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Background: Optical Coherence Tomography angiography (OCT‐A) is a novel tool that allows the detection of retinal vascular changes. The comparison of OCT‐A measures with brain vascular burden is essential to validate the former as a surrogate of cerebrovascular damage and biomarker of cognitive decline. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus assessed by OCT‐A with measures of cerebrovascular pathology quantified by brain magnetic resonance imaging (MRI) in a cohort of non‐demented individuals. Method: Clinical, demographical, APOE ε4 status, amyloid status, OCT‐A and brain MRI data from non‐demented participants of the FACEHBI and BIOFACE studies were included. We analyzed the association of macular VD in four quadrants (superior, nasal, inferior and temporal) with brain vascular burden using the Fazekas scale scores (dichotomized in two categories, 0‐1 vs 2‐3), assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) quantified by Freesurfer assessed in a multiple regression analysis. We additionally explored the associations of macular VD with hippocampal volume, ventricles volume and Alzheimer Disease cortical signature (ADCS) thickness assessed in a multiple regression analysis. All analyses were adjusted for age and dyslipidemia. Result: The study cohort comprised 188 participants: 89 with subjective cognitive decline (SCD) and 99 with mild cognitive impairment (MCI). Logistic regression analysis showed no significant differences of regional macular VD between the two Fazekas groups (all, p>0.05). Multiple regression analysis showed no significant association of regional macular VD with the volume of WMH (all, p>0.05). Macular VD in the nasal and inferior quadrants were positively associated with hippocampal volume (p = 0.009 and p = 0.021, respectively), but no association was detected with ventricles volume or ADCS thickness (both p>0.05). Conclusion: Our study showed that retinal vascular measures (regional macular VD in the superficial vascular plexus) assessed by OCT‐A were not associated with brain vascular damage quantified by the Fazekas scale and the WMH burden n in a cohort of non‐demented research participants with SCD and MCI. [ABSTRACT FROM AUTHOR]

Background: Clinical diagnosis of Alzheimer's disease (AD) increasingly incorporate CSF biomarkers. However due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, it is recommended to establish in‐house cutoffs defining the positivity/negativity of CSF biomarkers. Method: We quantified CSF Aβ1‐42, t‐Tau, p181Tau and Aβ1‐40 with standard INNOTEST® ELISA and Lumipulse G® chemiluminescence enzyme‐immunoassay (CLEIA) performed on automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with Alzheimer Disease (AD, n=37) and amyloid‐negative Subjective Cognitive Decline subjects (SCD, n=45), cognitively stable during 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) were analyzed. Kappa agreement of single biomarkers and Aβ1‐42/Aβ1‐40 was performed in an independent group of mild cognitive impairment (MCI) and dementia patients (n=68). Result: Cutoffs 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 were good but lower for Aβ1‐42/Aβ1‐40. Conclusion: We established internal cutoffs to discriminate AD patients from amyloid‐negative SCD individuals. Results obtained by both methods are not interchangeable but showed good agreement. We propose automated CLEIA as a good alternative to manual ELISA together with Aβ1‐42/Aβ1‐40 to classify amyloidosis. [ABSTRACT FROM AUTHOR]