José Contador’s research while affiliated with Parc de Salut Mar and other places

Global implementation of blood tests for Alzheimer’s disease (AD) would be facilitated by easily scalable, cost-effective and accurate tests. In the present study, we evaluated plasma phospho-tau217 (p-tau217) using predefined biomarker cutoffs. The study included 1,767 participants with cognitive symptoms from 4 independent secondary care cohorts in Malmö (Sweden, n = 337), Gothenburg (Sweden, n = 165), Barcelona (Spain, n = 487) and Brescia (Italy, n = 230), and a primary care cohort in Sweden (n = 548). Plasma p-tau217 was primarily measured using the fully automated, commercially available, Lumipulse immunoassay. The primary outcome was AD pathology defined as abnormal cerebrospinal fluid Aβ42:p-tau181. Plasma p-tau217 detected AD pathology with areas under the receiver operating characteristic curves of 0.93–0.96. In secondary care, the accuracies were 89–91%, the positive predictive values 89–95% and the negative predictive values 77–90%. In primary care, the accuracy was 85%, the positive predictive values 82% and the negative predictive values 88%. Accuracy was lower in participants aged ≥80 years (83%), but was unaffected by chronic kidney disease, diabetes, sex, APOE genotype or cognitive stage. Using a two-cutoff approach, accuracies increased to 92–94% in secondary and primary care, excluding 12–17% with intermediate results. Using the plasma p-tau217:Aβ42 ratio did not improve accuracy but reduced intermediate test results (≤10%). Compared with a high-performing mass-spectrometry-based assay for percentage p-tau217, accuracies were comparable in secondary care. However, percentage p-tau217 had higher accuracy in primary care and was unaffected by age. In conclusion, this fully automated p-tau217 test demonstrates high accuracy for identifying AD pathology. A two-cutoff approach might be necessary to optimize performance across diverse settings and subpopulations.

INTRODUCTION Blood‐based biomarkers for Alzheimer's disease (AD) have been widely studied, but direct comparisons of several biomarkers in clinical settings remain limited. METHODS In this cross‐sectional study, plasma biomarkers from 197 participants in the BIODEGMAR cohort (Hospital del Mar, Barcelona) were analyzed. Participants were classified based on AD cerebrospinal fluid (CSF) core biomarkers. We assessed the ability of plasma p‐tau181, p‐tau217, p‐tau231, t‐tau, and Aβ42/40 to classify Aβ pathology status. RESULTS Plasma p‐tau biomarkers had a greater diagnostic performance and larger effect sizes compared to t‐tau and Aβ42/40 assays in detecting Aβ pathology. Among them, plasma p‐tau217 consistently outperformed the others, demonstrating superior area under the curves. Furthermore, p‐tau217 showed the strongest correlation between plasma and CSF levels, underscoring its potential as a reliable surrogate for CSF biomarkers. DISCUSSION Several plasma biomarkers, targeting different epitopes and using different platforms, demonstrated high performance in detecting AD in a memory clinic setting. Highlights Plasma p‐tau biomarkers demonstrated higher diagnostic performance and larger effect sizes than t‐tau and Aβ42/40 assays in detecting Alzheimer's disease. Among the p‐tau biomarkers, p‐tau217 assays consistently outperformed the others, providing superior classification of Aβ pathology status across different phosphorylation sites. p‐tau217 assays showed the strongest correlation between plasma and CSF levels, indicating its potential as a reliable surrogate for CSF biomarkers. Several plasma p‐tau biomarkers can be used in a specialized memory clinic to accurately detect Alzheimer's disease.

Background Alzheimer's disease (AD) is a devastating neurodegenerative disease with delayed diagnosis until the manifestation of symptoms. Although the emergence of blood‐based biomarkers offers hope for easy detection of AD, existing AD‐associated blood biomarkers, known as the “blood ATN biomarkers”, mainly capture the pathological hallmarks of AD, overlooking other AD‐associated biological processes such as inflammation and vascular dysfunctions. Therefore, developing a blood‐based biomarker assay that captures dysregulation beyond the ATN biomarkers may help advance early detection and staging of AD, enabling a comprehensive examination of the disease status Method We leveraged ultrasensitive proteomic technology to develop a blood‐based, multiplex biomarker assay for AD. This assay simultaneously measures the levels of 21 blood proteins associated with different biological pathways, including neurodegeneration, inflammation, innate immunity, vascular functions, and metabolic activities. Moreover, we developed an AD risk scoring system by integrating the level changes of these 21 proteins in a machine learning‐based model. The performance of this 21‐protein biomarker assay in AD classification and indication of AD‐related endophenotypes was evaluated in three independent cohorts Result We showed that this 21‐protein biomarker assay accurately classifies AD (AUC = 0.9407–0.9867) and mild cognitive impairment (MCI) (AUC = 0.8434–0.8945). It also indicates amyloid pathology in Chinese and European populations. Moreover, this assay simultaneously evaluates changes in five biological processes, providing comprehensive assessment of disease status and revealing heterogeneity of AD among individuals. Notably, dysregulations of biological processes upon AD progression are different between Chinese and European populations, particularly in biological pathways related to inflammation and vascular functions. Conclusion Our findings demonstrate the utility of a blood‐based multi‐pathway biomarker assay for early screening and staging of AD in clinical settings and provide insights for patient stratification and the development of precision medicine.

Background Blood‐based biomarkers offer a non‐invasive and cost‐effective means for Alzheimer's disease (AD) detection. In this study, we performed a direct comparison of these novel biomarkers in a memory clinic population to facilitate their implementation into clinical practice. Method We included a total of 208 patients with cognitive complaints from the BIODEGMAR cohort at Hospital del Mar (Barcelona, Spain). CSF was used as standard‐of‐truth and patients were categorized as having an AD CSF profile with two approaches: Lumipulse CSF Aβ42/p‐tau181<10.25 (N=208) or Elecsys® p‐tau181/Aβ42>0.022 (N=157). The following biomarkers were measured in paired plasma and CSF samples: Aβ42 and p‐tau181 (Lumipulse CSF , plasma , Fujirebio), Aβ42 and p‐tau181 (NeuroToolKit (NTK) a panel of robust prototype assays (Roche Diagnostics International Ltd), and p‐tau181, p‐tau217, p‐tau231 and MAP‐T (NULISA, Alamar). P‐value and effect size of the group comparison (AD vs non‐AD CSF profiles) were calculated using a Mann‐Whitney U test. ROC curve analysis evaluated plasma biomarkers accuracy to discriminate AD from non‐AD CSF profiles. Spearman test was used to assess the correlation between plasma and CSF biomarkers. Result Demographic information of the study cohort is reported in Table 1 for both classification thresholds. All plasma biomarkers were significantly different between the AD and non‐AD CSF profile groups, but the effect size varied among them. Specifically, NULISA p‐tau217, Lumipulse p‐tau181 and NULISA p‐tau231 reported the greatest effect sizes for the Lumipulse threshold and NULISA p‐tau217, NTK p‐tau181, Lumipulse p‐tau181 and NULISA p‐tau231 reported the greatest effect sizes for the Elecsys® threshold (Table 1). ROC curve analysis performed consistently over the two thresholds with the following discrimination accuracy (AUC) values (from highest to lowest): NULISA p‐tau217, NTK p‐tau181, NULISA p‐tau231, Lumipulse p‐tau181, NULISA p‐tau181 (Figure 1). In comparison to the p‐tau assays, Aβ42 reported modest effect size and AUCs. NULISA p‐tau217 and NULISA p‐tau231 had the highest correlation between plasma and CSF (Spearman ρ=0.75 and ρ=0.60, respectively, with both FDR corrected p‐values<0.001), Figure 2. Conclusion In a memory clinic population, various plasma Aβ and tau plasma biomarkers, targeting different epitope and using different platforms, demonstrated high performance in distinguishing patients with biologically defined AD from those without.

Background Arterial spin labelling (ASL) is a non‐invasive MRI technique for quantifying cerebral blood flow (CBF), used for monitoring changes over the course of a disease or treatment. A crucial parameter in ASL is the post‐labelling delay (PLD), determined by the time it takes for blood to travel from the labeling location to the tissue under investigation. Time‐encoded ASL (te‐ASL) utilizes multiple PLDs for more accurate quantification. This study aims to enhance our understanding of CBF changes across the AD continuum, emphasizing the utility of te‐ASL over single‐PLD ASL in detecting early CBF changes. Method Fifty‐nine adults (≥ 60 years) along the AD continuum (24 cognitively unimpaired [CU] Aβ‐, 18 CU Aβ+, and 17 cognitively impaired [CI] Aβ+; Table 1) underwent CBF measurements using te‐ASL. Single‐PLD CBF measurements were derived based on the longest PLD (2000 ms) of the te‐ASL acquisition. CBF measurements were averaged across brain areas previously reported to be hypoperfused in AD. Associations between mean CBF and CSF biomarkers of Aβ, phosphorylated tau (pTau) proteins, synaptic dysfunction (GAP43, neurogranin, SNAP25, synaptotagmin‐1), and neurodegeneration (neurofilament light [NfL]), as well as cognitive scores, were investigated in CU participants. CSF Aβ42 and Aβ40 were assessed with Roche NeuroToolKit immunoassays, while pTau181 was measured with the Elecsys® Phospho‐Tau (181P) CSF immunoassay (Roche Diagnostics International Ltd). Sex and age were confounding variables. Result te‐ASL exhibited superior sensitivity in detecting CBF hypoperfusion in CI Aβ+ subjects compared to single‐PLD ASL (Figure 1). Te‐ASL also revealed CBF hypoperfusion in CU Aβ+ subjects. In CU individuals, lower CBF correlated with decreased levels of CSF Aβ42/40 and increased levels of CSF pTau181, GAP43, neurogranin, SNAP25, and NfL (p < 0.05; Figure 2). No association was found between mean CBF and cognitive scores. Conclusion This study provides compelling evidence that CBF reduction occurs earlier in the AD continuum than previously thought. te‐ASL emerges as a more sensitive tool than single‐PLD ASL for detecting subtle CBF changes across AD stages. Importantly, lower CBF in CU individuals correlates with multiple AD biomarkers, highlighting its potential as an early biomarker for AD progression.

Plasma phosphorylated-tau217 (p-tau217) has been shown to be one of the most accurate diagnostic markers for Alzheimer’s disease. No studies have compared the clinical performance of p-tau217 as assessed by the fully automated Lumipulse and single molecule array (SIMOA) AlZpath p-tau217. The study included 392 participants, 162 with Alzheimer’s disease, 70 with other neurodegenerative diseases with CSF biomarkers and 160 healthy controls. Plasma p-tau217 levels were measured using the Lumipulse and ALZpath SIMOA assays. The ability of p-tau217 assessed by both techniques to discriminate Alzheimer’s disease from other neurodegenerative diseases and controls was investigated using receiver operating characteristic analyses. The p-tau217 levels measured by the two techniques demonstrated a strong correlation, showing a consistent relationship with CSF p-tau181 levels. In head-to-head comparison, Lumipulse and SIMOA showed similar diagnostic accuracy for differentiating Alzheimer’s disease from other neurodegenerative diseases [area under the curve (AUC) 0.952, 95% confidence interval (CI) 0.927–0.978 versus 0.955, 95% CI 0.928–0.982, respectively] and healthy controls (AUC 0.938, 95% CI 0.910–0.966 and 0.937, 95% CI 0.907–0.967 for both assays). This study demonstrated the high precision and diagnostic accuracy of p-tau217 for the clinical diagnosis of Alzheimer’s disease using fully automated or semi-automated techniques.

Background Alzheimer’s disease (AD) blood biomarkers alone can detect amyloid‐β (Aβ) pathology in cognitively unimpaired (CU) individuals. We assessed whether combining different plasma biomarkers improves the detection of Aβ‐positivity and identifies rapid amyloid deposition in CU individuals. Method CU participants from the ALFA+ cohort were included. Among them, 361 had CSF Aβ42/40 and 328 amyloid PET‐scans [194 with two longitudinal scans; mean interval=3.35 (0.56) years]. Plasma Aβ42/40, p‐tau181, p‐tau231, GFAP, NfL (Simoa‐based) and p‐tau217 and t‐tau (MSD‐based) were measured at baseline (Table 1). We used simple and multiple logistic models to estimate Aβ‐positivity (defined as CSF Aβ42/40<0.071 or amyloid‐PET>12 Centiloids) or Aβ accumulation rate (“Fast accumulators” defined as >3 Centiloids/year). The model contained plasma biomarkers and demographics (age and sex) as covariates. We selected as "best model" (BM) that with lowest AIC. We defined parsimonious models as those with an AUC not significantly different (DeLong test) from BM or from each other yet outperforming single biomarkers and/or demographics models (FDR corrected). For the positive agreement closest to 90%, we calculated savings in lumbar punctures and amyloid PET‐scans. Result For CSF Aβ‐positive detection, BM included plasma Aβ42/40, p‐tau181, p‐tau217, p‐tau231, GFAP and t‐tau (AUC=0.84). All simpler biomarkers combinations included plasma Ab42/40 and p‐tau231 (Table 2A). For PET Ab‐positive detection, BM included plasma Aβ42/40, p‐tau181, p‐tau217, GFAP, NFL and age (AUC=0.88). All simpler biomarkers combinations included plasma Ab42/40 and p‐tau217 (Table 2B). Regarding fast accumulators’ detection, plasma p‐tau217 was the single biomarker with the highest performance (AUC=0.70). BM included plasma Aβ42/40, p‐tau217, p‐tau231 and GFAP (AUC= 0.76). BM and the plasma Aβ42/40, p‐tau217 and GFAP (AUC=0.75) combination were the only models that outperformed the age and sex combination and single biomarkers, except for plasma p‐tau217, Aβ42/40 (AUC=0.69) or GFAP (AUC=0.68) alone (Table 2C). The combination of biomarkers could save up to 11% of lumbar punctures or 44% of amyloid‐PET to detect Ab‐positive CU individuals and 16% amyloid‐PETs to detect fast Aβ‐accumulation compared to the best single plasma biomarker (Table 2). Conclusion In CU individuals, diverse combinations of plasma biomarkers detect Aβ‐positivity and future Aβ‐accumulation with high accuracy and can lead to substantial cost savings in AD detection.

Recently approved anti-amyloid immunotherapies for Alzheimer’s disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing.

INTRODUCTION: Blood-based biomarkers for Alzheimer's disease (AD) have been widely studied, but direct comparisons of several biomarkers in clinical settings remain limited. METHODS: In this cross-sectional study, plasma biomarkers from 197 participants in the BIODEGMAR cohort at Hospital del Mar (Barcelona) were analysed. Participants were classified based on AD cerebrospinal fluid (CSF) core biomarkers. We assessed the ability of plasma p-tau181, p-tau217, p-tau231, t-tau, and Aβ42/40 to classify Aβ status. RESULTS: Plasma p-tau biomarkers had a greater diagnostic performance and larger effect sizes compared to t-tau and Aβ42/40 assays in detecting biologically defined AD. Among them, plasma p-tau217 consistently outperformed the others, demonstrating superior AUC. Furthermore, p-tau217 showed the strongest correlation between plasma and CSF levels, underscoring its potential as a reliable surrogate for CSF biomarkers. DISCUSSION: Several plasma biomarkers, targeting different epitopes and using different platforms, demonstrated high performance in distinguishing biologically defined AD in a memory clinic setting.