The lack of readily available biomarkers is a significant hindrance towards progressing to effective therapeutic and preventative strategies for Alzheimer’s disease (AD). Blood-based biomarkers have potential to overcome access and cost barriers and greatly facilitate advanced neuroimaging and cerebrospinal fluid biomarker approaches. Despite the fact that preanalytical processing is the largest source of variability in laboratory testing, there are no currently available standardized preanalytical guidelines. The current international working group provides the initial starting point for such guidelines for standardized operating procedures (SOPs). It is anticipated that these guidelines will be updated as additional research findings become available. The statement provides (1) a synopsis of selected preanalytical methods utilized in many international AD cohort studies, (2) initial draft guidelines/SOPs for preanalytical methods, and (3) a list of required methodological information and protocols to be made available for publications in the field in order to foster cross-validation across cohorts and laboratories.

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ContextThere is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer's disease (AD) at the population level.ObjectiveTo generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.Design, Setting, ParticipantsAnalysis of serum biomarker proteins were conducted on 197 Alzheimer's disease (AD) participants and 199 control participants from the Texas Alzheimer's Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.Major Outcome MeasuresAlzheimer's disease.Results11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49–14.47), the likelihood ratio of not having AD based on the algorithm (LR−) = 3.55 (SE = 1.15; 2.22–5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86–69.47).ConclusionsIt is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.

Background There is a significant need for rapid and cost-effective biomarkers of Alzheimer’s disease (AD) for advancement of clinical practice and therapeutic trials. Objective The aim of the current study was to cross-validate our previously published serum-based algorithm on an independent assay platform as well as validate across tissues and species. Preliminary analyses were conducted to examine the utility in distinguishing AD from non-AD neurological disease (Parkinson’s Disease). Methods Serum proteins from our previously published algorithm were quantified from 150 AD cases and 150 controls on the Meso Scale Discovery (MSD) platform. Serum samples were analyzed from 49 Parkinson’s disease (PD) cases and compared to a random sample of 51 AD cases and 62 controls. Support vector machines (SVM) were used to discriminate PD vs. AD vs. NC. Human and AD mouse model microvessel images were quantified with HAMAMATSU imaging software. Mouse serum biomarkers were assayed via MSD. Results Analysis of 21 serum proteins from 150 AD cases and 150 controls yielded an algorithm with sensitivity and specificity of 0.90 for correctly classifying AD. This multi-marker approach was then validated across species and tissue. Assay of the top proteins in human and AD mouse model brain microvessels correctly classified 90–100% of the samples. SVM analyses were highly accurate at distinguishing PD vs. AD vs. NC. Conclusions This serum-based biomarker panel should be tested in a community-based setting to determine its utility as a first-line screen for AD and non-AD neurological diseases for primary care providers.

IntroductionThis study combined data across four independent cohorts to examine the positive and negative predictive values of an Alzheimer's disease (AD) blood test if implemented in primary care.MethodsBlood samples from 1329 subjects from multiple independent, multiethnic, community-based, and clinic-based cohorts were analyzed. A “locked-down” referent group of 1128 samples was generated with 201 samples randomly selected for validation purposes. Random forest analyses were used to create the AD blood screen. Positive (PPV) and negative (NPV) predictive values were calculated.ResultsIn detecting AD, PPV was 0.81, and NPV was 0.95 while using the full AD blood test. When detecting mild cognitive impairment, PPV and NPV were 0.74 and 0.93, respectively. Preliminary analyses were conducted to detect any “neurodegenerative disease”. The full 21-protein AD blood test yielded a PPV of 0.85 and NPV of 0.94.DiscussionThe present study creates the first-ever multiethnic referent sample that spans community-based and clinic-based populations for implementation of an AD blood screen.