Biology subjects

Schindler, S. E.

Publications and source records attributed to Schindler, S. E..

5 recordsLinked to original sources

bioRxiv2026

Biomarker Variability Limits Individualized Amyloid Time Estimation in Alzheimer Disease

ObjectiveDisease progression modeling (DPM) or "amyloid time" is increasingly used to stage Alzheimer disease (AD). DPM performance depends on within-individual heterogeneity in rates of pathological accumulation as well as test-retest reliability of the biomarker. The relative contributions of these variabilities have not been systematically assessed. This would be particularly relevant if extrapolations from DPM were to be used to make individual-level predictions for research, clinical trials, or potentially future clinical practice. MethodsWe conducted simulation studies incorporating empirically-derived noise properties from amyloid biomarkers to assess the contributions of inter- and intra-individual variability. Findings generalized in an autosomal dominant AD cohort with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and plasma biomarkers and in a sporadic AD cohort with both amyloid PET and plasma biomarkers. We assessed group level DPM performance via mean average error (MAE) and root mean squared error (RMSE). At the individual level, we evaluated distinctness of distributions of biomarker levels associated with specific disease timings. ResultsInter-individual variability was the dominant source of error in temporal estimates. Intra-individual variability reduced estimate stability. Optimal performance occurred in biomarkers with positive average accumulation rates where a subset of individuals had exceptionally high levels of accumulation. In research study data, amyloid PET outperformed CSF and plasma biomarkers. InterpretationDPM is fundamentally constrained by dynamic range, variability, and test-retest reliability of the biomarker of interest. Current DPM approaches are more robust at the group level, particularly when applied to biomarkers with more than 10-15% variability like fluid biomarkers. FundingNational Institute on Aging, Alzheimers Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korean Ministry of Health & Welfare and Ministry of Science and ICT, Spanish Institute of Health.

bioinformatics↗

bioRxiv2026

History of Traumatic Brain Injury with Loss of Consciousness and APOE ϵ4 Carriers Synergistically Increase Late-Life Amyloid PET Burden

BackgroundTraumatic brain injury with loss of consciousness (TBI-LOC) is an established risk factor for dementia, yet the pathways linking remote TBI to Alzheimers disease (AD) biology remain incompletely defined. APOE {varepsilon}4 is the strongest genetic predictor of amyloid accumulation in late-onset AD, it may moderate the long-term consequences of head injury. This study investigates whether history TBI-LOC independently contributes or synergistically interacts with APOE {varepsilon}4 to amplify late-life amyloid and tau burden. Methods429 participants completed the Ohio State University TBI screening tool and an amyloid PET scan (centiloids). A subcohort (n=352) also underwent tau PET. TBI history was classified by recency (<10 vs >10 years) and severity (no TBI, dazing/confusion [TBI-DZ], TBI-LOC). Analyses were stratified by degree of clinical impairment as assessed by Clinical Dementia Rating (CDR=0 vs CDR>0). Logistic and linear regression models examined associations between TBI and amyloid, adjusting for age, sex, education, and APOE {varepsilon}4, including an APOE*TBI-LOC status interaction term, while Fishers exact tests evaluated TBI recency and biomarker positivity. ResultsIn CDR=0 participants (n=365), 119 reported a history of TBI, comprising 56 TBI-DZ and 63 TBI-LOC. TBI-LOC but not TBI-DZ, correlated with elevated amyloid PET levels (p<0.001; [4.6-17]). Furthermore, an interaction between APOE {varepsilon}4 and TBI-LOC indicated that TBI-LOC augmented the amyloid-related risk associated with the APOE {varepsilon}4 allele (p=0.003; [4.3-21]). The interaction persisted when stratified by TBI recency with only remote TBI-LOC (occurring more than 10 years prior) associated with increased amyloid PET (p=0.003 [5.2-25]). No association between TBI and tau was identified in a subset with tau PET, and no TBI-amyloid correlations were observed among symptomatic participants (CDR>0; n=64) suggesting a ceiling effect of pathology once clinical dementia is present. ConclusionsHistory of remote TBI-LOC is linked to elevated amyloid PET levels in later life, particularly among APOE {varepsilon}4 carriers with a CDR=0. The robust findings for amyloid, contrasted with null tau results and the reduced association in symptomatic cases underscore the importance of considering TBI history when screening for preclinical AD and assessing early-stage risk.

neuroscience↗

bioRxiv2024

Revealing heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementias underlying heterogeneity, compared to analyzing the entire cohort as a single homogeneous group. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.

neuroscience↗

bioRxiv2023

Apolipoprotein E O-glycosylation is associated with amyloid plaques and APOE genotype.

Although the APOE {varepsilon}4 allele is the strongest genetic risk factor for sporadic Alzheimers disease (AD), the relationship between apolipoprotein (apoE) and AD pathophysiology is not yet fully understood. Relatively little is known about the apoE protein species, including post-translational modifications, that exist in the human periphery and CNS. To better understand these apoE species, we developed a LC-MS/MS assay that simultaneously quantifies both unmodified and O-glycosylated apoE peptides. The study cohort included 47 older individuals (age 75.6 {+/-} 5.7 years [mean {+/-} standard deviation]), including 23 individuals (49%) with cognitive impairment. Paired plasma and cerebrospinal fluid samples underwent analysis. We quantified O-glycosylation of two apoE protein residues - one in the hinge region and one in the C-terminal region - and found that glycosylation occupancy of the hinge region in the plasma was significantly correlated with plasma total apoE levels, APOE genotype and amyloid status as determined by CSF A{beta}42/A{beta}40. A model with plasma glycosylation occupancy, plasma total apoE concentration, and APOE genotype distinguished amyloid status with an AUROC of 0.89. These results suggest that plasma apoE glycosylation levels could be a marker of brain amyloidosis, and that apoE glycosylation may play a role in the pathophysiology of AD. HighlightsO_LISimultaneous quantification of unmodified and O-glycosylated apoE via LC-MS/MS. C_LIO_LITotal plasma apoE varies by APOE genotype in an isoform, dose-dependent fashion. C_LIO_LICNS-derived apoE is more extensively O-glycosylated than peripherally derived apoE. C_LIO_LIHinge region glycosylation occupancy of plasma apoE is associated with amyloidosis. C_LI

biochemistry↗

bioRxiv2022

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

BackgroundEstimates of "brain-predicted age" quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD), but has not been well explored in preclinical AD. Prior studies have typically modeled BAG with structural magnetic resonance imaging (MRI), but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. MethodsWe trained three models to predict age from FC, volumetric (Vol), or multimodal MRI (Vol+FC) in 390 control participants (18-89 years old). In independent samples of 144 older adult controls, 154 preclinical AD participants, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid, tau, and neurodegeneration, as well as a global cognitive composite. ResultsAll models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG and Vol+FC-BAG were marginally reduced in preclinical AD participants compared to controls. In CI participants only, elevated Vol-BAG and Vol+FC-BAG were associated with more advanced AD pathology and lower cognitive performance. ConclusionsBoth FC-BAG and Vol-BAG are elevated in CI participants. However, FC and volumetric MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to preclinical AD pathology, while Vol-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model captures these modality-specific patterns, and further, improves sensitivity to healthy age differences. FundingThis work was supported by the National Institutes of Health (P01-AG026276, P01-AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimers Association (SG-20-690363-DIAN).

neuroscience↗