Plasma 24-metabolite Panel Predicts Preclinical Transition to Clinical Stages of Alzheimer’s Disease

Abstract

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer's disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70-80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0-1.0, and 0.981-1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

Metabolic network failures in Alzheimer's disease: A biochemical road map

“…The technical validation of each analyzed kit plate was performed using MetIDQ software based on results obtained and defined acceptance criteria for blank, zero samples, calibration standards and curves, low/medium/high-level QC samples, and measured signal intensity of internal standards over the plate. This is a highly useful platform that was used in hundreds of publications, including several studies in AD [11,12,15]. …”

“…Although these studies highlight specific metabolic underpinnings of AD, not all metabolomics findings have been replicated. For example, a metabolomics study of two separate cohorts—the Baltimore Longitudinal Study of Aging, and the Age, Gene/Environment Susceptibility-Reykjavik Study—did not replicate an earlier finding [11,12,15]. …”

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

“…Indeed, replication of individual biomarkers and predictive factors has been difficult, if not impossible. An Alzheimer’s disease panel of metabolites had poor predictive ability [90]. Similarly, a protein panel developed in one cohort could not accurately group a different cohort’s participants into correct age classes [91].…”

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

“…Nevertheless, there are a number of fields in which the “absolute” structural identification of organic compounds is critical; these include natural product chemistry [1,2], clinical biochemistry [3,4,5], drug assessment [6], and forensic science [7]. Classically, structural elucidation of a single metabolite or a class of isolated secondary metabolites (e.g., natural products), is solved by first principles and, is governed to a large extent by the sensitivity (or lack thereof) of nuclear magnetic resonance spectroscopy (NMR) [8] and mass spectrometry (MS) techniques (both are discussed in more detail below).…”