Clinical performance and robustness of blood‐based biomarkers for early detection of amyloid pathology associated with Alzheimer’s disease

Abstract

BackgroundAccurate and robust blood‐based biomarkers (BBBMs) of Alzheimer’s disease (AD) are required for identification of symptomatic patients with low likelihood of amyloid pathology before confirmatory diagnostic evaluation. Further evidence on the clinical performance and robustness of BBBMs is required to identify patients for clinical trials and in routine clinical practice. We evaluated the clinical performance and robustness of amyloid‐β 1–42 (Aβ42), amyloid‐β 1–40 (Aβ40), apolipoprotein E4 (ApoE4), phosphorylated‐tau 181 (pTau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) as BBBMs of AD.MethodAnalyses were performed retrospectively using plasma samples from AIBL, BioFINDER, and CREAD cohorts, comprising cognitively normal individuals and patients with subjective/objective cognitive impairment, mild AD, or amyloid screen failure. BBBMs were measured at two laboratories using automated Elecsys® prototype immunoassays (cobas e 601 and e 411 analyzers; all Roche Diagnostics International Ltd). Clinical performance of single BBBMs and combinations of BBBMs (using logistic regression) was assessed using area under the receiver operating characteristic curve (AUC‐ROC) analysis. Negative percent agreement (NPA), prevalence‐adjusted negative and positive predictive values (NPV and PPV), and screen‐out rate were compared at 85% positive percent agreement (PPA). Robustness, defined as the change in clinical performance when adding ±10% bias and random variability, was calculated at 85% PPA.ResultAcross cohorts, the best performing single BBBM was pTau181 (AUC: 81.6–89.3). The best performing combined BBBM models across cohorts were pTau181+Aβ42 (AUC: 85.6–93.4), pTau181+ApoE4 (AUC: 83.7–91.8), and pTau181+Aβ40 (AUC: 84.8–90.2); clinical performance for these combined BBBM models in terms of NPV and PPV was comparable, providing high NPVs (>90%) and screen‐out rates (>50%). For combined BBBM models including Aβ42 or Aβ40, robustness at 85% PPA was lower compared with models not including Aβ42 or Aβ40 across cohorts. Considering both clinical performance and robustness, pTau181 and pTau181+ApoE4 were the best performing single and combined BBBM models, respectively, across cohorts.ConclusionAcross cohorts, pTau181+ApoE4 was the best performing BBBM model for detecting patients with low likelihood of amyloid pathology. These findings support the suitability of these BBBMs to inform diagnostic assessments for patients with low likelihood of AD.

The potential clinical value of plasma biomarkers in Alzheimer's disease

“…The Elecsys Amyloid Plasma Panel is based on a retrospective study that assessed a panel of Elecsys prototype immunoassays for clinical performance and robustness in three cohorts. It was found that the p‐tau181 and apolipoprotein E4 composite plasma biomarker score was the best‐performing combination in terms of meeting the clinical performance and robustness requirements to rule out symptomatic individuals with a low likelihood of Aβ pathology 51 …”

“…50 High robustness indicates that diagnostic performance is unaffected by small differences in test conditions such as pre-analytical sample handling techniques, analytical variability, lab-to-lab, batch-to-batch, and operator differences, all of which are unavoidable in practice. 51 Clinical validation assesses whether the test serves its purpose in the intended use population, that is, whether the test is able to reliably diagnose people with the condition and, in doing so, inform clinical decision making. In real-world terms, a widely validated test with high robustness means that patients are less likely to be misclassified and, hence, are more likely to receive the correct support and treatment.…”

“…Typical experiments performed during analytical validation include, among others: (1) testing for the degree of agreement between replicate measurements of the same sample under the same conditions (precision), (2) testing for substances affecting the measurable concentration of the analyte sample (interferences), 49 and (3) testing for the ability to obtain results that are proportional to the concentration of analyte being measured (linearity) 50 . High robustness indicates that diagnostic performance is unaffected by small differences in test conditions such as pre‐analytical sample handling techniques, analytical variability, lab‐to‐lab, batch‐to‐batch, and operator differences, all of which are unavoidable in practice 51 . Clinical validation assesses whether the test serves its purpose in the intended use population, that is, whether the test is able to reliably diagnose people with the condition and, in doing so, inform clinical decision making.…”