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

Figure 1

image: Figure 1. Schematic overview of our study.
visualization Moreover

Credit: 2023 Salignon et al.

“[…] we see our work as an indication that combining different types of molecular data could be a general strategy for improving future aging clocks.”

BUFFALO, NY – June 30th2023 – A new research paper has been published on the cover of Aging (listed by MEDLINE/PubMed as “Aging (Albany NY)” and “Aging-US” by Web of Science) Volume 15, Number 12titled, “Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis.”

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different types of molecular data for predicting age in the same cohort and whether combining them would improve predictions. In this new study, researchers Jérôme Salignon, Omid R. Faridani, Tasso Miliotis, Georges E. Janssens, Ping Chen, Bader Zarrouki, Rickard Sandberg, Pia Davidsson, AND Christian G. Riedel from Karolinska Institutet, University of New South Wales, Garvan Medical Research InstituteAND AstraZeneca explored this at the level of proteins and small RNAs in 103 human blood plasma samples.

“Here we expand the limited portfolio of aging clock comparisons constructed from different types of molecular data from the same cohort.”

First, the researchers used a two-step mass spectrometry approach by measuring 612 proteins to screen for and quantify 21 proteins that changed in abundance with age. Specifically, proteins that increase with age have been enriched for components of the complement system. Next, they used small RNA sequencing to select and quantify a pool of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age and predicted to target genes related to growth, cancer and senescence. Finally, the team used the collected data to build age prediction models.

Among the different types of molecules, proteins produced the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best performing class of small RNAs (R² = 0.54 ± 0. 02). Interestingly, using protein and miRNA data together improved the predictions (R2 = 0.70 ± 0.01). To confirm these results it will be necessary to work in the future using larger sample sizes and a validation dataset.

“However, our study suggests that combining proteomics and miRNA data yields higher age predictions, possibly capturing a wider range of age-related physiological changes. It will be interesting to determine whether combining different types of molecular data works as a general strategy to improve future aging clocks.”

Read the full study: DOI: https://doi.org/10.18632/aging.204787

Corresponding author: Christian G. Riedel – christian.riedel@ki.se

Keyword: human blood plasma small RNA proteomics age prediction of aging

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From Aging-USA:

Launched in 2009, Aging (Aging-US) publishes articles of general interest and biological significance in all fields of aging and age-related disease research, including cancer, and now, with a special focus on the vulnerability of COVID-19 as an age-dependent syndrome. Topics in Aging go beyond traditional gerontology, including but not limited to cellular and molecular biology, human age-related diseases, pathologies in model organisms, signal transduction pathways (eg, p53, sirtuins, and PI-3K/AKT /mTOR, among others) and approaches to modulating these signaling pathways.

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