Abstract
Manual ELISA assays are the most commonly used methods for quantification of biomarkers; however, they often show inter- and intra-laboratory variability that limits their wide use. Here, we compared the Innotest ELISA method with two fully automated platforms (Lumipulse and Elecsys) to determine whether these new methods can provide effective substitutes for ELISA assays. We included 149 patients with AD (n = 34), MCI (n = 94) and non-AD dementias (n = 21). Aβ42, T-tau, and P-tau were quantified using the ELISA method (Innotest, Fujirebio Europe), CLEIA method on a Lumipulse G600II (Fujirebio Diagnostics), and ECLIA method on a Cobas e 601 (Roche Diagnostics) instrument. We found a high correlation between the three methods, although there were systematic differences between biomarker values measured by each method. Both Lumipulse and Elecsys methods were highly concordant with clinical diagnoses, and the combination of Lumipulse Aβ42 and P-tau had the highest discriminating power (AUC 0.915, 95% CI 0.822–1.000). We also assessed the agreement of AT(N) classification for each method with AD diagnosis. Although differences were not significant, the use of Aβ42/Aβ40 ratio instead of Aβ42 alone in AT(N) classification enhanced the diagnostic accuracy (AUC 0.798, 95% CI 0.649–0.947 vs. AUC 0.778, 95% CI 0.617–0.939). We determined the cut-offs for the Lumipulse and Elecsys assays based on the Aβ42/Aβ40 ratio ± status as a marker of amyloid pathology, and these cut-offs were consistent with those recommended by manufacturers, which had been determined based on visual amyloid PET imaging or diagnostic accuracy. Finally, the biomarker ratios (P-tau/Aβ42 and T-tau/Aβ42) were more consistent with the Aβ42/Aβ40 ratio for both Lumipulse and Elecsys methods, and Elecsys P-tau/Aβ42 had the highest consistency with amyloid pathology (AUC 0.994, 95% CI 0.986–1.000 and OPA 96.4%) at the ≥0.024 cut-off. The Lumipulse and Elecsys cerebrospinal fluid (CSF) AD assays showed high analytical and clinical performances. As both automated platforms were standardized for reference samples, their use is recommended for the measurement of CSF AD biomarkers compared with unstandardized manual methods, such as Innotest ELISA, that have demonstrated a high inter and intra-laboratory variability.
Highlights
Alzheimer’s disease (AD) is the most prevalent age-related neurodegenerative disease, accounting for 60–80% of cases of dementia
The aims of this study were (a) to assess the concordance between core AD biomarkers measured in cerebrospinal fluid (CSF) using Innotest, Lumipulse and Elecsys methods; (b) to evaluate the diagnostic accuracy of biomarkers and their ratios measured by each method; (c) to assess the discriminating power of AT(N) groups that were generated by the results of the different biomarkers for each of these three technologies and (d) to define the CSF cutoff points for both Lumipulse and Elecsys assays based on the Lumipulse amyloid β42 protein (Aβ42)/40 status
All CSF biomarker concentrations were significantly different between the three diagnostic groups, except Lumipulse Aβ40 (P > 0.05)
Summary
Alzheimer’s disease (AD) is the most prevalent age-related neurodegenerative disease, accounting for 60–80% of cases of dementia. The extracellular amyloid plaques arising from the accumulation of amyloid β42 protein (Aβ42) and intracellular neurofibrillary tangles formed by aggregations of hyperphosphorylated tau protein (P-tau) are the two main pathological hallmarks of AD (Serrano-Pozo et al, 2011) Both of these pathological characteristics are specific to AD, while neurodegeneration, characterized by an increase in total-tau protein (T-tau), is a non-specific biomarker that can be caused by several neurodegenerative diseases (Jack et al, 2018). Aβ42, P-tau, and T-tau are considered core AD biomarkers that can be measured in cerebrospinal fluid (CSF) Their use increases the accuracy of the diagnosis and prediction of the progression from mild cognitive impairment (MCI) to AD and can differentiate between AD and other causes of dementia or neuropsychiatric problems (Albert et al, 2011; McKhann et al, 2011; Sperling et al, 2011). The inclusion of these biomarkers in diagnosis benefits populations included in clinical trials (Jack et al, 2018)
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