Giorgia Perri, Chloe French, César Agostinis-Sobrinho, Atul Anand, Radiana Dhewayani Antarianto, Yasumichi Arai, Joseph A Baur, Omar Cauli, Morgane Clivaz-Duc, Giuseppe Colloca, Constantinos Demetriades, Chiara de Lucia, Giorgio Di Gessa, Breno S Diniz, Catherine L Dotchin, Gillian Eaglestone, Bradley T Elliott, Mark A Espeland, Luigi Ferrucci, James Fisher, Dimitris K Grammatopoulos, Novi S Hardiany, Zaki Hassan-Smith, Waylon J Hastings, Swati Jain, Peter K Joshi, Theodora Katsila, Graham J Kemp, Omid A Khaiyat, Dudley W Lamming, Jose Lara Gallegos, Frank Madeo, Andrea B Maier, Carmen Martin-Ruiz, Ian J Martins, John C Mathers, Lewis R Mattin, Reshma A Merchant, Alexey Moskalev, Ognian Neytchev, Mary Ni Lochlainn, Claire M Owen, Stuart M Phillips, Jedd Pratt, Konstantinos Prokopidis, Nicholas J W Rattray, María Rúa-Alonso, Lutz Schomburg, David Scott, Sangeetha Shyam, Elina Sillanpää, Michelle M C Tan, Ruth Teh, Stephanie W Tobin, Carolina J Vila-Chã, Luigi Vorluni, Daniela Weber, Ailsa Welch, Daisy Wilson, Thomas Wilson, Tongbiao Zhao, Elena Philippou, Viktor I Korolchuk, Oliver M Shannon
{"title":"An expert consensus statement on biomarkers of ageing for use in intervention studies","authors":"Giorgia Perri, Chloe French, César Agostinis-Sobrinho, Atul Anand, Radiana Dhewayani Antarianto, Yasumichi Arai, Joseph A Baur, Omar Cauli, Morgane Clivaz-Duc, Giuseppe Colloca, Constantinos Demetriades, Chiara de Lucia, Giorgio Di Gessa, Breno S Diniz, Catherine L Dotchin, Gillian Eaglestone, Bradley T Elliott, Mark A Espeland, Luigi Ferrucci, James Fisher, Dimitris K Grammatopoulos, Novi S Hardiany, Zaki Hassan-Smith, Waylon J Hastings, Swati Jain, Peter K Joshi, Theodora Katsila, Graham J Kemp, Omid A Khaiyat, Dudley W Lamming, Jose Lara Gallegos, Frank Madeo, Andrea B Maier, Carmen Martin-Ruiz, Ian J Martins, John C Mathers, Lewis R Mattin, Reshma A Merchant, Alexey Moskalev, Ognian Neytchev, Mary Ni Lochlainn, Claire M Owen, Stuart M Phillips, Jedd Pratt, Konstantinos Prokopidis, Nicholas J W Rattray, María Rúa-Alonso, Lutz Schomburg, David Scott, Sangeetha Shyam, Elina Sillanpää, Michelle M C Tan, Ruth Teh, Stephanie W Tobin, Carolina J Vila-Chã, Luigi Vorluni, Daniela Weber, Ailsa Welch, Daisy Wilson, Thomas Wilson, Tongbiao Zhao, Elena Philippou, Viktor I Korolchuk, Oliver M Shannon","doi":"10.1093/gerona/glae297","DOIUrl":null,"url":null,"abstract":"Biomarkers of ageing serve as important outcome measures in longevity-promoting interventions. However, there is limited consensus on which specific biomarkers are most appropriate for human intervention studies. This work aimed to address this need by establishing an expert consensus on biomarkers of ageing for use in intervention studies via the Delphi method. A three-round Delphi study was conducted using an online platform. In Round 1, expert panel members provided suggestions for candidate biomarkers of ageing. In Rounds 2 and 3, they voted on 500 initial statements (yes/no) relating to 20 biomarkers of ageing. Panel members could abstain from voting on biomarkers outside their expertise. Consensus was reached when there was ≥70% agreement on a statement/biomarker. Of the 460 international panel members invited to participate, 116 completed Round 1, 87 completed Round 2, and 60 completed Round 3. Across the 3 rounds, 14 biomarkers met consensus that spanned physiological (e.g., insulin-like growth factor 1, growth-differentiating factor-15), inflammatory (e.g., high sensitivity c-reactive protein, interleukin-6), functional (e.g., muscle mass, muscle strength, hand grip strength, Timed-Up-and-Go, gait speed, standing balance test, frailty index, cognitive health, blood pressure), and epigenetic (e.g., DNA methylation/epigenetic clocks) domains. Expert consensus identified 14 potential biomarkers of ageing which may be used as outcome measures in intervention studies. Future ageing research should identify which combination of these biomarkers has the greatest utility.","PeriodicalId":22892,"journal":{"name":"The Journals of Gerontology Series A: Biological Sciences and Medical Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journals of Gerontology Series A: Biological Sciences and Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/gerona/glae297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Biomarkers of ageing serve as important outcome measures in longevity-promoting interventions. However, there is limited consensus on which specific biomarkers are most appropriate for human intervention studies. This work aimed to address this need by establishing an expert consensus on biomarkers of ageing for use in intervention studies via the Delphi method. A three-round Delphi study was conducted using an online platform. In Round 1, expert panel members provided suggestions for candidate biomarkers of ageing. In Rounds 2 and 3, they voted on 500 initial statements (yes/no) relating to 20 biomarkers of ageing. Panel members could abstain from voting on biomarkers outside their expertise. Consensus was reached when there was ≥70% agreement on a statement/biomarker. Of the 460 international panel members invited to participate, 116 completed Round 1, 87 completed Round 2, and 60 completed Round 3. Across the 3 rounds, 14 biomarkers met consensus that spanned physiological (e.g., insulin-like growth factor 1, growth-differentiating factor-15), inflammatory (e.g., high sensitivity c-reactive protein, interleukin-6), functional (e.g., muscle mass, muscle strength, hand grip strength, Timed-Up-and-Go, gait speed, standing balance test, frailty index, cognitive health, blood pressure), and epigenetic (e.g., DNA methylation/epigenetic clocks) domains. Expert consensus identified 14 potential biomarkers of ageing which may be used as outcome measures in intervention studies. Future ageing research should identify which combination of these biomarkers has the greatest utility.