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Association of estimated glomerular filtration rate from serum creatinine and cystatin C with new-onset diabetes: a nationwide cohort study in China

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Abstract

Aims

The association between estimated glomerular filtration rate (eGFR) and the risk of diabetes remains uncertain. We aimed to examine the association between eGFR based on creatinine (eGFRcr), cystatin C (eGFRcys), or a combination of both (eGFRcr-cys) and new-onset diabetes, using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study.

Methods

A total of 4,775 participants with pertinent measurements and without diabetes at baseline from CHARLS were included in the final analysis. The eGFR was calculated by creatinine, cystatin C or a combination of both using the Chronic Kidney Disease Epidemiology Collaboration equations. The study outcome was new-onset diabetes, defined as physician-diagnosed diabetes or use of glucose-lowering drugs during follow-up, or fasting glucose ≥ 126 mg/dL, random glucose ≥ 200 mg/dL, or HbA1c ≥ 6.5% (48 mmol/mol) at the exit visit.

Results

The mean age of the study population was 59.6 years. The mean values for the eGFRcr, eGFRcys, and eGFRcr-cys were 92.4, 78.9 and 85.9 mL/min/1.73m2, respectively. Over 4 years of follow-up, 612 (12.8%) participants experienced diabetes. Participants with lower eGFRcr-cys (< 60 mL/min/1.73m2) had a significantly higher risk of new-onset diabetes (adjusted OR, 1.46; 95%CI: 1.02, 2.09), compared to those with eGFRcr-cys ≥ 60 mL/min/1.73m2. However, there was no significant association between eGFRcr (< 60 vs. ≥ 60 mL/min/1.73m2; adjusted OR, 1.27; 95%CI: 0.75, 2.17) or eGFRcys (adjusted OR, 1.04; 95%CI: 0.80, 1.36) and new-onset diabetes.

Conclusions

Lower eGFRcr-cys (< 60 mL/min/1.73m2), but not eGFRcr or eGFRcys, was significantly associated with an increased risk of new-onset diabetes in Chinese adults.

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Code availability

Analytic code will be made available from the corresponding author.

Availability of data and material

The data and study materials that support the findings of this study will be available at the CHARLS project website(http://charls.pku.edu.cn/).

References

  1. Zheng Y, Ley SH, Hu FB (2017) Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 14:88–98

    Article  Google Scholar 

  2. Qin X, Li J, Zhang Y, Ma W et al (2012) Prevalence and associated factors of diabetes and impaired fasting glucose in Chinese hypertensive adults aged 45 to 75 years. PLoS ONE 7:e42538

    Article  CAS  Google Scholar 

  3. Petrie JR, Guzik TJ, Touyz RM (2018) Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can J Cardiol 34:575–584

    Article  Google Scholar 

  4. He M, Qin X, Cui Y et al (2012) Prevalence of unrecognized lower extremity peripheral arterial disease and the associated factors in Chinese hypertensive adults. Am J Cardiol 110:1692–1698

    Article  Google Scholar 

  5. Miller WG, Jones GRD (2018) Estimated glomerular filtration rate; Laboratory implementation and current global status. Adv Chronic Kidney D 25:7–13

    Article  Google Scholar 

  6. Hsu C, Chertow GM, Curhan GC (2002) Methodological issues in studying the epidemiology of mild to moderate chronic renal insufficiency. Kidney Int 61:1567–1576

    Article  Google Scholar 

  7. Stevens LA, Schmid CH, Greene T et al (2009) Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int 75:652–660

    Article  CAS  Google Scholar 

  8. Dharnidharka VR, Kwon C, Stevens G (2002) Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis 40:221–226

    Article  CAS  Google Scholar 

  9. Inker LA, Schmid CH, Tighiouart H et al (2012) Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 367:20–29

    Article  CAS  Google Scholar 

  10. Stevens LA, Coresh J, Schmid CH et al (2008) Estimating GFR using serum cystatin c alone and in combination with serum creatinine: a pooled analysis of 3418 individuals with CKD. Am J Kidney Dis 51:395–406

    Article  CAS  Google Scholar 

  11. Eriksen BO, Mathisen UD, Melsom T et al (2010) Cystatin C is not a better estimator of GFR than plasma creatinine in the general population. Kidney Int 78:1305–1311

    Article  CAS  Google Scholar 

  12. Koppe L, Pelletier CC, Alix PM et al (2014) Insulin resistance in chronic kidney disease: New lessons from experimental models. Nephrol Dial Transpl 29:1666–1674

    Article  CAS  Google Scholar 

  13. Xie Y, Bowe B, Li T, Xian H, Yan Y, Al-Aly Z (2018) Higher blood urea nitrogen is associated with increased risk of incident diabetes mellitus. Kidney Int 93:741–752

    Article  CAS  Google Scholar 

  14. Pham H, Robinson-Cohen C, Biggs ML et al (2012) Chronic kidney disease, insulin resistance, and incident diabetes in older adults. Clin J Am Soc Nephro 7:588–594

    Article  CAS  Google Scholar 

  15. Lorenzo C, Nath SD, Hanley AJG, Abboud HE, Gelfond JAL, Haffner SM (2009) Risk of type 2 diabetes among individuals with high and low glomerular filtration rates. Diabetologia 52:1290–1297

    Article  CAS  Google Scholar 

  16. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: The china health and retirement longitudinal study (CHARLS). Int J Epidemiol 43:61–68

    Article  Google Scholar 

  17. Levey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612

    Article  Google Scholar 

  18. Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K (2012) Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med 156:785–795

    Article  Google Scholar 

  19. Nerpin E, Riserus U, Ingelsson E et al (2008) Insulin sensitivity measured with euglycemic clamp is independently associated with glomerular filtration rate in a Community-Based cohort. Diabetes Care 31:1550–1555

    Article  Google Scholar 

  20. Kobayashi S, Maesato K, Moriya H, Ohtake T, Ikeda T (2005) Insulin resistance in patients with chronic kidney disease. Am J Kidney Dis 45:275–280

    Article  CAS  Google Scholar 

  21. Spoto B, Pisano A, Zoccali C (2016) Insulin resistance in chronic kidney disease: a systematic review. Am J Physiol-Renal 311:F1087–F1108

    Article  CAS  Google Scholar 

  22. Dogra G, Irish A, Chan D, Watts G (2006) Insulin resistance, inflammation, and blood pressure determine vascular dysfunction in CKD. Am J Kidney Dis 48:926–934

    Article  CAS  Google Scholar 

  23. Meigs JB, Hu FB, Rifai N, Manson JE (2004) Biomarkers of endothelial dysfunction and risk of type 2 diabetes mellitus. ACC Curr J Rev 13:22

    Article  Google Scholar 

  24. Giacco F, Brownlee M (2010) Oxidative stress and diabetic complications. Circ Res 107:1058–1070

    Article  CAS  Google Scholar 

  25. Onozato ML, Tojo A, Goto A, Fujita T, Wilcox CS (2002) Oxidative stress and nitric oxide synthase in rat diabetic nephropathy: Effects of ACEI and ARB. Kidney Int 61:186–194

    Article  CAS  Google Scholar 

  26. Eid A, Bodin S, Ferrier B et al (2006) Intrinsic gluconeogenesis is enhanced in renal proximal tubules of zucker diabetic fatty rats. J Am Soc Nephrol 17:398–405

    Article  CAS  Google Scholar 

  27. Jacob S, Henriksen EJ, Fogt DL, Dietze GJ (1996) Effects of trandolapril and verapamil on glucose transport in insulin-resistant rat skeletal muscle. Metabolism 45:535–541

    Article  CAS  Google Scholar 

  28. Tikellis C, Wookey PJ, Candido R, Andrikopoulos S, Thomas MC, Cooper ME (2004) Improved islet morphology after blockade of the renin- angiotensin system in the ZDF rat. Diabetes 53:989–997

    Article  CAS  Google Scholar 

  29. Yamamoto T, Nakagawa T, Suzuki H et al (2007) Urinary angiotensinogen as a marker of intrarenal angiotensin II activity associated with deterioration of renal function in patients with chronic kidney disease. J Am Soc Nephrol 18:1558–1565

    Article  CAS  Google Scholar 

  30. Shlipak MG, Matsushita K, Arnlov J et al (2013) Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med 369:932–943

    Article  CAS  Google Scholar 

  31. Schaeffner ES, Ebert N, Delanaye P et al (2012) Two novel equations to estimate kidney function in persons aged 70 years or older. Ann Int Med 157:471

    Article  Google Scholar 

  32. Fan L, Inker LA, Rossert J et al (2014) Glomerular filtration rate estimation using cystatin C alone or combined with creatinine as a confirmatory test. Nephrol Dial Transpl 29:1195–1203

    Article  CAS  Google Scholar 

  33. Rule AD, Bailey KR, Schwartz GL, Khosla S, Lieske JC, Melton LJ (2009) For estimating creatinine clearance measuring muscle mass gives better results than those based on demographics. Kidney Int 75:1071–1078

    Article  Google Scholar 

  34. Shardlow A, McIntyre NJ, Fraser S et al (2017) The clinical utility and cost impact of cystatin C measurement in the diagnosis and management of chronic kidney disease: a primary care cohort study. Plos Med 14:e1002400

    Article  Google Scholar 

  35. Glassock RJ, Rule AD (2017) Optimally predicting mortality with kidney function markers is not the same as optimally determining how kidney function predicts mortality. Nephrol Dial Transplant 32:585–587

    Article  CAS  Google Scholar 

  36. Lee S, Park S, Ko S et al (2010) Insulin resistance and inflammation may have an additional role in the link between cystatin C and cardiovascular disease in type 2 diabetes mellitus patients. Metabolism 59:241–246

    Article  CAS  Google Scholar 

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Acknowledgements

This analysis uses data from the China Health and Retirement Longitudinal Study (CHARLS). We thank the CHARLS research team and the field team for collecting and providing the data. We thank all volunteers and staff involved in this research.

Funding

The study was supported by the National Natural Science Foundation of China [81973133, 81730019].

Author information

Authors and Affiliations

  1. National Clinical Research Center for Kidney Disease, State Key Laboratory for Organ Failure Research, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangdong Provincial Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China

    Zhuxian Zhang, Panpan He, Chun Zhou, Mengyi Liu, Chengzhang Liu, Huan Li, Yuanyuan Zhang, Guobao Wang, Min Liang & Xianhui Qin

  2. Institute of Biomedicine, Anhui Medical University, Hefei, 230032, China

    Chengzhang Liu & Qinqin Li

  3. Spine Disease Research Institute, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China

    Yan Zhang

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  1. Zhuxian Zhang
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Contributions

Zhuxian Zhang and Xianhui Qin designed and conducted the study; Zhuxian Zhang, Panpan He and Xianhui Qin performed the data management and statistical analyses; Zhuxian Zhang and Xianhui Qin drafted the manuscript; all authors read and approved the final manuscript.

Corresponding author

Correspondence to Xianhui Qin.

Ethics declarations

Conflict of interest

Dr. Xianhui Qin reports grants from the National Natural Science Foundation of China [81973133, 81730019]. No other disclosures were reported.

Ethics approval

The CHARLS was approved by the Biomedical Ethics Review Committee of Peking University, Beijing, China (IRB00001052-11015).

Consent to participate

All participants provided written informed consent.

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All authors read and approved the final manuscript.

Additional information

This article belongs to the topical collection Diabetic Nephropathy, managed by Giuseppe Pugliese.

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Zhang, Z., He, P., Zhou, C. et al. Association of estimated glomerular filtration rate from serum creatinine and cystatin C with new-onset diabetes: a nationwide cohort study in China. Acta Diabetol 58, 1269–1276 (2021). https://doi.org/10.1007/s00592-021-01719-5

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  • DOI: https://doi.org/10.1007/s00592-021-01719-5

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