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. 2025 Apr 7;80(5):glae297.
doi: 10.1093/gerona/glae297.

An Expert Consensus Statement on Biomarkers of Aging for Use in Intervention Studies

Giorgia Perri  1 Chloe French  2 César Agostinis-Sobrinho  3   4 Atul Anand  5 Radiana Dhewayani Antarianto  6   7 Yasumichi Arai  8 Joseph A Baur  9 Omar Cauli  10   11 Morgane Clivaz-Duc  12 Giuseppe Colloca  13 Constantinos Demetriades  14   15 Chiara de Lucia  16   17 Giorgio Di Gessa  18 Breno S Diniz  19 Catherine L Dotchin  20   21 Gillian Eaglestone  22 Bradley T Elliott  23 Mark A Espeland  24 Luigi Ferrucci  25 James Fisher  26 Dimitris K Grammatopoulos  27   28 Novi S Hardiany  29 Zaki Hassan-Smith  30   31 Waylon J Hastings  32 Swati Jain  33 Peter K Joshi  34   35 Theodora Katsila  36 Graham J Kemp  37 Omid A Khaiyat  38 Dudley W Lamming  39 Jose Lara Gallegos  40   41 Frank Madeo  42   43 Andrea B Maier  44   45 Carmen Martin-Ruiz  46 Ian J Martins  47 John C Mathers  1 Lewis R Mattin  23 Reshma A Merchant  48 Alexey Moskalev  49   50 Ognian Neytchev  51 Mary Ni Lochlainn  52 Claire M Owen  20 Stuart M Phillips  53 Jedd Pratt  54 Konstantinos Prokopidis  55 Nicholas J W Rattray  56 María Rúa-Alonso  3   57 Lutz Schomburg  58 David Scott  59   60 Sangeetha Shyam  61   62 Elina Sillanpää  63   64 Michelle M C Tan  20   65 Ruth Teh  66 Stephanie W Tobin  67 Carolina J Vila-Chã  3 Luigi Vorluni  68 Daniela Weber  69 Ailsa Welch  70 Daisy Wilson  71 Thomas Wilson  72 Tongbiao Zhao  73 Elena Philippou  74   75 Viktor I Korolchuk  76 Oliver M Shannon  1
Affiliations

An Expert Consensus Statement on Biomarkers of Aging for Use in Intervention Studies

Giorgia Perri et al. J Gerontol A Biol Sci Med Sci. .

Abstract

Biomarkers of aging 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 aging for use in intervention studies via the Delphi method. A 3-round Delphi study was conducted using an online platform. In Round 1, expert panel members provided suggestions for candidate biomarkers of aging. In Rounds 2 and 3, they voted on 500 initial statements (yes/no) relating to 20 biomarkers of aging. 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 (eg, insulin-like growth factor 1, growth-differentiating factor-15), inflammatory (eg, high sensitivity C-reactive protein, interleukin-6), functional (eg, muscle mass, muscle strength, hand grip strength, Timed-Up-and-Go, gait speed, standing balance test, frailty index, cognitive health, blood pressure), and epigenetic (eg, DNA methylation/epigenetic clocks) domains. Expert consensus identified 14 potential biomarkers of aging which may be used as outcome measures in intervention studies. Future aging research should identify which combination of these biomarkers has the greatest utility.

Keywords: Consensus; Delphi method; Longevity.

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Conflict of interest statement

J.A.B reports receiving research funding and materials from Pfizer, Calico, Elysium Health, and Metro International Biotech and consulting fees from Pfizer, Elysium Health, Altimmune, and Cytokinetics. M.E. received research funding from the National Institutes of Health and the Alzheimer’s Association and consults with Nestle and Annovis Bio. W.J.H. is a paid consultant for Bayer Healthcare. P.K.J. is a paid consultant to Humanity Inc., a company focused on measuring and developing interventions for Biological Age. P.K.J. is partly remunerated under a Humanity Inc. share option scheme. P.K.J. is founder of Geromica, a consultancy providing advice on measurement of health and aging. D.W.L. has received funding from, and is a scientific advisory board member of Aeovian Pharmaceuticals, which seeks to develop novel, selective mTOR inhibitors for the treatment of various diseases. F.M. has equity interest in TLL The Longevity labs and Samsara Therapeutics. A.B.M. is cofounder of Chi Longevity and Chief Medical Officer of NU. S.P. reports non-financial support from Enhanced Recovery, other from Exerkine, personal fees from Nestle Health Sciences, outside the submitted work; In addition, S.P. has a patent 3052324 issued to Exerkine, and a patent 16/182891 issued to Exerkine. D.S. has received honoraria from Pfizer, Amgen, and Abbott Nutrition. S.S. received consulting fees from Abbott Laboratories Sdn Bhd. Z.H.-S. has received speaker fees/honoraria from pharmaceutical companies including Kyowa Kirin, UCB, and Ascendis, in recent years. He is the editor of Osteoporosis Review and part of the Clinical Research Committee for the Royal Osteoporosis Society. The other authors have no conflict to declare.

Figures

None
A graphical abstract to depict the methodology of results of this Delphi study.
Figure 1.
Figure 1.
Flow diagram of the Delphi process and results with indications of biomarkers and statements reaching consensus across each round. Numbers in parentheses indicate the numbers of statements reaching consensus (yes or no). *In Round 2, two biomarkers were amalgamated thus reducing the total number of statements from 500 to 475 and resulting in a total of 13 accepted (yes) by the end of Round 2, 125 undecided, and 59 removed biomarkers.
Figure 2.
Figure 2.
Summary of overall responses (yes/no) to biomarkers. (A) 20 biomarkers from Round 2, (B) 4 biomarkers recirculated for Round 3, and (C) Total recommended biomarkers. Black bars indicate % of responses denoted to not recommend the biomarker and dark gray bars indicate % of responses denoted to recommend the biomarker. Dashed line indicates the 70% threshold. GDF-15: growth differentiation factor 15; HbA1c: glycated hemoglobin; HGS: hand grip strength; hsCRP: high sensitivity C-reactive protein; IGF-1: insulin-like growth factor 1; IL-6: interleukin-6; SBT: standing balance test; TNF-α: tumor necrosis factor alpha; TUG: Timed-Up-and-Go.
Figure 3.
Figure 3.
A summary of overall responses across Round 2 and Round 3. Statements are divided by those that were removed as agreement was ≤50% (white), those that were undecided as agreement was 51%–69% (striped), those that reached consensus for “No” with agreement at ≥70% (black), and those that reached consensus for “Yes” with agreement at ≥70% (dark gray).
Figure 4.
Figure 4.
A summary of responses in each major theme in (A) Round 2 and (B) Round 3. Statements are divided by those that were removed as agreement was ≤50% (white), those that were undecided as agreement was 51%–69% (striped), those that reached consensus for “No” with agreement at ≥70% (black), and those that reached consensus for “Yes” with agreement at ≥70% (dark gray). Technicalities included precision, reliability, and mechanical validation; Function# included generalisabilty, prediction of biological age and responsiveness; Function* included requirement of models/software for, and age acceleration; Suitability for various settings included field, frail and cognitively impaired participants and access to materials; Intervention duration included acute, short, medium and long.

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