Chinese expert consensus on skeletal muscle aging biomarkers (2024): a protocol_Chinese Journal of Evidence-Based Medicine

Authors：

LI Daiping ¹ , HUANG Ning ¹ , GE Meiling ¹ , LIU Xiaolei ¹ , ZHANG Yong ² , TIAN Xu ³ , YIN Pengbin ⁴ , LIU Yong ⁵ , YANG Zhongli ¹ ,  YUE Jirong ¹ , DONG Birong ¹

1. Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
2. State Key Laboratory for Complex, Severe, and Rare Diseases, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100005, P. R. China;
3. Guangzhou Bioland Laboratory, Guangzhou 510005, P. R. China;
4. Senior Department of Orthopedics, the Fourth Medical Center of PLA General Hospital, National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing 100048, P. R. China;
5. College of Life Sciences, Wuhan University, Wuhan 430072, P. R. China;

Corresponding author：

YUE Jirong, Email: yuejirong11@hotmail.com

Keywords：

Skeletal muscle aging; Biomarkers; Expert consensus; Aging; Protocol

DOI：

10.7507/1672-2531.202404140

Video：

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With the continuous aging of the population in China, the aging of skeletal muscle in the elderly has seriously affected national health and poses a severe challenge to the public health system. Early detection of skeletal muscle aging, and early warning, prevention, and treatment are of great significance for achieving healthy aging. In order to select a series of clinically operable biomarkers for skeletal muscle aging, and to further standardize the early identification and precise diagnosis of skeletal muscle aging, a multidisciplinary team of experts have registered and written this protocol to provide a detailed introduction to the planning process for the development of the consensus.

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2.	Jing Y, Zuo Y, Yu Y, et al. Single-nucleus profiling unveils a geroprotective role of the FOXO3 in primate skeletal muscle aging. Protein Cell, 2023, 14(7): 497-512.
3.	Ding H, Chen S, Pan X, et al. Transferrin receptor 1 ablation in satellite cells impedes skeletal muscle regeneration through activation of ferroptosis. J Cachexia Sarcopenia Muscle, 2021, 12(3): 746-768.
4.	Aging Biomarker Consortium, Zhang L, Guo J, et al. A framework of biomarkers for vascular aging: a consensus statement by the Aging Biomarker Consortium. Life Med, 2023, 2(4): 3.
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6.	Aging Biomarker Consortium, Jia YJ, Wang J, et al. A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium. Life Med, 2023, 2(3): 5.
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11.	Sousa-Uva M, Head SJ, Thielmann M, et al. Methodology manual for European Association for Cardio-Thoracic Surgery (EACTS) clinical guidelines. Eur J Cardiothorac Surg, 2015, 48(6): 809-816.
12.	Brouwers MC, Kerkvliet K, Spithoff K, et al. The AGREE reporting checklist: a tool to improve reporting of clinical practice guidelines. BMJ, 2016, 354: i4852.
13.	杨楠, 赵巍, 潘旸, 等. 针对临床实践指南科学性、透明性和适用性的评级工具研发. 中华医学杂志, 2022, 102(30): 2329-2337.
14.	Chen Y, Yang K, Marušic A, et al. A reporting tool for practice guidelines in health care: the RIGHT statement. Ann Intern Med, 2017, 166(2): 128-132.
15.	陈耀龙, 马艳芳, 周奇, 等. 谁应该参与临床实践指南的制订. 协和医学杂志, 2019, 10(5): 524-530.
16.	Guyatt GH, Oxman AD, Vist GE, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008, 336(7650): 924-926.
17.	Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 2017, 358: j4008.
18.	Higgins JP, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343: d5928.
19.	Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
20.	Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
21.	Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010, 25(9): 603-605.
22.	Rostom A, Dubé C, Cranney A, et al. Appendix D. Quality assessment forms. Rockville (MD): Agency for Healthcare Research and Quality (US), 2004.

1. Yang LJ, Wu GH, Yang YL, et al. Nutrition, physical exercise, and the prevalence of sarcopenia in elderly residents in nursing homes in China. Med Sci Monit, 2019, 25: 4390-4399.
2. Jing Y, Zuo Y, Yu Y, et al. Single-nucleus profiling unveils a geroprotective role of the FOXO3 in primate skeletal muscle aging. Protein Cell, 2023, 14(7): 497-512.
3. Ding H, Chen S, Pan X, et al. Transferrin receptor 1 ablation in satellite cells impedes skeletal muscle regeneration through activation of ferroptosis. J Cachexia Sarcopenia Muscle, 2021, 12(3): 746-768.
4. Aging Biomarker Consortium, Zhang L, Guo J, et al. A framework of biomarkers for vascular aging: a consensus statement by the Aging Biomarker Consortium. Life Med, 2023, 2(4): 3.
5. Aging Biomarker Consortium, Zhang WW, Che Y, et al. A biomarker framework for cardiac aging: the Aging Biomarker Consortium consensus statement. Life Med, 2023, 2(5): 3.
6. Aging Biomarker Consortium, Jia YJ, Wang J, et al. A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium. Life Med, 2023, 2(3): 5.
7. Aging Biomarker Consortium, Suo JL, Gan YB, et al. A framework of biomarkers for skeletal aging: a consensus statement by the Aging Biomarker Consortium. Life Med, 2023, 2(6): 1.
8. WHO. WHO handbook for guideline development, 2nd ed. Vienna: World Health Organization, 2014: 1-167.
9. Schünemann HJ, Wiercioch W, Etxeandia I, 等. 指南2. 0: 为成功制订指南而系统研发的全面清单. 中国循证医学杂志, 2014, 14(9): 1135-1149.
10. 陈耀龙, 杨克虎, 王小钦, 等. 中国制订/修订临床诊疗指南的指导原则(2022版). 中华医学杂志, 2022, 102(10): 697-703.
11. Sousa-Uva M, Head SJ, Thielmann M, et al. Methodology manual for European Association for Cardio-Thoracic Surgery (EACTS) clinical guidelines. Eur J Cardiothorac Surg, 2015, 48(6): 809-816.
12. Brouwers MC, Kerkvliet K, Spithoff K, et al. The AGREE reporting checklist: a tool to improve reporting of clinical practice guidelines. BMJ, 2016, 354: i4852.
13. 杨楠, 赵巍, 潘旸, 等. 针对临床实践指南科学性、透明性和适用性的评级工具研发. 中华医学杂志, 2022, 102(30): 2329-2337.
14. Chen Y, Yang K, Marušic A, et al. A reporting tool for practice guidelines in health care: the RIGHT statement. Ann Intern Med, 2017, 166(2): 128-132.
15. 陈耀龙, 马艳芳, 周奇, 等. 谁应该参与临床实践指南的制订. 协和医学杂志, 2019, 10(5): 524-530.
16. Guyatt GH, Oxman AD, Vist GE, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008, 336(7650): 924-926.
17. Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 2017, 358: j4008.
18. Higgins JP, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343: d5928.
19. Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
20. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
21. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010, 25(9): 603-605.
22. Rostom A, Dubé C, Cranney A, et al. Appendix D. Quality assessment forms. Rockville (MD): Agency for Healthcare Research and Quality (US), 2004.

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