Plasma phospholipids identify antecedent memory impairment in older adults

Abstract

Alzheimer's disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels, structural and functional magnetic resonance imaging and the recent use of brain amyloid imaging or inflammaging, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer's disease with the required sensitivity and specificity. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer's disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer's disease within a 2-3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer's disease.

Figure 1

Memory composite z-scores and trend plots for the ten-metabolite panel in the discovery phase. (a) Box and whisker plot shows the composite memory z-scores (Z_mem) of the combined discovery and validation samples (Supplementary Table 3). The performance of the Converter group (C_pre, Converters at baseline) after phenoconversion (C_post) is plotted for direct comparison. The plot shows Z_mem, as described in Supplementary Table 3. The dotted line centered on 0 represents the median memory composite z-score for the entire cohort of 525 participants, and the black horizontal line represents the cut-off for impairment (−1.35 s.d.). Error bars represent ±s.e.m. As defined, all converters had nonimpaired memory at baseline and impaired memory after phenoconversion. NC, n = 73; C_pre, n = 28; C_post, n = 28; and aMCI/AD, n = 46. (b) The SID-MRM-MS–based quantitative profiling data was subjected to the nonparametric Kruskal-Wallis test using the STAT pack module (Biocrates). Results are shown for a panel of ten metabolites in the NC group (n = 53), C_pre (n = 18), C_post (n = 18) and aMCI/AD (n = 35) groups, respectively. The abundance of each metabolite is plotted as normalized concentrations units (nM). The black solid bars within the boxplot represent the median abundance, and the dotted line represents mean abundance for the given group. Error bars represent ± s.d. QC, quality control samples. The P values for analytes between groups were P ≤ 0.05. The two metabolites with P values <0.005 are indicated with an asterisk. Each Kruskal-Wallis test was followed by Mann-Whitney U-tests for post hoc pairwise comparisons (NC versus C_pre and NC versus aMCI/AD). Significance was adjusted for multiple comparisons using Bonferroni’s method (P < 0.025).

Figure 2

ROC results for the lipidomics analyses. (a–c) Plots of ROC results from the models derived from the three phases of the lipidomics analysis. Simple logistic models using only the metabolites identified in each phase of the lipidomics analysis were developed and applied to determine the success of the models for classifying the C_pre and NC groups. The red line in each plot represents the AUC obtained from the discovery-phase LASSO analysis (a), the targeted analysis of the ten metabolites in the discovery phase (b) and the application of the ten-metabolite panel developed from the targeted discovery phase in the independent validation phase (c). The ROC plots represent sensitivity (i.e., true positive rate) versus 1 – specificity (i.e., false positive rate).