A Comprehensive Analysis of Glycemic Variability and Time in Ranges Assessed using CGM in Diabetic People with diabetes with Microvascular Complications: A Single-Center Retrospective Real-World Evidence Study on Indian Population

Authors

  • Dr Mudit Director & Diabetologist Dharma Diabetes & Metabolic Clinic
  • Srivani Palukuri Clinical Research Scientist & Project Lead, Real-World Evidence Department, THB: Technology | Healthcare | Big Data Analytics 7(2) Bays, Kinship Square, Sector 32, Gurgaon, Haryana, India.
  • Saurav Deka AVP-Real World Evidence Real-World Evidence Department, THB: Technology | Healthcare | Big Data Analytics 7(2) Bays, Kinship Square, Sector 32, Gurgaon, Haryana India

DOI:

https://doi.org/10.14738/jbemi.91.11756

Abstract

Background: Regular assessment of glycemic variability is important to maintain good control of glycemic levels resulting delaying the onset or progression of microvascular complications.

Objective: To investigate association of glycemic variability (GV) and time in glucose ranges measured using CGM with diabetes microvascular complications.

Methodology: Retrospective CGM data collected for total 214 types 1 and 2 people with diabetes with (G1) and without (G2) microvascular complications. The daily glucose target range (TIR) was 70-180 mg/dL and variations in time above range (TAR), >180 mg/dL, and time below range (TBR), <70 mg/dL, considered as glucose excursions. The GV was assessed by SD, CV, MAGE, and MODD.

Results: On average, 7 and 12% of G1 people with diabetes had spent higher time in <54 (>1%) and >250 mg/dL (>5%) glucose values. In G1, around 15% of people with diabetes had hypoglycemic events (>15 mins/day) than G2. In insulin taking people with diabetes, MAGE values were about 20 (G1) and 15% (G2) higher than their respective group averages. The logistic regression had resulted significant association of TBR-54 [OR 2.7; (95% CI 1.2-6.1)] and CV [OR 1.1 (1.0-1.1)] with complications. The risk increased 1.5 times [OR 4.1 (1.4-11.7)] with night-time and 2.7 times [OR 7.5 (2.1-26.4)] during daytime hypoglycemia (≥45 minutes).

Conclusion: For every 1% increase (15 minutes) of time spent in <54 mg/dL had increased 2.7 times the risk for complications. It is equally important to focus on achieving % of time spent in both target glycemic values and hyper and hypoglycemia to the extent possible.

References

Thomas, N., Kapoor, N., Velavan, J., & Vasan, K. S. (2018). Chapter 9. Gadgets and glucose monitoring: A practical guide to diabetes mellitus. Jaypee Brothers Medical Publishers Pvt.

The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New England Journal of Medicine, 329(14), 977–986.

UK Prospective Diabetes Study (UKPDS) Group. (1998). Intensive blood-glucose control with sulfonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes. Lancet, 652, 837–853.

Action to Control Cardiovascular Risk in Diabetes Study Group, Gerstein, H. C., Miller, M. E., Byington, R. P., Goff, D. C., Bigger, J. T., Buse, J. B., Cushman, W. C., Genuth, S., Ismail-Beigi, F., Grimm, R. H., Probstfield, J. L., Simons-Morton, D. G., & Friedewald, W. T. Effects of intensive glucose lowering in type 2 diabetes. (2008). New England Journal of Medicine, 358(24), 2545–2559.

Powers, A. C., Stafford, J. M., & Rickels, M. R. (2018). Chapter 398. Diabetes Mellitus Complications: Harrison’s Principles of Internal Medicine 2nd volume Twentiath Edition, 2. McGraw-Hill Education.

Orasanu, G., & Plutzky, J. (2009). The pathologic continuum of diabetic vascular disease. Journal of the American College of Cardiology, 53(5) Suppl., S35–S42.

Fowler, M. J. (2008). Microvascular and macrovascular complications of diabetes. Clinical Diabetes, 26(2), 77–82.

Chawla, A., Chawla, R., & Jaggi, S. (2016). Microvasular and macrovascular complications in diabetes mellitus: Distinct or continuum? Indian Journal of Endocrinology and Metabolism, 20(4), 546–551.

Bansal, D., Gudala, K., Muthyala, H., Esam, H. P., Nayakallu, R., & Bhansali, A. (2014). Prevalence and risk factors of development of peripheral diabetic neuropathy in type 2 diabetes mellitus in a tertiary care setting. Journal of Diabetes Investigation, 5(6), 714–721.

Gross, J. L., de Azevedo, M. J., Silveiro, S. P., Canani, L. H., Caramori, M. L., & Zelmanovitz, T. (2005). Diabetic nephropathy: Diagnosis, prevention, and treatment. Diabetes Care, 28(1), 164–176. 134

Unnikrishnan, R. I., Rema, M., Pradeepa, R., Deepa, M., Shanthirani, C. S., Deepa, R., & Mohan, V. (2007). Prevalence and risk factors of diabetic nephropathy in an urban South Indian population: The Chennai Urban Rural Epidemiology Study (CURES 45). Diabetes Care, 30(8), 2019–2024.

Rema, M., Premkumar, S., Anitha, B., Deepa, R., Pradeepa, R., & Mohan, V. (2005). Prevalence of diabetic retinopathy in urban India: The Chennai Urban Rural Epidemiology Study (CURES) eye study, I. Investigative Ophthalmology and Visual Science, 46(7), 2328–2333.

Raman, R., Rani, P. K., Reddi Rachepalle, S., Gnanamoorthy, P., Uthra, S., Kumaramanickavel, G., & Sharma, T. (2009). Prevalence of diabetic retinopathy in India: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study Report 2. Ophthalmology, 116(2), 311–318.

Beck, R. W., Bergenstal, R. M., Riddlesworth, T. D., Kollman, C., Li, Z., Brown, A. S., & Close, K. L. (2019). Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care, 42(3), 400–405.

Lu, J., Ma, X., Zhou, J., Zhang, L., Mo, Y., Ying, L., Lu, W., Zhu, W., Bao, Y., Vigersky, R. A., & Jia, W. (2018). Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care, 41(11), 2370–2376.

Sartore, G., Chilelli, N. C., Burlina, S., & Lapolla, A. (2013). Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes. Acta Diabetologica, 50(3), 437–442.

Xu, F., Zhao, L. H., Su, J. B., Chen, T., Wang, X. Q., Chen, J. F., Wu, G., Jin, Y., & Wang, X. H. (2014). The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c. Diabetology and Metabolic Syndrome, 6(1), 139.

Hu, Y. M., Zhao, L. H., Zhang, X. L., Cai, H. L., Huang, H. Y., Xu, F., Chen, T., Wang, X. Q., Guo, A. S., Li, J. A., & Su, J. B. (2018). Association of glycaemic variability evaluated by continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients. Endocrine, 60(2), 292–300.

Yeoh, E., Lim, B. K., Fun, S., Tong, J., Yeoh, L. Y., Sum, C. F., Subramaniam, T., & Lim, S. C. (2018). Efficacy of selfmonitoring of blood glucose versus retrospective continuous glucose monitoring in improving glycaemic control in diabetic kidney disease patients. Nephrology (Carlton), 23(3), 264–268.

Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., Cobelli, C., Dassau, E., DeVries, J. H., Donaghue, K. C., Dovc, K., Doyle, F. J., Garg, S., Grunberger, G., Heller, S., . . . Phillip, M. (2019). Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care, 42(8), 1593–1603.

Czerwoniuk, D., Fendler, W., Walenciak, L., & Mlynarski, W. (2011). GlyCulator: A glycemic variability calculation tool for continuous glucose monitoring data. Journal of Diabetes Science and Technology, 5(2), 447–451.

Wiernsperger, N. F. (2001). In defense of microvascular constriction in diabetes. Clinical Hemorheology and Microcirculation, 25(2), 55–62.

Sheetz, M. J., & King, G. L. (2002). Molecular understanding of hyperglycemia’s adverse effects for diabetic complications. JAMA, 288(20), 2579–2588.

He, Z., Rask-Madsen, C., & King, G. L. (2004). Pathogenesis of diabetic microvascular complications. In R. A. De Fronzo, E. Ferrannini, H. Keen & P. Zimmet (Eds.), International textbook of diabetes mellitus, 2 (pp. 1135–1159). John Wiley & Sons.

Wong, T. Y., Liew, G., Tapp, R. J., Schmidt, M. I., Wang, J. J., Mitchell, P., Klein, R., Klein, B. E., Zimmet, P., & Shaw, J. (2008). Relation between fasting glucose and retinopathy for diagnosis of diabetes: Three population-based cross-sectional studies. Lancet, 371(9614), 736–743.

Xing, D., Kollman, C., Beck, R. W., Tamborlane, W. V., Laffel, L., Buckingham, B. A., Wilson, D. M., Weinzimer, S., Fiallo-Scharer, R., Ruedy, K. J., & Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. (2011). Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring. Diabetes Technology and Therapeutics, 13(3), 351–358. 135

Riddlesworth, T. D., Beck, R. W., Gal, R. L., Connor, C. G., Bergenstal, R. M., Lee, S., & Willi, S. M. (2018). Optimal sampling duration for continuous glucose monitoring to determine long-term glycemic control. Diabetes Technology and Therapeutics, 20(4), 314–316.

Johnson, M. L., Martens, T. W., Criego, A. B., Carlson, A. L., Simonson, G. D., & Bergenstal, R. M. (2019). Utilizing the ambulatory glucose profile to standardize and implement continuous glucose monitoring in clinical practice. Diabetes Technology and Therapeutics, 21(S2), S217–S225.

Ponirakis, G., Petropoulos, I. N., Alam, U., Ferdousi, M., Asghar, O., Marshall, A., Azmi, S., Jeziorska, M., Mahfoud, Z. R., Boulton, A. J. M., Efron, N., Nukada, H., & Malik, R. A. (2019). Hypertension contributes to neuropathy in patients with type 1 diabetes. American Journal of Hypertension, 32(8), 796–803.

Ohkubo, Y., Kishikawa, H., Araki, E., Miyata, T., Isami, S., Motoyoshi, S., Kojima, Y., Furuyoshi, N., & Shichiri, M. (1995). Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: A randomized prospective 6-year study. Diabetes Research and Clinical Practice, 28(2), 103–117.

Vigersky, R. A., & McMahon, C. (2019). The relationship of hemoglobin A1C to time-in-range in patients with diabetes. Diabetes Technology and Therapeutics, 21(2), 81–85.

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Published

2022-02-23

How to Cite

Dr Mudit, Srivani Palukuri, & Saurav Deka. (2022). A Comprehensive Analysis of Glycemic Variability and Time in Ranges Assessed using CGM in Diabetic People with diabetes with Microvascular Complications: A Single-Center Retrospective Real-World Evidence Study on Indian Population. British Journal of Healthcare and Medical Research, 9(1), 114–136. https://doi.org/10.14738/jbemi.91.11756