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

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“(…) we view our work as an indication that combining different molecular data types may become a general strategy for improving the aging clock in the future.”

Credit: 2023 Salignon et al.

“(…) we view our work as an indication that combining different molecular data types may become a general strategy for improving the aging clock in the future.”

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

Aging clocks, constructed from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, several studies have compared the suitability of different molecular data types for predicting age within the same cohort and whether combining them improves prediction. In this new study, the 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 Institute, University of New South Wales, Garvan Medical Research InstituteAnd AstraZeneca explored this at the protein and small RNA levels in 103 human blood plasma samples.

“Here we extend a limited portfolio of comparisons between aging clocks constructed from different types of molecular data from the same cohort.”

First, the researchers used a two-step mass spectrometry approach that measured 612 proteins to select and measure 21 proteins whose numbers changed with age. In particular, proteins that increase with age are enriched for components of the complement system. Next, they used small RNA sequencing to select and measure a set of 315 small RNAs that change a lot with age. Most are microRNAs (miRNAs), regulated with age, and thought to target genes associated with growth, cancer, and aging. Finally, the team used the collected data to create an age prediction model.

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 the protein and miRNA data together improved the predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and validation datasets will be required to confirm these results.

“Nevertheless, our study demonstrates that combining proteomic and miRNA data yields superior age prediction, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine whether combining different molecular data types serves as a general strategy for improving the aging clock in the future.”

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

Corresponding author: Christian G. Riedel – (email protected)

Keywords: Human blood plasma Small RNA proteomics predicts age-aging

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Launched in 2009, Aging (Aging-US) published papers of general interest and biological significance in all areas of research on aging and age-related diseases, including cancer—and now, with a particular focus on COVID-19 susceptibility as an age-dependent syndrome. topic in Aging goes beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g. p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

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