Blood MicroRNAs: Predicting Alzheimer’s Progression

Researchers discovered the serum micro-ribonucleic acid (miRNA) signature in Alzheimer’s Disease (AD) in a recent study published in Alzheimer’s & Dementia. They also investigated specific MicroRNAs that could potentially identify when mild cognitive impairment (MCI) progresses to Alzheimer’s Disease (AD).

Context
A neurodegenerative condition called Alzheimer’s disease is characterized by a steady deterioration in cognitive function. When AD is discovered at a later stage, treatment results are not good. In order to detect patients with early- or late-stage MCI (EMCI) and forecast its conversion to AD, new diagnostic approaches are thus needed.

The diagnostic instruments used today are pricy and invasive. Short non-coding RNAs known as microRNAs regulate the state of proteostasis within a system. These could be affordable, non-invasive indicators for AD. MicroRNAs have the ability to operate paracrine, influence many mRNA targets, and take part in inter-organ communication. These molecules are also very stable in cell-free environments and resistant to cycles of thaw and freeze, which makes them a preferred logistical choice in clinical settings.

About the Study

Using recognized AD biomarkers, the researchers in this work examined the miRNA signatures of early MCI, late MCI, and AD to see if the signatures correlated with the clinical disease status.

Serum samples were collected from participants in the AD Neuroimaging Initiative (ADNI) so that miRNA expression could be examined. Serum samples from 803 of the 847 ADNI subjects were subjected to small RNA sequencing analysis, with low-quality samples being excluded. Out of the 803 participants, the EMCI, LMCI, AD, and control groups included 272, 217, 149, and 165 individuals, respectively. With at least 10 reads per sample, linear mixed regressions were used to assess miRNAs expressed in 95% or more of the samples.

The miRNAs having significant impact sizes in the regression analysis were examined using machine learning (ML) models. The training and testing datasets made up the machine learning data (fraction, 0.3). Researchers eliminated miRNAs having area under the receiver-operating characteristic curve (AUC) values ≤0.5, which indicate that these miRNAs lacked predictive potential. There were 73 possibilities after the top five miRNAs for each condition were chosen. For these candidates, the ML technique determined every possible pairing of up to three miRNAs.

Serum miRNA performance was compared to ADAScog13 readings and invasive indicators of AD in the cerebrospinal fluid (CSF) by the researchers. Apart from the levels of phosphorylated tau and amyloid beta in CSF, they also contrasted the results of the Mini-Mental State Examination (MMSE). Additionally, they examined participant-provided follow-up phenotypic data for 144 months following blood collection. Using Gene Ontology (GO) analysis, the biological pathways regulated by the miRNAs found in this study were examined.

In summary
According to the study, serum miRNA profiles can be utilized to predict the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) and serve as biomarkers for the condition. The accuracy of AD prediction can be raised by combining these markers with cognitive assessments such as the MMSE.

The results are relevant to the general public because they may lessen the need for intrusive and expensive testing by characterizing the at-risk group of the aging population using serum miRNAs. These signatures should be improved upon, validated, and combined with cognitive screening tools in future studies.

For more information: The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals,  Alzheimer’s & Dementia, https://doi.org/10.1002/alz.14157