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Open Access 01-12-2023 | Albuminuria | Research

Differential correlation between time in range and eGFR or albuminuria in type 2 diabetes

Authors: Xuguang Jin, Xinyi Yang, Yixin Xu, Jingjing Liang, Chunyan Liu, Qingyu Guo, Wei Wang, Zhouqin Feng, Yanyu Yuan, Hui Zhou, Zhen Zhang, Wenwen Jiang, Yue Liang, Bin Lu, Jiaqing Shao, Yong Zhong, Ping Gu

Published in: Diabetology & Metabolic Syndrome | Issue 1/2023

Abstract

Introduction

As a CGM-derived indicator, ‘time in range’ (TIR) is emerging as a key indicator for accurate assessment of glycaemic control. However, there is few report focusing on the correlation of TIR with albumuria and renal fuction. The aim of this work was to investigate whether TIR, as well as nocturnal TIR and hypoglycaemic events is related to the presence and severity of albuminuria and decrease of eGFR in type 2 diabetes.

Research design and methods

A total of 823 patients were enrolled in this study. All patients received continuous glucose monitoring, TIR indicating the percentage of time that blood glucose was in the range of 3.9–10.0 mmol/L. The Spearman analysis was applied to analyze the relationship between TIR (or nocturnal TIR) and ACR. Logistic regression was used to explore whether TIR (or nocturnal TIR) is an independent risk factor for albuminuria.

Results

The prevalence of albuminuria decreased with increasing TIR quartiles. Binary logistic regression revealed that TIR as well as nocturnal TIR was obviously related to the presence of albuminuria. Multiple regression analysis found that only nocturnal TIR was obviously related to the severity of albuminuria. In our study, eGFR was significantly associated with the number of hypoglycemic events.

Conclusions

In T2DM patients, TIR and nocturnal TIR is associated with the presence of albuminuria independent of HbA1c and GV metrics. Nocturnal TIR shows better correlation than TIR. The role of TIR especially nocturnal TIR in the evaluation of diabetes kidney disease should be emphasized.

UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet. 1998;352(9131):837–53.CrossRef

The DCCTR, Group, “The Diabetes Control and Complications Trial (DCCT). Design and methodologic considerations for the feasibility phase. The DCCT Research Group. Diabetes. 1986;35(5):530–45.CrossRef

Beck RW, Connor CG, Mullen DM, Wesley DM, Bergenstal RM. The fallacy of average: how using HbA1c alone to assess Glycemic Control can be misleading. Diabetes Care Aug. 2017;40(8):994–9.CrossRef

Qu Y, Jacober SJ, Zhang Q, Wolka LL, DeVries JH. Rate of hypoglycemia in insulintreated patients with type 2 diabetes can be predicted from glycemic variability data. Diabetes Technol Ther Nov. 2012;14(11):1008–12.CrossRef

Lachin JM, Genuth S, Nathan DM, Zinman B, Rutledge BN, DCCT/EDIC Research Group. Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial–revisited. Diabetes. 2008;57(4):995–1001.CrossRefPubMed

American Diabetes Association. 6. Glycemic targets: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):73–S84. https://doi.org/10.2337/dc21-S006.CrossRef

Bellido V, Pinés-Corrales PJ, Villar-Taibo R, Ampudia-Blasco FJ. Time-in-range for monitoring glucose control: is it time for a change? Diabetes Res Clin Pract. 2021;177:108917. https://doi.org/10.1016/j.diabres.2021.108917.CrossRefPubMed

Yano Y, Hayakawa M, Kuroki K, et al. Nighttime blood pressure, nighttime glucose values, and target-organ damages in treated type 2 diabetes patients. Atherosclerosis. 2013;227(1):135–9. https://doi.org/10.1016/j.atherosclerosis.2012.12.006.CrossRefPubMed

Young MW, Time Travels. A 40-Year journey from Drosophila’s clock mutants to Human Circadian Disorders (Nobel lecture). Angew Chem Int Ed Engl. 2018;57(36):11532–9. https://doi.org/10.1002/anie.201803337.CrossRefPubMed

10.

La Fleur SE, Kalsbeek A, Wortel J, Buijs RM. A suprachiasmatic nucleus generated rhythm in basal glucose concentrations. J Neuroendocrinol. 1999;11(8):643–52. https://doi.org/10.1046/j.1365-2826.1999.00373.x.CrossRefPubMed

11.

Challet E, Malan A, Turek FW, Van Reeth O. Daily variations of blood glucose, acid-base state and PCO2 in rats: effect of light exposure. Neurosci Lett. 2004;355(1–2):131–5. https://doi.org/10.1016/j.neulet.2003.10.041.CrossRefPubMed

12.

Cailotto C, La Fleur SE, Van Heijningen C, et al. The suprachiasmatic nucleus controls the daily variation of plasma glucose via the autonomic output to the liver: are the clock genes involved? Eur J Neurosci. 2005;22(10):2531–40. https://doi.org/10.1111/j.1460-9568.2005.04439.x.CrossRefPubMed

13.

Yoo JH, Choi MS, Ahn J, et al. Association between continuous glucose monitoring-derived time in Range, other Core Metrics, and Albuminuria in Type 2 diabetes. Diabetes Technol Ther. 2020;22(10):768–76. https://doi.org/10.1089/dia.2019.0499.CrossRefPubMed

14.

Ranjan AG, Rosenlund SV, Hansen TW, Rossing P, Andersen S, Nørgaard K. Improved Time in Range over 1 year is Associated with reduced Albuminuria in individuals with sensor-augmented insulin pump-treated type 1 diabetes. Diabetes Care. 2020;43(11):2882–5. https://doi.org/10.2337/dc20-0909.CrossRefPubMed

15.

Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011 Sep 20;155(6):408]. Ann Intern Med. 2009;150(9):604–12. https://doi.org/10.7326/0003-4819-150-9-200905050-00006.CrossRefPubMedPubMedCentral

16.

Effect of intensive therapy on the development and progression of diabetic nephropathy in the Diabetes Control and Complications Trial. The Diabetes Control and Complications (DCCT) Research Group. Kidney Int. 1995;47(6):1703–20. https://doi.org/10.1038/ki.1995.236.CrossRef

17.

Stratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321(7258):405–12. https://doi.org/10.1136/bmj.321.7258.405.CrossRefPubMedPubMedCentral

18.

Cox DJ, Kovatchev BP, Julian DM, et al. Frequency of severe hypoglycemia in insulinde-pendent diabetes mellitus can be predicted from self-monitoring blood glucose data. J Clin Endocrinol Metab. 1994;79:1659–62.PubMed

19.

Qu Y, Jacober SJ, Zhang Q, Wolka LL, DeVries JH. Rate of hypoglycemia in insulin-treated patients with type 2 diabetes can be predicted fromglycemic variability data. Diabetes Technol Ther. 2012;14:1008–12.CrossRefPubMed

20.

National Institute of Diabetes and Digestive and Kidney Diseases Health Information Center. Sickle cell trait & other hemoglobinopathies & diabetes (for providers) [Internet]. Available from https://www.niddk.nih.gov/health-information/diagnostic-tests/sickle-cell-trait-hemoglobinopathiesdiabetes. Accessed 12 January 2018.

21.

Bry L, Chen PC, Sacks DB. Effects of hemoglobin variants and chemically modified derivatives on assays for glycohemoglobin. Clin Chem. 2001;47:153–63. CrossRefPubMed

22.

Ford ES, Cowie CC, Li C, Handelsman Y, Bloomgarden ZT. Iron-deficiency anemia, noniron-deficiency anemia and HbA1c among adults in the US. J Diabetes. 2011;3:67–73. CrossRefPubMed

23.

Nielsen LR, Ekbom P, Damm P, et al. HbA1c levels are significantly lower in early and late pregnancy. Diabetes Care. 2004;27:1200–1.CrossRefPubMed

24.

Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose Monitoring Data Interpretation: recommendations from the International Consensus on Time in Range. Diabetes Care. 2019;42(8):1593–603. https://doi.org/10.2337/dci19-0028.CrossRefPubMedPubMedCentral

25.

Bellido V, Aguilera E, Cardona-Hernandez R, et al. Expert Recommendations for using Time-in-range and other continuous glucose monitoring Metrics to achieve patient-centered Glycemic Control in people with diabetes [published online ahead of print, 2022 Apr 26]. J Diabetes Sci Technol. 2022;19322968221088601. https://doi.org/10.1177/19322968221088601.

26.

Jin SM, Kim TH, Oh S, et al. Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring. Diabet Med. 2015;32(2):274–9. https://doi.org/10.1111/dme.12607.CrossRefPubMed

27.

Subramanian S, Hirsch IB. Diabetic kidney disease: is there a role for glycemic variability? Curr Diab Rep. 2018;18(3):13. https://doi.org/10.1007/s11892-018-0979-3. Published 2018 Feb 15.CrossRefPubMed

28.

Wakasugi S, Mita T, Katakami N, et al. Associations between continuous glucose monitoring-derived metrics and diabetic retinopathy and albuminuria in patients with type 2 diabetes. BMJ Open Diabetes Res Care. 2021;9(1):e001923. https://doi.org/10.1136/bmjdrc-2020-001923.CrossRefPubMedPubMedCentral

29.

Varghese JS, Ho JC, Anjana RM, et al. Profiles of intraday glucose in type 2 diabetes and their association with complications: an analysis of continuous glucose Monitoring Data. Diabetes Technol Ther. 2021;23(8):555–64. https://doi.org/10.1089/dia.2020.0672.CrossRefPubMedPubMedCentral

30.

Kuroda N, Kusunoki Y, Osugi K, et al. Relationships between time in range, glycemic variability including hypoglycemia and types of diabetes therapy in japanese patients with type 2 diabetes mellitus: Hyogo Diabetes Hypoglycemia Cognition Complications study. J Diabetes Investig. 2021;12(2):244–53. https://doi.org/10.1111/jdi.13336.CrossRefPubMed

31.

Feng ZQ, Guo QY, Wang W, et al. Time in range, especially overnight time in range, is associated with sudomotor dysfunction in patients with type 1 diabetes. DiabetolMetabSyndr. 2021;13(1):119. https://doi.org/10.1186/s13098-021-00739-z. Published 2021 Oct 26.CrossRef

32.

Haffner D, Grund A, Leifheit-Nestler M. Renal effects of growth hormone in health and in kidney disease. Pediatr Nephrol. 2021;36(8):2511–30. https://doi.org/10.1007/s00467-021-05097-6.CrossRefPubMedPubMedCentral

33.

Verrotti A, Cieri F, Petitti MT, Morgese G, Chiarelli F. Growth hormone and IGF-I in diabetic children with and without microalbuminuria. Diabetes NutrMetab. 1999;12(4):271–6.

34.

Mahran YF. New insights into the protection of growth hormone in cisplatin-induced nephrotoxicity: the impact of IGF-1 on the Keap1-Nrf2/HO-1 signaling. Life Sci. 2020;253:117581. https://doi.org/10.1016/j.lfs.2020.117581.CrossRefPubMed

35.

Roy MS, Roy A, Brown S. Increased urinary-free cortisol outputs in patients with diabetes. J Diabetes Complications. 1998;12(1):24–7. https://doi.org/10.1016/s1056-8727(97)00006-8.CrossRefPubMed

36.

Chiodini I, Adda G, Scillitani A, et al. Cortisol secretion in patients with type 2 diabetes: relationship with chronic complications. Diabetes Care. 2007;30(1):83–8. https://doi.org/10.2337/dc06-1267.CrossRefPubMed

37.

Hikichi T, Tateda N, Miura T. Alteration of melatonin secretion in patients with type 2 diabetes and proliferative diabetic retinopathy. Clin Ophthalmol. 2011;5:655–60. CrossRefPubMedPubMedCentral

38.

Afsar B, ElsurerAfsar R, Sag AA, et al. Sweet dreams: therapeutic insights, targeting imaging and physiologic evidence linking sleep, melatonin and diabetic nephropathy. Clin Kidney J. 2020;13(4):522–30. https://doi.org/10.1093/ckj/sfz198. Published 2020 Feb 6.CrossRefPubMedPubMedCentral

39.

Krolewski AS. Progressive renal decline: the new paradigm of diabetic nephropathy in type 1 diabetes. Diabetes Care. 2015;38(6):954–62. https://doi.org/10.2337/dc15-0184.CrossRefPubMedPubMedCentral

40.

Gilbert RE. Proximal Tubulopathy: Prime Mover and Key Therapeutic Target in Diabetic kidney disease. Diabetes. 2017;66(4):791–800. https://doi.org/10.2337/db16-0796.CrossRefPubMed

41.

Hasegawa K, Wakino S, Simic P, et al. Renal tubular Sirt1 attenuates diabetic albuminuria by epigenetically suppressing Claudin-1 overexpression in podocytes. Nat Med. 2013;19(11):1496–504. https://doi.org/10.1038/nm.3363.CrossRefPubMedPubMedCentral

42.

Li S, Jia Y, Xue M, et al. Inhibiting Rab27a in renal tubular epithelial cells attenuates the inflammation of diabetic kidney disease through the miR-26a-5p/CHAC1/NF-kB pathway. Life Sci. 2020;261:118347. https://doi.org/10.1016/j.lfs.2020.118347.CrossRefPubMed

43.

Khanimov I, Zingerman B, Korzetz A, et al. Association between estimated GFR and incident hypoglycaemia during hospitalization. Nephrol (Carlton). 2022;27(2):162–70. https://doi.org/10.1111/nep.13984.CrossRef

Title: Differential correlation between time in range and eGFR or albuminuria in type 2 diabetes
Authors: Xuguang Jin
Xinyi Yang
Yixin Xu
Jingjing Liang
Chunyan Liu
Qingyu Guo
Wei Wang
Zhouqin Feng
Yanyu Yuan
Hui Zhou
Zhen Zhang
Wenwen Jiang
Yue Liang
Bin Lu
Jiaqing Shao
Yong Zhong
Ping Gu
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Albuminuria
Hypoglycemia
Macroalbuminuria
Microalbuminuria
Type 2 Diabetes
Published in: Diabetology & Metabolic Syndrome / Issue 1/2023
Electronic ISSN: 1758-5996
DOI: https://doi.org/10.1186/s13098-023-01071-4

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