Figdore, Daniel J

Abstract Background Blood-based biomarkers (BBBs) are a growing area of study for improving Alzheimer’s disease (AD) clinical evaluation. BBBs have been demonstrated to correlate with AD CSF biomarkers as well as neuroimaging results for assessment of amyloid pathology. Currently BBBs may be used as a screening tool for patient selection in clinical trials, and alongside more well characterized biomarkers like amyloid-PET and CSF markers for evaluation of individuals presenting with cognitive impairment. A comparative study of several AD BBBs assays was performed to assess bias between two platforms, as well as correlation to markers of amyloid pathology. Methods The study included 146 plasma samples from 112 cognitively normal (CN), and 34 mild cognitive impairment (MCI) or AD dementia individuals. Plasma Aβ42, Aβ40, and pTau181 concentrations were obtained using the Fujirebio Lumipulse and Quanterix Simoa immunoassays. Assay comparison between Fujirebio and Quanterix were performed for Aβ42, Aβ40, Aβ42/Aβ40 ratio, pTau181, and pTau181/Aβ42 ratio using Passing-Bablok regression and regression slopes were obtained. Amyloid-PET was performed using Pittsburg Compound B, and a standard uptake value ratio (SUVR) of 1.48 was used to classify amyloid status. In the CN group, 69 were amyloid negative, and 43 were amyloid positive. In the MCI/AD dementia group, 8 were amyloid negative and 26 were amyloid positive. Spearman’s rank correlation coefficient (rho) was used to determine correlation between assays and correlation of each with Amyloid-PET SUVRs. Results Between Fujirebio (y) and Quanterix (x) assays, measured Aβ42 and Aβ40 concentrations showed a large bias with y = 5.23x − 14.25, and y = 4.42x − 264.87, respectively. A moderate to strong correlation was observed between assays for Aβ42 and Aβ40 with rho values of 0.641 (P < 0.0001), and 0.498 (P < 0.0001), respectively. The ratio of Aβ42/Aβ40 corrected this bias with y = 0.96x + 0.03 and rho = 0.509 (P < 0.0001). pTau181 showed little bias between methods with y = 0.92x + 0.48, and a rho = 0.592 (P < 0.0001). However, the pTau181/Aβ42 ratio showed a large bias driven mostly by differences in the Aβ42 concentrations with y = 0.27x + 0.04 and rho = 0.574 (P < 0.0001). Quanterix assay results correlated with amyloid-PET SUVR yielded rho coefficients of AB42/40 ratio = −0.219 (P = 0.079), pTau181 = 0.380 (P < 0.0001), and pTau181/Aβ42 ratio = 0.466 (P < 0.0001). Fujirebio assay results correlated with amyloid-PET SUVR showed AB42/40 ratio rho = −0.551 (P < 0.0001), pTau181 rho = 0.370 (P < 0.0001), and pTau181/Aβ42 ratio rho = 0.391 (P < 0.0001). Conclusion This study demonstrates that some BBBs methods differ significantly between manufacturers. Little bias was observed between the Aβ42/Aβ40 and pTau181 determinations for the evaluated platforms; however, significant bias was observed in Aβ42, Aβ40, and the pTau181/Aβ42 ratio. The Fujirebio platform demonstrated stronger correlation of the Aβ42/Aβ40 ratio with amyloid-PET SUVR, while Quanterix showed better correlation of the pTau181/Aβ42 ratio. The pTau181 assays showed similar correlation to amyloid-PET SUVR. Bias between assays as well as clinical correlation differences may preclude the generalization of clinical performance of AD BBBs assays between platforms.

Abstract Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer’s disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer’s disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.

Abstract Staging the severity of Alzheimer’s disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer’s Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer’s clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1–2, 3–4, 5–6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1–42 and Aβ1–40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78–0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72–0.85 versus C = 0.73–0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer’s disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

AbstractBackgroundNeurofilament light chain (NfL) is an emerging blood‐based biomarker useful for assessing neuro‐axonal injury in several neurological conditions. Plasma NfL concentrations reflect the extent of axonal damage, making it a general marker of neurodegenerative disease progression. This study evaluates the analytical performance characteristics of the Quanterix NfL Advantage method in EDTA‐plasma.MethodThe assay was performed per manufacturer’s instructions on the Quanterix Simoa HD‐X Analyzer. Analytical validation included analyte stability, imprecision, lower limit of quantification (LLOQ), analytical measurement range (AMR), accuracy, and interferences. One‐sided reference intervals were established using quantile regression methods among 1100 cognitive normal individuals, age 20 to 95 years without known chronic kidney disease, stroke, or myocardial infarction.ResultNfL was stable in plasma three days ambient, seven days refrigerate, 90 days frozen (‐20C), and over 3 freeze/thaw cycles. Intra and inter‐assay imprecision, expressed as % coefficient of variation (%CV), ranged from 3.4 – 6.0% and 5.9 – 11.6% respectively. The LLOQ was evaluated using patient plasma pools at concentrations of 1.7 and 3.6 pg/mL. %CVs were 16.8% and 9.7% respectively, and the LLOQ was set at 3.0 pg/mL. Accuracy was assessed by spiking 5 plasma samples with NfL calibratoran 87% average recovery (range 81‐94%) was observed. The AMR of 3.0 – 1500 pg/mL was verified with a regression slope of 0.93 and r2 > 0.99. Additionally, plasma admixtures covering the AMR showed acceptable linear response (slope of 1.02 and r² >0.99). The assay was not affected by icterus to 100 mg/dL, lipemia to 1000 mg/dL, hemolysis to 1000 mg/dL at NfL concentrations >30 pg/mL or to 200 mg/dL at NfL concentrations ≤30 pg/mL. NfL increased with age but did not differ by sex. The data supported age‐based reference intervals broken down into 10‐year intervals. The 97.5th percentile limits were: 20s: ≤8.4 pg/mL, 30s: ≤11.4 pg/mL, 40s: ≤15.4 pg/mL, 50s: ≤20.8 pg/mL, 60s: ≤28.0 pg/mL, 70s: ≤37.9 pg/mL, 80+: ≤51.2 pg/mL.ConclusionThe analytical performance characteristics of the plasma Quanterix NfL advantage assay were deemed acceptable for clinical laboratory implementation. The establishment of NfL reference intervals will aid in the clinical interpretation of patient plasma NfL concentrations.