1. Harman, D. The aging process: major risk factor for disease and death. Proc. Natl Acad. Sci. USA 88, 5360–5363 (1991).

2. Baht, G. S. et al. Exposure to a youthful circulation rejuvenates bone repair through modulation of β-catenin. Nat. Commun. 6, 7131 (2015).

3. Conboy, I. M. et al. Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nature 433, 760–764 (2005).

4. Huang, Q. et al. A young blood environment decreases aging of senile mice kidneys. J. Gerontol. A Biol. Sci. Med. Sci. 73, 421–428 (2018).

5. Katsimpardi, L. et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science 344, 630–634 (2014).

6. Loffredo, F. S. et al. Growth differentiation factor 11 is a circulating factor that reverses age-related cardiac hypertrophy. Cell 153, 828–839 (2013).

7. Salpeter, S. J. et al. Systemic regulation of the age-related decline of pancreatic beta-cell replication. Diabetes 62, 2843–2848 (2013).

8. Sinha, M. et al. Restoring systemic GDF11 levels reverses age-related dysfunction in mouse skeletal muscle. Science 344, 649–652 (2014).

9. Villeda, S. A. et al. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature 477, 90–94 (2011).

10. Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014).

11. Valdes, A. M., Glass, D. & Spector, T. D. Omics technologies and the study of human ageing. Nat. Rev. Genet. 14, 601–607 (2013).

12. Stegeman, R. & Weake, V. M. Transcriptional signatures of aging. J. Mol. Biol. 429, 2427–2437 (2017).

13. Aramillo Irizar, P. et al. Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly. Nat. Commun. 9, 327 (2018).

14. Castellano, J. M. et al. Human umbilical cord plasma proteins revitalize hippocampal function in aged mice. Nature 544, 488–492 (2017).

15. Di Angelantonio, E. et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet 390, 2360–2371 (2017).

16. Gubbi, S. et al. Effect of exceptional parental longevity and lifestyle factors on prevalence of cardiovascular disease in offspring. Am. J. Cardiol. 120, 2170–2175 (2017).

17. Zhou, J. & Rossi, J. Aptamers as targeted therapeutics: current potential and challenges. Nat. Rev. Drug Discov. 16, 440 (2017).

18. Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).

19. Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010).

20. Austad, S. N. & Fischer, K. E. Sex differences in lifespan. Cell Metab. 23, 1022–1033 (2016).

21. Ostan, R. et al. Gender, aging and longevity in humans: an update of an intriguing/neglected scenario paving the way to a gender-specific medicine. Clin. Sci. 130, 1711–1725 (2016).

22. Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018).

23. Cohen, A. A. Aging across the tree of life: the importance of a comparative perspective for the use of animal models in aging. Biochim. Biophys. Acta. Mol. Basis Dis. 1864, 2680–2689 (2018).

24. Guiraud, S. et al. Identification of serum protein biomarkers for utrophin based DMD therapy. Sci. Rep. 7, 43697 (2017).

25. Wang, R. N. et al. Bone morphogenetic protein (BMP) signaling in development and human diseases. Genes Dis. 1, 87–105 (2014).

26. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

27. Sattlecker, M. et al. Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 10, 724–734 (2014).

28. Sullivan, K. D. et al. Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation. Sci. Rep. 7, 14818 (2017).

29. Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532–2541 (2016).

30. Carayol, J. et al. Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator. Nat. Commun. 8, 2084 (2017).

31. Go, A. S. et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 127, e6–e245 (2013).

32. Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat. Rev. Endocrinol. 14, 576–590 (2018).

33. Franceschi, C. et al. The continuum of aging and age-related diseases: common mechanisms but different rates. Front. Med. 5, 61 (2018).

34. Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

35. Castellano, J. M., Kirby, E. D. & Wyss-Coray, T. Blood-borne revitalization of the aged brain. JAMA Neurol. 72, 1191–1194 (2015).

36. Wiklund, F. E. et al. Macrophage inhibitory cytokine-1 (MIC-1/GDF15): a new marker of all-cause mortality. Aging Cell 9, 1057–1064 (2010).

37. Cohen, E. & Dillin, A. The insulin paradox: aging, proteotoxicity and neurodegeneration. Nat. Rev. Neurosci. 9, 759–767 (2008).

38. Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

39. Sha, S. J., et al. Safety, tolerability, and feasibility of young plasma infusion in the plasma for Alzheimer symptom amelioration study: a randomized clinical trial. JAMA Neurol. 76, 35–40 (2018).

40. Mehan, M. R. et al. Protein signature of lung cancer tissues. PLoS One 7, e35157 (2012).

41. Britschgi, M. et al. Modeling of pathological traits in Alzheimer’s disease based on systemic extracellular signaling proteome. Mol. Cell Proteomics 10, M111 008862 (2011).

42. Franceschi, C. et al. Genetics of healthy aging in Europe: the EU-integrated project GEHA (GEnetics of Healthy Aging). Ann. NY Acad. Sci. 1100, 21–45 (2007).

43. Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, 2011).

44. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statis. Soc. B 57, 289–300 (1995).

45. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

46. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

47. Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

48. Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for Gene Ontology. https://doi.org/10.18129/B9.bioc.topGO (2016).

49. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

50. Carlson, M. org.Hs.eg.db: genome wide annotation for human. https://doi.org/10.18129/B9.bioc.org.Hs.eg.db (2017).

51. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

52. Castellano, J. M. et al. In vivo assessment of behavioral recovery and circulatory exchange in the peritoneal parabiosis model. Sci. Rep. 6, 29015 (2016).

53. Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: efficient manipulation of biological strings. https://doi.org/10.18129/B9.bioc.Biostrings (2019).

54. Dray, S. & Dufour, A. B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw 22, 1–20 (2007).

55. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

56. Lehallier, B. et al. Combined plasma and cerebrospinal fluid signature for the prediction of midterm progression from mild cognitive impairment to Alzheimer disease. JAMA Neurol. 73, 203–212 (2016).

57. Wang, M., Zhao, Y. & Zhang, B. Efficient test and visualization of multi-set intersections. Sci. Rep. 5, 16923 (2015).