Early detection of Alzheimer's disease using small RNAs. Results from the EPAD cohort

Abstract

Background: Alzheimer's disease (AD) is the most common form of dementia, and early diagnosis is crucial to enable effective interventions. Currently, Alzheimer's disease is diagnosed through cognitive assessments, brain imaging and fluid biomarkers focused on determining amyloid (A) and, tau (T) protein levels as well as neurodegeneration (N) in the AT(N) framework. Prognostic biomarkers for predicting cognitive decline within the amyloid positive (Aβ+) individuals would further strengthen the framework.

Objectives: This study evaluated small RNAs as novel auxiliary biomarkers, independent of the AT(N) framework, either alone or in combination with established protein markers, for detecting the earliest cognitive decline in AD.

Design: The European Prevention of Alzheimer's Disease (EPAD) clinical trial platform is a prospective, multi-center study designed to investigate biomarkers for preclinical and prodromal AD.

Setting: Peripheral whole blood RNA sequencing was performed on participants across Europe with no cognitive impairment or very mild cognitive impairment (MCI), stratified by cerebrospinal fluid amyloid levels.

Participants: 1,913 participants, 50 years or older and free of dementia diagnosis at enrollment, were analyzed.

Intervention: (if any) Not applicable.

Measurements: Ultra-deep small RNA sequencing was performed on whole blood samples using a refined blocking protocol to eliminate highly abundant erythroid small RNAs, and thereby to open sequencing bandwidth for the discovery of less abundant biomarker RNAs. Biomarker RNAs were deconvolved into plasma or blood cell origin and analyzed for functional relevance. We define high and low amyloid groups based on a cutoff on the p-tau₁₈₁/Aβ_1-42 ratio as determined from cerebrospinal fluid.

Results: We identified a combination of small RNAs that predicted early cognitive decline (Clinical Dementia Rating of 0.5) with an area under the receiver-operator curve of ∼0.7. Notably, when focusing on individuals with cognitive decline and high amyloid burden (Aβ+), the predictive accuracy improved to an AUC of 0.77. This performance could be extended to the entire cohort when combining blood RNA and CSF amyloid markers (AUC 0.76). We conducted bioinformatic analyses to interrogate the likely functional relevance of these small RNAs, uncovering several links to dementia-relevant pathways, including neuronal, cardiovascular, and inflammatory activities. Our findings also suggest that small nucleolar RNAs warrant further investigation as potential disease-relevant markers, in addition to microRNAs.

Conclusions: Integrating small RNA biomarkers with protein-based assays offers preliminary evidence for stratifying MCI, particularly within the amyloid positive continuum. Small nucleolar RNAs and microRNAs warrant further exploration as complementary diagnostic tools, and their use may enable more precise and effective interventions.

Keywords: AT(N) framework; Blood RNA biomarker; Early Onset Alzheimer’s Disease (EOAD); European Prevention for Alzheimer’s Dementia (EPAD); Machine learning; Mild Cognitive Impairment (MCI); miRNA; small nucleolar RNA (snoRNA).

Fig. 1

Machine learning predictions of CDR cognitive impairment. ROC curves and AUCs were obtained for 100 randomly picked test set splits of patient samples. (a-c) Propensity score matching was applied to samples before splitting, admitting all small RNAs detected in at least 99% of samples for logistic regression machine learning. (b + e) shows ROC curves only for the low amyloid subset of patients, whereas (c + f) shows the high amyloid subset. (d-f) Only small RNAs from the mir-186, mir-363, and mir-150 precursors were admitted, combined with the CSF markers Aβ_1-42, p-tau, and t-tau.

Fig. 2

Cell type deconvolution of most important small RNAs at precursor level.

Fig. 3

Functional analysis of important small RNAs. (a) Co-expression network between small RNA classes (precursor level). Nodes are connected if the precursor expression vectors correlated with r>0.7 (r=Spearman rank correlation). Red boxes: higher expression in impaired individuals, blue boxes: higher in normal individuals. Thick box borders indicate most important precursors from ML models. (b) Functional categories of AD-related gene targets of most important miRNAs.