Algeciras‐Schimnich, Alicia

AbstractINTRODUCTIONWe compare the stability of phosphorylated tau (p‐tau)217, amyloid beta (Aβ)42, Aβ40, Aβ42/40, and p‐tau217/Aβ42 at different storage temperatures.METHODSTen ethylenediaminetetraacetic acid–plasma sample aliquots stored at frozen, refrigerated, and ambient temperatures were tested at various timepoints up to 30 days. The mean percent change from baseline was calculated.RESULTSAβ42 and Aβ40 concentrations decreased by >15% after 6 hours at ambient temperature and after 24 hours refrigerated, while p‐tau217 remained stable over 72 hours with < 10% deviation from baseline at either storage temperature. The Aβ42/40 ratio remained relatively constant as each analyte concentration decreased concurrently, while the p‐tau217/Aβ42 ratio deviated from baseline over time. All biomarkers were stable up to 30 days frozen.DISCUSSIONDifferences in the stability of Aβ42 and p‐tau217 may lead to altered p‐tau217/Aβ42 ratio results if samples are not handled properly during the pre‐analytical testing phase. Ideally, samples should be frozen promptly and sent to the laboratory frozen.Highlights Plasma phosphorylated tau (p‐tau)217, amyloid beta (Aβ)42, Aβ40, Aβ42/40, and p‐tau217/Aβ42 stability was assessed. P‐tau217 was stable, but Aβ42 and Aβ40 started decreasing by 6 hours at ambient temperature. The analyte stability differences led to falsely increased p‐tau217/Aβ42 ratios. Increases > 15% were seen in p‐tau217/Aβ42 after 6 hours at ambient temperature or 24 hours at 4°C.

Abstract INTRODUCTION Limited data exist on the utility of plasma biomarkers to predict incident abnormal amyloid positron emission tomography (PET). In this study we evaluate the association of plasma Alzheimer's disease (AD) biomarkers with amyloid PET progression among initially amyloid PET negative (A−) individuals. METHODS We included 290 A−, cognitively unimpaired Mayo Clinic Study of Aging participants. We estimated the association of each baseline plasma biomarker with progression from A− to A+ and with rate of amyloid PET change. RESULTS Interquartile range differences in amyloid beta 42/40, percent phosphorylated tau 217 (%p‐tau217), and Amyloid Probability Score 2 were associated with 1.29 ( P = 0.09), 1.38 ( P < 0.001), and 1.20 ( P = 0.05) increases, respectively, in the hazard of progression from A− to A+ and 0.27 ( P = 0.16), 0.50 ( P = 0.007), and 0.28 ( P = 0.15) Centiloid/year increases, respectively, in annual rate of amyloid PET change. DISCUSSION Plasma %p‐tau217 may be a useful screening tool to enrich for participants with increased likelihood of progressing from normal to abnormal amyloid PET in a primary prevention trial. Highlights Plasma phosphorylated tau 217 was associated with amyloid positron emission tomography progression, negative to positive. The associations were weaker for amyloid beta 42/40 and Amyloid Probability Score 2. Age and apolipoprotein E ε4 carriership were also important predictors. These markers may be useful for enrichment of a primary prevention trial.

AbstractBackgroundEvidence for abnormal amyloid‐β (Aβ) plaque accumulation is necessary prior to initiating anti‐amyloid therapy in early symptomatic Alzheimer’s disease (AD). While the clinical trials for lecanemab and related drugs utilized positron emission tomography (PET) to demonstrate brain amyloidosis, current appropriate use recommendations for clinical practice consider PET or cerebrospinal fluid (CSF) biomarkers as satisfactory for this purpose. Here, we present four clinical cases where CSF biomarker results were discordant from amyloid PET, with the potential to result in erroneous treatment targeting.MethodPatients were seen at a tertiary care subspecialty clinic in consideration for lecanemab therapy. All patients had office examination, neuropsychological assessment, structural magnetic resonance imaging (MRI), amyloid PET (with either 11C‐Pittsburgh compound B or 18F‐florbetapir), CSF AD biomarkers (Roche Elecsys p‐tau181/Aβ42 and Fujirebio Lumipulse Aβ42/Aβ40 ratios), and apolipoprotein E (APOE) allele testing. Amyloid PET interpretations were made by consensus visual review from experienced readers.ResultAll four patients had clinical diagnoses of mild cognitive impairment or mild dementia (Table 1). Two patients had positive CSF p‐tau181/Aβ42 and Aβ42/40 ratios with negative amyloid PET (indicating sparse to no neuritic plaques). Case 1 displayed aphasia (characterized by anomia and deep dysgraphia) and imaging findings possibly compatible with semantic dementia. Case 2 displayed a gait abnormality and imaging signs of abnormal CSF dynamics. Two other patients had negative CSF p‐tau181/Aβ42 measures with positive amyloid PET (indicating moderate to frequent neuritic plaques). In those instances, the clinical syndrome and imaging studies were strongly supportive of AD and the CSF Aβ42/40 ratio was “likely positive” (Table 1). For one patient (Case 4), tau PET and Lumipulse plasma p‐tau217 concentration were both positive.ConclusionClinicians prescribing anti‐amyloid therapies should be aware of the possibility for CSF biomarkers to be discordant from amyloid PET status in select scenarios. These cases highlight the value of considering PET imaging in the presence of clinically atypical features, concern for mixed etiology, or strong clinical suspicion for AD despite negative CSF biomarkers. Larger series are needed to identify broader patterns contributing to biomarker discordance by assay, modality (PET versus CSF/plasma measures), and medical/neurologic comorbidities.

AbstractBackgroundThe NIA Health Disparities Research (NIA‐HD) Framework organizes factors in four domains (i.e., environmental, sociocultural, behavioral, and biological), which work together over the lifetime to influence health and health disparities.MethodIn previous work, we employed a 2‐stage approach using factor and principal component (PC) analyses to identify patterns of factors in the domains of the NIA‐HD Framework. We found three PCs that identified related groupings from more than one NIA‐HD domain. PC1: retirement status, residence type, multivitamin use, heart disease, Charlson comorbidity index, walking/balance difficulties, and Beck Depression Inventory (BDI)‐II questions – mainly from the somatic dimension; PC2: physical activity/exercise, social and group activities, education, occupation, cardiometabolic risk factors, and self‐rated health; PC3: national and state area deprivation index ranking and BDI‐II questions – mainly in the cognitive dimension. Age and sex‐adjusted linear regression models were used to assess the cross‐sectional association of each PC with AD‐plasma biomarkers measured using the Quanterix Simoa platform in the Mayo Clinic Study of Aging (MCSA) participants. AD‐plasma biomarkers included Aβ 42/40, phosphorylated‐tau 181 (p‐tau181), plasma glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). All analyses were considered statistically significant at a p <0.05.ResultThere were 1300 cognitively unimpaired participants, 50 years old or older, with available plasma biomarkers (mean age (SD) 68.6 (9.5) years; 49.1% were female) at the current study baseline. PC1 and PC3 were associated with higher NfL and p‐tau 181 values (p<0.05). PC1 and PC2 were associated with higher GFAP values (p<0.05). No PCs were associated with the Aβ 42/40 ratio. Findings were mostly similar after additionally adjusting for global cognitive performance and chronic kidney disease.ConclusionWe found that patterns in a wide range of environmental, sociocultural, behavioral, and biological factors were associated with AD plasma biomarkers. Longitudinal studies are warranted to validate findings and support multi‐faceted approaches to address health disparities and achieve disease delay and prevention.

AbstractBackgroundImplementation of Alzheimer disease blood‐based biomarkers (AD BBBs) in the clinical laboratory requires careful evaluation of test performance and selection of interpretative cut‐points to prevent patient misclassification that may lead to further evaluation, misdiagnosis, and/or potentially unnecessary treatment.MethodParticipants with mild cognitive impairment or mild dementia (n = 427) and with an EDTA‐plasma sample available within 6 months of amyloid‐PET imaging were selected through the Mayo Clinic Study of Aging and the Mayo Clinic Alzheimer’s Disease Research Center in Rochester, Minnesota. Amyloid‐PET was performed with 11C‐Pittsburg Compound B; abnormal amyloid (A+) was defined as Centiloid ≥25 (SUVR≥1.52) (n = 274 A+, 153 A−). EDTA‐plasma samples were measured using the ALZpath Simoa pTau217 assay and the Fujirebio Lumipulse pTau217, Aβ42/Aβ40 and pTau217/Aβ42 ratio assays. The clinical performance of a 1‐cutpoint as well as a 2‐cutpoint model that incorporates an intermediate range were assessed. For the 2‐cutpoint model, various combinations of sensitivity and specificity were examined to identify thresholds that maximize overall test performance.ResultWhile both pTau217 assays displayed high ROC AUC (>0.90), neither assay achieved ≥90% in both sensitivity and specificity using a 1‐cutpoint model. Using a 2‐cutpoint model for the Lumipulse pTau217 assay, the combination of 92% sensitivity and 96% specificity to define lower and upper cutpoints provided the optimal balance of false positives and false negatives, while keeping 20% or fewer results categorized as intermediate. For the ALZpath assay, 92% sensitivity and 96% specificity could only be achieved with 39% of the patients in this cohort falling into the intermediate range. Using this approach, the Lumipulse pTau217 assay would reduce more costly CSF or PET testing by 80%.ConclusionCutpoints for the identification of amyloid pathology were defined in a cohort of individuals with MCI and mild dementia. Information obtained on the performance of these defined cut‐points was used to select the most appropriate assay and cut‐points to implement in clinical practice.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total β‐amyloid Aβ42 relative to Aβ40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

AbstractBackgroundThere is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease. However, the association between EKG‐based age and underlying neuropathology has not been studied.MethodWe used a cohort of 336 participants from the Mayo Clinic Study of Aging, a population‐based prospective study of residents in Olmsted County, Minnesota, with antemortem EKG and post‐mortem brain autopsy (Table 1 & 2). Age was predicted from EKG using a validated convolutional neural network algorithm. We used ordinal logistic regression to identify associations between lower neuropathologic scores of AD (Thal phase, neuritic plaque score, Braak stage, and overall AD neuropathologic change) and the Age‐Gap, defined as EKG‐predicted age minus chronological age. Results were controlled for true age, sex, and APOE e4 carrier status. Neuropathologic scales were grouped into none, low, intermediate, and high categories. Observations were weighted by the inverse of years between last EKG and time of death to account for the time difference between acquisition of EKG and autopsy.ResultWe found that an increasing Age‐Gap (i.e. older predicted age) was inversely associated with lower overall AD neuropathologic change (OR 0.93 [0.89, 0.97], p = 0.002). Investigation of the components of AD neuropathologic change showed that an increasing Age‐Gap was also inversely associated with lower amyloid metrics such as Thal phase (OR 0.85 [0.80, 0.89], p < 0.001) and neuritic plaque scores (OR 0.91 [0.87, 0.96], p < 0.001), but not tau metrics like Braak staging (OR 1.05 [1.00, 1.10], p = 0.05).ConclusionIn this work, we show that older predicted age on EKG, a noninvasive biomarker associated with vascular and biological aging, is associated with higher amyloid burden and neuropathologic change of AD.

AbstractBackgroundNeurofilament Light Chain (NfL) is a blood biomarker of axonal injury and neurodegeneration that can be used in a variety of neurological disorders. Despite the potential clinical use of plasma NfL across multiple neurological disorders, there is increasing evidence that underlying comorbidities such as renal impairment associated with chronic kidney disease (CKD) and cardiovascular diseases can increase NfL concentrations. The objective of this study was to determine the relationship between plasma NfL concentrations and renal function (CKD staging) in individuals without known neurological conditions.MethodsIndividuals (n=62) seen in an outpatient setting at Mayo Clinic who had various stages of renal impairment (stages 1‐5 by CKD‐EPI 2021 estimated glomerular filtration rate (eGFR) equation) and did not have co‐existing conditions that are known to elevate plasma NfL were retrospectively identified through electronic medical record review. EDTA plasma from these patients collected within +/‐ 30 days of the eGFR determination was identified and stored frozen (‐80°C) until the time of testing. Samples were tested using the Simoa® NF‐light™ V2 Advantage Kit (Quanterix). NfL concentrations in pg/mL were used to calculate age‐adjusted Z‐scores (https://shiny.dkfbasel.ch/baselnflreference/) using an online calculator. Correlation analysis was performed to assess associations of continuous eGFR (mL/min/ 1.73 m2) with plasma NfL age‐adjusted Z‐scores.ResultsA strong negative correlation between NfL Z‐score and eGFR was observed (Spearman’s ρ= ‐0.806). Mean NfL Z‐scores (ranges) in each CKD stage were ‐0.4(‐2.3‐1.4) for stage 1 (N=9), 0.9(‐0.8‐1.9) for stage 2 (N=10), 1.9(‐1.2‐3.4) for stage 3 (N=29), 2.9(2.7‐3.5) for stage 4 (N=6), and 3.2(1.6‐3.7) for stage 5 (N=8); with all groups showing significant differences (P>0.05) except between groups 4 and 5 (P=0.09).ConclusionsPlasma NfL concentrations were increased in individuals with a decreased eGFR. Kidney impairment is an important potential confounding factor to consider when utilizing NfL as a biomarker of neurodegeneration in the clinical setting.