Glucotypes reveal new patterns of glucose dysregulation

Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called “glucotypes” that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.

[1]  Robert Cuddihy,et al.  Characterizing glucose exposure for individuals with normal glucose tolerance using continuous glucose monitoring and ambulatory glucose profile analysis. , 2008, Diabetes technology & therapeutics.

[2]  Wójcicki Jm,et al.  J"-index. A new proposition of the assessment of current glucose control in diabetic patients. , 1995 .

[3]  J. Holst,et al.  Loss of Incretin Effect Is a Specific, Important, and Early Characteristic of Type 2 Diabetes , 2011, Diabetes Care.

[4]  J. C. Kuenen,et al.  Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study , 2010, Diabetologia.

[5]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[6]  O. Nerman,et al.  The Association between HbA1c, Fasting Glucose, 1-Hour Glucose and 2-Hour Glucose during an Oral Glucose Tolerance Test and Cardiovascular Disease in Individuals with Elevated Risk for Diabetes , 2014, PloS one.

[7]  Toni Giorgino,et al.  Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package , 2009 .

[8]  S. Fowler,et al.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. , 2002 .

[9]  Takashi Washio,et al.  Proceedings of the 2011 SIAM International Conference on Data Mining , 2011 .

[10]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[11]  T. Valle,et al.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. , 2001, The New England journal of medicine.

[12]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[13]  B. Corvilain,et al.  Glucose Variability Assessed by Low Blood Glucose Index Is Predictive of Hypoglycemic Events in Patients With Type 1 Diabetes Switched to Pump Therapy , 2013, Diabetes Care.

[14]  L. Groop,et al.  Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. , 2018, The lancet. Diabetes & endocrinology.

[15]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[16]  Wolfgang Rathmann,et al.  Prediabetes: a high-risk state for diabetes development , 2012, The Lancet.

[17]  Antonio Ceriello,et al.  The Effect of Glucose Variability on the Risk of Microvascular Complications in Type 1 Diabetes , 2007, Diabetes Care.

[18]  Volkan Tuzcu,et al.  Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[19]  G. Reaven,et al.  Evaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test , 1994, Diabetologia.

[20]  R. Hovorka,et al.  ISEC: a program to calculate insulin secretion. , 1996, Computer methods and programs in biomedicine.

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

[22]  A. Garber.  Postprandial dysmetabolism and the heart. , 2012, Heart failure clinics.

[23]  K. Nomura,et al.  Glycemic Profiles of Healthy Individuals with Low Fasting Plasma Glucose and HbA1c , 2011, ISRN endocrinology.

[24]  G. Reaven,et al.  Assessment of Insulin Resistance with the Insulin Suppression Test and the Euglycemic Clamp , 1981, Diabetes.

[25]  J. H. Kim,et al.  Glycemic Variability: How Do We Measure It and Why Is It Important? , 2015, Diabetes & metabolism journal.

[26]  E. Segal,et al.  Personalized Nutrition by Prediction of Glycemic Responses , 2015, Cell.

[27]  Michael Brownlee,et al.  The Effect of Glucose Variability on the Risk of Microvascular Complications in Type 1 Diabetes , 2007, Diabetes Care.

[28]  Ifeyinwa E. Achumba,et al.  Sensor Data Acquisition and Processing Parameters for Human Activity Classification , 2014, Sensors.

[29]  Helen X. Gao,et al.  American Diabetes Association Standards of Medical Care in Diabetes 2017 , 2017, Journal of diabetes.

[30]  Bo Zhang,et al.  The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study , 2008, The Lancet.

[31]  B. Zinman,et al.  Impaired Fasting Glucose and Impaired Glucose Tolerance , 2007, Diabetes Care.

[32]  E. Bonora.  Postprandial peaks as a risk factor for cardiovascular disease: epidemiological perspectives. , 2002, International journal of clinical practice. Supplement.

[33]  Brian J. Parker,et al.  Systematic Clustering of Transcription Start Site Landscapes , 2011, PloS one.

[34]  H. Jung.  Clinical Implications of Glucose Variability: Chronic Complications of Diabetes , 2015, Endocrinology and metabolism.

[35]  Jeppe Sturis,et al.  Estimation of Insulin Secretion Rates from C-Peptide Levels: Comparison of Individual and Standard Kinetic Parameters for C-Peptide Clearance , 1992, Diabetes.

[36]  Jean-Paul Cristol,et al.  Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. , 2006, JAMA.

[37]  Eamonn J. Keogh,et al.  A Complexity-Invariant Distance Measure for Time Series , 2011, SDM.

[38]  Ram Rajagopal,et al.  Smart Meter Driven Segmentation: What Your Consumption Says About You , 2013, IEEE Transactions on Power Systems.

[39]  Peter A Baghurst,et al.  Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: an automated algorithm. , 2011, Diabetes technology & therapeutics.

[40]  Stephen W. Sorensen,et al.  The Cost-Effectiveness of Lifestyle Modification or Metformin in Preventing Type 2 Diabetes in Adults with Impaired Glucose Tolerance , 2005, Annals of Internal Medicine.