A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression

Abstract

To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer’s Disease (AD) and normal aged controls (NL), and to determine if this signature can also predict progression from mild cognitive impairment (MCI) to AD, analysis of CSF samples was done on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The profiles of 320 peptides from baseline CSF samples of 287 subjects over a 3–6 year period were analyzed. As expected, the peptide most able to differentiate between AD vs. NL was found to be Apolipoprotein E. Other peptides, some of which are not classically associated with AD, such as heart fatty acid binding protein, and the neuronal pentraxin receptor, also differentiated disease states. A sixteen-analyte signature was identified which differentiated AD vs. NL with an area under the receiver operating characteristic curve of 0.89, which was better than any combination of amyloid beta (1–42), tau, and phospho-181 tau. This same signature, when applied to a new and independent data set, also strongly predicted both probability and rate of future progression of MCI subjects to AD, better than traditional markers. These data suggest that multivariate peptide signatures from CSF predict MCI to AD progression, and point to potentially new roles for certain proteins not typically associated with AD.

VGF in cerebrospinal fluid, when combined with conventional biomarkers, enhances prediction of conversion from mild cognitive impairment to Alzheimer’s Disease

“…Recently, our group and others have identified a group of novel plasma and cerebrospinal fluid (CSF) biomarkers that fall outside the traditional Aβ cascade. Many of these markers have been shown to be useful in the prediction of MCI to AD conversion [14–20]. For example, we used a hypothesis-free bioinformatics approach to identify a panel of 16 peptides in CSF initially identified as showing high diagnostic accuracy for AD vs. control, that was highly predictive of conversion from mild cognitive impairment (MCI) to AD in an independent group of subjects and outperformed conventional CSF markers such as Aβ, tau derivatives and their ratios [20].…”

“…Many of these markers have been shown to be useful in the prediction of MCI to AD conversion [14–20]. For example, we used a hypothesis-free bioinformatics approach to identify a panel of 16 peptides in CSF initially identified as showing high diagnostic accuracy for AD vs. control, that was highly predictive of conversion from mild cognitive impairment (MCI) to AD in an independent group of subjects and outperformed conventional CSF markers such as Aβ, tau derivatives and their ratios [20]. These studies highlight non-canonical pathological cascades that may both provide useful tools for clinical practice and clinical trials purposes, and may also reveal new insights about disease mechanisms underlying AD.…”

“…Therefore, in the current study, we examined the potential for VGF in the CSF, when combined with conventional biomarkers of CSF Aβ1-42, total tau (tTau) and pTau-181 and hippocampal volume, to enhance the diagnostic and prognostic accuracy of these markers. The focus of this work is on the VGF peptide fragment with sequence NSEPQDEGELFQGVDPR (“VGF.NSEP”) since it previously emerged as a strong predictor in a panel of peptides that predict MCI to AD conversion [20], though other VGF peptide fragments are also examined. Unlike our previous studies involving hypothesis-free approaches to identify optimal peptides to include in biomarker signatures [20, 33, 34], the current study was focused on the utility of VGF.…”

“…The focus of this work is on the VGF peptide fragment with sequence NSEPQDEGELFQGVDPR (“VGF.NSEP”) since it previously emerged as a strong predictor in a panel of peptides that predict MCI to AD conversion [20], though other VGF peptide fragments are also examined. Unlike our previous studies involving hypothesis-free approaches to identify optimal peptides to include in biomarker signatures [20, 33, 34], the current study was focused on the utility of VGF. Using data from two independent groups in the ADNI cohort: one group of AD and control subjects and a separate group of MCI subjects, it was found that VGF, when combined with conventional biomarkers, enhanced both the diagnostic accuracy of these markers and the ability of these markers to predict MCI to AD conversion.…”

DL-CHI: a dictionary learning-based contemporaneous health index for degenerative disease monitoring

“…The effective monitoring of degenerative patient conditions represents a significant challenge in many clinical decisionmaking problems and has given rise to the development of numerous mathematical and computational models [1][2][3][4]. Developing a knowledge-driven contemporaneous health index (CHI) that can precisely reflect the underlying patient condition across the course of the condition's progression holds a unique value, like facilitating a range of clinical decision-making opportunities [5][6][7], enhancing the continuity of care, and facilitating imperfect data, subjecting them to all kinds of statistical errors.…”