Association of point in range with β-cell function and insulin sensitivity of type 2 diabetes mellitus in cold areas

Abstract

Background and Objective: Self-monitoring of blood glucose (SMBG) is crucial for achieving a glycemic target and upholding blood glucose stability, both of which are the primary purpose of anti-diabetic treatments. However, the association between time in range (TIR), as assessed by SMBG, and β-cell insulin secretion as well as insulin sensitivity remains unexplored. Therefore, this study aims to investigate the connections between TIR, derived from SMBG, and indices representing β-cell functionality and insulin sensitivity. The primary objective of this study was to elucidate the relationship between short-term glycemic control (measured as points in range [PIR] ) and both β-cell function and insulin sensitivity. Methods: This cross-sectional study enrolled 472 hospitalized patients with type 2 diabetes mellitus (T2DM). To assess β-cell secretion capacity, we employed the insulin secretion-sensitivity index-2 (ISSI-2) and (ΔC-peptide_0-120/Δglucose_0-120) × Matsuda index, while insulin sensitivity was evaluated using the Matsuda index and HOMA-IR. Since SMBG offers glucose data at specific point-in-time, we substituted TIR with PIR. According to clinical guidelines, values falling within the range of 3.9-10 mmol were considered "in range, " and the corresponding percentage was calculated as PIR. Results: We observed significant associations between higher PIR quartiles and increased ISSI-2, (ΔC-peptide_0-120/Δglucose_0-120) × Matsuda index, Matsuda index (increased) and HOMA-IR (decreased) (all P < 0.001). PIR exhibited positive correlations with log ISSI-2 (r = 0.361, P < 0.001), log (ΔC-peptide_0-120/Δglucose_0-120) × Matsuda index (r = 0.482, P < 0.001), and log Matsuda index (r = 0.178, P < 0.001) and negative correlations with log HOMA-IR (r = -0.288, P < 0.001). Furthermore, PIR emerged as an independent risk factor for log ISSI-2, log (ΔC-peptide_0-120/Δglucose_0-120) × Matsuda index, log Matsuda index, and log HOMA-IR. Conclusion: PIR can serve as a valuable tool for assessing β-cell function and insulin sensitivity.

Keywords

time in range / points in range / self-monitoring of blood glucose / β-cell function / insulin sensitivity

References

[[1]] Porte D. β-cells in type II diabetes mellitus. Diabetes, 1991; 40(2): 166-180.

[[2]] Defronzo R A. Lilly lecture 1987. The triumvirate: Beta-cell, muscle, liver. A collusion responsible for NIDDM. Diabetes, 1988; 37(6): 667-687.

[[3]] Stratton I M, Adler A I, Neil H A, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ, 2000; 321(7258): 405-412.

[[4]] Kramer C K, Choi H, Zinman B, et al. Glycemic variability in patients with early type 2 diabetes: the impact of improvement in β-cell function. Diabetes Care, 2014; 37(4): 1116-1123.

[[5]] Liu L, Liu J, Xu L, et al. Lower mean blood glucose during short-term intensive insulin therapy is associated with long-term glycemic remission in patients with newly diagnosed type 2 diabetes: Evidence-based recommendations for standardization. J Diabetes Investig, 2018; 9(4): 908-916.

[[6]] Rohlfing C L, Wiedmeyer H M, Little R R, et al. Defining the relationship between plasma glucose and HbA1c: analysis of glucose profiles and HbA1c in the Diabetes Control and Complications Trial. Diabetes Care, 2002; 25(2): 275-278.

[[7]] Nitin S. HbA1c and factors other than diabetes mellitus affecting it. Singapore Med J, 2010; 51(8): 616-22. PMID: 20848057.

[[8]] Lu J, Ma X, Zhou J, et al. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care, 2018; 41(11): 2370-2376.

[[9]] García-Lorenzo B, Rivero-Santana A, Vallejo-Torres L, et al. Cost-effectiveness analysis of real-time continuous monitoring glucose compared to self-monitoring of blood glucose for diabetes mellitus in Spain. Endocrinol Diabetes Nutr, 2018; 65(7): 380-386.

[[10]] Young L A, Buse J B, Weaver M A, et al. Glucose self-monitoring in non-insulin-treated patients with type 2 diabetes in primary care settings: A randomized trial. JAMA Intern Med, 2017; 177(7): 920-929.

[[11]] Cutruzzolà A, Irace C, Parise M, et al. Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes. Nutr Metab Cardiovasc Dis, 2020; 30(10): 1800-1805.

[[12]] The Diabetes Research in Children Network DirecNet Study Group. Eight-point glucose testing versus the continuous glucose monitoring system in evaluation of glycemic control in type 1 diabetes. J Clin Endocrinol Metab, 2005; 90(6): 3387-3391.

[[13]] Brackney, Elisabeth D. Enhanced self-monitoring blood glucose in non-insulin requiring Type 2 diabetes: A qualitative study in primary care. J Clin Nurs, 2018; 27(9-10): 2120-2131.

[[14]] Kohnert K D, Augstein P, Zander E, et al. Glycemic variability correlates strongly with postprandial beta-cell dysfunction in a segment of type 2 diabetic patients using oral hypoglycemic agents. Diabetes Care, 2009; 32(6): 1058-1062.

[[15]] Inker L A, Schmid C H, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J M ed, 2012; 367(1): 20-29.

[[16]] Kramer C K, Choi H, Zinman B, et al. Determinants of reversibility of β-cell dysfunction in response to short-term intensive insulin therapy in patients with early type 2 diabetes. Am J Physiol Endocrinol Metab, 2013; 305(11): E1398-E1407.

[[17]] Retnakaran R, Shen S, Hanley A J, et al. Hyperbolic relationship between insulin secretion and sensitivity on oral glucose tolerance test. Obesity (Silver Spring), 2008; 16(8): 1901-1907.

[[18]] Retnakaran R, Qi Y, Goran M I, et al. Evaluation of proposed oral disposition index measures in relation to the actual disposition index. Diabet Med, 2009; 26(12): 1198-1203.

[[19]] Matsuda M, DeFronzo R A. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care, 1999; 22(9): 1462-1470.

[[20]] Matthews D R, Hosker J P, Rudenski A S, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia, 1985; 28(7): 412-419.

[[21]] Defronzo R A, Tripathy D, Schwenke D C, et al. Prediction of diabetes based on baseline metabolic characteristics in individuals at high risk. Diabetes Care, 2013; 36(11): 3607-3612.

[[22]] Beck R W, Bergenstal R M, Riddlesworth T D, et al. Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes care, 2019; 42(3): 400-405.

[[23]] Klatman E L, Jenkins A J, Ahmedani M Y, et al. Blood glucose meters and test strips: global market and challenges to access in low-resource settings. Lancet Diabetes Endocrinol, 2019; 7(2): 150-160.

[[24]] Weinstock R S, Braffett B H, McGuigan P, et al. Self-monitoring of blood glucose in youth-onset type 2 diabetes: results from the TODAY study. Diabetes Care, 2019; 42(5): 903-909.

[[25]] Schnell O, Klausmann G, Gutschek B, et al. Impact on diabetes self-management and glycemic control of a new color-based SMBG meter. J Diabetes Sci Technol, 2017; 11(6): 1218-1225.

[[26]] Zhang Y, Dai J, Han X, et al. Glycemic variability indices determined by self-monitoring of blood glucose are associated with β-cell function in Chinese patients with type 2 diabetes. Diabetes Res Clin Pract, 2020; 164: 108152.

[[27]] Kilpatrick E S, Rigby A S, Goode K, et al. Relating mean blood glucose and glucose variability to the risk of multiple episodes of hypoglycaemia in type 1 diabetes. Diabetologia, 2007; 50(12): 2553-2561.

[[28]] Schnell O, Barnard K, Bergenstal R, et al. Clinical utility of SMBG: recommendations on the use and reporting of SMBG in clinical research. Diabetes Care, 2015; 38(9): 1627-1633.

[[29]] Swisa A, Glaser B, Dor Y. Metabolic stress and compromised identity of pancreatic beta cells. Front Genet, 2017; 8: 21.

[[30]] Veres A, Faust A L, Bushnell H L, et al. Charting cellular identity during human in vitro β-cell differentiation. Nature, 2019; 569(7756): 368-373.

[[31]] Sun J, Ni Q, Xie J, et al. β-Cell dedifferentiation in patients with T2D with adequate glucose control and nondiabetic chronic pancreatitis. J Clin Endocrinol Metab, 2019; 104(1): 83-94.

[[32]] Wang Z, York N W, Nichols C G, et al. Pancreatic β-cell dedifferentiation in diabetes and redifferentiation following insulin therapy. Cell Metab, 2014; 19(5): 872-882.

[[33]] Murata M, Adachi H, Oshima S, et al. Glucose fluctuation and the resultant endothelial injury are correlated with pancreatic β-cell dysfunction in patients with coronary artery disease. Diabetes Res Clin Pract, 2017; 131: 107-115.

[[34]] Wu N, Shen H, Liu H, et al. Acute blood glucose fluctuation enhances rat aorta endothelial cell apoptosis, oxidative stress and pro-inflammatory cytokine expression in vivo. Cardiovasc Diabetol, 2016; 15(1): 109.

[[35]] Kahn S E, Prigeon R L, McCulloch D K, et al. Quantification of the relationship between insulin sensitivity and β-cell function in human subjects: evidence for a hyperbolic function. Diabetes, 1993; 42: 1663-1667.

[[36]] Wang T, Lu J, Shi L, et al. Association of insulin resistance and β-cell dysfunction with incident diabetes among adults in China: a nationwide, population-based, prospective cohort study. Lancet Diabetes Endocrinol, 2020; 8(2): 115-124.

[[37]] Keane K N, Cruzat V F, Carlessi R, et al. Molecular events linking oxidative stress and inflammation to insulin resistance and β-cell dysfunction. Oxid Med Cell Longev, 2015; 2015: 181643.

[[38]] Weng J, Li Y, Xu W, et al. Effect of intensive insulin therapy on β-cell function and glycaemic control in patients with newly diagnosed type 2 diabetes: a multicentre randomised parallel-group trial. Lancet, 2008; 371(9626): 1753-1760.

[[39]] Ma M, Liu H, Yu J. Triglyceride is independently correlated with insulin resistance and islet beta cell function: a study in population with different glucose and lipid metabolism states. Lipids Health Dis, 2020; 19(1): 121.

[[40]] Fiorentino T V, Succurro E, Marini M A, et al. HDL cholesterol is an independent predictor of β-cell function decline and incident type 2 diabetes: A longitudinal study. Diabetes Metab Res Rev, 2020; 36(4): e3289.

[[41]] Rutti S, Ehses J A, Sibler R A, et al. Low and high-density lipoproteins modulate function, apoptosis and proliferation of primary human and murine pancreatic beta cells. Endocrinology, 2009; 150: 4521-4530.

[[42]] Young K A, Maturu A, Lorenzo C, et al. The triglyceride to high-density lipoprotein choles- terol (TG/HDL-C) ratio as a predictor of insulin resistance, b-cell function, and diabetes in His- panics and African Americans. J Diabetes Complications, 2019; 33: 118-122.

[[43]] Ochoa-Rosales C, Portilla-Fernandez E, Nano J, et al. Epigenetic link between statin therapy and type 2 diabetes. Diabetes Care, 2020; 43(4): 875-884.

[[44]] Yang Y M, Shin B C, Son C, et al. An analysis of the associations between gender and metabolic syndrome components in Korean adults: a national cross-sectional study. BMC Endocr Disord, 2019; 19(1): 67.

[[45]] Arslanian S, Bacha F, Grey M, et al. Evaluation and management of youth-onset type 2 diabetes: a position statement by the American Diabetes Association. Diabetes Care, 2018; 41(12): 2648-2668.

[[46]] Hope S V, Knight B A, Shields B M, et al. Random non-fasting C-peptide: bringing robust assessment of endogenous insulin secretion to the clinic. Diabet Med, 2016; 33(11): 1554-1558.

[[47]] Arslanian S, El Ghormli L, Bacha F, et al. Adiponectin, insulin sensitivity, β-cell function, and racial/ethnic disparity in treatment failure rates in TODAY. Diabetes Care, 2017; 40(1): 85-93.