Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Area of Science:

• Biomarkers of Aging
• Molecular Biology
• Proteomics
• Genomics

Background:

• Aging clocks utilize molecular data for applications in medicine, forensics, and ecology.
• Limited research compares different molecular data types for age prediction within the same cohort.
• Investigating combined molecular data for enhanced age prediction is crucial.

Purpose of the Study:

• To compare the efficacy of proteins and small RNAs (specifically microRNAs) in predicting biological age.
• To determine if combining proteomic and microRNA data improves age prediction accuracy.
• To identify age-associated proteins and microRNAs in human blood plasma.

Main Methods:

• Mass spectrometry was used to quantify 612 proteins, identifying 21 age-related proteins.
• Small RNA sequencing identified 315 age-related small RNAs, predominantly microRNAs.
• Age-predictive models were built using proteomic data, microRNA data, and a combination of both.

Main Results:

• Proteins provided the most accurate age prediction model (R² = 0.59 ± 0.02).
• MicroRNAs (miRNAs) were the best-performing small RNAs, with R² = 0.54 ± 0.02.
• Combining protein and miRNA data significantly improved age prediction (R² = 0.70 ± 0.01).

Conclusions:

• Combined proteomic and miRNA data yield superior age predictions compared to individual molecular types.
• This integrated approach may capture a broader spectrum of age-related physiological changes.
• Further validation with larger sample sizes is needed to confirm these findings for developing future aging clocks.