Blood‐based biomarkers for Alzheimer's disease

Review3 December 2021Open Access Blood-based biomarkers for Alzheimer's disease Antoine Leuzy Corresponding Author Antoine Leuzy [email protected] orcid.org/0000-0003-4542-7879 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Search for more papers by this author Niklas Mattsson-Carlgren Niklas Mattsson-Carlgren orcid.org/0000-0002-8885-7724 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Department of Neurology, Skåne University Hospital, Lund, Sweden Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden Search for more papers by this author Sebastian Palmqvist Sebastian Palmqvist orcid.org/0000-0002-9267-1930 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Memory Clinic, Skåne University Hospital, Lund, Sweden Search for more papers by this author Shorena Janelidze Shorena Janelidze orcid.org/0000-0003-2869-8378 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Search for more papers by this author Jeffrey L Dage Jeffrey L Dage orcid.org/0000-0002-2192-1699 Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA Search for more papers by this author Oskar Hansson Corresponding Author Oskar Hansson [email protected] orcid.org/0000-0001-8467-7286 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Memory Clinic, Skåne University Hospital, Lund, Sweden Search for more papers by this author Antoine Leuzy Corresponding Author Antoine Leuzy [email protected] orcid.org/0000-0003-4542-7879 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Search for more papers by this author Niklas Mattsson-Carlgren Niklas Mattsson-Carlgren orcid.org/0000-0002-8885-7724 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Department of Neurology, Skåne University Hospital, Lund, Sweden Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden Search for more papers by this author Sebastian Palmqvist Sebastian Palmqvist orcid.org/0000-0002-9267-1930 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Memory Clinic, Skåne University Hospital, Lund, Sweden Search for more papers by this author Shorena Janelidze Shorena Janelidze orcid.org/0000-0003-2869-8378 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Search for more papers by this author Jeffrey L Dage Jeffrey L Dage orcid.org/0000-0002-2192-1699 Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA Search for more papers by this author Oskar Hansson Corresponding Author Oskar Hansson [email protected] orcid.org/0000-0001-8467-7286 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Memory Clinic, Skåne University Hospital, Lund, Sweden Search for more papers by this author Author Information Antoine Leuzy *,1, Niklas Mattsson-Carlgren1,2,3, Sebastian Palmqvist1,4, Shorena Janelidze1, Jeffrey L Dage5 and Oskar Hansson *,1,4 1Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden 2Department of Neurology, Skåne University Hospital, Lund, Sweden 3Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden 4Memory Clinic, Skåne University Hospital, Lund, Sweden 5Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA *Corresponding author. Tel: +46 (0)222 0660; E-mail: [email protected] *Corresponding author. Tel: +46 (0)40 331000; E-mail: [email protected] EMBO Mol Med (2021)e14408https://doi.org/10.15252/emmm.202114408 See the Glossary for abbreviations used in this article. PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Neurodegenerative disorders such as Alzheimer's disease (AD) represent a mounting public health challenge. As these diseases are difficult to diagnose clinically, biomarkers of underlying pathophysiology are playing an ever-increasing role in research, clinical trials, and in the clinical work-up of patients. Though cerebrospinal fluid (CSF) and positron emission tomography (PET)-based measures are available, their use is not widespread due to limitations, including high costs and perceived invasiveness. As a result of rapid advances in the development of ultra-sensitive assays, the levels of pathological brain- and AD-related proteins can now be measured in blood, with recent work showing promising results. Plasma P-tau appears to be the best candidate marker during symptomatic AD (i.e., prodromal AD and AD dementia) and preclinical AD when combined with Aβ42/Aβ40. Though not AD-specific, blood NfL appears promising for the detection of neurodegeneration and could potentially be used to detect the effects of disease-modifying therapies. This review provides an overview of the progress achieved thus far using AD blood-based biomarkers, highlighting key areas of application and unmet challenges. Glossary Amyloid PET Aβ-specific ligands for use with positron emission tomography Antibodies immune proteins produced in response to an antigen Area under the receiver operating curve a measure of the ability of a classifier to distinguish between positive and negative classes (i.e., normal and abnormal) Aβ plaques abnormal extracellular deposits of the Aβ peptide Biomarkers a biomarker is an objectively measurable parameter that can be treated as an indicator of biological processes or responses to a treatment Enzyme-linked immunosorbent assays though various ELISA variants all are characterized by the following elements: an antigen, one or several antibodies specific to that antigen, and a system to quantify the amount of antigen present Head-to-head study a study design in which two methods are directly compared using data from the same individuals Immunomagnetic reduction platforms a technique in which the concentration is measured by comparing changes in magnetic responses between free and conjugated magnetic nanoparticles Immunoprecipitation mass spectrometry assay an assay combining immunoprecipitation and mass spectrometry. Using this approach, desired analytes are first selectively captured from solution prior to analysis with mass spectrometry Mild cognitive impairment a state of cognitive impairment intermediate between those due to normal aging and dementia (i.e., objective cognitive deficits beyond that expected given their age and education yet of insufficient severity to meet criteria for dementia) Neurofibrillary tangles abnormal intracellular accumulations of the tau protein Neuroinflammation astrocytic and microglial activation Preanalytical variables these include tube type and time from blood collection to centrifugation and pipetting of the plasma Sensitivity the ability of a test to correctly identify patients with a disease Single-molecule array a digital assay technique allowing for the measurement of single-molecule immunocomplexes Specificity the ability of a test to correctly identify people without the disease Tau PET tau-specific ligands for use with positron emission tomography Introduction Neurodegenerative disorders such as Alzheimer's disease (AD) are the leading causes of dementia and carry immense social and economic costs. Worldwide, an estimated 50 million people currently live with dementia, with this figure projected to exceed 80 million by 2030 (Prince et al, 2016). With increasing age as the greatest risk factor for dementia, a driving factor behind these rising prevalence figures is increased longevity (Winblad et al, 2016); as such, these disorders represent a major and increasing global health challenge. On clinical grounds alone, the differential diagnosis of AD can prove challenging, even for dementia experts (Beach et al, 2012; Salloway et al, 2014). Accurate prognosis and disease monitoring are also difficult when relying on clinical information only. As a result, biomarkers have come to play an increasingly important role in the field. Providing an objective measure of relevant pathophysiology in vivo, biomarkers are now included in modern research diagnostic criteria for AD (Dubois et al, 2007, 2014; Albert et al, 2011; Jack et al, 2011, 2018; McKhann et al, 2011; Sperling et al, 2011a) and are recommended for use in clinical trials by regulatory agencies (Hampel et al, 2010). Further, the use of biomarkers is important in the context of treatment, both in terms of ensuring that AD patients access available symptomatic treatments and in terms of providing an accurate prognosis early on in the disease course should disease-modifying therapies become available for AD (Abbasi, 2018). The AD biomarker research field is now moving from studies of group-level associations to subject-level diagnosis and prognosis in real-world scenarios. Examples of this include recent work aiming to assess the risk for cognitive decline using biomarkers in patients with mild cognitive impairment (MCI) (van Maurik et al, 2017, 2019a, 2019b; Cullen et al, 2021a). However, a substantial irreversible neuronal loss can already be seen by this stage—which may reduce the likelihood of disease-modifying therapies to prevent dementia onset (Sperling et al, 2011b). There is therefore now an increasing focus also on cognitively unimpaired (CU) older individuals at risk for progression to AD dementia on the basis of biomarker evidence of brain AD pathology (Sperling et al, 2014; Cullen et al, 2021b). Due to it being in direct contact with the central nervous system, cerebrospinal fluid (CSF) has proven an ideal source of information for the detection and measurement of biochemical abnormalities within the brain (Hampel et al, 2012); examples include CSF amyloid-β (Aβ) 42—alone, and in ratio with Aβ40 (Aβ42/Aβ40)—reflecting Aβ deposition; phosphorylated tau (P-tau), reflecting tau pathology; and neurofilament light (NfL) (Khalil et al, 2018), reflecting neurodegeneration. These abnormalities can also be measured using positron emission tomography (PET) with compounds (tracers) specific for Aβ, tau, and synaptic impairment. The global use of CSF and imaging-based biomarkers remains limited owing to the perceived invasiveness of lumbar punctures and the high cost and low availability of PET imaging (Duits et al, 2016). This has led to a growing interest in the use of blood-based biomarkers, with recent work showing promising results (Nakamura et al, 2018; Palmqvist et al, 2019b; Schindler et al, 2019; Janelidze et al, 2020a; Karikari et al, 2020b, 2021). In the present review, we aim to provide an overview of the progress achieved thus far using AD blood-based biomarkers and to highlight key areas of application and remaining challenges. Markers of Aβ pathology (Aβ42/Aβ40) Following the discovery of multiple C-terminal forms of Aβ, the 42-amino acid isoform of Aβ (Aβ42) was found to be highly aggregation-prone and predominant in diffuse and cored plaques in AD (Iwatsubo et al, 1994; Tamaoka et al, 1994). Using enzyme-linked immunosorbent assays (ELISAs) specific to Aβ42, a marked reduction in CSF Aβ42 was seen in AD (Motter et al, 1995; Blennow & Hampel, 2003), with levels shown to correlate inversely with cortical plaque load at post-mortem (Strozyk et al, 2003; Tapiola et al, 2009) and in biopsy studies (Seppala et al, 2012). Combining Aβ42 with Aβ40 (using a ratio) corrects inter-individual differences in Aβ processing and possible preanalytical confounders and increases concordance with amyloid PET (Hansson et al, 2019). Assays Plasma Aβ assays include ELISA-based immunoassays on the Luminex xMAP (Hansson et al, 2010), single-molecule array (Simoa) (Janelidze et al, 2016), Elecsys (Palmqvist et al, 2019b), and immunomagnetic reduction (IMR) platforms as well as immunoprecipitation mass spectrometry (IP/MS) assays (Pannee et al, 2014; Ovod et al, 2017; Schindler et al, 2019). For the IP/MS assays, plasma Aβ is enriched via immunoprecipitation with Aβ antibodies coated onto paramagnetic beads. These antibodies are either directed to the mid-region (Ovod et al, 2017; Nakamura et al, 2018) or N-terminal (Pannee et al, 2014) part of Aβ. Stable isotope-labeled synthetic Aβ peptides (e.g., Aβ42 and Aβ40) are then used as mass spectrometry quantification standards. In the study by Nakamura et al, however (Nakamura et al, 2018), Aβ38 was used as a single stable isotope-labeled standard for all Aβ isoforms. Early work using Luminex xMAP technology in plasma failed to replicate the observed decrease of Aβ42 seen in CSF (Song et al, 2011; Toledo et al, 2013; Rembach et al, 2014; Swaminathan et al, 2014; Olsson et al, 2016), likely due to the use of clinical diagnosis as the standard of truth, analytical limitations inherent to these methods (e.g., epitope masking by hydrophobic Aβ peptides (Kuo et al, 1999)) and, possibly, to peripheral tissues contributing to the global pool of plasma Aβ (Li et al, 1998; Kuo et al, 2000; Roher et al, 2009; Hansson et al, 2010). In 2016, however, using a Simoa assay for Aβ—a technique allowing for the reduction of matrix effects via predilution of samples due to its very high analytical sensitivity (Rissin et al, 2010, 2011; Zetterberg et al, 2011)—plasma Aβ was found to be reduced in AD compared to controls and patients with MCI and vascular dementia and to separate abnormal from normal amyloid PET scans with moderate accuracy (AUC between 0.62 and 0.68) (Janelidze et al, 2016; Verberk et al, 2018). Higher AUCs have since been reported using a modified version of the Simoa assay with different antibodies (Verberk et al, 2020b). Simoa studies were followed by several studies using IP/MS (Ovod et al, 2017; Nakamura et al, 2018; Schindler et al, 2019). Using amyloid PET status as outcome, plasma Aβ42/Aβ40 showed high accuracy in CU individuals (Schindler et al, 2019) and across CU individuals and patients with mild-to-moderate AD (AUCs of between 0.84 and 0.97) (Ovod et al, 2017; Nakamura et al, 2018). In a recent study that compared several IP/MS assays and immunoassays using a head-to-head design (Janelidze et al, 2021), certain IP/MS methods were shown to have superior performance to other IP/MS methods and all immunoassays using CSF Aβ42/40 and Aβ-PET status as outcome. Though promising and highlighting the potential of plasma Aβ as an AD biomarker, IP/MS- and Simoa-based studies are comparatively costly and require extensive development before they can be used in primary care or in screening large numbers of participants for AD clinical trials. Although this recent progress with high precision plasma Aβ measures has resulted in commercially available lab-developed blood tests for the detection of AD pathology, fully automated, high-throughput, and highly reliable analysis methods would facilitate implementation more broadly in clinical practice. Indeed, in the study by Janelidze et al (2021) comparing IP/MS and immunoassays, the Elecsys immunoassays (Roche Diagnostics) (Hansson et al, 2018) showed the numerically highest AUC (0.740). This is likely due to the immunoassays being fully automated and having very high analytical reliability and precision. Additional work using the Elecsys immunoassays for plasma Aβ42/Aβ40 showed that subjects could be differentiated based on their Aβ status with an AUC of 0.80 (Palmqvist et al, 2019b). The addition of APOE ε4 status—and, to a lesser extent, T-tau and NfL—increased the AUC significantly to around 0.85–0.87, though accuracy was lower compared to those reported in the IP/MS studies (Ovod et al, 2017; Nakamura et al, 2018; Schindler et al, 2019). In a recent study, however, a head-to-head comparison of plasma Aβ42/Aβ40 quantified with commercially available ELISA kits (EUROIMMUN) and prototype SIMOA assays (Amyblood; ADx NeuroSciences) (De Meyer et al, 2020) that used the same sets of monoclonal detector and capture antibodies showed that both provided identical accuracy for detecting amyloid PET status in a cohort of nondemented elderly individuals. The superior performance of these novel ELISAs can be attributed to technological advancements, including the use of C- and N-terminal antibodies (Pesini et al, 2012), improved conjugation method (Cirrito et al, 2003; Lopez et al, 2008), and an improved understanding of the effects of preanalystical variables (Lachno et al, 2009). While head-to-head comparisons are required between the different ELISAs, their improved performance carries potentially important implications due to their being much more widely available than Simoa. Recently, novel ready-to-use Simoa-based immunoassays (“Amyblood”) were developed to detect full-length Aβ1–42 and Aβ1–40 (Thijssen et al, 2021b), with the Amyblood Aβ42/Aβ40 ratio showing technical and clinical performance comparable to the Quanterix triplex and Euroimmun ELISAs but superior specificity and selectivity than the Quanterix triplex kit. A major limitation of plasma Aβ42/40, however, is that its levels are only decreased by 10–20% in individuals with cerebral Aβ pathology, compared to 40–60% for CSF Aβ42/40 (Nakamura et al, 2018; Verberk et al, 2018; Palmqvist et al, 2019a, 2019b; Schindler et al, 2019). This is likely due to plasma Aβ levels being affected by Aβ metabolism outside the brain (Li et al, 1998; Kuo et al, 2000; Roher et al, 2009; Hansson et al, 2010). As a result, plasma Aβ42/40 levels can be affected by small measurement variations caused by preanalytical handling (such as tube type and time from blood collection to centrifugation and pipetting of the plasma) and analytical performance (Rozga et al, 2019). This, in turn, can affect subject-level classification (i.e., negative or positive for Aβ pathology). Given the more robust changes seen for Aβ42/40 in CSF—as well as the robustness of this measure to the interfering effects of preanalytical factors (Hansson et al, 2019)—CSF Aβ42/40 has overall shown a higher diagnostic accuracy than plasma Aβ42/40 and is less susceptible to variations in its optimal cut-point (Schindler et al, 2019). Possibly, combining plasma Aβ42/40 with P-tau or GFAP using an algorithm may make plasma Aβ42/40 more robust to preanalytical factors; however, this has not yet been studied. Differential diagnosis of AD dementia Thus far, only one study has examined the ability of plasma Aβ42/Aβ40 to differentiate AD dementia from non-AD dementia disorders (Palmqvist et al, 2020). Using immunoassays, however (Euroimmun ELISAs)—as opposed to mass spectrometry methods—the study reported poor diagnostic accuracy (AUC of 0.62) when using clinically diagnosed participants (AD dementia [n = 121] vs a non-AD group [n = 99] including 45 patients with Parkinson's disease (without or with dementia) or multiple system atrophy, 21 with progressive supranuclear palsy or corticobasal syndrome, 12 with vascular dementia, and 21 with behavioral variant frontotemporal dementia or primary progressive aphasia). The AUC of plasma Aβ42/Aβ40 was higher, however, when using neuropathologically confirmed cases (AUC of 0.72 for intermediate to high likelihood of AD [n = 34] vs non-AD [n = 47], where primary neuropathological diagnoses included PART (seven possible, 11 definite), 13 PD, three PSP, two VaD, three with white matter changes due to infarcts, one ALS, one multiple sclerosis, one showing diffuse astrocytoma, two CBD, one FTLD with TDP-43 pathology, and two with NFT predominant dementia) (Palmqvist et al, 2020). Prediction of AD dementia and cognitive decline in MCI Varying discriminative performance has been seen when differentiating MCI patients who converted to AD dementia from those that did not using plasma Aβ42/Aβ40 adjusted for age (AUC of 0.67) (Simren et al, 2021) and age, sex, education, and baseline MMSE (AUC ranging from 0.66 to 0.86 depending on cohort and assay) (Cullen et al, 2021a) (Table EV1). In a prospective study examining plasma Aβ42/Aβ40 and the risk of conversion from amnestic MCI to AD dementia, however, plasma Aβ42/Aβ40 at baseline adjusted for age, APOE ε4 status, and education carried an increased risk of progression (˜70%) to AD dementia over 2 years (Perez-Grijalba et al, 2019), with an AUC of 0.86 by comparison to stable MCI. Prediction of AD dementia and cognitive decline in CU In longitudinal studies that have examined the association between plasma Aβ42/Aβ40 and the risk of progression to MCI or AD dementia in CU individuals using up to 6 years follow-up, time-dependent receiver operating characteristic curves—a method which takes into account interindividual differences in follow-up and conversion times—showed that Simoa-based plasma Aβ42/Aβ40 had AUC values ≥ 0.85 across all yearly time points (range 0.85–0.92) (Stockmann et al, 2020). Plasma Aβ42/Aβ40 has also been shown to be associated with progression to both MCI and AD dementia (Verberk et al, 2018, 2021), independent of potential confounders (education; APOE carriership; or medication use for hypertension, hypercholesterolemia, and diabetes) and measures of neuroinflammation and neurodegeneration (plasma GFAP and NfL). Lower (more abnormal) plasma Aβ42/Aβ40 has also been shown to relate to a more pronounced decline in composite cognitive scores over time (adjusted for sex, age, education, treatment group, BMI, CDR, GDS score) (Giudici et al, 2020). This finding also held when using longitudinal MMSE scores. Similar results have been reported using domain-specific findings, including attention, memory, language, and executive functioning (Verberk et al, 2020a). Markers of tau pathology (P-tau181, P-tau217, and P-tau231) The identification of hyperphosphorylated tau as a major constituent of neurofibrillary tangles (Grundke-Iqbal et al, 1986a, 1986b) led to the development of CSF assays for P-tau (Iqbal & Grundke-Iqbal, 1997) targeting specific serine and threonine amino acid residues. Though multiple phosphorylation sites exist on the tau protein (Portelius et al, 2008), the most commonly used assays for P-tau detect phosphorylation at threonine 181 (P-tau181). Using this measure, increased CSF P-tau181 has been consistently shown in AD (Blennow et al, 2015). Using several different outcomes, however—including the separation of AD dementia from non-AD neurodegenerative disorders and correlations with amyloid and tau PET—P-tau217 has shown somewhat better performance than P-tau181 (Janelidze et al, 2020b). Recently, P-tau231 has also been detected in CSF (Buerger et al, 2003; Suarez-Calvet et al, 2020) and plasma (Ashton et al, 2021c). Although previous studies assumed that soluble P-tau measures reflected tau pathology in AD (Jack et al, 2018), direct correlations between soluble P-tau and neuropathology or PET measures of tau typically only found moderate correlations (Buerger et al, 2006, 2007; Mattsson et al, 2017b; La Joie et al, 2018). More recent studies have instead linked changes in soluble P-tau (for both CSF and plasma) to the accumulation of Aβ (Sato et al, 2018; Mattsson-Carlgren et al, 2020a) and shown that changes in soluble P-tau precede tau aggregation in AD as measured by PET or with neuropathology (Mattsson-Carlgren et al, 2021). Assays In contrast to CSF where commercial P-tau181 assays target mid-region forms of tau phosphorylated at threonine 181 (Vanderstichele et al, 2006; Leitao et al, 2019; Lifke et al, 2019), the development of assays for P-tau in blood have focused on N-terminal to mid-region tau fragments following the discovery that this is the predominant tau species in blood (Sato et al, 2018). For instance, Tatebe and colleagues (Tatebe et al, 2017) developed a Simoa-based plasma P-tau181 assay by replacing the detection antibody in an existing T-tau assay with a monoclonal antibody specific for P-tau181. This study was the first to report elevated levels of plasma P-tau181 in AD dementia, yet the assay suffered from insufficient analytical sensitivity. Using electrochemiluminescence (ECL)-based methods developed by Eli Lilly, significant increases in plasma P-tau181 and P-tau217 have been reported in AD (Mielke et al, 2018; Janelidze et al, 2020a). Novel P-tau181 and P-tau217 assays targeting P-tau isoforms containing the N-terminal amino acid 6–18 epitope were then later developed (Karikari et al, 2020b, 2021). Though designed for use with blood, these assays also work for the quantification of P-tau in CSF (Janelidze et al, 2020a; Karikari et al, 2020a; Suarez-Calvet et al, 2020). Two additional Simoa-based assays from Janssen targeting P-tau217 and phosphorylation at amino acid 212 have also been recently described (Triana-Baltzer et al, 2020, 2021) as well as a study providing a direct comparison of modified versions of the ECL-based Eli Lilly assays for P-tau217 and P-tau181 (Thijssen et al, 2021a) that differed only in their epitope-specific capture antibodies. Quantification of P-tau231 in plasma has also recently been described using a phospho-specific cis-conformational monoclonal antibody ADx253 as a capture antibody and a biotin-conjugated N-terminal anti-tau mouse monoclonal antibody for detection (Ashton et al, 2021c). A schematic overview of P-tau assays is provided in Fig 1. Figure 1. A schematic overview of the included P-tau assays Schematic illustration of full-length tau-441, including N-terminal, proline-rich region, microtubuli binding domain, and C-terminal. Anti-tau antibodies are indicated for each of the five included P-tau assays under the respective epitope region. P-tau181UGOT is the P-tau181 assay from the University of Gothenburg, as detailed in Karikari et al (2021). For P-tau231ADx, the inverted V symbol represents a biotin-conjugated N-terminal anti-tau mouse monoclonal antibody, as detailed in Ashton et al (2021c). Download figure Download PowerPoint Differential diagnosis of AD dementia Increasingly, biomarkers are being incorporated into the clinical work-up of patients presenting with cognitive impairment, in part due to the difficulty in differentiating AD from related non-AD neurodegenerative disorders early on in the disease course (Beach et al, 2012; Salloway et al, 2014). In recent studies that have examined AD plasma biomarkers (Janelidze et al, 2020a; Karikari et al, 2020b; Palmqvist et al, 2020; Thijssen et al, 2020), diagnostic accuracies (AUC) for the separation of AD from non-AD dementia disorders using clinical diagnosis as the standard of truth have ranged from 0.81 (Palmqvist et al, 2020) to 0.89 (Thijssen et al, 2020). In a larger cohort, plasma P-tau181 was shown to differentiate AD dementia from non-AD dementia disorders with a somewhat higher AUC (0.94), similar to those achieved using CSF P-tau181 or tau PET (Janelidze et al, 2020a). More importantly, plasma P-tau181 has been shown to be able to differentiate AD dementia from other neurodegenerative disorders using neuropathologically confirmed cases, with AUCs ranging from 0.85 (Janelidze et al, 2020a) to 0.95 (Thijssen et al, 2020). High accuracy for plasma P-tau181 for discriminating AD from non-pathologies when using neuropathological diagnoses as standard of truth was also reported (AUC of 0.97) using measures taken 8 years prior to autopsy (Lantero Rodriguez et al, 2020). Using plasma P-tau217, AD dementia cases were separated from non-AD disorders with an AUC of 0.96 (Palmqvist et al, 2020) (Table EV1). Using neuropathologically confirmed cases, AUCs ranged from 0.89 to 0.98 (Palmqvist et al, 2020) for intermediate to high likelihood of AD, respectively (Mirra et al, 1991; 1997). In a recent head-to-head study comparing plasma P-tau181 and P-tau217 (Thijssen et al, 2021a)—measured using electrochemiluminescence-based assays differing only in the biotinylated antibody epitope—though both measures showed high AUC values for differentiating pathology-confirmed AD from pathologically confirmed FTLD, P-tau217 slightly outperformed P-tau181 (AUC 0.93 vs 0.91) when separating clinically diagnosed AD from FTLD syndromes. Plasma P-tau231 has also been recently shown to have high accuracy for AD using clinical (AUC of 0.93) and neuropathological diagnoses (AUC of 0.99) using samples drawn 4.2 years on average prior to post-mortem (Ashton et al, 2021c). Prediction of AD dementia and cognitive decline in MCI In patients with MCI, baseline plasma P-tau181 levels have been shown to be increased in those who progressed to AD dementia compared to those that did not develop dementia or that developed dementia due to other causes (Janelidze et al, 2020a; Therriault et al, 2021) (Table EV2). Plasma P-tau181 levels were also elevated in Aβ-positive MCI who progressed to AD dementia by comparison to both Aβ-positive and Aβ-negative CU and MCI who did not convert to AD dementia. After adjusting for age, sex, and education, higher baseline plasma P-tau181 levels were associated with a greater risk of progression to AD dementia, with a hazard ratio nearly identical to that for CSF P-tau181. Similar findings were obtained when adjusting plasma P-tau181 for plasma T-tau, Aβ42/Aβ40, and NfL (Janelidze et al, 2020a). In a related study, survival analysis showed that high baseline plasma P-tau181 was associated with an increased risk of progression to AD dementia in MCI over 84 months, as compared with Aβ-negative CU, with similar findings observed using a shorter follow-up interval of 48 months (Karikari et al, 2021). Using both follow-up intervals, the performance of plasma P-tau181 was similar to that for