Purpose We describe the outcome of the Biomarkers Consortium CSF Proteomics Project, a public-private partnership of government, academia, non-profit, and industry. The goal of this study was to evaluate a multiplexed mass spectrometry-based approach for the qualification of candidate Alzheimer’s Disease (AD) biomarkers using CSF samples from the AD Neuroimaging Initiative (ADNI). Experimental Design Reproducibility of sample processing, analytic variability, and ability to detect a variety of analytes of interest were thoroughly investigated. Multiple approaches to statistical analyses assessed whether panel analytes were associated with baseline pathology (MCI, AD) vs. Healthy Controls (CN) or associated with progression for MCI patients, and included: (i) univariate association analyses, (ii) univariate prediction models, (iii) exploratory multivariate analyses, and (iv) supervised multivariate analysis. Results A robust targeted mass spectrometry-based approach for the qualification of candidate AD biomarkers was developed. The results identified several peptides with potential diagnostic or predictive utility, with the most significant differences observed for the following peptides for differentiating (including peptides from Hemoglobin A (HBA), Hemoglobin B (HBB), and Superoxide dismutase (SODE)) or predicting (including peptides from Neuronal pentraxin-2 (NPTX2), Neurosecretory protein VGF (VGF), and Secretogranin-2 (SCG2)) progression vs. non-progression from mild cognitive impairment to AD. Conclusions and Clinical Relevance These data provide potential insights into the biology of CSF in AD and MCI progression and provide a novel tool for AD researchers and clinicians working to improve diagnostic accuracy, evaluation of treatment efficacy, and early diagnosis.

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The objective of this analysis was to develop a semi-mechanistic nonlinear disease progression model using an expanded set of covariates that captures the longitudinal change of Alzheimer's Disease Assessment Scale (ADAS-cog) scores from the Alzheimer's Disease Neuroimaging Initiative study that consisted of 191 Alzheimer disease patients who were followed for 2 years. The model describes the rate of progression and baseline disease severity as a function of influential covariates. The covariates that were tested fell into 4 categories: (1) imaging volumetric measures, (2) serum biomarkers, (3) demographic and genetic factors, and (4) baseline cognitive tests. Covariates found to affect baseline disease status were years since disease onset, hippocampal volume, and ventricular volume. Disease progression rate in the model was influenced by age, total cholesterol, APOE ε4 genotype, Trail Making Test (part B) score, and current levels of impairment as measured by ADAS-cog. Rate of progression was slower for mild and severe Alzheimer patients compared with moderate Alzheimer patients who exhibited faster rates of deterioration. In conclusion, this model describes disease progression in Alzheimer patients using novel covariates that are important for understanding the worsening of ADAS-cog scores over time and may be useful in the future for optimizing study designs through clinical trial simulations.

BackgroundPatients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer’s dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD.MethodsWe have conducted a comprehensive study using a large number of samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection.ResultsWe have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587.ConclusionsOur results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.

Hypothetical models of AD progression typically relate clinical stages of AD to sequential changes in CSF biomarkers, imaging, and cognition. However, quantifying the continuous trajectories proposed by these models over time is difficult because of the difficulty in relating the dynamics of different biomarkers during a clinical trial that is significantly shorter than the duration of the disease. We seek to show that through proper synchronization, it is possible to de-convolve these trends and quantify the periods of time associated with different pathophysiological changes associated with Alzheimer's disease (AD). We developed a model that replicated the observed progression of ADAS-Cog 13 scores and used this as a more precise estimate of diseaseduration and thus pathologic stage. We then synchronized cerebrospinal fluid (CSF) and imaging biomarkers according to our new disease timeline. By de-convolving disease progression via ADAS-Cog 13, we were able to confirm the predictions of previous hypothetical models of disease progression as well as establish concrete timelines for different pathobiological events. Specifically, our work supports a sequential pattern of biomarker changes in AD in which reduction in CSF A␤ 42 and brain atrophy precede the increases in CSF tau and phospho-tau.