Urinary peptidomic profiles to address age-related disabilities: a prospective population study
Authors: Dries S Martens, Lutgarde Thijs, Agnieszka Latosinska, Sander Trenson, Justyna Siwy, Zhen-Yu Zhang, Congrong Wang, Joachim Beige, Antonia Vlahou, Stefan Janssens, Harald Mischak, Tim S Nawrot, Jan A Staessen, Kei Asayama, Murielle Bochud, José Boggia, Jana Brguljan-Hitij, Ying-Mei Feng, Yu-Mei Gu, Azusa Hara, Qi-Fang Huang, Yu Jin, Jitka Seidlerová, Yan-Ping Liu, Jesus Melgarejo, Paula Moliterno, Augustine N Odili, Thibault Petit, Anke Raaijmakers, Rudolph Schutte, Jan A Staessen, Katarzyna Stolarz-Skrzypek, Lutgarde Thijs, Valérie Tikhonoff, Ji-Guang Wang, Fangfei Wei, Dongmei Wei, Wen-Yi Yang, Yuling Yu, Zhenyu Zhang, Dries S Martens, Tim S Nawrot, Harry A Roels, Congrong Wang, Agnieszka Latosinska, Harald Mischak, Justyna Siwy, Tine Willum-Hansen, Gladys E Maestre
Journal: The Lancet Healthy Longevity (2021)

BackgroundThe Global Burden of Diseases, Injuries, and Risk Factors Study 2019 called for innovation in addressing age-related disabilities. Our study aimed to identify and validate a urinary peptidomic profile (UPP) differentiating healthy from unhealthy ageing in the general population, to test the UPP predictor in independent patient cohorts, and to search for targetable molecular pathways underlying age-related chronic diseases.MethodsIn this prospective population study, we used data from participants in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), done in northern Belgium from 1985 to 2019, and invited participants to a follow-up examination in 2005–10. Participants were eligible if their address was within 15 km of the examination centre and if they had not withdrawn consent in any of the previous examination cycles (1985–2004). All participants (2005–10) were also invited to an additional follow-up examination in 2009–13. Participants who took part in both the 2005–10 follow-up examination and in the additional 2009–13 follow-up visit constituted the derivation dataset, which included their 2005–10 data, and the time-shifted internal validation dataset, which included their 2009–13 data. The remaining participants who only had 2005–10 data constituted the synchronous internal validation dataset. Participants were excluded from analyses if they were incapacitated, had not undergone UPP, or had either missing or outlying (three SDs greater than the mean of all consenting participants) values of body-mass index, plasma glucose, or serum creatinine. The UPP was assessed by capillary electrophoresis coupled with mass spectrometry. The multidimensional UPP signature reflecting ageing was generated from the derivation dataset and validated in the time-shifted internal validation dataset and the synchronous validation dataset. It was further validated in patients with diabetes, COVID-19, or chronic kidney disease (CKD). In FLEMENGHO, the mortality endpoints were all-cause, cardiovascular, and non-cardiovascular mortality; other endpoints were fatal or non-fatal cancer and musculoskeletal disorders. Molecular pathway exploration was done using the Reactome and Kyoto Encyclopedia of Genes and Genomes databases.Findings778 individuals (395 [51%] women and 383 [49%] men; aged 16·2–82·1 years; mean age 50·9 years [SD 15·8]) from the FLEMENGHO cohort had a follow-up examination between 2005 and 2010, of whom 559 participants had a further follow-up from Oct 28, 2009, to March 19, 2013, and made up the derivation (2005–10) and time-shifted internal validation (2009–13) datasets. 219 were examined once and constituted the synchronous internal validation dataset (2005–10). With correction for multiple testing and multivariable adjustment, chronological age was associated with 210 sequenced peptides mainly showing downregulation of collagen fragments. The trained model relating chronological age to UPP, derived by elastic net regression, included 54 peptides from 17 proteins. The UPP-age prediction model explained 76·3% (r=0·87) of chronological age in the derivation dataset, 54·4% (r=0·74) in the time-shifted validation dataset, and 65·3% (r=0·81) in the synchronous internal validation dataset. Compared with chronological age, the predicted UPP-age was greater in patients with diabetes (chronological age 50·8 years [SE 0·37] vs UPP-age 56·9 years [0·30]), COVID‑19 (53·2 years [1·80] vs 58·5 years [1·67]), or CKD (54·6 years [0·97] vs 62·3 years [0·85]; all p<0·0001). In the FLEMENGHO cohort, independent of chronological age, UPP-age was significantly associated with various risk markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use. Over a median of 12·4 years (IQR 10·8–13·2), total mortality, cardiovascular mortality, and osteoporosis in the population was associated with UPP-age independent of chronological age, with hazard ratios per 10 year increase in UPP-age of 1·54 (95% CI 1·22–1·95) for total mortality, 1·72 (1·20–2·47) for cardiovascular mortality, and 1·40 (1·06–1·85) for osteoporosis and fractures. The most relevant molecular pathways informed by the proteins involved deregulation of collagen biology and extracellular matrix maintenance.InterpretationThe UPP signature indicative of ageing reflects fibrosis and extracellular matrix remodelling and was associated with risk factors and adverse health outcomes in the population and with accelerated ageing in patients. Innovation in addressing disability should shift focus from the ontology of diseases to shared disease mechanisms, in particular ageing-related fibrotic degeneration.FundingEuropean Research Council, Ministry of the Flemish Community, OMRON Healthcare.