Skip to main content
SpringerLink
Log in

Association between Preexisting Sarcopenia and Stroke in Patients with Type 2 Diabetes Mellitus

  • Original Research
  • Published:
The journal of nutrition, health & aging

Abstract

Objectives

This propensity score–matched population-based cohort study compared stroke risk between patients with type 2 diabetes mellitus with and without preexisting sarcopenia.

Research Design and Methods

We used data from Taiwan’s National Health Insurance Research Database for the period from January 2008 to December 2019. We recruited patients with type 2 diabetes mellitus and categorized them into two groups at a ratio of 1:1 on the basis of diagnosed sarcopenia. The matching variables were age, sex, income level, urbanization level, diabetes severity (adapted Diabetes Complications Severity Index [aDCSI Scores]), Charlson Comorbidity Index (CCI), other comorbidities associated with stroke, smoking status, medication use, and types of antidiabetic medications. The matching process yielded a final cohort of 104,120 patients (52,060 and 52,060 in the sarcopenia and nonsarcopenia groups, respectively) who were eligible for inclusion in subsequent analyses.

Results

In the multivariate Cox regression analysis, the adjusted hazard ratio (aHR; 95% CI) of stroke for the sarcopenia diabetes group compared with the control group was 1.13 (1.{vn10}, 1.16; P < 0.001), after controlling for age, sex, CCI, and aDCSI scores. The incidence rates of stroke for the sarcopenia and nonsarcopenia groups were 295.98 and 260.68 per 10,000 person-years, respectively. The significant IRR (95% CI) of stroke was 1.14 (1.09, 1.17) for the sarcopenia diabetes group compared with the nonsarcopenic diabetes group.

Conclusion

Preexisting sarcopenia increased the risk of stroke in patients with type 2 diabetes mellitus.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2

Similar content being viewed by others

Availability of Data and Material

The datasets supporting the study conclusions are included within this manuscript and its additional files.

Abbreviations

HR:

hazard ratio

aHR:

adjusted hazard ratio

CI:

confidence interval

PSM:

propensity score matching

ICD-9-CM:

International Classification of Diseases, Ninth Revision, Clinical Modification

ICD-10-CM:

International Classification of Diseases, Tenth Revision, Clinical Modification

CCI:

Charlson Comorbidity Index

IQR:

interquartile range

aDCSI:

adapted Diabetes Complications Severity Index

NHI:

National Health Insurance

NHIRD:

National Health Insurance Research Database

IR:

incidence rate

IRR:

incidence rate ratios

IPTW:

inverse probability of treatment weighting.

References

  1. Arvanitakis, Z. et al. Diabetes is related to cerebral infarction but not to AD pathology in older persons. Neurology 67, 1960–1965, doi:https://doi.org/10.1212/01.wnl.0000247053.45483.4e (2006).

    Article  CAS  PubMed  Google Scholar 

  2. Janghorbani, M. et al. Prospective study of type 1 and type 2 diabetes and risk of stroke subtypes: the Nurses’ Health Study. Diabetes Care 30, 1730–1735, doi:https://doi.org/10.2337/dc06-2363 (2007).

    Article  PubMed  Google Scholar 

  3. Emerging Risk Factors, C. et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375, 2215–2222, doi:https://doi.org/10.1016/S0140-6736(10)60484-9 (2010).

    Article  Google Scholar 

  4. Luitse, M. J., Biessels, G. J., Rutten, G. E. & Kappelle, L. J. Diabetes, hyperglycaemia, and acute ischaemic stroke. Lancet Neurol 11, 261–271, doi:https://doi.org/10.1016/S1474-4422(12)70005-4 (2012).

    Article  PubMed  Google Scholar 

  5. Peters, S. A., Huxley, R. R. & Woodward, M. Diabetes as a risk factor for stroke in women compared with men: a systematic review and meta-analysis of 64 cohorts, including 775,385 individuals and 12,539 strokes. Lancet 383, 1973–1980, doi:https://doi.org/10.1016/S0140-6736(14)60040-4 (2014).

    Article  PubMed  Google Scholar 

  6. Garg, A. & Grundy, S. M. Management of dyslipidemia in NIDDM. Diabetes Care 13, 153–169, doi:https://doi.org/10.2337/diacare.13.2.153 (1990).

    Article  CAS  PubMed  Google Scholar 

  7. O’Brien, T., Nguyen, T. T. & Zimmerman, B. R. Hyperlipidemia and diabetes mellitus. Mayo Clin Proc 73, 969–976, doi:https://doi.org/10.4065/73.10.969 (1998).

    Article  PubMed  Google Scholar 

  8. Miura, H. et al. Diabetes mellitus impairs vasodilation to hypoxia in human coronary arterioles: reduced activity of ATP-sensitive potassium channels. Circ Res 92, 151–158, doi:https://doi.org/10.1161/01.res.0000052671.53256.49 (2003).

    Article  CAS  PubMed  Google Scholar 

  9. Kleindorfer, D. O. et al. 2021 Guideline for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline From the American Heart Association/American Stroke Association. Stroke 52, e364–e467, doi:https://doi.org/10.1161/STR.0000000000000375 (2021).

    Article  PubMed  Google Scholar 

  10. Terry, T., Raravikar, K., Chokrungvaranon, N. & Reaven, P. D. Does aggressive glycemic control benefit macrovascular and microvascular disease in type 2 diabetes? Insights from ACCORD, ADVANCE, and VADT. Curr Cardiol Rep 14, 79–88, doi:https://doi.org/10.1007/s11886-011-0238-6 (2012).

    Article  PubMed  Google Scholar 

  11. Genuth, S. et al. Implications of the United kingdom prospective diabetes study. Diabetes Care 26 Suppl 1, S28–32, doi:https://doi.org/10.2337/diacare.26.2007.s28 (2003).

    PubMed  Google Scholar 

  12. Wang, T. et al. Type 2 diabetes mellitus is associated with increased risks of sarcopenia and pre-sarcopenia in Chinese elderly. Sci Rep 6, 38937, doi:https://doi.org/10.1038/srep38937 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mesinovic, J., Zengin, A., De Courten, B., Ebeling, P. R. & Scott, D. Sarcopenia and type 2 diabetes mellitus: a bidirectional relationship. Diabetes Metab Syndr Obes 12, 1057–1072, doi:https://doi.org/10.2147/DMSO.S186600 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hausman, G. J., Basu, U., Du, M., Fernyhough-Culver, M. & Dodson, M. V. Intermuscular and intramuscular adipose tissues: Bad vs. good adipose tissues. Adipocyte 3, 242–255, doi:https://doi.org/10.4161/adip.28546 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Morley, J. E., Malmstrom, T. K., Rodriguez-Manas, L. & Sinclair, A. J. Frailty, sarcopenia and diabetes. J Am Med Dir Assoc 15, 853–859, doi:https://doi.org/10.1016/j.jamda.2014.10.001 (2014).

    Article  PubMed  Google Scholar 

  16. Banerjee, M. & Saxena, M. Interleukin-1 (IL-1) family of cytokines: role in type 2 diabetes. Clin Chim Acta 413, 1163–1170, doi:https://doi.org/10.1016/j.cca.2012.03.021 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Michaud, M. et al. Proinflammatory cytokines, aging, and age-related diseases. J Am Med Dir Assoc 14, 877–882, doi:https://doi.org/10.1016/j.jamda.2013.05.009 (2013).

    Article  PubMed  Google Scholar 

  18. Tuttolomondo, A., Di Raimondo, D., di Sciacca, R., Pinto, A. & Licata, G. Inflammatory cytokines in acute ischemic stroke. Curr Pharm Des 14, 3574–3589, doi:https://doi.org/10.2174/138161208786848739 (2008).

    Article  CAS  PubMed  Google Scholar 

  19. Carlson, N. G. et al. Inflammatory cytokines IL-1 alpha, IL-1 beta, IL-6, and TNF-alpha impart neuroprotection to an excitotoxin through distinct pathways. J Immunol 163, 3963–3968 (1999).

    Article  CAS  PubMed  Google Scholar 

  20. Qiao, Y. S. et al. The Association Between Diabetes Mellitus and Risk of Sarcopenia: Accumulated Evidences From Observational Studies. Front Endocrinol (Lausanne) 12, 782391, doi:https://doi.org/10.3389/fendo.2021.782391 (2021).

    Article  PubMed  Google Scholar 

  21. Pacifico, J. et al. Prevalence of sarcopenia as a comorbid disease: A systematic review and meta-analysis. Exp Gerontol 131, 110801, doi:https://doi.org/10.1016/j.exger.2019.110801 (2020).

    Article  PubMed  Google Scholar 

  22. Wu, S. Y., Fang, S. C., Shih, H. J., Wen, Y. C. & Shao, Y. J. Mortality associated with statins in men with advanced prostate cancer treated with androgen deprivation therapy. Eur J Cancer 112, 109–117, doi:https://doi.org/10.1016/j.ejca.2018.11.032 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Lin, K. C., Chen, T. M., Yuan, K. S., Wu, A. T. H. & Wu, S. Y. Assessment of Predictive Scoring System for 90-Day Mortality Among Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma Who Have Completed Concurrent Chemoradiotherapy. JAMA Netw Open 3, e1920671, doi:https://doi.org/10.1001/jamanetworkopen.2019.20671 (2020).

    Article  PubMed  Google Scholar 

  24. Wu, S. Y., Chang, C. L., Chen, C. I. & Huang, C. C. Comparison of Acute and Chronic Surgical Complications Following Robot-Assisted, Laparoscopic, and Traditional Open Radical Prostatectomy Among Men in Taiwan. JAMA Netw Open 4, e2120156, doi:https://doi.org/10.1001/jamanetworkopen.2021.20156 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhang, J., Lu, C. Y., Chen, H. M. & Wu, S. Y. Neoadjuvant Chemotherapy or Endocrine Therapy for Invasive Ductal Carcinoma of the Breast With High Hormone Receptor Positivity and Human Epidermal Growth Factor Receptor 2 Negativity. JAMA Netw Open 4, e211785, doi:https://doi.org/10.1001/jamanetworkopen.2021.1785 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Liu, W. C. et al. Definitive radiotherapy or surgery for early oral squamous cell carcinoma in old and very old patients: A propensity-score-matched, nationwide, population-based cohort study. Radiother Oncol 151, 214–221, doi:https://doi.org/10.1016/j.radonc.2020.08.016 (2020).

    Article  PubMed  Google Scholar 

  27. National Health Insurance Administration. NHI profile [cited July 9, 2018]., <https://www.nhi.gov.tw/English/Content_List.aspx?n=8FC0974BBFEFA56D&topn=ED4A30E51A609E49> (

  28. Sun, M. Y., Chang, C. L., Lu, C. Y., Wu, S. Y. & Zhang, J. Q. Sarcopenia as an Independent Risk Factor for Specific Cancers: A Propensity Score-Matched Asian Population-Based Cohort Study. Nutrients 14, doi:https://doi.org/10.3390/nu14091910 (2022).

  29. Huang, Y. M., Chen, W. M., Chen, M., Shia, B. C. & Wu, S. Y. Sarcopenia Is an Independent Risk Factor for Severe Diabetic Nephropathy in Type 2 Diabetes: A Long-Term Follow-Up Propensity Score-Matched Diabetes Cohort Study. J Clin Med 11, doi:https://doi.org/10.3390/jcm11112992 (2022).

  30. Anker, S. D., Morley, J. E. & von Haehling, S. Welcome to the ICD-10 code for sarcopenia. J Cachexia Sarcopenia Muscle 7, 512–514, doi:https://doi.org/10.1002/jcsm.12147 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lin, M. H. et al. Hyperlipidemia and Statins Use for the Risk of New Diagnosed Sarcopenia in Patients with Chronic Kidney: A Population-Based Study. Int J Environ Res Public Health 17, doi:https://doi.org/10.3390/ijerph17051494 (2020).

  32. Chien, M. Y., Huang, T. Y. & Wu, Y. T. Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc 56, 1710–1715, doi:https://doi.org/10.1111/j.1532-5415.2008.01854.x (2008).

    Article  PubMed  Google Scholar 

  33. Chang, H. Y., Weiner, J. P., Richards, T. M., Bleich, S. N. & Segal, J. B. Validating the adapted Diabetes Complications Severity Index in claims data. Am J Manag Care 18, 721–726 (2012).

  34. Jimenez Caballero, P. E. et al. Charlson comorbidity index in ischemic stroke and intracerebral hemorrhage as predictor of mortality and functional outcome after 6 months. J Stroke Cerebrovasc Dis 22, e214–218, doi:https://doi.org/10.1016/j.jstrokecerebrovasdis.2012.11.014 (2013).

    Article  PubMed  Google Scholar 

  35. Austin, P. C. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 10, 150–161, doi:https://doi.org/10.1002/pst.433 (2011).

    Article  PubMed  Google Scholar 

  36. Nguyen, T. L. et al. Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance. BMC Med Res Methodol 17, 78, doi:https://doi.org/10.1186/s12874-017-0338-0 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zhang, Z., Kim, H. J., Lonjon, G., Zhu, Y. & written on behalf of, A. M. E. B.-D. C. T. C. G. Balance diagnostics after propensity score matching. Ann Transl Med 7, 16, doi:https://doi.org/10.21037/atm.2018.12.10 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Pirracchio, R., Resche-Rigon, M. & Chevret, S. Evaluation of the propensity score methods for estimating marginal odds ratios in case of small sample size. BMC Med Res Methodol 12, 70, doi:https://doi.org/10.1186/1471-2288-12-70 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yuan, Y., Yung, Y.-F. & Stokes, M. in Proceedings at the SAS Global Forum 2017 Conference. (SAS Institute Cary, NC).

  40. Austin, P. C. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med 33, 1242–1258, doi:https://doi.org/10.1002/sim.5984 (2014).

    Article  PubMed  Google Scholar 

  41. Kahm, K. et al. Health Care Costs Associated With Incident Complications in Patients With Type 2 Diabetes in Germany. Diabetes Care 41, 971–978, doi:https://doi.org/10.2337/dc17-1763 (2018).

    Article  PubMed  Google Scholar 

  42. Vaidya, V., Gangan, N. & Sheehan, J. Impact of cardiovascular complications among patients with Type 2 diabetes mellitus: a systematic review. Expert Rev Pharmacoecon Outcomes Res 15, 487–497, doi:https://doi.org/10.1586/14737167.2015.1024661 (2015).

    Article  PubMed  Google Scholar 

  43. Cleasby, M. E., Jamieson, P. M. & Atherton, P. J. Insulin resistance and sarcopenia: mechanistic links between common co-morbidities. J Endocrinol 229, R67–81, doi:https://doi.org/10.1530/JOE-15-0533 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Lu, C. W. et al. Sarcopenic obesity is closely associated with metabolic syndrome. Obes Res Clin Pract 7, e301–307, doi:https://doi.org/10.1016/j.orcp.2012.02.003 (2013).

    Article  PubMed  Google Scholar 

  45. Atkins, J. L. et al. Sarcopenic obesity and risk of cardiovascular disease and mortality: a population-based cohort study of older men. J Am Geriatr Soc 62, 253–260, doi:https://doi.org/10.1111/jgs.12652 (2014).

    Article  PubMed  Google Scholar 

  46. Hammoud, T., Tanguay, J. F. & Bourassa, M. G. Management of coronary artery disease: therapeutic options in patients with diabetes. J Am Coll Cardiol 36, 355–365, doi:https://doi.org/10.1016/s0735-1097(00)00732-4 (2000).

    Article  CAS  PubMed  Google Scholar 

  47. Taegtmeyer, H., McNulty, P. & Young, M. E. Adaptation and maladaptation of the heart in diabetes: Part I: general concepts. Circulation 105, 1727–1733, doi:https://doi.org/10.1161/01.cir.0000012466.50373.e8 (2002).

    Article  CAS  PubMed  Google Scholar 

  48. Haffner, S. M., Mykkanen, L., Festa, A., Burke, J. P. & Stern, M. P. Insulin-resistant prediabetic subjects have more atherogenic risk factors than insulin-sensitive prediabetic subjects: implications for preventing coronary heart disease during the prediabetic state. Circulation 101, 975–980, doi:https://doi.org/10.1161/01.cir.101.9.975 (2000).

    Article  CAS  PubMed  Google Scholar 

  49. Austin, P. C. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res 46, 399–424, doi:https://doi.org/10.1080/00273171.2011.568786 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  50. O’Donnell, M. J. et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet 376, 112–123, doi:https://doi.org/10.1016/S0140-6736(10)60834-3 (2010).

    Article  PubMed  Google Scholar 

  51. Di Carlo, A. Human and economic burden of stroke. Age Ageing 38, 4–5, doi:https://doi.org/10.1093/ageing/afn282 (2009).

    Article  PubMed  Google Scholar 

  52. Katan, M. & Luft, A. Global Burden of Stroke. Semin Neurol 38, 208–211, doi:https://doi.org/10.1055/s-0038-1649503 (2018).

    Article  PubMed  Google Scholar 

  53. Yoo, S. Z. et al. Role of exercise in age-related sarcopenia. J Exerc Rehabil 14, 551–558, doi:https://doi.org/10.12965/jer.1836268.134 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Minn, Y. K. & Suk, S. H. Higher skeletal muscle mass may protect against ischemic stroke in community-dwelling adults without stroke and dementia: The PRESENT project. BMC Geriatr 17, 45, doi:https://doi.org/10.1186/s12877-017-0433-4 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Srikanthan, P., Hevener, A. L. & Karlamangla, A. S. Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: findings from the National Health and Nutrition Examination Survey III. PLoS One 5, e10805, doi:https://doi.org/10.1371/journal.pone.0010805 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Charpentier, G., Riveline, J. P. & Varroud-Vial, M. Management of drugs affecting blood glucose in diabetic patients with renal failure. Diabetes Metab 26 Suppl 4, 73–85 (2000).

    CAS  PubMed  Google Scholar 

  57. Snyder, R. W. & Berns, J. S. Use of insulin and oral hypoglycemic medications in patients with diabetes mellitus and advanced kidney disease. Semin Dial 17, 365–370, doi:https://doi.org/10.1111/j.0894-0959.2004.17346.x (2004).

    Article  PubMed  Google Scholar 

  58. Tanasescu, M., Leitzmann, M. F., Rimm, E. B. & Hu, F. B. Physical activity in relation to cardiovascular disease and total mortality among men with type 2 diabetes. Circulation 107, 2435–2439, doi:https://doi.org/10.1161/01.CIR.0000066906.11109.1F (2003).

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. These authors have contributed equally to this study (joint primary authors)

Authors and Affiliations

  1. Department of General Surgery, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan

    K.-C. Chai

  2. Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, Taipei, Taiwan

    W.-M. Chen, M. Chen, B.-C. Shia & Szu-Yuan Wu

  3. Artificial Intelligence Development Center, Fu Jen Catholic University, Taipei, Taiwan

    W.-M. Chen, M. Chen, B.-C. Shia & Szu-Yuan Wu

  4. Department of Food Nutrition and Health Biotechnology, College of Medical and Health Science, Asia University, Taichung, Taiwan

    Szu-Yuan Wu

  5. Big Data Center, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan

    Szu-Yuan Wu

  6. Division of Radiation Oncology, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan

    Szu-Yuan Wu

  7. Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan

    Szu-Yuan Wu

  8. Centers for Regional Anesthesia and Pain Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan

    Szu-Yuan Wu

  9. College of Medical and Health Science, Asia University, Taichung, Taiwan

    Szu-Yuan Wu

Authors
  1. K.-C. Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W.-M. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B.-C. Shia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Szu-Yuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and Design: Kang-Chuang Chai; Wan-Ming Chen; Mingchih Chen; Ben-Chang Shia; Szu-Yuan Wu.

Corresponding author

Correspondence to Szu-Yuan Wu.

Ethics declarations

The study protocols were reviewed and approved by the Institutional Review Board of Tzu-Chi Medical Foundation (IRB109-015-B).

Additional information

Competing Interests

The authors have no potential conflicts of interest to declare.

Research Funding

Lo-Hsu Medical Foundation, LotungPoh-Ai Hospital, supports Szu-Yuan Wu’s work (Funding Number: 10908, 10909, 11001, 11002, 11003, 11006, and 11013).

Financial Support

Lo-Hsu Medical Foundation, LotungPoh-Ai Hospital, supports Szu-Yuan Wu’s work (Funding Number: 10908, 10909, 11001, 11002, 11003, 11006, and 11013).

Collection and Assembly of Data

Kang-Chuang Chai; Wan-Ming Chen.

Data Analysis and Interpretation

Kang-Chuang Chai; Wan-Ming Chen; Szu-Yuan Wu.

Administrative Support

Szu-Yuan Wu.

Manuscript Writing

Kang-Chuang Chai; Wan-Ming Chen; Mingchih Chen; Ben-Chang Shia; Szu-Yuan Wu.

Final Approval of Manuscript

All authors.

Competing Interests

The authors have no potential conflicts of interest to declare. The datasets supporting the study conclusions are included within the manuscript.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chai, KC., Chen, WM., Chen, M. et al. Association between Preexisting Sarcopenia and Stroke in Patients with Type 2 Diabetes Mellitus. J Nutr Health Aging 26, 936–944 (2022). https://doi.org/10.1007/s12603-022-1846-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12603-022-1846-0

Key words

Search

Navigation