Research Paper

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

Jérôme Salignon1,2, *, , Omid R. Faridani1,3,4, *, , Tasso Miliotis5, , Georges E. Janssens1, , Ping Chen1, , Bader Zarrouki6, , Rickard Sandberg1,7, , Pia Davidsson5, , Christian G. Riedel1,2, ,

  • 1 Department of Medicine, Integrated Cardio Metabolic Centre (ICMC), Karolinska Institutet, Huddinge 14157, Sweden
  • 2 Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge 14157, Sweden
  • 3 Lowy Cancer Research Centre, School of Medical Sciences, University of New South Wales, Sydney, Australia
  • 4 Garvan Institute of Medical Research, Sydney, Australia
  • 5 Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
  • 6 Bioscience Metabolism, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
  • 7 Department of Cellular and Molecular Biology, Ludwig Institute for Cancer Research, Karolinska Institutet, Solna 17165, Sweden
* Equal contribution

Received: August 30, 2022       Accepted: May 26, 2023
How to Cite

Copyright: © 2023 Salignon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


FDR: False Discovery Rate; L2FC: Log2 Fold Change; miRNAs: microRNAs; MMSE: mean Mini-Mental State Examination; BMI: Body Mass Index; HRM-MS: Hyper Reaction Monitoring mass spectrometry; DIA: Data Independent Acquisition; MRM-MS: Multiple Reaction Monitoring mass spectrometry; tRFs: fragments of tRNAs; tRNAs: transfer RNAs; rRNAs: ribosomal RNAs; snRNAs: small nuclear RNAs; snoRNAs: small nucleolar RNAs.