scholarly journals Biological Markers and Alzheimer Disease: A Canadian Perspective

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Hyman M. Schipper

Decreased -amyloid1-42and increased phospho-tau protein levels in the cerebrospinal fluid (CSF) are currently the most accurate chemical neurodiagnostics of sporadic Alzheimer disease (AD). A report (2007) of the Third Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (2006) recommended that biological markers shouldnotbe currently requisitioned by primary care physicians in the routine investigation of subjects with memory complaints. Consideration for such testing should prompt patient referral to a specialist engaged in dementia evaluations or a Memory Clinic. The specialist should consider having CSF biomarkers (-amyloid1-42and phospho-tau) measured at a reputable facility in restricted cases presenting with atypical features and diagnostic confusion, but not as a routine procedure in all individuals with typical sporadic AD phenotypes. We submit that developments in the field of AD biomarker discovery since publication of the 3rd CCCDTD consensus data do not warrant revision of the 2007 recommendations.

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1043
Author(s):  
Carmen Peña-Bautista ◽  
Lourdes Álvarez-Sánchez ◽  
Inés Ferrer ◽  
Marina López-Nogueroles ◽  
Antonio José Cañada-Martínez ◽  
...  

Background: Alzheimer disease (AD) is an increasingly common neurodegenerative disease, especially in countries with aging populations. Its diagnosis is complex and is usually carried out in advanced stages of the disease. In addition, lipids and oxidative stress have been related to AD since the earliest stages. A diagnosis in the initial or preclinical stages of the disease could help in a more effective action of the treatments. Methods: Isoprostanoid biomarkers were determined in plasma samples from preclinical AD participants (n = 12) and healthy controls (n = 31) by chromatography and mass spectrometry (UPLC-MS/MS). Participants were accurately classified according to cerebrospinal fluid (CSF) biomarkers and neuropsychological examination. Results: Isoprostanoid levels did not show differences between groups. However, some of them correlated with CSF biomarkers (t-tau, p-tau) and with cognitive decline. In addition, a panel including 10 biomarkers showed an area under curve (AUC) of 0.96 (0.903–1) and a validation AUC of 0.90 in preclinical AD prediction. Conclusions: Plasma isoprostanoids could be useful biomarkers in preclinical diagnosis for AD. However, these results would require a further validation with an external cohort.


Author(s):  
S.C Burnham ◽  
P.M. Coloma ◽  
Q.-X. Li ◽  
S. Collins ◽  
G. Savage ◽  
...  

BACKGROUND: The National Institute on Aging and Alzheimer’s Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer’s disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer’s disease. OBJECTIVES: To stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)). DESIGN: Individuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A-T-(N)-; A+T-(N)-; A+T+(N)-; A+T-(N)+; A+T+(N)+; A-T+(N)-; A-T-(N)+; A-T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers. SETTING: Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study. PARTICIPANTS: One-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays. INTERVENTION (if any): Not applicable. MEASUREMENTS: Three CSF biomarkers, namely amyloid β1-42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test – Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores. RESULTS: Thirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework’s definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures. CONCLUSIONS: Increasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials.


2017 ◽  
Vol 13 (7S_Part_21) ◽  
pp. P1014-P1014
Author(s):  
Suzanne E. Schindler ◽  
Elizabeth M. Herries ◽  
Jack H. Ladenson ◽  
John C. Morris ◽  
Anne M. Fagan

2018 ◽  
Vol 75 (4) ◽  
pp. 488 ◽  
Author(s):  
Yen Ying Lim ◽  
Pawel Kalinowski ◽  
Robert H. Pietrzak ◽  
Simon M. Laws ◽  
Samantha C. Burnham ◽  
...  

Hippocampus ◽  
2010 ◽  
Vol 22 (5) ◽  
pp. 1040-1050 ◽  
Author(s):  
Ana Ricobaraza ◽  
Mar Cuadrado-Tejedor ◽  
Sonia Marco ◽  
Isabel Pérez-Otaño ◽  
Ana García-Osta

2004 ◽  
Vol 35 (3) ◽  
pp. 232-245 ◽  
Author(s):  
Barbara Borroni ◽  
Monica DiLuca ◽  
Flaminio Cattabeni ◽  
Alessandro Padovani

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
S. Y. Liao ◽  
N. G. Casanova ◽  
C. Bime ◽  
S. M. Camp ◽  
H. Lynn ◽  
...  

AbstractThe lack of successful clinical trials in acute respiratory distress syndrome (ARDS) has highlighted the unmet need for biomarkers predicting ARDS mortality and for novel therapeutics to reduce ARDS mortality. We utilized a systems biology multi-“omics” approach to identify predictive biomarkers for ARDS mortality. Integrating analyses were designed to differentiate ARDS non-survivors and survivors (568 subjects, 27% overall 28-day mortality) using datasets derived from multiple ‘omics’ studies in a multi-institution ARDS cohort (54% European descent, 40% African descent). ‘Omics’ data was available for each subject and included genome-wide association studies (GWAS, n = 297), RNA sequencing (n = 93), DNA methylation data (n = 61), and selective proteomic network analysis (n = 240). Integration of available “omic” data identified a 9-gene set (TNPO1, NUP214, HDAC1, HNRNPA1, GATAD2A, FOSB, DDX17, PHF20, CREBBP) that differentiated ARDS survivors/non-survivors, results that were validated utilizing a longitudinal transcription dataset. Pathway analysis identified TP53-, HDAC1-, TGF-β-, and IL-6-signaling pathways to be associated with ARDS mortality. Predictive biomarker discovery identified transcription levels of the 9-gene set (AUC-0.83) and Day 7 angiopoietin 2 protein levels as potential candidate predictors of ARDS mortality (AUC-0.70). These results underscore the value of utilizing integrated “multi-omics” approaches in underpowered datasets from racially diverse ARDS subjects.


2019 ◽  
Author(s):  
Daniel Stamate ◽  
Min Kim ◽  
Petroula Proitsi ◽  
Sarah Westwood ◽  
Alison Baird ◽  
...  

AbstractINTRODUCTIONMachine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer’s Disease (AD). Here we set out to test the performance of metabolites in blood to categorise AD when compared to CSF biomarkers.METHODSThis study analysed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n=883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV).RESULTSOn the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively.DISCUSSIONThis study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders


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