Objective:

To identify plasma microRNA (miRNA) signatures that may aid in early detection of individuals at risk for developing Alzheimer's disease (AD) and predicting the progression from mild cognitive impairment (MCI) to AD.

Approach:

• Study Design: The study analyzed plasma samples from 803 participants, including individuals with early MCI (EMCI), late MCI (LMCI), AD, and cognitively normal controls, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
• miRNA Detection: Small RNA sequencing detected 300 miRNAs, which were evaluated by machine learning algorithms to identify signatures associated with different diagnostic stages.

Key Findings:

• Distinct miRNA signatures could predict MCI and AD diagnoses.
• A miRNA signature of miR-142-3p, miR-98-5p, and miR-9985 predicted AD with an accuracy comparable to total Tau (AUC = 0.72).
• A combination of miR-590-3p, miR-369-3p, and miR-9985 predicted EMCI patients with an AUC of 0.71.
• For LMCI, a signature of miR-4429, miR-22-5p, and miR-1306 showed similar predictive accuracy (AUC = 0.71).
• A miRNA signature of miR-125b-5p, miR-18a-5p, and miR-26b-5p predicted the conversion from EMCI to AD with an AUC of 0.70.
• A signature of miR-338-3p, miR-584-5p, and miR-142-3p predicted LMCI to AD conversion with an AUC of 0.75.

Interpretation:

Limitations:

• Further validation in more diverse populations is needed.
• Longitudinal studies are required to confirm the clinical applicability of these findings.

Conclusion:

Sources:

• Alzheimer’s & Dementia