Diagnostic Biomarkers of Amyloid and Tau Pathology in Alzheimer's Disease: An Overview of Tests for Clinical Practice in the United States and Europe.

Amyloid and tau biomarkers for Alzheimer's disease are widely recognized diagnostic tools for the identification of Alzheimer's disease pathology antemortem and are recommended by the most recent clinical and research guidelines. Approved biomarkers include positron emission tomography (PET)- and fluid-based markers derived from cerebrospinal fluid and, more recently, plasma. These biomarkers are still infrequently used in clinical practice, potentially due to challenges in access to and understanding of individual assay information and methodology. We provide an overview of the diagnostic biomarkers for amyloid and tau pathology that are currently available in the US and/or EU for clinical use. Available performance data from both labels/instructions for use and the scientific literature (with focus on autopsy or PET as standard of truth) are summarized to help healthcare providers navigate the biomarker landscape. All available PET amyloid and tau biomarkers demonstrate high accuracy in identifying amyloid and tau Alzheimer's disease pathology, respectively, at autopsy. Among cerebrospinal fluid biomarkers, all showed accurate prediction of Alzheimer's disease pathology, either based on autopsy or PET findings; greater accuracy was evident for concentration ratios (Aß42/40 or P-tau181/Aß42) versus individual biomarker concentrations. Among plasma biomarkers, Aß42/40 and P-tau181 demonstrated high agreement with PET findings. Overall, we conclude that commercially available PET, cerebrospinal fluid and plasma assays accurately identify Alzheimer's disease amyloid and tau pathology. The recent development of fully automated tests for fluid-based biomarkers improves test reliability. The continued development of plasma biomarkers holds promise for the future management of patients with Alzheimer's disease.

Application of the NIA-AA Research Framework: Towards a Biological Definition of Alzheimer's Disease Using Cerebrospinal Fluid Biomarkers in the AIBL Study.

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 ß1-42, 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.

Constructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer's disease.

In epidemiology, cohort studies utilised to monitor and assess disease status and progression often result in short-term and sparse follow-up data. Thus, gaining an understanding of the full-term disease pathogenesis can be difficult, requiring shorter-term data from many individuals to be collated. We investigate and evaluate methods to construct and quantify the underlying long-term longitudinal trajectories for disease markers using short-term follow-up data, specifically applied to Alzheimer's disease. We generate individuals' follow-up data to investigate approaches to this problem adopting a four-step modelling approach that (i) determines individual slopes and anchor points for their short-term trajectory, (ii) fits polynomials to these slopes and anchor points, (iii) integrates the reciprocated polynomials and (iv) inverts the resulting curve providing an estimate of the underlying longitudinal trajectory. To alleviate the potential problem of roots of polynomials falling into the region over which we integrate, we propose the use of non-negative polynomials in Step 2. We demonstrate that our approach can construct underlying sigmoidal trajectories from individuals' sparse, short-term follow-up data. Furthermore, to determine an optimal methodology, we consider variations to our modelling approach including contrasting linear mixed effects regression to linear regression in Step 1 and investigating different orders of polynomials in Step 2. Cubic order polynomials provided more accurate results, and there were negligible differences between regression methodologies. We use bootstrap confidence intervals to quantify the variability in our estimates of the underlying longitudinal trajectory and apply these methods to data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate their practical use. Copyright © 2017 John Wiley & Sons, Ltd.

A blood-based predictor for neocortical Aß burden in Alzheimer's disease: results from the AIBL study.

Dementia is a global epidemic with Alzheimer's disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aß (extracellular ß-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aß accumulation, therefore, providing a platform for developing a population-based screen.

Comparison of the cardiopulmonary effects of subcutaneously administered epinephrine and terbutaline in patients with reversible airway obstruction.

The cardiopulmonary effects of epinephrine and terbutaline were compared in a doubleblind crossover study in 23 subjects with chronic obstructive airway disease. On each of three days each subject received a single subcutaneous dose of saline, 0.25 mg of epinephrine or 0.5 mg of terbutaline. Treatment with epinephrine produced significant increases in forced vital capacity (FVC), forced expiratory volume in one second (FEV-1), maximal expiratory flow rate (MEFR) and maximal mid-expiratory flow (MMEF). Terbutaline caused even more pronounced increases in all four parameters and exhibited a longer duration of action. Neither drug altered arterial pH, arterial oxygen pressure (PaO-2), or arterial carbon dioxide pressure (PaCO-2). With regard to cardiovascular effects, no alterations in either systolic or diastolic pressure were observed. Administration of epinephrine and terbutaline caused statistically significant increases in heart rate. The effect of terbutaline was more pronounced that that of epinephrine. In addition, terbutaline caused a heart rate-related depression of the T-wave of the lead 2 ECG. Neither drug altered any of the hematologic, hemochemical or urinary parameters monitored before and after treatment. Side effects were seen in eight subjects after administration of saline solution, in 13 subjects after epinephrine and in 19 subjects after terbutaline. None of these side effects was considered clinically serious and none required treatment. It is concluded from this study that subcutaneously administered terbutaline is a more effective bronchodilator than epinephrine.