Glycemic Variability: Risk Factors, Assessment, and Control

Glycemic variability (GV) a well-established risk factor for hypoglycemia and a suspected risk factor for diabetes complications. GV is also a marker of the instability of a person’s metabolic system, expressed as frequent high and low glucose excursions and overall volatile glycemic control. In this review, the author discusses topics related to the assessment, quantification, and optimal control of diabetes, including (1) the notion that optimal control of diabetes, that is, lowering of HbA1c—the commonly accepted gold-standard outcome—can be achieved only if accompanied by simultaneous reduction of GV; (2) assessment and visualization of the two principal dimensions of GV, amplitude and time, which is now possible via continuous glucose monitoring (CGM) and various metrics quantifying GV and the risks associated with hypo- and hyperglycemic excursions; and (3) the evolution of diabetes science and technology beyond quantifying GV and into the realm of GV control via pharmacological agents, for example, GLP-1 receptor agonists and DPP-4 inhibitors, which have pronounced variability-reducing effect, or real-time automated closed-loop systems commonly referred to as the “artificial pancreas.” The author concludes that CGM allows close tracking over time, and therefore precise quantification, of glycemic variability in diabetes. The next step—optimal control of glucose fluctuations—is also taken by medications with pronounced GV-lowering effect primarily in type 2 diabetes, and by automated insulin delivery in type 1 diabetes. Contemporary CGM-based artificial pancreas systems use specific GV representations as input signals, and thus their main objective is to minimize GV and, from there, optimize glycemic control.

[1]  W. F. Taylor,et al.  Mean Amplitude of Glycemic Excursions, a Measure of Diabetic Instability , 1970, Diabetes.

[2]  I. Hirsch.  Glycemic Variability and Diabetes Complications: Does It Matter? Of Course It Does! , 2015, Diabetes Care.

[3]  D. Drucker,et al.  The incretin system: glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes , 2006, The Lancet.

[4]  P. Reichard,et al.  Complications in IDDM are caused by elevated blood glucose level: The Stockholm Diabetes Intervention Study (SDIS) at 10-year follow up , 1996, Diabetologia.

[5]  J. Hans DeVries,et al.  Glucose Variability: Where It Is Important and How to Measure It , 2013, Diabetes.

[6]  D. Cox,et al.  Symmetrization of the Blood Glucose Measurement Scale and Its Applications , 1997, Diabetes Care.

[7]  P. Cryer.  Hypoglycemia: The Limiting Factor in the Management of IDDM , 1994, Diabetes.

[8]  P. Cryer.  Glycemic Goals in Diabetes: Trade-off Between Glycemic Control and Iatrogenic Hypoglycemia , 2014, Diabetes.

[9]  W. Tamborlane,et al.  Effect of Intensive Insulin Therapy on Glycemic Thresholds for Counterregulatory Hormone Release , 1988, Diabetes.

[10]  J. Lachin,et al.  Mortality in Type 1 Diabetes in the DCCT/EDIC Versus the General Population , 2016, Diabetes Care.

[11]  C. Cobelli,et al.  Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes , 2016, Diabetes Care.

[12]  V. Basevi.  Standards of Medical Care in Diabetes—2010 , 2010, Diabetes Care.

[13]  I. Deary,et al.  A Structural Equation Model for Predictors of Severe Hypoglycaemia in Patients with Insulin‐dependent Diabetes Mellitus , 1997 .

[14]  K. Utsunomiya,et al.  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. , 2011, Diabetes technology & therapeutics.

[15]  Frits Holleman,et al.  Glucose variability; does it matter? , 2010, Endocrine reviews.

[16]  Eyal Dassau,et al.  International Consensus on Use of Continuous Glucose Monitoring , 2017, Diabetes Care.

[17]  P. Reichard,et al.  Mortality and Treatment Side-Effects During Long-Term Intensified Conventional Insulin Treatment in the Stockholm Diabetes Intervention Study , 1994, Diabetes.

[18]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[19]  Boris P. Kovatchev,et al.  Metrics for glycaemic control — from HbA1c to continuous glucose monitoring , 2017, Nature Reviews Endocrinology.

[20]  B. Zinman,et al.  Effect of Glycemic Exposure on the Risk of Microvascular Complications in the Diabetes Control and Complications Trial—Revisited , 2008, Diabetes.

[21]  J. Santiago.  Lessons From the Diabetes Control and Complications Trial , 1993, Diabetes.

[22]  K. J. Polley.  Intervention study , 2007, Calcified Tissue International.

[23]  W. Tamborlane,et al.  Epidemiology of Diabetes Interventions and Complications (EDIC). Design, implementation, and preliminary results of a long-term follow-up of the Diabetes Control and Complications Trial cohort. , 1999, Diabetes care.

[24]  Diederick Grobbee,et al.  Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. , 2008, The New England journal of medicine.

[25]  Roman Hovorka,et al.  Closed-loop insulin delivery: from bench to clinical practice , 2011, Nature Reviews Endocrinology.

[26]  L. Monnier,et al.  Glycemic Variability , 2008, Diabetes Care.

[27]  J. Schlichtkrull,et al.  [M-VALUE, AN INDEX FOR BLOOD SUGAR CONTROL IN DIABETICS]. , 1964, Ugeskrift for laeger.

[28]  C. Cobelli,et al.  The Artificial Pancreas in 2016: A Digital Treatment Ecosystem for Diabetes , 2016, Diabetes Care.

[29]  L. Kessler,et al.  Glucose Variability , 2016, Journal of diabetes science and technology.

[30]  Howard Zisser,et al.  Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic β Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers , 2008, Journal of diabetes science and technology.

[31]  C. Cobelli,et al.  Artificial Pancreas: Past, Present, Future , 2011, Diabetes.

[32]  Lauren M. Huyett,et al.  Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms , 2014, Diabetes Care.

[33]  Stephen D Patek,et al.  Hypoglycemia Prevention via Pump Attenuation and Red-Yellow-Green “Traffic” Lights Using Continuous Glucose Monitoring and Insulin Pump Data , 2010, Journal of diabetes science and technology.

[34]  James V. Neel,et al.  lessons from , 2010 .

[35]  B. Davis,et al.  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 , 2015, Diabetes Care.

[36]  P. Valensi,et al.  Continuous glucose profiles with vildagliptin versus sitagliptin in add-on to metformin: results from the randomized Optima study. , 2012, Diabetes & metabolism.

[37]  Michael Brownlee,et al.  Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic complications. , 2006, JAMA.

[38]  Daniel J Cox,et al.  Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes. , 2002, Diabetes technology & therapeutics.

[39]  David Rodbard,et al.  New and improved methods to characterize glycemic variability using continuous glucose monitoring. , 2009, Diabetes technology & therapeutics.

[40]  Howard C. Zisser,et al.  Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report , 2016, Diabetes Care.

[41]  Hypoglycemia in the Diabetes Control and Complications Trial , 1997, Diabetes.

[42]  B. Kovatchev.  The artificial pancreas in 2017: The year of transition from research to clinical practice , 2018, Nature Reviews Endocrinology.

[43]  S. Schinner,et al.  Intensive Blood Glucose Control and Vascular Outcomes in Patients with Type 2 Diabetes , 2009 .

[44]  J. Nerup,et al.  Glycosylated haemoglobin and steady-state mean blood glucose concentration in type 1 (insulin-dependent) diabetes , 1982, Diabetologia.

[45]  Dejian Lai,et al.  Glucose Variability in a 26-Week Randomized Comparison of Mealtime Treatment With Rapid-Acting Insulin Versus GLP-1 Agonist in Participants With Type 2 Diabetes at High Cardiovascular Risk , 2016, Diabetes Care.

[46]  David Rodbard,et al.  The Challenges of Measuring Glycemic Variability , 2012, Journal of diabetes science and technology.

[47]  W. Tamborlane,et al.  The Artificial Pancreas: Are We There Yet? , 2014, Diabetes Care.

[48]  R. Eaton,et al.  Incretin effects of increasing glucose loads in man calculated from venous insulin and C-peptide responses. , 1986, The Journal of clinical endocrinology and metabolism.

[49]  W. Tamborlane,et al.  Defective glucose counterregulation after strict glycemic control of insulin-dependent diabetes mellitus. , 1987, The New England journal of medicine.

[50]  B P Kovatchev,et al.  Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. , 1998, Diabetes care.

[51]  R. Bergenstal.  Glycemic Variability and Diabetes Complications: Does It Matter? Simply Put, There Are Better Glycemic Markers! , 2015, Diabetes Care.

[52]  R. Holman,et al.  Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. , 1998 .

[53]  Michael E. Miller,et al.  Effects of intensive glucose lowering in type 2 diabetes. , 2008, The New England journal of medicine.

[54]  I. Hirsch,et al.  Should minimal blood glucose variability become the gold standard of glycemic control? , 2005, Journal of diabetes and its complications.

[55]  P. Cryer,et al.  Glucose counterregulation, hypoglycemia, and intensive insulin therapy in diabetes mellitus. , 1985, The New England journal of medicine.

[56]  P. Cryer,et al.  Identification of type I diabetic patients at increased risk for hypoglycemia during intensive therapy. , 1983, The New England journal of medicine.

[57]  J. Hans DeVries,et al.  Glucose variability is associated with intensive care unit mortality* , 2010, Critical care medicine.

[58]  B. Kovatchev,et al.  Lixisenatide reduces glycaemic variability in insulin‐treated patients with type 2 diabetes , 2017, Diabetes, obesity & metabolism.

[59]  C. Cobelli,et al.  Artificial Pancreas Goes Outpatient: A New Diabetes Ecosystem , 2013, Journal of diabetes science and technology.

[60]  S. Schinner,et al.  Effects of Intensive Glucose Lowering in Type 2 Diabetes , 2009 .

[61]  David Rodbard,et al.  Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control. , 2009, Diabetes technology & therapeutics.

[62]  J. Shaw,et al.  International Expert Committee Report on the Role of the A1C Assay in the Diagnosis of Diabetes , 2009, Diabetes Care.

[63]  D G Schlundt,et al.  Frequency of severe hypoglycemia in insulin-dependent diabetes mellitus can be predicted from self-monitoring blood glucose data. , 1994, The Journal of clinical endocrinology and metabolism.

[64]  P. Cryer,et al.  Hypoglycaemia: The limiting factor in the glycaemic management of Type I and Type II Diabetes* , 2002, Diabetologia.

[65]  F. Service.  Glucose Variability , 2013, Diabetes.

[66]  M. Nauck,et al.  Reduced incretin effect in Type 2 (non-insulin-dependent) diabetes , 2006, Diabetologia.

[67]  Daniel J Cox,et al.  A novel analytical method for assessing glucose variability: using CGMS in type 1 diabetes mellitus. , 2006, Diabetes technology & therapeutics.

[68]  A. McCall,et al.  The Median is Not the Only Message: A Clinician's Perspective on Mathematical Analysis of Glycemic Variability and Modeling in Diabetes Mellitus , 2009, Journal of diabetes science and technology.

[69]  B. Kovatchev,et al.  How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes? , 2015, Diabetes research and clinical practice.

[70]  Michael E. Miller,et al.  ACTION TO CONTROL CARDIOVASCULAR RISK IN DIABETES STUDY GROUP. EFFECTS OF INTENSIVE GLUCOSE LOWERING IN TYPE 2 DIABETES , 2010 .

[71]  Boris Kovatchev,et al.  Peculiarities of the Continuous Glucose Monitoring Data Stream and Their Impact on Developing Closed-Loop Control Technology , 2008, Journal of diabetes science and technology.

[72]  Boris Kovatchev,et al.  Statistical tools to analyze continuous glucose monitor data. , 2009, Diabetes technology & therapeutics.

[73]  The Relationship of Glycemic Exposure (HbA1c) to the Risk of Development and Progression of Retinopathy in the Diabetes Control and Complications Trial , 1995, Diabetes.

[74]  A. Shapiro,et al.  Assessment of the severity of hypoglycemia and glycemic lability in type 1 diabetic subjects undergoing islet transplantation. , 2004, Diabetes.