Researchers say this breakthrough could lead to more effective prevention strategies and future therapies.
Study: The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals. Image Credit: ART-ur/Shutterstock.com
In a recent study published in Alzheimer’s & Dementia, researchers established the serum micro-ribonucleic acid (miRNA) signature in Alzheimer's Disease (AD). They also investigated miRNAs that could predict the transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD).
Background
Alzheimer's disease is a neurodegenerative disease characterized by progressive cognitive decline. The identification of AD at advanced stages leads to poor treatment outcomes. Thus, novel diagnostic techniques are required to identify individuals in early-stage MCI (EMCI) or late-stage MCI (LMCI) and predict their conversion to AD.
Current diagnostic tools are invasive and costly. MicroRNAs are short, non-coding RNAs that control proteostasis at the system level. They are potential minimally invasive and inexpensive biomarkers for AD. MicroRNAs can affect multiple mRNA targets, act paracrine, and participate in inter-organ communication. Moreover, these molecules are highly stable in cell-free environments and resistant to thaw-freeze cycles, making them logistically desirable in clinical settings.
About the study
In the present study, researchers investigated the miRNA signatures of early MCI, late MCI, and AD and determined whether the signatures correlate with clinical disease state using established AD biomarkers.
Researchers obtained serum samples from AD Neuroimaging Initiative (ADNI) participants to analyze miRNA expression. Small RNA sequencing analyzed serum samples from 803 of 847 ADNI participants, excluding samples of poor quality. Among 803 participants, 272, 217, 149, and 165 belonged to the EMCI, LMCI, AD, and control groups, respectively. Linear mixed regressions analyzed miRNAs expressed in 95% or more samples with at least ten reads in each sample.
Machine learning (ML) models tested the miRNAs with significant effect sizes in the regression analysis. The ML data comprised the training and testing datasets (fraction, 0.3). Researchers excluded miRNAs without predictive power, i.e., those with area under the receiver-operating characteristic curve (AUC) values ≤0.5. For each condition, selecting the best five miRNAs resulted in 73 candidates. The ML method derived all potential combinations of up to three miRNAs for these candidates.
The researchers compared serum miRNA performance to ADAScog13 values and invasive biomarkers of AD in the cerebrospinal fluid (CSF). In addition to amyloid beta and phosphorylated tau protein levels in CSF, they compared the Mini-Mental State Examination (MMSE) scores. They also analyzed follow-up phenotypic data provided by the participants for 144 months after blood collection. Gene Ontology (GO) analysis investigated the biological pathways controlled by the miRNAs identified in the present study.
Results
The serum miRNA signature for AD comprised miR.98.5p, miR.142.3p, and miR.9985 (AUC, 0.7). Serum miR.369.3p, miR.590.3p, and miR.9985 identified EMCI (AUC, 0.7). The three miRNAs showing the best performance to identify LMCI included miR.22.5p, miR.1306, and miR.4429 (AUC, 0.7). Concerning AD prediction, Abeta levels and the Abeta/tTAU or Abeta/pTAU181 ratios in the cerebrospinal fluid and MMSE testing outperformed the serum miRNA signature.
Serum miRs for MECI performed better than CSF biomarkers (AUC from 0.5 to 0.6) and the MMSE test (AUC, 0.6). To predict LMCI, the miR signature performed similarly to CSF biomarkers (AUC, 0.7) and the MMSE (AUC, 0.7). To predict EMCI to AD conversion, miR.18a.5p, miR.26b.5p, and miR.125b.5p (AUC, 0.7) performed better than CSF biomarkers (AUC, 0.6). In identifying LMCI to AD converters, serum miR.142.3p, miR.338.3p, and miR.584.5p (AUC, 0.8) outperformed the cerebrospinal fluid biomarkers.
ADAScog13 estimated the conversion from early-stage MCI to Alzheimer’s disease (AUC, 0.6) and LMCI-AD (AUC, 0.7) with lower accuracy than the miRNA signatures. Using serum miR.532.3p and miR.1306.3p expression improved the accuracy of predicting conversion from EMCI and LMCI to AD. Combining the miRNAs for EMCI prediction with MMSE data moderately enhanced the accuracy of identifying LMCI patients (0.75 from 0.71).
The miRNA profiles of EMCI, LMCI, and AD indicate separate molecular mechanisms. MiRNAs representing EMCI showed specific associations with oxidative phosphorylation and ferroptosis. The findings are consistent with previous research showing that disordered energy and iron metabolism occur early in AD pathophysiology. Contrastingly, only in the late-stage MCI signature of miRNAs did the researchers find mechanisms indicating interleukin-17 (IL-17) signaling and vascular injury, which are related to AD development.
Conclusion
The study showed that serum miRNA signatures can be used as biomarkers for Alzheimer's disease and predict the transition from MCI to AD. Combining these signatures with neuropsychological tests like the MMSE can increase the accuracy of AD prediction.
The findings are of public interest since using serum miRNAs to characterize the at-risk segment of the aging population could reduce invasive and costly testing. Future research should refine and confirm these signatures and integrate them with cognitive screening measures.
New insights into the mechanisms of "Niemann-Pick type C" in children