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Background: Clinical diagnosis of Alzheimer's disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST® ELISA and Lumipulse G® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer's disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815−27). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects. [ABSTRACT FROM AUTHOR]

Background: Stroke recurrence (SR) after an ischemic stroke is an important cause of death and disability. We conducted a hospital-based study to evaluate the role of biological age (b-Age: age-related DNA-methylation changes) as a risk factor for SR. Methods: We included 587 patients in the acute phase of stroke, assessed at one tertiary stroke center (Hospital del Mar: Barcelona, Spain). B-Age was estimated with 5 different methods based on DNA methylation, and Hannum's method was the one that better performed. We analyzed the relationships between b-Age, chronological age, sex, vascular risk factors, coronary and peripheral arterial disease, atrial fibrillation, initial neurological severity assessed by National Institutes of Health Stroke Scale (NIHSS), transient ischemic attack (TIA) in the 7 days preceding the index stroke, and symptomatic atherosclerosis. Stroke recurrence definition include: new symptoms that suggest a new ischemic event had occurred within 3 months after stroke onset and worsening by four points in the initial neurological severity (measured by National Institutes of Health Stroke Scale (NIHSS) score). Results: Logistic regression analysis associated b-Age with SR [p = 0.003; OR = 1.06 (95% CI: 1.02–1.09)], independently of chronological age [p = 0.022; OR = 0.96 (95% CI 0.94–1.00)], symptomatic atherosclerosis (stenosis > 50% in the symptomatic territory), transient ischemic attack (TIA) in the 7 days preceding the index stroke, and initial NIHSS. The b-Age of patients with SR was 2.7 years older than patients without SR. Conclusions: Patients with SR were biologically older than those without SR. B-Age was independently associated with high risk of developing SR. [ABSTRACT FROM AUTHOR]