Seshadri, Sudha

Background New antiamyloid therapies (AAT) for treating patients with early Alzheimer disease (AD) have become available in the past 3 years. Although AAT can be beneficial in slowing progression of AD, they also come with potentially serious side effects, particularly for patients requiring anticoagulation or thrombolysis for cardiovascular conditions. Given that most patients with AD are older and likely to have cardiovascular risk factors, there is urgent need for additional guidance for clinicians about potential risks and benefits associated with AAT for patients with cardiovascular comorbidities, best practices for management of these patients, and special considerations with respect to emergency evaluation and treatment of patients on AAT who develop acute neurological symptoms. Methods The American Heart Association convened 75 multidisciplinary experts for the Amyloid‐Related Imaging Abnormalities Summit: Cardiovascular and Stroke Considerations for β‐Amyloid Immunotherapy in AD in July 2025. These experts were divided into 8 work groups to evaluate the current evidence related to AAT usage in patients with cardiovascular risk factors and conditions and develop consensus‐based guidance for clinicians. Results This statement outlines the outcomes of the Summit, including considerations for prescribers, primary care professionals, emergency personnel, and health care system stakeholders. These considerations are intended to improve identification of patients as appropriate candidates for AAT, management of patients with co‐occurring AD and cardiovascular conditions, and their emergency evaluation and treatment. The statement also includes guidance for shared decision‐making, implementation of system changes for improving dementia care, and recommendations for future research and data collection to add to the body of evidence for treating patients with concomitant AD and cardiovascular conditions. Conclusions Building on lessons learned from the establishment of stroke systems of care, the consensus‐based considerations outlined in this document can serve as a foundation for AD systems of care aimed at ensuring optimal use of these ground‐breaking therapies.

Abstract INTRODUCTION Prospective studies of blood‐based biomarkers reflecting pathogenic processes, other than Alzheimer‐specific pathologies, that contribute to dementia in diverse cohorts are lacking. METHODS We jointly fitted linear mixed‐effect models and proportional hazards models to examine associations of midlife glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble cluster of differentiation‐14 (sCD14), neurofilament light chain (NfL), total tau (t‐tau), and ubiquitin C‐terminal hydrolase L1 (UCHL1) levels in serum (collected in 1993 to 1995) with cognitive decline and incident dementia (ascertained over 29 years through 2022) in the community‐based Atherosclerosis Risk in Communities Study. sCD14 and YKL‐40 were measured in 3082 participants and the other four biomarkers in 1766 participants. RESULTS Higher serum GFAP, YKL‐40, sCD14, and NfL were associated with faster cognitive decline and elevated dementia rate (e.g., 1 standard deviation [SD] higher log‐base2 YKL‐40 was associated with −0.11SD faster 25‐year cognitive decline (95% confidence interval [CI]: −0.15,−0.07) and 45% higher dementia hazard (hazard ratio [HR]: 1.45 [95% CI: 1.25, 1.68]). Higher t‐tau and UCHL1 were also associated with faster cognitive decline. DISCUSSION Midlife blood biomarkers reflecting glial and neuronal dysfunction and neuroinflammation are associated with early cognitive impairment.

Abstract Background Prospective investigations on blood biomarkers for Alzheimer's disease and related‐dementias (ADRDs) and cognitive impairment provide important insights into their clinical utility in early disease diagnosis and risk stratification, but have been primarily limited to AD pathologies (e.g. amyloid‐beta and hyperphosphorylated‐tau aggregation). Studies of biomarkers reflecting other pathogenic processes that contribute to ADRDs, especially in community‐based cohorts, are lacking. We examined associations of blood biomarkers reflecting glial and neuronal dysfunction and neuroinflammation, including soluble cluster of differentiation‐14 (sCD14), chitinase‐3‐like protein‐1 (YKL‐40), glial‐fibrillary acidic protein (GFAP), neurofilament‐light chain (NfL), total‐Tau, and Ubiquitin C‐terminal hydrolase L1 (UCH‐L1) with cognitive decline and incident dementia in ARIC study participants. Methods Blood biomarkers were measured in 1,864 ARIC Visit 3 (1993‐1995) participants using the Meso Scale Discovery assay (YKL‐40), the Quanterix Simoa Neurology 4‐Plex assay (total‐Tau, NfL, GFAP, UCH‐L1), and a commercial ELISA assay (sCD14, R&D Systems); global cognition z‐scores were derived from three cognitive tests (measuring memory, language, and executive function) administered up to six times over 25 years, and incident dementia cases were ascertained via in‐person cognitive assessments (expert‐adjudicated), informant‐based interviews, and hospitalization and death records through 2018. We used shared parameter models to simultaneously fit linear mixed‐effect models and proportional hazard models to estimate 25‐year cognitive decline and dementia rates separately for each biomarker (log‐base2‐transformed), adjusting for covariates (Table). Results Among 1,864 participants (mean±SD age:63±4.5 years, 60% female, 49% self‐reported Black), 27% developed dementia. Higher levels of each biomarker were associated with faster cognitive decline. All but UCH‐L1 were associated with elevated dementia risk (Table). For example, one standard deviation (SD) higher log‐base2 YKL‐40 was associated with ‐0.10 SD steeper 25‐year cognitive decline (95%CI:‐0.15,‐0.04) and 59% higher dementia hazard (hazard ratio (HR):1.59 (95%CI:1.20,2.10); likewise, sCD14 was associated with an additional cognitive decline of ‐0.06 (95%CI:‐0.11,‐0.01) and 38% higher dementia hazard (HR:1.38 (95%CI:1.08,1.76)). Conclusions Blood biomarkers reflecting neuropathological processes, including microglial activation, astrocytic reactivity, and neuronal degeneration, were associated with faster cognitive decline and increased dementia risk, suggesting that they may aid in risk stratification and intervention strategies for ADRDs. Future analyses will examine whether these associations differ by race and APOE‐ε4 s tatus.

Abstract Background Our objective is to examine the generalizability of the relationships between four MRI markers of CVD: DTI‐derived free water (FW), fractional anisotropy (FA), perivascular space (PVS) and white matter hyperintensities (WMH) volume, with vascular risk factors (VRF) and cognition in the baseline POINTER Imaging and Framingham Heart Study (FHS) cohorts. Method We studied 907 U.S. POINTER and 797 FHS Offspring participants with available baseline demographic, clinical and imaging data (Table 1). Continuous FW, FA, WMH (log‐transformed) and PVS measurements were regressed against total cranial volume (TCV). Cognitive outcomes included a Global Cognitive Composite and executive function composite score for POINTER and FHS. For each MRI marker, we used linear and logistic regressions to assess the effect of age, sex, hypertension, diabetes and smoking status, adjusting for ethnicity. We then assessed, for each cognitive outcome, the individual effect of MRI markers using separate linear regressions adjusted for demographic and clinical factors, as well as TCV‐adjusted gray matter volume to control for atrophy. Result POINTER and FHS differed across most demographic and biomarker characteristics (Table 1). Despite these differences, there were similar associations between demographic and VRF and the 4 MRI vascular markers in POINTER and FHS (Figure 1): increased age was associated with worse outcomes for the 4 markers, hypertension was associated with increased FW, PVS and lower FA, diabetes was associated with increased FW only, smoking status did not impact any of the four markers. Only exception was that female had lower FW and increased WMH in POINTER and increased PVS in FHS. Associations between CVD measures and cognition were also similar across cohorts: reduced cognitive performance was significantly associated with both decreased FA and increased FW (Figure 2), but not with WMH nor PVS. Conclusion Our study explores four imaging markers representing diverse mechanisms by which CVD could impact brain structure and function in the POINTER and FHS studies. Despite differences in cohort characteristics, we found mostly consistent CVD relationships with demographic, VRF, and cognitive measures between cohorts which supports the generalizability of the relation of these markers with VRF and cognition identified in the POINTER cohort.

Objective To establish a framework for validating candidate biomarkers of cerebral small vessel diseases (SVD) associated with cognitive impairment and characterize individuals enrolled by the MarkVCID2 consortium under this framework. Methods Participants age 60 to 90 years were enrolled across 17 MarkVCID2 sites. Recruitment was targeted to enrich in cognitive symptoms (mild dementia, mild cognitive impairment, subjective cognitive decline), defined risk factors (diabetes mellitus, advanced hypertension), and Black/African American, White, and Hispanic/Latino subgroups. Enrolled participants underwent baseline visits that included cognitive testing, multimodal magnetic resonance imaging (MRI), and biofluid collection. Provisional risk for SVD‐related cognitive decline was estimated primarily by baseline cognitive symptoms plus SVD risk factors. Adjudicated risk status was estimated by cognitive symptoms plus presence of moderate‐to‐severe white matter hyperintensities, microbleeds, or lacunes on baseline MRI. Results MarkVCID2 enrolled 1883 individuals age 73.4 ± 7.5 years, 65.0% female, 24.2% Hispanic, and 27.1% non‐Hispanic Black. Among enrollees, 44.8% were provisionally designated high‐risk. After baseline MRI, 48.5% were categorized as adjudicated high‐risk status, with substantial recategorization both from low‐ to high‐risk (primarily because of MRI lesions without SVD risk factors) and high‐ to low‐risk (primarily suspected cognitive impairment at screening not confirmed by baseline testing). Interpretation MarkVCID2 baseline data indicate successful enrollment of diverse individuals enriched in factors associated with SVD‐related cognitive decline. Changes over 3 years of longitudinal follow‐up will be analyzed to validate the candidate biomarkers for 2 projected contexts of use: subject selection (identifying likelihood of future SVD progression) and study outcome (efficiently measuring SVD progression). ANN NEUROL 2026;99:449–458

Importance SARS-CoV-2 infection has been linked to neurotoxic effects and cognitive deficits. Objective To determine whether decreases in cognitive function were accelerated after SARS-CoV-2 infection compared with individuals not infected. Design, Setting, and Participants Multicenter, prospective cohort study from 2016 to 2022 among 3525 participants alive on March 1, 2020, and enrolled in The Atherosclerosis Risk in Communities (ARIC) study and the Collaborative Cohort of Cohorts for COVID-19 Research study who completed a prepandemic cognitive assessment and a pandemic-era assessment of SARS-CoV-2 infection. Final analyses performed in November 2024. Exposure SARS-CoV-2 infection determined via self-report of a positive SARS-CoV-2 test or health care professional diagnosis of COVID-19, a positive SARS-CoV-2 antinucleocapsid antibody response, or presence of an administrative code for COVID-19 on medical records. Main outcomes and measures A neuropsychological battery assessed multiple cognitive domains, and a cocalibrated confirmatory factor analysis generated factor scores for global cognitive function. The primary outcome was the rate of excess change in cognitive function. Results The 3525 eligible participants had a mean (SD) age of 80.8 (4.7) years, 2085 (59.1%) were female, 752 (21.4%) were Black, and 2773 (78.6%) were White. SARS-CoV-2 infection was detected among 307 participants (8.7%), 103 of whom (33.6%) were hospitalized. Among uninfected participants, the mean annualized change in cognitive function was −0.09 (95% CI, −0.13 to −0.04). Compared with this rate, change was faster (β = −0.06; 95% CI, −0.09 to −0.02) among participants hospitalized for infection, but not different from participants who were infected but not hospitalized (β = 0.00; 95% CI, −0.02 to 0.03). The association among participants hospitalized for infection was evident in the cognitive domains of memory and executive function, but not language. Conclusions and relevance This cohort study of older participants found accelerated decreases in cognition among individuals hospitalized for SARS-CoV-2 infection, but not nonhospitalized infection, in comparison with individuals not yet infected.

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.

AbstractBackgroundCerebral small vessel disease (SVD) related vascular contributions represent a major factor contributing to cognitive decline and dementia (VCID) in older adults. However, there has not been a validated biomarker for the diagnosis and treatment monitoring of this condition. Recently, the US National Institute on Neurological Disorders and Stroke (NINDS) funded the MarkVCID Consortium to identify and validate clinical‐trial‐ready biomarkers for VCID. Cerebrovascular reactivity (CVR) MRI is one of the selected biomarkers that underwent multi‐site testing in the Consortium. The present study aimed to report the relationship between CVR and cognitive function at independent sites, based on a pre‐specified hypothesis.MethodCVR, the ability of cerebral small vessels to dilate upon stimulus, is thought to directly reflect physiological function of the brain microvasculature. Based on previous single‐site findings, the pre‐defined hypothesis was that CVR will be associated with the global cognitive function measured by Montreal Cognitive Assessment (MoCA) after adjusting for age, sex, and education, and this association will be observed in data collected and analyzed at each individual site.A total of 264 older participants from three sites were included (Table 1). Each site performed an identical CVR MRI procedure using 5% CO2 inhalation, and a standardized clinical and cognitive evaluation. Multi‐linear regression analysis was conducted on a site‐by‐site basis to examine the pre‐defined hypothesis.ResultGray‐matter CVR showed a positive association with MoCA score after adjustment for age, sex, and education, in which participants with higher CVR had higher MoCA scores. This relationship was reproduced at each site (Figure 1, p<0.05 for each), confirming our pre‐specified hypothesis.In the secondary analysis of all data together, higher gray‐matter CVR was found to be significantly associated with better executive function measure of item response theory (IRT) score (β=2.87, p=0.003).ConclusionThe present study evaluated the relationship between CVR and cognition in a multi‐center setting. CVR was found to be positively associated with global cognitive function measured by the MoCA, which was shown to be reproducible across different sites with diverse cohorts. These findings support the utility of CVR as a biomarker in future clinical trials of SVD and VCID.

AbstractBackgroundGenome‐wide association studies (GWAS) in Alzheimer’s disease (AD) leveraging endophenotypes beyond case/control diagnosis, such as brain amyloid β pathology, have shown promise in identifying novel variants and understanding their potential functional impact. In this study, we leverage two brain amyloid β pathology measurement modalities, PET imaging and neuropathology, to address sample size limitations and to discover novel genetic drivers of disease.MethodWe conducted a meta‐analysis on an amyloid PET imaging GWAS (N = 7,036, 35% amyloid positive, 53.67% female, age = 71) and an autopsy GWAS of brain amyloidosis (N = 6,519, 63.08% amyloid positive, 51.34% female, age at death = 83), both adjusted for covariates including age, sex and principal components of genetic ancestry. All participants in both GWAS were unrelated individuals of European descent. We defined amyloid positivity as moderate or frequent neuritic plaques using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) staging scores within each autopsy cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET imaging cohort to identify the cohort and tracer‐specific cut‐offs that differentiate amyloid positive and negative populations.ResultThe genome‐wide significant results from the meta‐analysis identified three known AD loci (APOE, CR1, and BIN1) and a novel chromosome 17 locus (rs35635959, intergenic, MAF = 0.27, OR = 1.18, p = 1.47 × 10‐8). The MAGMA gene‐level analysis excluding the APOE region suggests significant associations between rs35635959 and COASY, PLEKHH3, and TUBG2 on chromosome 17, implying the potential roles of these genes on amyloidosis. Functional annotations of rs35635959 leveraging brain eQTL databases indicate its association with differential gene expression of COASY and TUBG2 in AD brains. ROSMAP bulk and single‐nucleus RNAseq analyses link TUBG2 downregulation to the higher amyloid burden and AD diagnosis while suggesting such observations are enriched in excitatory and inhibitory neurons, reaching FDR significance.ConclusionThis study is the most extensive European GWAS of brain amyloidosis, and our findings replicate known AD loci and identify a possible novel locus (index SNP rs35635959) on chromosome 17. Functional annotations of the novel variant indicate TUBG2, implicating in microtubule organization, warrants further assessment. Ongoing efforts aim to validate these novel effects using independent datasets.

Abstract INTRODUCTION Multi‐omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP‐AD) expanded brain multi‐omics profiling to multi‐ethnic donors. RESULTS We generated multi‐omics data and curated and harmonized phenotypic data from BA ( n = 306), LA ( n = 326), or BA and LA ( n = 4) brain donors plus non‐Hispanic White ( n = 252) and other ( n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION The inclusion of traditionally underrepresented groups in multi‐omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD. Highlights Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi‐ethnic populations. Brain multi‐omics data is generated from Black American, Latin American, and non‐Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.