects) versus fluctuations (i.e., within-person effects) in inflammation. Participants (N = 235) were older adults (Mage= 75.1, range = 60-92) from a longitudinal study that included neuropsychological assessments and blood draws every six months for up to 12 years. Systemic inflammation was operationalized as serum interleukin-6 (IL-6). The Trail Making Test measured psychomotor processing speed (TMT-A), task-switching (TMT-B), and executive functioning (TMT-B-TMT-A). Multilevel models tested associations between cognition and inflammation and moderation by gender. Models controlled for relevant covariates (e.g., age, gender, education, body mass index, cardiovascular medication count). Higher mean IL-6 was associated with slower TMT-A performance for women (γ = .036, p = .04) but not men (γ = -.01, p = .45). Higher mean IL-6 was associated with slower TMT-B performance (γ = .036, p = .02), but not after covariate adjustment (γ = .016, p = .28) and there was no significant moderation by gender. There were no significant associations between within-person IL-6 and any outcome, between- or within--person IL-6 and TMT B-A performance, or any 3-way interactions between gender, IL-6, and study wave on any outcome. Psychomotor speed, but not executive function, was sensitive to changes in systemic inflammation in a gender-specific way. -Gender-specific differences in neurological aging remain a fruitful direction and critical target for future intervention research aimed at addressing preclinical Alzheimer's disease and accelerated aging.

Using memory recall response time on digital cognitive testing to detect subtle cognitive changes among cognitively unimpaired older adults

BACE1 as an early biomarker and its relevance to risk factors for Alzheimer's disease.