O'Bryant, Sid

Abstract The Centiloid scale is the standard for Amyloid (Aβ) PET quantification, widely used in research, clinical settings, and trial stratification. However, variability between tracers and scanners remains a challenge. This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalising implausible longitudinal trajectories during training. The model was trained using data from 2,098 participants (6,762 Aβ PET scans) in AIBL/ADNI and validated using 15,806 Aβ PET scans from 10,543 participants across 10 external datasets. DeepSUVR increased correlation between tracers, and reduced variability in the Aβ-negatives. It showed the strongest association with cognition, highest AUC against visual reads and best longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting lower Centiloid increase per year in the A4 study. DeepSUVR advances Aβ PET quantification, outperforming standard approaches, which is particularly important for consistent decision making and to detect subtle and early changes in clinical interventions.

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, PD). 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 PD cases and compared to a random sample of 51 AD cases and 62 controls. Support vector machines (SVM) were used to discriminate PD versus AD versus controls. 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 versus AD versus controls. 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.

AbstractIntroductionThis 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.

Abstract During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this “silent” stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

AbstractIntroductionThis study investigated the comparability of potential Alzheimer's disease (AD) biomarkers across blood fractions and assay platforms.MethodsNonfasting serum and plasma samples from 300 participants (150 AD patients and 150 controls) were analyzed. Proteomic markers were obtained via electrochemiluminescence or Luminex technology. Comparisons were conducted via Pearson correlations. The relative importance of proteins within an AD diagnostic profile was examined using random forest importance plots.ResultsOn the Meso Scale Discovery multiplex platform, 10 of the 21 markers shared >50% of the variance across blood fractions (serum amyloid A R2 = 0.99, interleukin (IL)10 R2 = 0.95, fatty acid‐binding protein (FABP) R2 = 0.94, I309 R2 = 0.94, IL‐5 R2 = 0.94, IL‐6 R2 = 0.94, eotaxin3 R2 = 0.91, IL‐18 R2 = 0.87, soluble tumor necrosis factor receptor 1 R2 = 0.85, and pancreatic polypeptide R2 = 0.81). When examining protein concentrations across platforms, only five markers shared >50% of the variance (beta 2 microglobulin R2 = 0.92, IL‐18 R2 = 0.80, factor VII R2 = 0.78, CRP R2 = 0.74, and FABP R2 = 0.70).DiscussionThe current findings highlight the importance of considering blood fractions and assay platforms when searching for AD relevant biomarkers.

Abstract The lack of readily available biomarkers is a significant hindrance toward 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 to foster cross‐validation across cohorts and laboratories.

AbstractAlzheimer's disease is the public health crisis of the 21st century. There is a clear need for a widely available, inexpensive and reliable method to diagnosis Alzheimer's disease in the earliest stages, track disease progression, and accelerate clinical development of new therapeutics. One avenue of research being explored is blood based biomarkers. In April 2012, the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation convened top scientists from around the world to discuss the state of blood based biomarker development. This manuscript summarizes the meeting and the resultant discussion, including potential next steps to move this area of research forward.

Objectives To investigate whether demographic (age and education) adjustments for the Mini‐Mental State Examination ( MMSE ) attenuate mean score discrepancies between African‐American and Caucasian adults and whether demographically adjusted MMSE scores improve the diagnostic classification accuracy of dementia in African‐American adults over unadjusted MMSE scores. Design Cross‐sectional study. Setting Community‐dwelling adults participating in the Mayo Clinic A lzheimer's Disease Patient Registry and A lzheimer's Disease Research Center. Participants Three thousand two hundred fifty‐four adults (2,819 Caucasian, 435 African American) aged 60 and older. Measurements MMSE score at study entry. Results African‐American adults had significantly lower unadjusted MMSE scores (23.0 ± 7.4) than Caucasian adults (25.3 ± 5.4). This discrepancy persisted despite adjustment of MMSE scores for age and years of education using established regression weights or newly derived weights. Controlling for dementia severity at baseline and adjusting MMSE scores for age and quality of education attenuated this discrepancy. In African‐American adults, an age‐ and education‐adjusted MMSE cut score of 23/24 provided optimal dementia classification accuracy, but this represented only a modest improvement over an unadjusted MMSE cut score of 22/23. The posterior probability of dementia in African‐American adults is presented for various unadjusted MMSE cut scores and prevalence rates of dementia. Conclusion Age, dementia severity at study entry, and quality of educational experience are important explanatory factors in understanding the existing discrepancies in MMSE performance between Caucasian and African‐American adults. These findings support the use of unadjusted MMSE scores when screening older African Americans for dementia, with an unadjusted MMSE cut score of 22/23 yielding optimal classification accuracy.