Age Prediction from Human Blood Plasma: Comparative Analysis | Aging-US
Channel: Aging (Aging-US)
Date: 2023-06-30

Aging-US published this research paper as the cover for Volume 15, Issue 12, entitled, "Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis" by researchers from Department of Medicine, Integrated Cardio Metabolic Centre (ICMC), Karolinska Institutet, Huddinge 14157, Sweden; Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge 14157, Sweden; Lowy Cancer Research Centre, School of Medical Sciences, University of New South Wales, Sydney, Australia; Garvan Institute of Medical Research, Sydney, Australia; Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden; Bioscience Metabolism, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden; Department of Cellular and Molecular Biology, Ludwig Institute for Cancer Research, Karolinska Institutet, Solna 17165, Sweden.
#aging  #plasma  #rna  #proteomics  #prediction #clocks  #research  #researchpaper #openaccess  #openscience  #peerreview  #journal  #publication #publishing  #meded  #agingshort #video

DOI - https://doi.org/10.18632/aging.204787

Corresponding author - Christian G. Riedel - christian.riedel@ki.se

Abstract

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 molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set 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, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded 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, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

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Keywords - aging, human blood plasma, small RNAs, proteomics, age prediction

About Aging-US

Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go 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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