The study population consisted of 199 nondemented participants: 161 cognitively normal (CN) participants and 38 patients with amnestic mild cognitive impairment (aMCI). The two groups did not differ in age (p = 0.06), sex (p = 0.72) or years of education (p = 0.86). The CN participants stemmed from the Flemish Prevent AD Cohort KU Leuven (F-PACK), a larger longitudinal community-recruited study cohort of 180 CN elderly volunteers , preregistered under EudraCT 2009-014475-45 . At inclusion, the F-PACK cohort was stratified for APOE-ε4 genotype such that half of the included individuals carried at least one APOE-ε4 allele [18, 20]. Among the F-PACK inclusion criteria, participants had to score within the normal range on detailed neuropsychological evaluation and have a Mini-Mental State Examination (MMSE) score of ≥ 27/30 and a Clinical Dementia Rating (CDR) scale score of 0. At baseline, participants underwent [18F]flutemetamol amyloid-PET and structural MRI. EDTA plasma samples at baseline, sampled between 2009 and 2016, were available for 165 F-PACK participants; however, four participants were excluded due to technical errors in the SIMOA Amyblood assays (coefficient of variation (CV) > 20%), yielding a CN subgroup of 161 participants.
aMCI patients (n = 38) stemmed from a consecutive academic memory clinic recruited longitudinal observational cohort, the Biomarker-based adaptive development in Alzheimer’s disease (BioAdaptAD) cohort, preregistered under EudraCT 2013-004671-12 . All aMCI patients were recruited from the Memory Clinic of the University Hospitals Leuven. Among the BioAdaptAD inclusion criteria, participants had to be clinically followed with a current clinical diagnosis of aMCI. The aMCI participants all had unknown amyloid-PET or CSF status at the time of inclusion in the BioAdaptAD study. Clinical disease duration was on average 4.5 ± 3.2 years. Following the BioAdaptAD study protocol, aMCI participants received a [18F]florbetaben amyloid-PET scan, a structural MRI, EDTA blood sampling between 2015 and 2016 and detailed neuropsychological assessment.
All participants underwent amyloid-PET on a 16-slice Biograph PET/CT scanner (Siemens, Erlangen, Germany) and structural MRI on a 3-T Achieva scanner (Philips, Best, The Netherlands), with the exception of one CN subject and three aMCI patients who had contraindications for MRI. For the latter four subjects, the mean MRI images calculated from amyloid-PET negative subjects of the respective cohorts were used for segmentation and calculation of the deformation field used in normalising the PET data. PET measurements were acquired in a 90- to 120-min post-injection window, and the standardised uptake value ratio was calculated in a composite volume of interest (SUVRcomp) using participant-specific cerebellar grey matter as a reference region . Amyloid-PET positivity was defined as a SUVRcomp above predefined cut-offs equal to 1.38 for [18F]flutemetamol PET  and 1.29 for [18F]florbetaben PET. For calculation of these cut-offs, we used the same methodology as the one employed in a previous study . For both tracers, SUVRcomp values were converted to Centiloid (CL) values to allow correlation between cerebral amyloid burden and plasma biomarkers across the CN and aMCI subgroups (see Appendix 1).
Intermediate amyloid burden was defined as CL values between 20 and 50 and high amyloid burden as CL ≥ 50 . Twenty-two (14%) CN participants showed intermediate amyloid burden (CL range 22.2–47.8), while eight (5%) showed high amyloid burden (CL range 66.25–184.9). aMCI patients generally had a higher prevalence of amyloid-PET positivity than CN participants, with four patients (11%) showing intermediate amyloid burden (CL range 27.0–36.9) and nine (24%) showing high amyloid burden (CL range 51.5–103.1).
Cerebrospinal fluid assays
CSF samples were available for a subset of both subgroups (37 CN, 19 aMCI). In both subgroups, a lumbar puncture was performed with a 22G traumatic needle between L3/L4 and L4/L5. The CSF samples of CN participants were processed according to the F-PACK protocol; the collected CSF was transferred to a PP tube (Greiner Bio-One, 82050-278), followed by centrifugation at 1264g at 4 °C and aliquotation in 1.5-mL low-binding PP tubes (Kartell, 298). The CSF samples of aMCI patients were collected within the multicentre BioAdaptAD study, which adhered to a similar protocol; the collected CSF was transferred to a PP tube (Sarstedt, 62.610.018), followed by centrifugation for 10 min at 3000g at RT and aliquotation in 1.5-mL low-binding PP cryovials (Sarstedt, 72.703). The low-binding PP tubes were then placed on dry ice. Finally, all samples of both subgroups were stored at − 80 °C within 2 h after sampling. CSF Aβ1–42 and t-tau levels were determined by means of INNOTEST ELISAs (Fujirebio, Ghent, Belgium). In line with the International Working Group (IWG)-2 criteria, which commends combined analysis of CSF Aβ1–42 and p-tau or t-tau, we included CSF Aβ1–42/t-tau as a CSF-based AD biomarker.
Plasma collection and processing
Blood was collected in K2EDTA-coated polyethylene terephthalate tubes (BD Diagnostics, BD367864). Samples of CN participants were processed according to the F-PACK study protocol, starting with centrifugation at 1200g for 10 min at 4 °C, followed by transfer of supernatant to polypropylene (PP) cryovials (Thermo Fisher Scientific, 363401, 500 μL plasma per tube) and subsequent storage at − 20 °C for 24 h before moving them to − 80 °C. aMCI patient samples were collected within the multicentre BioAdaptAD study, which adhered to a different protocol; samples were first centrifuged at 3000g for 15 min with subsequent division of the supernatant into PP cryovials (Sarstedt, 72.703) stored at − 80 °C within 2 h after sampling.
We quantified EDTA plasma Aβ1–40 and Aβ1–42 with commercially available ELISA kits (EUROIMMUN, Lübeck, Germany), as well as with prototype SIMOA Amyblood assays (UMC Amsterdam and ADx NeuroSciences), which use the same sets of monoclonal antibodies: the 3D6 antibody, which is an N-terminal antibody that binds to residues 1–5 of the Aβ peptide, was used as the detector antibody and the C-terminal antibodies 21F12 and 2G3 were used as capture antibodies to capture respectively plasma Aβx–42 and Aβx–40 (Table 1). This differs from the singleplex and 3-Plex SIMOA assays (Quanterix, Lexington, MA, USA) employing the 6E10 antibody as a capture and detector antibody, respectively. The 6E10 antibody does not specifically target the N-terminus, but instead binds an RHD sequence located at residues 5–7 of the Aβ peptide [11, 12]. As a result, these SIMOA assays detect amyloid fragments of various lengths (Aβx–42 and Aβx–40) . The Quanterix SIMOA assays use a different C-terminal antibody for Aβx–42 (H31L21), but the same C-terminal antibody for Aβx–40 as used in the SIMOA Amyblood assays and EUROIMMUN ELISAs (Thijssen, under review ).
EDTA plasma t-tau was quantified with a prototype ELISA designed by ADx NeuroSciences, which included an N-terminal detector antibody and a capture antibody targeting residues 194–204 of the tau protein (Table 1).
Plasma amyloid and tau measurements
EUROIMMUN ELISA assays were performed manually according to the manufacturer’s protocol, and absorbance spectra were obtained with the CLARIOstar Plus microplate reader (BMG Labtech, Ortenberg, Germany). The lyophilized calibrators of multiple ELISA kits from the same lot were first reconstituted and then pooled per Aβ isoform in order to standardise the calibrator material among the different ELISA kits used. Subsequently, the reconstituted calibrators were aliquoted in separate PP tubes (Qiagen, 19560) per ELISA plate and stored at − 20 °C until testing. SIMOA Amyblood assays were performed as described earlier , and in-house developed ready-to-use calibrators were employed, which were composed of the same recombinant proteins (rPeptide, Athens, USA) as the ELISA calibrators.
The prototype ELISA for plasma t-tau included in-house developed ready-to-use calibrators constituted of recombinant t-tau protein (rPeptide, Athens, USA). No SIMOA-based quantification of plasma t-tau was performed. Consequently, the SIMOA-based Aβ1–42/t-tau ratio is a combination of the SIMOA Aβ1–42 measure and the ELISA t-tau measure.
The quality control (QC) panel was identical in all assays and was selected from a collection of 30 plasma samples donated by CN volunteers other than those in the F-PACK cohort. QC selection aimed at identifying one sample with consistently high levels of both amyloid isoforms (QC1), two samples with intermediate levels (QC2/QC3) and one sample with consistently low levels (QC4) of both amyloid isoforms when quantified by means of EUROIMMUN ELISA. For amyloid immunoassays, two additional QC samples were included consisting of an in-house prepared buffer spiked with respectively low (QC5) and high concentrations (QC6) of both recombinant Aβ1–40 and Aβ1–42 peptides identical to those used in the calibrators. Subsequently, all QCs were divided into 150-μL aliquots in PP vials (Sarstedt, 730.105) and stored at − 80 °C so that one vial was available for every ELISA and Amyblood run. The QCs provided by the EUROIMMUN ELISA kit (C1–2) were also reported (Table 1). No SIMOA-specific QC samples were available. It was observed that the Aβ1–42 concentration in the intermediate control sample QC2 was lower than in the high control sample QC1 when measured with ELISA, while it was higher when measured with the SIMOA assay. Of note, the QC panel was selected based on ELISA data and not SIMOA data. Moreover, the Aβ1–42 concentrations in the low, intermediate and high QC samples are all within a relatively close range, presumably because they all stemmed from CN volunteers. This, in addition to the substantial measurement difference in terms of values generated between the two platforms, is thought to cause the between-platform discrepancy in Aβ1–42 concentrations within the QC1 and QC2 sample.
Within all assays, plasma samples were randomised for analyses and all samples were analysed in duplicate within a total of four runs in four consecutive days. No correction for inter-assay variation was required, as inter-assay CVs were all below 15% (mean 7.10, range 2.58–13.55). Every vial was subjected to only one freeze/thaw cycle. All measured concentrations exceeded the limits of detection (LoDs) and limits of quantification (LoQs) and fell within the calibration ranges of the respective assays. The time interval between blood collection and measurement of plasma biomarkers was longer for the CN subgroup (median 6.51, IQR 5.36–7.78 years) than for the aMCI subgroup (median 3.24, IQR 3.04–3.83 years) (p < 0.0001), but did not differ between amyloid-PET negative (amyloid-PET−ve) and amyloid-PET positive (amyloid-PET+ve) participants within either subgroup (all p > 0.74).
Statistical analyses were performed using GraphPad Prism 8.4.2 (GraphPad Software Inc., La Jolla, CA, USA) and MedCalc 19.0.3 (MedCalc, Ostend, Belgium) software. Normality was assessed with D’Agostino-Pearson test. Demographic continuous variables were compared between amyloid-PET groups with unpaired t tests or Mann-Whitney U tests in case of two groups, depending on normality, and with Kruskall-Wallis tests in case of three or more groups. Contingency tables were analysed by means of χ2 tests for categorical variables at a significance level of 0.05. Correlations between demographic variables and plasma biomarkers were assessed within the full nondemented cohort as well as in the CN and aMCI subgroups. Bonferroni correction was applied to adjust for multiple comparisons with two separate immunoassay platforms (ELISA and SIMOA, Bonferroni correction: α = 0.05/k compared platforms, k = 2, α = 0.03). In order to derive effect sizes for plasma levels depending on amyloid status, robust d values were calculated using the R package “WRS2” in R statistical software, version 3.6.2 (2019-12-12) (The R Foundation for Statistical Computing, https://www.r-project.org/). Robust d values are an alternative to Cohen’s standardised mean difference effect size  and do not assume a normal distribution of variables.
As primary outcome analysis, the performance of plasma Aβ1–42/Aβ1–40 to detect cerebral amyloidosis was compared between the ELISA and SIMOA platform using receiver operating characteristic (ROC) analyses for detecting amyloid-PET positivity based on binary classification of SUVRcomp values in the full nondemented cohort as well as in the subgroups (CN and aMCI, respectively). The areas under the ROC curve (AUCs) with 95% CIs were reported as measures of performance. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values (NPVs) were calculated for optimal cut-offs at maximised Youden index. For all biomarkers and their ratios, this AUC was compared to the AUC value adjusted for age and APOE-ε4 genotype. To obtain this adjusted AUC value with 95% CIs, we first calculated a binary logistic regression model with amyloid-PET positivity as binary dependent variable and the plasma biomarker as well as age and APOE-ε4 genotype as independent variables. In a next step, the result of this binary logistic regression, i.e. predicted probabilities, was entered in a ROC analysis to obtain the final adjusted AUC value. APOE-ε4 genotype was specified by means of a dummy variable (non-carrier = 0, heterozygous carrier = 1, homozygous carrier = 2). Adjusted AUCs were only reported if they significantly differed from the unadjusted AUC. In addition, the adjusted AUCs were compared to the AUC of a basic demographic model, including only age and APOE-ε4 genotype as independent variables, but no plasma biomarker or plasma biomarker ratio. Pairwise comparisons between ROC curves were performed with the DeLong method .
As a second objective, the correspondence of plasma biomarkers versus established AD biomarkers was assessed using Spearman rank correlations for ELISA and SIMOA measurements of plasma Aβ1–42/Aβ1–40 and Aβ1–42/t-tau ratios with (i) continuous Centiloid values as a measure for amyloid-PET binding and (ii) CSF Aβ1–42/t-tau. The latter contained data of a subgroup of cases for whom CSF samples were available (n = 56).
As a final objective, we examined the agreement of plasma amyloid measurements (commutability) between platforms in the entire nondemented study cohort (n = 199) using Mann-Whitney U tests to assess differences in median plasma Aβ measurements, Spearman rank correlations and Passing-Bablok regression analyses. The difference between the two assays is also shown graphically using non-parametric percentile Bland-Altman bias plots for which, by definition, the Y axis represents the difference between the two immunoassay platforms and the X axis represents the average of these measures. This allows the assessment of whether one method consistently under- or overestimates measurements of the same variable as compared to the other method.