Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer’s Disease

Background: Lipidomics may provide insight into biochemical processes driving Alzheimer’s disease (AD) pathogenesis and ensuing clinical trajectories. Objective: To identify a peripheral lipidomics signature associated with AD pathology and investigate its potential to predict clinical progression. Methods: We used Bayesian elastic net regression to select plasma lipid classes associated with the CSF pTau/Aβ42 ratio as a biomarker of AD pathology in preclinical and prodromal AD cases from the ADNI cohort. Consensus clustering of the selected lipid classes was used to identify lipidomic endophenotypes and study their association with clinical progression. Results: In the APOE4-adjusted model, ether-glycerophospholipids, lyso-glycerophospholipids, free-fatty acids, cholesterol esters, and complex sphingolipids were found to be associated with the CSF pTau/Aβ 42 ratio. We found an optimal number of five lipidomic endophenotypes in the prodromal and preclinical cases, respectively. In the prodromal cases, these clusters differed with respect to the risk of clinical progression as measured by clinical dementia rating score conversion. Conclusion: Lipid alterations can be captured at the earliest phases of AD. A lipidomic signature in blood may provide a dynamic overview of an individual’s metabolic status and may support identifying different risks of clinical progression.

Quantitative assessment of AD markers using naked eyes: point-of-care testing with paper-based lateral flow immunoassay

Aβ42 is one of the most extensively studied blood and Cerebrospinal fluid (CSF) biomarkers for the diagnosis of symptomatic and prodromal Alzheimer’s disease (AD). Because of the heterogeneity and transient nature of Aβ42 oligomers (Aβ42Os), the development of technologies for dynamically detecting changes in the blood or CSF levels of Aβ42 monomers (Aβ42Ms) and Aβ42Os is essential for the accurate diagnosis of AD. The currently commonly used Aβ42 ELISA test kits usually mis-detected the elevated Aβ42Os, leading to incomplete analysis and underestimation of soluble Aβ42, resulting in a comprised performance in AD diagnosis. Herein, we developed a dual-target lateral flow immunoassay (dLFI) using anti-Aβ42 monoclonal antibodies 1F12 and 2C6 for the rapid and point-of-care detection of Aβ42Ms and Aβ42Os in blood samples within 30 min for AD diagnosis. By naked eye observation, the visual detection limit of Aβ42Ms or/and Aβ42Os in dLFI was 154 pg/mL. The test results for dLFI were similar to those observed in the enzyme-linked immunosorbent assay (ELISA). Therefore, this paper-based dLFI provides a practical and rapid method for the on-site detection of two biomarkers in blood or CSF samples without the need for additional expertise or equipment. Graphical Abstract

Olfactory Measures as Predictors of Conversion to Mild Cognitive Impairment and Alzheimer’s Disease

Background: Early biomarkers of prodromal Alzheimer’s disease (AD) are critical both to initiate interventions and to choose participants for clinical trials. Odor threshold, odor identification and odor familiarity are impaired in AD. Methods: We investigated the relative abilities of standard screening (MMSE) and olfactory measures to predict transitions from cognitively normal (CN) to mild cognitive impairment (MCI), from CN to AD, and MCI to AD. The archival sample of 497, from the UCSD ADRC, included participants who were CN, MCI, AD and converters to MCI or AD. Apoe ε4 status, a genetic risk factor, was available for 256 participants, 132 were ε4 carriers. A receiver operating characteristic curve (ROC) curve plots the trade-off between sensitivity and specificity. Area under the ROC curve (AUC) was used to determine diagnostic accuracy. Results: Different measures were better predictors at specific stages of disease risk; e.g., odor familiarity, odor identification and the combination showed higher predictive value for converting from MCI to AD in ε4 carriers than the MMSE. Combining odor familiarity and odor identification produced an AUC of 1.0 in ε4 carriers, MMSE alone was 0.58. Conclusions: Olfactory biomarkers show real promise as non-invasive indicators of prodromal AD. The results support the value of combining olfactory measures in assessment of risk for conversion to MCI and to AD.