Glycemic Patterns and Factors Associated with Post-Hemodialysis Hyperglycemia among End-Stage Renal Disease Patients undergoing Maintenance Hemodialysis

Authors

  • Abdul Hanif Khan Yusof Khan University Putra Malaysia https://orcid.org/0000-0002-8975-2174
  • Nor Fadhlina Zakaria Universiti Putra Malaysia
  • Muhammad Adil Zainal Abidin International Islamic University Malaysia (IIUM), Jalan Hospital Campus, Kuantan, Pahang
  • Christopher Tiam Seong Lim University Putra Malaysia
  • Nor Azmi Kamaruddin The National University of Malaysia (HUKM), Kuala Lumpur

DOI:

https://doi.org/10.15605/jafes.035.01.12

Keywords:

renal dialysis, glycemic variability, diabetes complications, hyperglycemia, risk factors, Asians

Abstract

*Visual Abstracts prepared by Dr. Monica Therese Cating-Cabral

Introduction. Chronic and post-prandial hyperglycemia are independent risk factors for diabetic complications. Glycemic patterns among hemodialysis end-stage-renal-disease (ESRD) differ as glucose metabolism changes with declining kidney function with more pronounced glycemic fluctuations. The objectives of this study are to determine glycemic patterns on hemodialysis days, the magnitude of post-hemodialysis rebound hyperglycemia (PHH) and their associated factors.

Methodology. 148 patients on hemodialysis were analysed, 91 patients had end-stage-diabetic-renal disease (DM-ESRD), and 57 patients had end-stage-non-diabetic renal disease (NDM-ESRD). Glycemic patterns and PHH data were obtained from 11-point and 7-point self-monitoring blood glucose (SMBG) profiles on hemodialysis and
non-hemodialysis days. PHH and its associated factors were analysed with logistic regression.

Results. Mean blood glucose on hemodialysis days was 9.33 [SD 2.7] mmol/L in DM-ESRD patients compared to 6.07 [SD 0.85] mmol/L in those with NDM-ESRD (p<0.001). PHH occurred in 70% of patients and was more pronounced in DM-ESRD compared to NDM-ESRD patients (72.5% vs 27.5%; OR 4.5). Asymptomatic hypoglycemia was observed
in 18% of patients. DM-ESRD, older age, previous IHD, obesity, high HbA1c, elevated highly-sensitive CRP and low albumin were associated with PHH.


Conclusion. DM-ESRD patients experienced significant PHH in our cohort. Other associated factors include older age, previous IHD, obesity, high HbA1c, elevated hs-CRP and low albumin.

Downloads

Download data is not yet available.

Author Biographies

Abdul Hanif Khan Yusof Khan, University Putra Malaysia

Department of Medicine, Faculty of Medicine and Health Sciences

Nor Fadhlina Zakaria, Universiti Putra Malaysia

Department of Medicine, Faculty of Medicine and Health Sciences

Muhammad Adil Zainal Abidin, International Islamic University Malaysia (IIUM), Jalan Hospital Campus, Kuantan, Pahang

Kuliyyah of Medicine

Christopher Tiam Seong Lim, University Putra Malaysia

Department of Medicine, Faculty of Medicine and Health Sciences

Nor Azmi Kamaruddin, The National University of Malaysia (HUKM), Kuala Lumpur

Department of Medicine

References

Couser WG, Remuzzi G, Mendis S, Tonelli M. The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney Int. 2011; 80(12):1258–70. https://www.ncbi.nlm.nih.gov/pubmed/21993585. https://doi.org/10.1038/ki.2011.368.

Collins AJ, Foley RN, Herzog C, et al. Excerpts from the US Renal Data System 2009 Annual Data Report. Am J Kidney Dis. 2010; 55(1 Suppl 1):S1-420. https://www.ncbi.nlm.nih.gov/pubmed/20082919. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829836. https://doi.org/10.1053/j.ajkd.2009.10.009.

Wong H, Goh B. Twenty Third Report of The Malaysian Dialysis and Transplant 2015 [Internet]. Kuala Lumpur 2017; 2017. Available from: http://www.msn.org.my.

Kalantar-Zadeh K, Derose SF, Nicholas S, Benner D, Sharma K, Kovesdy CP. Burnt-Out Diabetes: Impact of chronic kidney disease progression on the natural course of diabetes mellitus. J Ren Nutr. 2009; 19(1):33–7. https://www.ncbi.nlm.nih.gov/pubmed/19121768. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652655. https://doi.org/10.1053/j.jrn.2008.11.012.

Abe M, Kalantar-Zadeh K. Haemodialysis-induced hypoglycaemia and glycaemic disarrays. Nat Rev Nephrol. 2015; 11(5):302–13. https://www.ncbi.nlm.nih.gov/pubmed/25848881. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015632. https://doi.org/10.1038/nrneph.2015.38.

Abe M, Kaizu K, Matsumoto K. Plasma insulin is removed by hemodialysis: Evaluation of the relation between plasma insulin and glucose by using a dialysate with or without glucose. Ther Apher Dial. 2007; 11(4):280–7. https://www.ncbi.nlm.nih.gov/pubmed/17661834. https://doi.org/10.1111/j.1744-9987.2007.00491.x.

Rhee CM, Leung AM, Kovesdy CP, Lynch KE, Brent GA, Kalantar-Zadeh K. Updates on the management of diabetes in dialysis patients. Semin Dial. 2014; 27(2):135–45. https://www.ncbi.nlm.nih.gov/pubmed/24588802. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960718. https://doi.org/10.1111/sdi.12198.

Ricks J, Molnar MZ, Kovesdy CP, et al. Glycemic control and cardiovascular mortality in hemodialysis patients with diabetes: A 6-year cohort study. Diabetes. 2012; 61(3):708–15. https://www.ncbi.nlm.nih.gov/pubmed/22315308. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3282812. https://doi.org/10.2337/db11-1015.

Monnier L, Colette C, Owens D. The glycemic triumvirate and diabetic complications: Is the whole greater than the sum of its component parts? Diabetes Res Clin Pract. 2012; 95(3):303–11. https://www.ncbi.nlm.nih.gov/pubmed/22056719. https://doi.org/10.1016/j.diabres.2011.10.014.

Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007; 39(2):175–91. https://www.ncbi.nlm.nih.gov/pubmed/17695343. https://doi.org/10.3758/bf03193146.

Jin YP, Su XF, Yin GP, et al. Blood glucose fluctuations in hemodialysis patients with end stage diabetic nephropathy. J Diabetes Complications. 2015; 29(3):395–9. https://www.ncbi.nlm.nih.gov/pubmed/25681043. https://doi.org/10.1016/j.jdiacomp.2014.12.015.

Home P. Contributions of basal and post-prandial hyperglycaemia to micro- and macrovascular complications in people with type 2 diabetes. Curr Med Res Opin. 2005; 21(7):989–98. https://www.ncbi.nlm.nih.gov/pubmed/16004665. https://doi.org/10.1185/030079905x49662.

Colette C, Monnier L. Acute glucose fluctuations and chronic sustained hyperglycemia as risk factors for cardiovascular diseases in patients with type 2 diabetes. Horm Metab Res. 2007; 39(9):683–6. https://www.ncbi.nlm.nih.gov/pubmed/17846977. https://doi.org/10.1055/s-2007-985157.

Weber C, Schnell O. The assessment of glycemic variability and its impact on diabetes-related complications: An overview. Diabetes Technol Ther. 2009; 11(10):623–33. https://www.ncbi.nlm.nih.gov/pubmed/19821754. https://doi.org/10.1089/dia.2009.0043.

Monnier L, Colette C, Owens DR. Glycemic variability: The third component of the dysglycemia in diabetes. Is it important? How to measure it? J Diabetes Sci Technol. 2008; 2(6):1094–100. https://www.ncbi.nlm.nih.gov/pubmed/19885298. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2769808. https://doi.org/10.1177/193229680800200618.

Hu G, Qiao Q, Tuomilehto J. Glucose tolerance and cardiovascular mortality. Cardiovasc Rev Reports. 2001; 22(11):649–54. https://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14103450.

Ceriello A. Postprandial hyperglycemia and diabetes complications: Is it time to treat? Diabetes. 2005; 54(1):1–7. https://www.ncbi.nlm.nih.gov/pubmed/15616004. https://doi.org/10.2337/diabetes.54.1.1.

Kazempour-Ardebili S, Lecamwasam VL, Dassanyake T, et al. Assessing glycemic control in maintenance hemodialysis patients with type 2 diabetes. Diabetes Care. 2009; 32(7):1137–42. https://www.ncbi.nlm.nih.gov/pubmed/19196889. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2699727. https://doi.org/10.2337/dc08-1688.

Mirani M, Berra C, Finazzi S, et al. Inter-day glycemic variability assessed by continuous glucose monitoring in insulin-treated type 2 diabetes patients on hemodialysis. Diabetes Technol Ther. 2010;12(10):749–53. https://www.ncbi.nlm.nih.gov/pubmed/20809678. https://doi.org/10.1089/dia.2010.0052.

Gai M, Merlo I, Dellepiane S, et al. Glycemic pattern in diabetic patients on hemodialysis: Continuous Glucose Monitoring (CGM) analysis. Blood Purif. 2014; 38(1):68–73. https://www.ncbi.nlm.nih.gov/pubmed/25300368. https://doi.org/10.1159/000362863.

Rizzo MR, Barbieri M, Marfella R, Paolisso G. Reduction of oxidative stress and inflammation by blunting daily acute glucose fluctuations in patients with type 2 diabetes: Role of dipeptidyl peptidase-IV inhibition. Diabetes Care. 2012; 35(10):2076–82. https://www.ncbi.nlm.nih.gov/pubmed/22688551.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3447848. https://doi.org/10.2337/dc12-0199.

Shyangdan DS, Royle P, Clar C, Sharma P, Waugh N, Snaith A. Glucagon-like peptide analogues for type 2 diabetes mellitus. Cochrane Database Syst Rev. 2011;2017(12). https://www.ncbi.nlm.nih.gov/pubmed/21975753. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486297. https://doi.org/10.1002/14651858.CD006423.pub2.

Mori Y, Taniguchi Y, Sezaki K, Yokoyama J, Utsunomiya K. Liraglutide narrows the range of circadian glycemic variations in Japanese type 2 diabetes patients and nearly flattens these variations in drug naive type 2 diabetes patients: A continuous glucose monitoring based study. Diabetes Technol Ther. 2011; 13(11):1139–44. https://www.ncbi.nlm.nih.gov/pubmed/21877924. https://doi.org/10.1089/dia.2011.0137.

Tesfaye S, Boulton AJM, Dyck PJ, et al. Diabetic neuropathies: Update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care. 2010; 33(10):2285–93. https://www.ncbi.nlm.nih.gov/pubmed/20876709. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945176. https://doi.org/10.2337/dc10-1303.

Kohnert KD, Augstein P, Heinke P, et al. Chronic hyperglycemia but not glucose variability determines HbA1c levels in well-controlled patients with type 2 diabetes. Diabetes Res Clin Pract. 2007; 77(3):420–6. https://www.ncbi.nlm.nih.gov/pmc/articles/17331614. https://doi.org/10.1016/j.diabres.2007.01.021.

Borg R, Kuenen JC, Carstensen B, et al. Associations between features of glucose exposure and A1C: The A1C-Derived Average Glucose (ADAG) study. Diabetes. 2010; 59(7):1585–90. https://www.ncbi.nlm.nih.gov/pubmed/20424232. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889756. https://doi.org/10.2337/db09-1774.

Nathan DM, Kuenen J, Borg R, et al. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008; 31(8):1473–8. https://www.ncbi.nlm.nih.gov/pubmed/18540046. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2742903. https://doi.org/10.2337/dc08-0545.

Sartore G, Chilelli NC, Burlina S, et al. The importance of HbA1c and glucose variability in patients with type 1 and type 2 diabetes: Outcome of continuous glucose monitoring (CGM). Acta Diabetol. 2012; 49(Suppl 1.):S153-60. https://www.ncbi.nlm.nih.gov/pubmed/22466072. https://doi.org/10.1007/s00592-012-0391-4.

Fang FS, Li ZB, Li CL, Tian H, Li J, Cheng XL. Influence of glycemic variability on the HbA1c level in elderly male patients with type 2 diabetes. Intern Med. 2012; 51(22):3109–13. https://www.ncbi.nlm.nih.gov/pubmed/23154714. https://doi.org/10.2169/internalmedicine.51.8077.

Tanaka C, Saisho Y, Tanaka K, et al. Factors associated with glycemic variability in Japanese patients with diabetes. Diabetol Int. 2014;5(1):36–42. https://doi.10.1007/s13340-013-0129-8.

Coelho S. What is the role of HbA1c in Diabetic Hemodialysis Patients? Semin Dial. 2016; 29(1):19–23. https://www.ncbi.nlm.nih.gov/pubmed/26138753. https://doi.org/10.1111/sdi.12408.

Hoshino J, Mehrotra R, Rhee CM, et al. Using hemoglobin A1c to derive mean blood glucose in peritoneal dialysis patients. Am J Nephrol. 2013; 37(5):413–20. https://www.ncbi.nlm.nih.gov/pubmed/23594745. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3844668. https://doi.org/10.1159/000349929.

Gu Z, Du Y, Liu Y, et al. Effect of aging on islet beta-cell function and its mechanisms in Wistar rats. Age (Dordr). 2012; 34(6):1393–403. https://www.ncbi.nlm.nih.gov/pubmed/21898034. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3528366. https://doi.org/10.1007/s11357-011-9312-7.

Tschen SI, Dhawan S, Gurlo T, Bhushan A. Age-dependent decline in β-cell proliferation restricts the capacity of β-cell regeneration in mice. Diabetes. 2009; 58(6):1312–20. https://www.ncbi.nlm.nih.gov/pubmed/19228811. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682690. https://doi.org/10.2337/db08-1651.

DeFronzo RA, Abdul-Ghani MA. Preservation of β-cell function:The key to diabetes prevention. J Clin Endocrinol Metab. 2011;96(8):2354–66. https://www.ncbi.nlm.nih.gov/pubmed/21697254. https://doi.org/10.1210/jc.2011-0246.

Vella A, Zinsmeister AR. Predicting diabetes using measures of β-cell function. Diabetes. 2012; 61(3):562–3. https://www.ncbi.nlm.nih.gov/pubmed/22354930. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3282824. https://doi.org/10.2337/db11-1785.

FLAT-SUGAR Trial investigators, Probstfield JL, Hirsch I. Design of FLAT-SUGAR: Randomized trial of prandial insulin versus prandial glp-1 receptor agonist together with basal insulin and metformin for high-risk type 2 diabetes. Diabetes Care. 2015; 38(8):1558–66. https://www.ncbi.nlm.nih.gov/pubmed/26068865. https://doi.org/10.2337/dc14-2689.

Ridker PM. A test in context: High-sensitivity C-reactive protein. J Am Coll Cardiol. 2016; 67(6):712–23. https://www.ncbi.nlm.nih.gov/pubmed/26868696. https://doi.org/10.1016/j.jacc.2015.11.037.

De Mutsert R, Grootendorst DC, Axelsson J, et al. Excess mortality due to interaction between protein-energy wasting, inflammation and cardiovascular disease in chronic dialysis patients. Nephrol Dial Transplant. 2008; 23(9):2957–64. https://www.ncbi.nlm.nih.gov/pubmed/18400817. https://doi.org/10.1093/ndt/gfn167.

Van Tellingen A, Grooteman MPC, Schoorl M, et al. Intercurrent clinical events are predictive of plasma C-reactive protein levels in hemodialysis patients. Kidney Int. 2002; 62(2):632–8. https://www.ncbi.nlm.nih.gov/pubmed/12110028. https://doi.org/10.1046/j.1523-1755.2002.00470.x.

Snaedal S, Heimbürger O, Qureshi AR, et al. Comorbidity and acute clinical events as determinants of C-reactive Protein variation in hemodialysis patients: implications for patient survival. Am J Kidney Dis. 2009; 53(6):1024–33. https://www.ncbi.nlm.nih.gov/pubmed/19394732. https://doi.org/10.1053/j.ajkd.2009.02.008.

Shafi T, Jaar BG, Plantinga LC, et al. Association of residual urine output with mortality, quality of life, and inflammation in incident hemodialysis patients: The Choices for Healthy Outcomes in Caring for End-Stage Renal Disease (CHOICE) study. Am J Kidney Dis. 2010; 56(2):348–58. https://www.ncbi.nlm.nih.gov/pubmed/20605303. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2910835. https://doi.org/10.1053/j.ajkd.2010.03.020.

Kovesdy CP, Kalantar-Zadeh K. Why is protein-energy wasting associated with mortality in chronic kidney disease? Semin Nephrol. 2009; 29(1):3–14. https://www.ncbi.nlm.nih.gov/pubmed/19121469. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500837. https://doi.org/10.1016/j.semnephrol.2008.10.002.

Kato A, Takita T, Furuhashi M, Maruyama Y, Hishida A. Comparison of serum albumin, C-reactive protein and carotid atherosclerosis as predictors of 10-year mortality in hemodialysis patients. Hemodial Int. 2010; 14(2):226–32. https://www.ncbi.nlm.nih.gov/pubmed/20345387. https://doi.org/10.1111/j.1542-4758.2009.00432.x.

Rodbard D. Interpretation of continuous glucose monitoring data: Glycemic variability and quality of glycemic control. Diabetes Technol Ther. 2009; 11(Suppl 1):S55-67. https://www.ncbi.nlm.nih.gov/pubmed/19469679. https://doi.org/10.1089/dia.2008.0132.

Service FJ. Glucose variability. Diabetes. 2013; 62(5):1398–404. https://www.ncbi.nlm.nih.gov/pubmed/23613565. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3636651. https://doi.org/10.2337/db12-1396.

Published

2020-05-02

How to Cite

Yusof Khan, A. H. K., Zakaria, N. F., Zainal Abidin, M. A., Lim, C. T. S., & Kamaruddin, N. A. (2020). Glycemic Patterns and Factors Associated with Post-Hemodialysis Hyperglycemia among End-Stage Renal Disease Patients undergoing Maintenance Hemodialysis. Journal of the ASEAN Federation of Endocrine Societies, 35(1), 68–76. https://doi.org/10.15605/jafes.035.01.12

Issue

Section

Original Articles