Search

Background: The VALCODIS (Valencian Cognitive Diseases Study) cohort was designed and studied at the Hospital Universitari i Politècnic La Fe (Valencia, Spain) for the research of cognitive diseases, especially in the search for new biomarkers of Alzheimer's disease (AD). Methods: Participants in the VALCODIS cohort had cerebrospinal fluid (CSF) and blood samples, neuroimaging, and neuropsychological tests. The ApoE genotype was evaluated to identify its relationship with CSF biomarkers and neuropsychological tests in AD and non-AD participants. Results: A total of 1249 participants were included. They were mainly AD patients (n = 547) but also patients with other dementias (frontotemporal lobar dementia (n = 61), Lewy body dementia without AD CSF signature (n = 10), vascular dementia (n = 24) and other specific causes of cognitive impairment (n = 442), and patients with subjective memory complaints (n = 165)). In the ApoE genotype evaluation, significant differences were found for Aβ42 levels between genotypes in both AD and non-AD patients, as well as a negative correlation between tau values and a cognitive test in non-carriers and ε4 heterozygous. Conclusions: The VALCODIS cohort provides biologically diagnosed patients with demographical, clinical and biochemical data, and biological samples for further studies on early AD diagnosis. Also, the ApoE genotype evaluation showed correlations between CSF biomarkers and neuropsychological tests. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a complex disease that shares clinical features with other dementias. It is important to establish a specific and reliable diagnosis. Nowadays, AD diagnosis is based on cerebrospinal fluid (CSF) biomarkers. However, the corresponding cut‐offs differ amongst studies. This study aims to evaluate the CSF biomarkers in the AD differential diagnosis. Methods: Clinical relevant biomarkers (amyloid β42 (Aβ42), t‐Tau, p‐Tau, amyloid β40 (Aβ40), neurofilament light chain (NfL)) were determined in CSF samples from participants classified as AD (n = 124) and non‐AD (n = 148) patients from the Neurology Unit. They were included and evaluated consecutively (August 2018–October 2020). The clinical utility of these biomarkers was evaluated by AUC‐ROC curves and the corresponding cut‐off points were defined. Results: The results showed satisfactory accuracy (AUC‐ROC 0.91 for Aβ42, 0.890 for t‐Tau and 0.933 for p‐Tau); whilst Aβ40 and NfL did not show good discriminatory capacity (AUC‐ROC 0.557 and 0.738, respectively). The ratios Aβ42/Aβ40 and t‐Tau/Aβ42 improved the diagnosis indices of each individual biomarker, with AUC‐ROC of 0.980 and 0.971, respectively. Also, elevated levels of NfL were found in the frontotemporal dementia group compared with the other participant groups. Conclusions: The ratio Aβ42/Aβ40 showed the highest discriminating capacity between AD and non‐AD patients and might be useful in clinical practice. Regarding NfL, it is not a specific biomarker for AD; however, it might be helpful for the differential diagnosis of frontotemporal dementia. Nevertheless, further analysis in an external cohort is required in order to validate these results. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is the most common neurodegenerative disease worldwide. So, there is a need to identify AD early diagnosis and monitoring biomarkers in blood samples. The aim of this study was to analyse the utility of lipid peroxidation biomarkers in AD progression evaluation. Participants (n = 19) were diagnosed with AD at early stages (Time 0, T0), and they were re‐evaluated 2 years later (Time 1, T1). Plasma biomarkers from AD patients were determined at both times. Some analytes, such as dihomo‐isoprostanes (17‐epi‐17‐F2t‐dihomo‐IsoP, 17‐F2t‐dihomo‐IsoP, Ent‐7(RS)‐7‐F2t‐dihomo‐IsoP), and neuroprostanes (10‐epi‐10‐F4t‐NeuroP) showed very high probability of showing an increasing trend over time. Baseline values allowed to develop an affordable preliminary regression model to predict long‐term cognitive status. So, some lipid peroxidation biomarkers would deserve consideration as useful progression AD biomarkers. The developed prediction model would constitute an important minimally invasive approach in AD personalized prognosis and perhaps could have some interest also in experimental treatments evaluation. [ABSTRACT FROM AUTHOR]

Lipid peroxidation plays an important role in Alzheimer Disease, so corresponding metabolites found in urine samples could be potential biomarkers. The aim of this work is to develop a reliable ultra-performance liquid chromatography-tandem mass spectrometry analytical method to determine a new set of lipid peroxidation compounds in urine samples. Excellent sensitivity was achieved with limits of detection between 0.08 and 17 nmol L −1 , which renders this method suitable to monitor analytes concentrations in real samples. The method's precision was satisfactory with coefficients of variation around 5–17% (intra-day) and 8–19% (inter-day). The accuracy of the method was assessed by analysis of spiked urine samples obtaining recoveries between 70% and 120% for most of the analytes. The utility of the described method was tested by analyzing urine samples from patients early diagnosed with mild cognitive impairment or mild dementia Alzheimer Disease following the clinical standard criteria. As preliminary results, some analytes (17( RS )−10- epi -SC-Δ 15 −11-dihomo-IsoF, PGE 2 ) and total parameters (Neuroprostanes, Isoprostanes, Isofurans) show differences between the control and the clinical groups. So, these analytes could be potential early Alzheimer Disease biomarkers assessing the patients’ pro-oxidant condition. [ABSTRACT FROM AUTHOR]

Intoduction: The heterogenicity in Alzheimer's Disease (AD) progression hinders individual prognosis. The present work is an observational 2-year longitudinal study in patients with mild cognitive impairment due to AD (n= 52, with positive CSF biomarkers). The aim of this study is to predict which patients are at risk of fast progression. For this, 3 neuropsychological tests based on different domains (clinical dementia, cognition, delayed memory) and the sum of them were used., Method: The tests were performed at diagnosis time (T1) and two years after the diagnosis time (T2). Then, the corresponding progression models were developed using each individual test and their sum as a variable response., Results: As a result, the model based on cognition status to predict fast decline (differences in the Z score (T2-T1) <1.5 were considered fast declining) provided satisfactory performance (AUC 0.74, 83.3% of sensibility and 70.2% of specificity); the models based on clinical dementia and delayed memory to predict fast declining showed low AUC and sensitivity. Nevertheless, the model based on the sum of the 3 tests showed the highest AUC (0.79), low sensitivity (63.6%), and high specificity., Conclusion: The developed progression models could provide useful information to clinicians and AD patients regarding their fast/normal decline in general or specific domains., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)