Background The development of blood-based biomarker tests that are accurate and robust for Alzheimer’s disease (AD) pathology have the potential to aid clinical diagnosis and facilitate enrollment in AD drug trials. We developed a high-resolution mass spectrometry (MS)-based test that quantifies plasma Aβ42 and Aβ40 concentrations and identifies the ApoE proteotype. We evaluated robustness, clinical performance, and commercial viability of this MS biomarker assay for distinguishing brain amyloid status. Methods We used the novel MS assay to analyze 414 plasma samples that were collected, processed, and stored using site-specific protocols, from six independent US cohorts. We used receiver operating characteristic curve (ROC) analyses to assess assay performance and accuracy for predicting amyloid status (positive, negative, and standard uptake value ratio; SUVR). After plasma analysis, sites shared brain amyloid status, defined using diverse, site-specific methods and cutoff values; amyloid PET imaging using various tracers or CSF Aβ42/40 ratio. Results Plasma Aβ42/40 ratio was significantly (p < 0.001) lower in the amyloid positive vs. negative participants in each cohort. The area under the ROC curve (AUC-ROC) was 0.81 (95% CI = 0.77–0.85) and the percent agreement between plasma Aβ42/40 and amyloid positivity was 75% at the optimal (Youden index) cutoff value. The AUC-ROC (0.86; 95% CI = 0.82–0.90) and accuracy (81%) for the plasma Aβ42/40 ratio improved after controlling for cohort heterogeneity. The AUC-ROC (0.90; 95% CI = 0.87–0.93) and accuracy (86%) improved further when Aβ42/40, ApoE4 copy number and participant age were included in the model. Conclusions This mass spectrometry-based plasma biomarker test: has strong diagnostic performance; can accurately distinguish brain amyloid positive from amyloid negative individuals; may aid in the diagnostic evaluation process for Alzheimer’s disease; and may enhance the efficiency of enrolling participants into Alzheimer’s disease drug trials.
IMPORTANCEThe diagnostic evaluation for Alzheimer disease may be improved by a blood-based diagnostic test identifying presence of brain amyloid plaque pathology. OBJECTIVE To determine the clinical performance associated with a diagnostic algorithm incorporating plasma amyloid-β (Aβ) 42:40 ratio, patient age, and apoE proteotype to identify brain amyloid status. DESIGN, SETTING, AND PARTICIPANTS This cohort study includes analysis from 2 independent cross-sectional cohort studies: the discovery cohort of the Plasma Test for Amyloidosis Risk Screening (PARIS) study, a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, including 249 patients from 2018 to 2019, and MissionAD, a dataset of 437 biobanked patient samples obtained at screenings during 2016 to 2019. Data were analyzed from May to November 2020. EXPOSURES Amyloid detected in blood and by positron emission tomography (PET) imaging. MAIN OUTCOMES AND MEASURESThe main outcome was the diagnostic performance of plasma Aβ42:40 ratio, together with apoE proteotype and age, for identifying amyloid PET status, assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTSAll 686 participants (mean [SD] age 73.2 [6.3] years; 368 [53.6%] men; 378 participants [55.1%] with amyloid PET findings) had symptoms of mild cognitive impairment or mild dementia. The AUC of plasma Aβ42:40 ratio for PARIS was 0.79 (95% CI, 0.73-0.85) and 0.86 (95% CI, 0.82-0.89) for MissionAD. Ratio cutoffs for Aβ42:40 based on the Youden index were similar between cohorts (PARIS: 0.089; MissionAD: 0.092). A logistic regression model (LRM) incorporating Aβ42:40 ratio, apoE proteotype, and age improved diagnostic performance within each cohort (PARIS: AUC, 0.86 [95% CI, 0.81-0.91]; MissionAD: AUC, 0.89 [95% CI, 0.86-0.92]), and overall accuracy was 78% (95% CI, 72%-83%) for PARIS and 83% (95% CI, 79%-86%) for MissionAD. The model developed on the prospectively collected samples from PARIS performed well on the MissionAD samples (AUC, 0.88 [95% CI, 0.84-0.91]; accuracy, 78% [95% CI, 74%-82%]). Training the LRM on combined cohorts yielded an AUC of 0.88 (95% CI, 0.85-0.91) and accuracy of 81% (95% CI, 78%-84%). The output of this LRM is the Amyloid Probability Score (APS). For clinical use, 2 APS cutoff values were established yielding 3 categories, with low, intermediate, and high likelihood of brain amyloid plaque pathology. (continued) Key Points Question Is an amyloid probability score based on a mass spectrometrybased blood test measuring plasma amyloid β 42:40 ratio and apoE proteotype plus age, associated with identifying brain amyloidosis among patients with cognitive impairment? Findings In this cohort study of 686 participants from 2 separate studies, the developed Amyloid Probability Score showed high concordance with amyloid PET status, with an area under the curve of 0.88 and overall accuracy of 81%. The test's findings were significantly associated with the presence or absence of brain amyloidosi...
Sacubitril/valsartan (LCZ696) is the first angiotensin receptor neprilysin inhibitor approved to reduce cardiovascular mortality and hospitalization in patients with heart failure with reduced ejection fraction. As neprilysin (NEP) is one of several enzymes known to degrade amyloid-β (Aβ), there is a theoretical risk of Aβ accumulation following long-term NEP inhibition. The primary objective of this study was to evaluate the potential effects of sacubitril/valsartan on central nervous system clearance of Aβ isoforms in cynomolgus monkeys using the sensitive Stable Isotope Labeling Kinetics (SILK™)-Aβ methodology. The in vitro selectivity of valsartan, sacubitril, and its active metabolite sacubitrilat was established; sacubitrilat did not inhibit other human Aβ-degrading metalloproteases. In a 2-week study, sacubitril/valsartan (50mg/kg/day) or vehicle was orally administered to female cynomolgus monkeys in conjunction with SILK™-Aβ. Despite low cerebrospinal fluid (CSF) and brain penetration, CSF exposure to sacubitril was sufficient to inhibit NEP and resulted in an increase in the elimination half-life of Aβ1-42 (65.3%; p=0.026), Aβ1-40 (35.2%; p=0.04) and Aβtotal (29.8%; p=0.04) acutely; this returned to normal as expected with repeated dosing for 15days. CSF concentrations of newly generated Aβ (AUC) indicated elevations in the more aggregable form Aβ1-42 on day 1 (20.4%; p=0.039) and day 15 (34.7%; p=0.0003) and in shorter forms Aβ1-40 (23.4%; p=0.009), Aβ1-38 (64.1%; p=0.0001) and Aβtotal (50.45%; p=0.00002) on day 15. However, there were no elevations in any Aβ isoforms in the brains of these monkeys on day 16. In a second study cynomolgus monkeys were administered sacubitril/valsartan (300mg/kg) or vehicle control for 39weeks; no microscopic brain changes or Aβ deposition, as assessed by immunohistochemical staining, were present. Further clinical studies are planned to address the relevance of these findings.
BackgroundAccumulation of amyloid beta plaques and tau tangles in the brain are sequential pathological hallmarks of Alzheimer’s disease. Specific measurement of tau phosphorylation sites in plasma samples can be used to investigate the presence of both amyloid plaque and tau tangle brain pathology.MethodA tandem mass spectrometry assay that quantifies phosphorylation of tau at threonine residues 181 and 217 in plasma was developed. Both phosphorylated (phospho) and non‐phosphorylated (non‐phospho) peptides containing tau residues 181 and 217 as well as the ratio of phospho to non‐phospho at each of these phosphorylation sites can be calculated from a blood sample. We tested the concordance between these measures and brain amyloid burden in PARIS study participants, an IDEAS sub‐study in patients with mild cognitive impairment or dementia. K2EDTA plasma samples from PARIS participants (n=221) were analyzed for tau phosphorylation and Aβ42/40.ResultsThe amount of phosphorylation at both 181 and 217 sited were significantly higher in patients who were amyloid PET positive, while PET negative patients showed very low amounts of phosphorylation at 217 (1.2 pg/mL vs. 4.9 pg/mL; p <2e‐16). The ratio of phospho‐tau to non‐phospho‐tau 217 showed high correlation (Spearman’s rank correlation r=0.78; p <2e‐16) with quantitative amyloid PET, demonstrating that phosphorylation of tau on threonine‐217 can quantitatively identify the amount of amyloid burden. This ratio of phospho‐tau to non‐phospho‐tau also correlated better with amyloid status than the concentration of phospho‐tau alone, with area under the curve (AUC) for detecting brain amyloid burden improving from 0.74 (95%CI 0.67‐0.82) to 0.81 (95%CI 0.75‐0.88) for 181 and from 0.92 (95%CI 0.88‐0.96) to 0.95 (95%CI 0.92‐0.98) for 217. Combining the phospho‐tau‐217 with the Aβ42/40 ratios for the same sample further increased the AUC to 0.96 (95%CI 0.93‐0.99).ConclusionThis novel mass spectrometry‐based quantitative assay for plasma phospho‐tau and non‐phospho‐tau species has excellent analytical and clinical validity performance characteristics. Combining the ratio of tau phosphorylation at 217 with plasma Aβ42/40 can help identify brain amyloid pathology with a performance that rivals that of CSF or PET tests.
BackgroundIn an emerging biomarker era, scientists, clinicians and patients must trust that Alzheimer’s disease (AD) biomarkers are broadly applicable across populations. However, biomarker studies inadequately include minoritized populations – largely because biomarker collection is invasive and centralized, and outreach wholly insufficient. The AA‐FAIM study collected plasma and cognitive data from a Black cohort by leveraging existing resources from the Wisconsin Alzheimer’s Disease Research Center and Wisconsin Registry for Alzheimer’s Prevention. We compared two easily‐decentralized and low‐burden options for measuring preclinical changes–either alone or combined, testing associations of baseline plasma Aβ42/40 and intra‐individual cognitive variability (IICV) with longitudinal cognition.MethodsUsing AA‐FAIM participants’ cognitive data, we derived an estimate of baseline IICV from z‐scores, representing standard deviation across five indices from list learning, executive function, and confrontational naming tests. PrecivityADTM assays quantified baseline plasma Aβ42/40. After comparing baseline IICV associations for factor‐validity (Figure 1), we conducted linear mixed‐effects models examining associations of baseline plasma Aβ42/40, IICV, or the combination of markers with longitudinal cognition on eight outcomes. Fully adjusted models included age‐at‐assessment, gender, education, and random intercepts. Benjamini‐Hochberg (BH) corrections controlling false discovery rate at 5% were applied across outcomes. Using Bayesian Information Criterion (BIC), plasma Aβ42/40 models were assessed for comparative fits with and without IICV interactions and main effects.ResultsModels included between 163‐667 observations from 56‐174 participants (Table 1). In models without plasma, IICV*age was weakly significant only for Trails tests with higher baseline IICV being associated with worse trajectories (Table 2; uncorrected/corrected p‐values=0.057/0.227 (log Trails A) and 0.044/0.227 (log Trails B)). Plasma Aβ42/40 was significantly associated with trajectories across multiple cognitive outcomes regardless of IICV inclusion (Table 3). Model estimates suggested lower baseline Aβ42/40 was associated with worse performance over time (Figures 2‐3). All interactions survived correction with IICV in the model. Without IICV, only Trails B survived correction. BIC comparisons suggested removing the IICV*age interaction from plasma models, but retaining IICV main effects.ConclusionsBaseline plasma Aβ42/40 predicted cognitive trajectory in a Black cohort. Although IICV appeared less sensitive than plasma Aβ42/40 in moderating cognitive trajectory, models including both IICV and plasma Aβ42/40 may be better in predicting trajectories.
BackgroundAlzheimer’s disease (AD) models of disease progression have been built largely on non‐Hispanic White samples. Availability of AD blood‐based biomarkers may improve screening for AD risk and facilitate greater diversity in research studies; little is known about these biomarkers within African Americans (AA). Here, we characterized associations between plasma Ab42/40 and Positron Emission Tomography (PET) amyloid measures in an AA sample.MethodParticipants in the African Americans Fighting Alzheimer’s in Mid‐Life (AA‐FAIM) with >=1 PET scan and plasma Ab42/40 assay were included (n=34 participants; n=117 EDTA plasma samples). The ratio Ab42/40 was calculated from plasma Ab40 and Ab42 quantified by C2N Diagnostics (PrecivityAD™). PET 11C‐Pittsburgh compound B (PIB)‐derived amyloid measures included a mean cortical DVR (global PiB DVR), PET_A+ (DVR>1.19) and estimated PET_A+ duration at each plasma sample. In separate models where “time” was age or PET_A+ duration at each plasma sample (outcome=plasma Ab42/40), we used mixed effects models to estimate simple slopes from time*PET_A+ status interactions. Sensitivity, specificity and related statistics were calculated for two plasma_A+ cutoffs (0.0975, West et al., 2021; 0.089, Hu et al., 2022).ResultBaseline plasma sample and baseline PET scan average(sd) ages were 61(8.6) and 65(9), respectively. Closest plasma Ab42/40 explained 47% of variability in last global PiB DVR (mean(sd)=‐0.85(0.91) years plasma‐to‐PET). Seven(20.6%) participants were PET_A+. In longitudinal analyses, simple age‐slopes for plasma Ab42/40 change were ‐0.00061(0.00024) for PET_A+ and ‐0.00057(0.00018) for PET_A‐ (age*PET_A status interaction NS; model AICc=‐792.6). In a parallel mixed effect model, simple slopes were ‐0.00093(0.00026) for PET_A+ change/year and ‐0.00039(0.00012) for PET_A‐ change/year (interaction p∼0.06; AICc=‐800.6). Figures 1 and 2 depict relationships among plasma and PET amyloid measures. Positive and negative predictive values were 50.0% and 95.5% (0.0975 cut‐off) and 68% and 83% (0.089 cut‐off; see Figure 1 for additional statistics).ConclusionPreliminary results from this small African American sample provide encouraging data regarding plasma Ab42/40’s potential to screen for PET A+ in AAs. PET A+ duration explained more variability than age in plasma Ab42/40 trajectories. The on‐going AA‐FAIM study continues the critical effort to increase representation of AA in AD research, ensuring findings are broadly applicable.
BackgroundAfrican Americans (AA) are under‐represented in Alzheimer’s disease (AD) biomarker research. Blood‐based biomarkers for AD offer the promise of greater diversity in research studies, but little is known about the performance of these biomarkers within an AA cohort. We examined the association between modifiable and non‐modifiable risk factors on the trajectory of plasma measures of Aβ42/40 in a large well‐characterized AA cohort.MethodN=318 AA participants enrolled in in African Americans Fighting Alzheimer’s in Mid‐Life (AA‐FAIM) provided 690 plasma samples. The ratio Ab42/40 was calculated from plasma Aβ40 and Aβ42 quantified by C2N Diagnostics (PrecivityAD™; Figure 1). Age range was 39 ‐ 87 years at baseline (see Table 1); 52% of the cohort provided ≥2 plasma samples. Mixed‐effects linear models were used to test for longitudinal change in Aβ42/40 where years since baseline was the measure of time (M= 4.13 years, range = 1 to 16 years). To determine if risk factors modified Aβ42/40 trajectory, we tested the interactions between time and the following AD risk factors: APOE genetic risk, cardiovascular factors measured at baseline plasma sample (blood pressure, BMI, total cholesterol), and amyloid PET positivity.ResultRaw Aβ42/40 values plotted against time are shown in Figure 2. None of the risk factors modified the trajectory of Aβ42/40 (p values ranged .22 to .84), which declined by approximately 1% per year over baseline (p <.001; Table 2). After accounting for longitudinal trajectory, Ab42/40 was 3% lower among APOE ε4 carriers, while higher baseline total cholesterol was associated with higher Aβ42/40. In a subset of participants with PiB‐PET visual ratings (N=35), amyloid positive participants (N=10) had 12% lower plasma Aβ42/40 values than amyloid negative participants (p<.001) after accounting for longitudinal trajectory.ConclusionThese initial analyses conducted exclusively in African Americans suggest that clinical risk factors (total cholesterol, APOE) can influence brain amyloid pathology, as determined using plasma Aβ42/40. More research is needed to fully characterize AD pathology and dementia risk in AAs. This research is imperative in light of the disproportionate burden of dementia in the AA community and the increasing use of biomarkers in research and clinical practice.
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