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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Synthetic method of analogues for emerging infectious disease forecasting

The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. Mapping the genetics of gene expression in human microglia has identified several loci associated with disease-associated genetic variants in microglia-specific regulatory elements. However, identifying genetic effects on splicing is challenging because of the use of short sequencing reads. Here, we present the isoform-centric microglia genomic atlas (isoMiGA), which leverages long-read RNA sequencing to identify 35,879 novel microglia isoforms. We show that these isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ancestry meta-analysis of 555 human microglia short-read RNA sequencing samples from 391 donors, and found associations with genetic risk loci in Alzheimer’s and Parkinson’s disease. We nominate several loci that may act through complex changes in isoform and splice-site usage.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗

DOE OSTI2025

Ticking off Lyme disease: OspA mRNA vaccine halts infection in mouse model

Lyme disease, a condition caused by Borrelia burgdorferi sensu lato and transmitted to humans via ticks, affects approximately 676,000 individuals annually in the United States and Western Europe.1 Currently, there is no approved Lyme vaccine available for human use. A promising study by Tahir et al., published in Molecular Therapy Nucleic Acids, investigated the efficacy of mRNA and subunit vaccines targeting Lyme disease.2 This research demonstrated complete protection from infection in a tick-fed mouse model using an outer surface protein A (OspA) mRNA vaccine. Although mRNA vaccines have shown success against viral pathogens, their clinical application against bacterial diseases has been limited.3 Thus, this study represents an important step toward developing an effective mRNA vaccine against Lyme disease.

He, Wei↗

DOE OSTI2025

scRNA seq of an F1 cross of Marek’s disease resistant and susceptible chickens identifies allele specific expression signatures enriched in transcription modulators

Abstract Marek’s disease (MD), a T cell lymphoma disease in chickens, is caused by the Marek’s disease virus (MDV) found ubiquitously in the poultry industry. Genetically resistant Line 6 3 (L6) and susceptible Line 7 2 (L7) chickens have been instrumental to research on avian immune system response to MDV infection. In this study we characterized molecular signatures unique to splenic immune cell types across different genetic backgrounds 6 days after infection. Using three populations, L6, L7, and an F1 cross between L6xL7, we evaluated the immune cell transcriptome of responding cell types using single cell RNA sequencing. Several MDV genes were found expressed mainly in cytotoxic T cells while ICP4 and MEQ MDV genes were expressed across infected cell types. Using the F1 we quantified allele specific expression (ASE) of biallelic SNPs and found biased expression of parental alleles specific to immune cell subtypes. We identified 22 SNPs with ASE in response to MDV infection mapped to gene rich regions surrounding 59 genes of critical importance for chromatin remodeling and transcriptional regulation. Histone deacetylase genes (HDAC1 and HDAC8) had increased expression of L6 alleles, while small nuclear RNA genes (SNORA68 and SNORA72) expressed higher levels of L7 alleles with infection in T cell subsets. SNPs with ASE also mapped genes important for an adequate immune response including GNLY (cytotoxic activity) and PDIA3 (component of MHC class I peptide loading complex), and genes known to promote viral replication (MCM5 and EIF3M). These results show that functional variants associated with susceptibility to MD may have a bigger impact in subsets of immune cell types, and by characterizing the transcriptomes of these subtypes we can unravel molecular signatures specific to MD genomic resistance.

Science & Technology - Other Topics↗

DOE OSTI2025

Plasma phosphorylated tau217 strongly associates with memory deficits in the Alzheimer’s disease spectrum

Abstract Plasma phosphorylated tau (p-tau) biomarkers open unprecedented opportunities for identifying carriers of Alzheimer’s disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment. To address this question, we included 103 cognitively unimpaired elderly and 40 cognitively impaired, amyloid-β-positive individuals from the TRIAD cohort, in addition to 336 cognitively unimpaired and 216 cognitively impaired, amyloid-β-positive older adults from the BioFINDER-2 cohort. Participants had tau PET scans, amyloid PET scans or amyloid CSF, p-tau217, p-tau181 and p-tau231 blood measures, structural T1-MRI and cognitive assessments. In this cross-sectional study, we used regression models and correlation analyses to assess the relationship between plasma biomarkers and cognitive scores. Furthermore, we applied receiver operating characteristic curves to assess cognitive impairment across plasma biomarkers. Finally, we categorized participants into amyloid (A), p-tau (T1) and tau PET (T2) positive (+) or negative (−) profiles and ran non-parametric comparisons to assess differences across cognitive domains. We found that plasma p-tau217 was more associated with cognitive performance than p-tau181 and p-tau231 and that this relationship was particularly strong for memory scores (TRIAD: βp-tau217 = −0.53, βp-tau181 = −0.35 and βp-tau231 = −0.24; BioFINDER-2: βp-tau217 = −0.52, βp-tau181 = −0.24 and βp-tau231 = −0.29). Associations in amyloid-β-positive participants resembled these results, but other cognitive scores also showed strong associations in cognitively impaired individuals. Moreover, plasma p-tau217 outperformed plasma p-tau181 and plasma p-tau231 in identifying memory impairment (area under the curve values for TRIAD: p-tau217 = 0.86, p-tau181 = 0.77 and p-tau231 = 0.75; and for BioFINDER-2: p-tau217 = 0.86, p-tau181 = 0.76 and p-tau231 = 0.81) and in identifying executive function impairment only in the BioFINDER-2 cohort (p-tau217 = 0.82, p-tau181 = 0.76 and p-tau231 = 0.76). Lastly, we showed that subtle memory deficits were present in A+T1+T2− participants for plasma p-tau217 (P = 0.007) and plasma p-tau181 (P = 0.01) in the TRIAD cohort and for all biomarkers across cognitive domains in A+T1+T2− and A+T1+T2− individuals (P < 0.001 in all) in the BioFINDER-2 cohort. The A+T1+T2− individuals showed cognitive deficits in both cohorts (P < 0.001 in all). Together, our results suggest that plasma p-tau217 stands out as a biomarker capable of identifying memory deficits attributable to Alzheimer’s disease and that memory impairment certainly occurs in amyloid-β- and plasma p-tau-positive individuals who have no significant amounts of tau in the neocortex.

Neurosciences & Neurology↗

DOE OSTI2024

EMC3 regulates trafficking and pulmonary toxicity of the SFTPC I73T mutation associated with interstitial lung disease

The most common mutation in surfactant protein C gene (SFTPC), SFTPC I73T , causes interstitial lung disease with few therapeutic options. We previously demonstrated that EMC3, an important component of the multiprotein endoplasmic reticulum membrane complex (EMC), is required for surfactant homeostasis in alveolar type 2 epithelial (AT2) cells at birth. In the present study, we investigated the role of EMC3 in the control of SFTPC I73T metabolism and its associated alveolar dysfunction. Using a knock-in mouse model phenocopying the I73T mutation, we demonstrated that conditional deletion of Emc3 in AT2 cells rescued alveolar remodeling/simplification defects in neonatal and adult mice. Proteomic analysis revealed that Emc3 depletion reversed the disruption of vesicle trafficking pathways and rescued the mitochondrial dysfunction associated with I73T mutation. Affinity mass spectrometry analysis identified potential EMC3 interacting proteins in lung AT2 cells, including Valosin Containing Protein (VCP) and its interactors. Treatment of Sftpc I73T knock-in mice and SFTPC I73T expressing iAT2 cells derived from SFTPC I73T patient-specific iPSCs with the specific VCP inhibitor CB5083 restored alveolar structure and SFTPC I73T trafficking respectively. Taken together, the present work identifies the EMC complex and VCP in the metabolism of the disease-associated SFTPC I73T mutant, providing novel therapeutical targets for SFTPC I73T -associated interstitial lung disease.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Calibration verification for stochastic agent-based disease spread models

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

The key role of the ferroptosis mechanism in neurological diseases and prospects for targeted therapy

Neurological disorders represent a major global health concern owing to their intricate pathological processes. Ferroptosis, defined as a form of cell death that is reliant on iron, has been closely linked to various neurological conditions. The fundamental process underlying ferroptosis is defined by the excessive buildup of iron ions, which initiates lipid peroxidation processes leading to cellular demise. Neurons, as highly metabolically active cells, are susceptible to oxidative stress, and imbalances in iron metabolism can directly initiate the ferroptosis process. In neurodegenerative disorders like Alzheimer’s disease and Parkinson’s disease, ferroptosis driven by iron accumulation represents a fundamental pathological connection. Although the connection between ferroptosis and neurological diseases is clear, clinical application still faces challenges, such as precise regulation of iron metabolism, development of specific drugs, and assessment of efficacy. The limited comprehension of the ferroptosis mechanism hinders the development of personalized treatment approaches. Consequently, subsequent investigations must tackle these obstacles to facilitate the clinical application of ferroptosis-associated therapies in neurological disorders. This article provides a comprehensive overview of the most recent advancements regarding the underlying mechanisms of ferroptosis. Subsequently, the study investigates the mechanistic contributions of ferroptosis within the nervous system. In conclusion, we evaluate and deliberate on targeted therapeutic strategies associated with ferroptosis and neurological disorders.

Xie, Chenyu↗

DOE OSTI2024

Methods for promoting myelination and for treating demyelinating diseases

This invention provides novel methods for treating or ameliorating symptoms of demyelinating diseases such as multiple sclerosis. The methods involve administering to subjects in need of treatment a pharmaceutical composition that contains a therapeutically effective amount of taurine and also a compound that induces oligodendrocyte precursor cell (OPC) differentiation (e.g., T3, benztropine, clemastine or miconazole). Some of the methods additionally involve administration to the subject a known agent for treating demyelinating diseases (e.g., SIP receptor agonists) or a known disease modifying drug. The invention also provides methods for increasing myelination and methods for promoting OPC differentiation into oligodendrocytes. These methods entail contacting a population of OPCs with a combination of taurine and a known OPC differentiation-inducing agent such as T3, clemastine, benztropine or miconazole.

Lairson, Luke↗

DOE OSTI2024

Vascular endothelial growth factor receptor-1 (FLT1) interactions with amyloid-beta in Alzheimer’s disease: A putative biomarker of amyloid-induced vascular damage

We have identified FLT1 as a protein that changes during Alzheimer's disease (AD) whereby higher brain protein levels are associated with more amyloid, more tau, and faster longitudinal cognitive decline. Given FLT1's role in angiogenesis and immune activation, we hypothesized that FLT1 is upregulated in response to amyloid pathology, driving a vascular-immune cascade resulting in neurodegeneration and cognitive decline. We sought to determine (1) if in vivo FLT1 levels (CSF and plasma) associate with biomarkers of AD neuropathology or differ between diagnostic staging in an aged cohort enriched for early disease, and (2) whether FLT1 expression interacts with amyloid on downstream outcomes, such as phosphorylated tau levels and cognitive performance. Additionally, we sought to replicate FLT1 interactions in the brain. The results showed that higher levels of FLT1 in CSF and post-mortem brain tissue related to increased tau, particularly among amyloid positive individuals. These analyses help clarify the potential utility of FLT1 as a biomarker among individuals with evidence of brain amyloidosis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Brain phosphoproteomic analysis identifies diabetes‐related substrates in Alzheimer's disease pathology in older adults

INTRODUCTION: Type 2 diabetes increases the risk of Alzheimer's disease (AD) dementia. Insulin signaling dysfunction exacerbates tau protein phosphorylation, a hallmark of AD pathology. However, the comprehensive impact of diabetes on patterns of AD-related phosphoprotein in the human brain remains underexplored. METHODS: We performed tandem mass tag–based phosphoproteome profiling in post mortem human brain prefrontal cortex samples from 191 deceased older adults with and without diabetes and pathologic AD. RESULTS: Among 7874 quantified phosphosites, microtubule-associated protein tau (MAPT) phosphorylated at T529 and T534 (isoform 8 T212 and T217) were more abundant in AD and showed differential associations with diabetes. Network analysis of co-abundance patterns uncovered synergistic interactions between AD and diabetes, with one module exhibiting higher MAPT phosphorylation (15 MAPT phosphosites) and another displaying lower MAP1B phosphorylation (22 MAP1B phosphosites). DISCUSSION: This study offers phosphoproteomics insights into AD in diabetes, shedding light on mechanisms that can inform the development of therapeutics for dementia. Highlights: The risk of Alzheimer's disease (AD) dementia is increased among older adults living with diabetes. The patterns of AD-related phosphoprotein in the human brain in older adults are differential among older adults living with diabetes. Microtubule-associated protein tau phosphorylated at T529 and T534 (isoform 8 T212 and T217) showed differential associations with diabetes. Phosphosite co-abundance networks of synergistic interactions between AD and diabetes were identified.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Biofluid-based staging of Alzheimer’s disease

Recently, conceptual systems for the in vivo staging of Alzheimer’s disease (AD) using fluid biomarkers have been suggested. Thus, it is important to assess whether available fluid biomarkers can successfully stage AD into clinically and biologically relevant categories. In the TRIAD cohort, we explored whether p-tau217, p-tau205 and NTA-tau (biomarkers of early, intermediate and late AD pathology, respectively) have potential for biofluid-based staging in cerebrospinal fluid (CSF; n = 219) and plasma (n = 150), and compared them in a paired CSF and plasma subset (n = 76). Our findings suggest a good concordance between biofluid staging and underlying pathology when classifying amyloid-positivity into three categories based on neurofibrillary pathology: minimal/non-existent (p-tau217 positive), early-to-intermediate (p-tau217 and p-tau205 positivity), and advanced tau tangle deposition (p-tau217, p-tau205 and NTA-tau positive), as indexed by tau-PET. Discordant cases accounted for 4.6% and 13.3% of all CSF and plasma measurements respectively (9.2% and 11.8% in paired samples). Notably, CSF- and plasma-based staging matched one another in 61.7% of the cases, while approximately 32% of the remaining participants were one to three biofluid stages higher in CSF as compared to plasma. Overall, these exploratory results suggest that biofluid staging of AD holds potential for offering valuable insights into underlying AD hallmarks and disease severity. However, its applicability beyond molecular characterization at research settings has yet to be demonstrated.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

How short peptides disassemble tau fibrils in Alzheimer’s disease

Reducing fibrous aggregates of the protein tau is a possible strategy for halting the progression of Alzheimer’s disease (AD). Previously, we found that in vitro, the d-enantiomeric peptide (D-peptide) D-TLKIVWC disassembles ultra-stable tau fibrils extracted from the autopsied brains of individuals with AD (hereafter, these tau fibrils are referred to as AD-tau) into benign segments, with no energy source other than ambient thermal agitation. To consider D-peptide-mediated disassembly as a potential route to therapeutics for AD, it is essential to understand the mechanism and energy source of the disassembly action. Here, in this work, we show that the assembly of D-peptides into amyloid-like (‘mock-amyloid’) fibrils is essential for AD-tau disassembly. These mock-amyloid fibrils have a right-handed twist but are constrained to adopt a left-handed twist when templated in complex with AD-tau. The release of strain that accompanies the conversion of left-twisted to right-twisted, relaxed mock-amyloid produces a torque that is sufficient to break the local hydrogen bonding between tau molecules, and leads to the fragmentation of AD-tau. This strain-relief mechanism seems to operate in other examples of amyloid fibril disassembly, and could inform the development of first-in-class therapeutics for amyloid diseases.

Alzheimer's disease↗

DOE OSTI2025

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗

DOE OSTI2025

Ethical considerations in infectious disease modelling for public health policy: the case of school closures

Mathematical models of infectious diseases are frequently used as a tool to support public health policy and decisions around the implementation of interventions such as school closures. However, most publications on policy-relevant modelling lack an ethical framework and do not explicitly consider the ethical implications of the work. This creates a risk that the unintended consequences of interventions are overlooked or that models are used to justify decisions that are inconsistent with public health ethics. In this article, we focus on the case study of school closures as a commonly modelled intervention against pandemic influenza, COVID-19 and other infectious disease threats. We briefly review some of the key concepts in public health ethics and describe approaches to modelling the effects of school closures. We then identify a series of ethical considerations involved in modelling school closures. These include accounting for population heterogeneity and inequalities; including a diversity of viewpoints and expertise in model design; considering the distribution of benefits and harms; and model transparency and contextualization. Furthermore, we conclude with some recommendations to ensure that policy-relevant modelling is consistent with some key ethics values.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗