Post-Load Insulin Secretion Patterns are Associated with Glycemic Status and Diabetic Complications in Patients with Type 2 Diabetes Mellitus

Abstract

Background To examine whether the different patterns of post-load insulin secretion can identify the heterogeneity of type 2 diabetes mellitus (T2DM).

Methods Six hundred twenty-five inpatients with T2DM at Jining No. 1 People’s Hospital were recruited from January 2019 to October 2021. The 140 g steamed bread meal test (SBMT) was conducted on patients with T2DM, and glucose, insulin, and C-peptide levels were recorded at 0, 60, 120, and 180 min. To avoid the effect of exogenous insulin, patients were categorized into three different classes by latent class trajectory analysis based on the post-load secretion patterns of C-peptide. The difference in short- and long-term glycemic status and prevalence of complications distributed among the three classes were compared by multiple linear regression and multiple logistic regression, respectively.

Results There were significant differences in long-term glycemic status (e. g., HbA1c) and short-term glycemic status (e. g., mean blood glucose, time in range) among the three classes. The difference in short-term glycemic status was similar in terms of the whole day, daytime, and nighttime. The prevalence of severe diabetic retinopathy and atherosclerosis showed a decreasing trend among the three classes.

Conclusions The post-load insulin secretion patterns could well identify the heterogeneity of patients with T2DM in short- and long-term glycemic status and prevalence of complications, providing recommendations for the timely adjustment in treatment regimes of patients with T2DM and promotion of personalized treatment.

Key words

Type 2 diabetes mellitus - post-load insulin secretion - glycemic status - continuous glucose monitoring - diabetic complications

^* Jiajia Jiang and Yuhao Li contributed equally.

Publication History

Received: 12 September 2022
Received: 21 December 2022

Accepted: 12 January 2023

Article published online:
16 February 2023

• References

• 1 International Diabetes Federation. IDF diabetes atlas. Brussels, Belgium, 2021 https://diabetesatlas.org
• 2 Anjana RM, Baskar V, Nair ATN. et al. Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: A data-driven cluster analysis: The INSPIRED study. BMJ Open Diabetes Res Care 2020; 8: e001506
• 3 Mahajan A, Wessel J, Willems SM. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet 2018; 50: 559-571
• 4 Holman RR, Paul SK, Bethel MA. et al. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 2008; 359: 1577-1589
• 5 Nathan D. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med 2005; 353
• 6 Mdl A, Mae B, Aa C. et al. Applications and pitfalls of hemoglobin A1C and alternative methods of glycemic monitoring - ScienceDirect. J Diabetes Complications 2020; 34: 107585
• 7 Beck RW, Connor CG, Mullen DM. et al. The fallacy of average: How using HbA1c alone to assess glycemic control can be misleading. Diabetes Care 2017; 40: 994-999
• 8 Petersen MC, Shulman GI. Mechanisms of insulin action and insulin resistance. Physiol Rev 2018; 98: 2133-2223
• 9 Miya A, Nakamura A, Handa T. et al. Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment. J Diabetes Investig 2021; 12: 738-746
• 10 Wagner R, Heni M, Tabák AG. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med 2021; 27: 49-57
• 11 Stidsen JV, Henriksen JE, Olsen MH. et al. Pathophysiology-based phenotyping in type 2 diabetes: A clinical classification tool. Diabetes Metab Res Rev 2018; 34: e3005
• 12 Hayashi T, Boyko EJ, Sato KK. et al. Patterns of insulin concentration during the OGTT predict the risk of type 2 diabetes in Japanese Americans. Diabetes Care 2013; 36: 1229-1235
• 13 Proust-Lima C, Philipps V, Liquet B. Estimation of extended mixed models using latent classes and latent processes: The R package lcmm. J Stat Softw 2015; 78: 1-56
• 14 Rens VDS, Sijbrandij M, Winter SD. et al. The GRoLTS-Checklist: Guidelines for reporting on latent trajectory studies. Struct Equ Modeling 2017; 24: 451-467
• 15 Alberti GK, Zimmet PZ. et al. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation. Diabet Med 1998; 15: 539-553
• 16 Wilkinson CP, Iii FLF, Klein RE. et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 2003; 110: 1677-1682
• 17 Li B, Gao H, Li X. et al. Correlation between brachial-ankle pulse wave velocity and arterial compliance and cardiovascular risk factors in elderly patients with arteriosclerosis. Hypertens Res 2006; 29: 309-314
• 18 Yamashina A, Tomiyama H, Arai T. et al. Brachial-ankle pulse wave velocity as a marker of atherosclerotic vascular damage and cardiovascular risk. Hypertens Res 2003; 26: 615-622
• 19 Kim H-L, Kim S-H. Pulse wave velocity in atherosclerosis. Front Cardiovasc Med 2019; 6: 41
• 20 Hulman A, Witte DR. et al. Pathophysiological characteristics underlying different glucose response curves: A latent class trajectory analysis from the prospective EGIR-RISC study. Diabetes Care 2018; 41: 1740-1748
• 21 Kahn SE, Brenda M, William H. et al. Importance of early phase insulin secretion to intravenous glucose tolerance in subjects with type 2 diabetes mellitus. J Clin Endocrinol Metab 2001; 5824-5829
• 22 Stratton IM, Adler AI, Neil HAW. et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS35): Prospective observational study. Br Med J 2000; 321: 405-412
• 23 Welsh KJ, Kirkman MS, Sacks DB. Role of glycated proteins in the diagnosis and management of diabetes: Research gaps and future directions. Diabetes care 2016; 39: 1299-1306
• 24 Jingyi Lu, Ma X. et al. Association of Time in Range, as assessed by continuousglucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care 2018; 41: 2370-2376
• 25 Li J, Li Y, Ma W. et al. Association of Time in Range levels with Lower Extremity Arterial Disease in patients with type 2 diabetes. Diabetes Metab Syndr. 2020; 14: 2018-2085
• 26 Lu J, Wang C, Shen Y. et al. Time in Range in relation to all-cause and cardiovascular mortality in patients with type 2 diabetes: A prospective cohort study. Diabetes Care 2020; 44: dc201862
• 27 Song P, Fang Z, Wang H. et al. Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: A systematic review, meta-analysis, and modelling study. Lancet Glob Health 2020; 8: e721-e729
• 28 Obura M, Beulens J, Slieker R. et al. Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study. Diabet Med 2021; 38: e14428
• 29 Hulman A, Simmons RK, Vistisen D. et al. Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk. Endocrine 2016; 55: 1-8
• 30 Obura M, Beulens JWJ, Slieker R. et al. Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study. PLoS ONE 2020; 15: e0242360
• 31 Ahlqvist E, Storm P, Käräjämäki A. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 2018; 6: 361-369