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Olsen MT, Klarskov CK, Dungu AM, Hansen KB, Pedersen-Bjergaard U, Kristensen PL. Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review. J Diabetes Sci Technol 2024:19322968231221803. [PMID: 38179940 DOI: 10.1177/19322968231221803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages. METHODS A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163). RESULTS A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics. CONCLUSION This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.
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Affiliation(s)
- Mikkel Thor Olsen
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
| | - Carina Kirstine Klarskov
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
| | - Arnold Matovu Dungu
- Department of Pulmonary and Infectious Diseases, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
| | - Katrine Bagge Hansen
- Steno Diabetes Center Copenhagen, Copenhagen University Hospital-Herlev-Gentofte, Herlev, Denmark
| | - Ulrik Pedersen-Bjergaard
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Lommer Kristensen
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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2
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Kim JY, Yoo JH, Kim JH. Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality. Diabetes Technol Ther 2023; 25:883-892. [PMID: 37668665 DOI: 10.1089/dia.2023.0264] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Background: The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric that gives greater weight to hypoglycemia than to hyperglycemia and to extreme hypo/hyperglycemia over less extreme hypo/hyperglycemia. This study aimed at validating the effectiveness of GRI and at comparing it with time in range (TIR) in assessing glycemic quality in clinical practice. Methods: A total of 524 ninety-day CGM tracings of 194 insulin-treated adults with diabetes were included in the analysis. GRI was assessed according to standard metrics in ambulatory glucose profiles. Both cross-sectional and longitudinal analyses were performed to compare the GRI and TIR. Results: The GRI was strongly correlated not only with TIR (r = -0.974), but also with the coefficient of variation (r = 0.683). To identify whether the GRI differed by hypoglycemia even with a similar TIR, CGM tracings were grouped according to TIR (50% to <60%, 60% to <70%, 70% to <80%, and ≥80%). In each TIR group, the GRI increased as time below range (TBR)<70 mg/dL increased (P < 0.001 for all TIR groups). In longitudinal analysis, as TBR<70 mg/dL improved, the GRI improved significantly (P = 0.003) whereas TIR did not (P = 0.704). Both GRI and TIR improved as time above range (TAR)>180 mg/dL improved (P < 0.001 for both). The longitudinal change was easily identifiable on a GRI grid. Conclusions: The GRI is a useful tool for assessing glycemic quality in clinical practice and reflects hypoglycemia better than does TIR.
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Affiliation(s)
- Ji Yoon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jee Hee Yoo
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Jae Hyeon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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3
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Chan NB, Li W, Aung T, Bazuaye E, Montero RM. Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study. JMIR AI 2023; 2:e45450. [PMID: 38875568 PMCID: PMC11041419 DOI: 10.2196/45450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/15/2023] [Accepted: 02/24/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations. OBJECTIVE This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns. METHODS Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis. RESULTS In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs. CONCLUSIONS The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.
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Affiliation(s)
- Nicholas Berin Chan
- Informatics Research Centre, Henley Business School, University of Reading, Reading, United Kingdom
| | - Weizi Li
- Informatics Research Centre, Henley Business School, University of Reading, Reading, United Kingdom
| | - Theingi Aung
- Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
| | - Eghosa Bazuaye
- Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
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4
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Dimova R, Chakarova N, Del Prato S, Tankova T. The Relationship Between Dietary Patterns and Glycemic Variability in People with Impaired Glucose Tolerance. J Nutr 2023; 153:1427-1438. [PMID: 36906149 PMCID: PMC10196612 DOI: 10.1016/j.tjnut.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns. OBJECTIVES The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT). METHODS Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m2) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m2) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed. RESULTS Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI). CONCLUSIONS The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.
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Affiliation(s)
- Rumyana Dimova
- Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria.
| | - Nevena Chakarova
- Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Via Pietro Trivella, Italy
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5
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Donaldson LE, Vogrin S, So M, Ward GM, Krishnamurthy B, Sundararajan V, MacIsaac RJ, Kay TW, McAuley SA. Continuous glucose monitoring-based composite metrics: a review and assessment of performance in recent-onset and long-duration type 1 diabetes. Diabetes Technol Ther 2023. [PMID: 37010375 DOI: 10.1089/dia.2022.0563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.
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Affiliation(s)
- Laura E Donaldson
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
| | - Sara Vogrin
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia;
| | - Michelle So
- St Vincent's Institute of Medical Research, 85092, Melbourne, Victoria, Australia
- The Royal Melbourne Hospital, 90134, Department of Diabetes and Endocrinology, Parkville, Victoria, Australia
- Northern Health NCHER, 569275, Department of Endocrinology and Diabetes, Melbourne, Victoria, Australia;
| | - Glenn M Ward
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
| | - Balasubramanian Krishnamurthy
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Institute of Medical Research, 85092, Melbourne, Victoria, Australia;
| | - Vijaya Sundararajan
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia;
| | - Richard J MacIsaac
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
| | - Thomas Wh Kay
- St Vincent's Institute of Medical Research, 85092, Melbourne, Victoria, Australia;
| | - Sybil A McAuley
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
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Cappon G, Sparacino G, Facchinetti A. AGATA: A Toolbox for Automated Glucose Data Analysis. J Diabetes Sci Technol 2023:19322968221147570. [PMID: 36602030 DOI: 10.1177/19322968221147570] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave. METHODS Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis. RESULTS Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities. CONCLUSION Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.
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Affiliation(s)
- Giacomo Cappon
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
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7
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Piersanti A, Giurato F, Göbl C, Burattini L, Tura A, Morettini M. Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data. Diabetes Technol Ther 2023; 25:69-85. [PMID: 36223198 DOI: 10.1089/dia.2022.0237] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.
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Affiliation(s)
- Agnese Piersanti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Francesco Giurato
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Padova, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
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8
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Frank S, Hames KC, Jbaily A, Park JH, Stroyeck C, Price D. Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes. Diabetes Technol Ther 2022; 24:907-914. [PMID: 35920831 DOI: 10.1089/dia.2022.0189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Context: Plasma glucose or A1C criteria can be used to establish the diagnosis of type 2 diabetes (T2D). Objective: We examined whether continuous glucose monitoring (CGM) data from a single 10-day wear period could form the basis of an alternative diagnostic test for T2D. Design: We developed a binary classification diagnostic CGM (dCGM) algorithm using a dataset of 716 individual CGM sensor sessions from 563 participants with associated A1C measurements from seven clinical trials. Data from 470 participants were used for training and 93 participants for testing (49 normoglycemic [A1C <5.7%], 27 prediabetes, and 17 T2D [A1C ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying A1C measurement, which was assumed to provide the correct diagnosis. Results: The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant A1C thresholds, dCGM specificity among normoglycemic participants was 98% (48/49 correctly classified), and for participants with suboptimally controlled diabetes (A1C ≥7%, above the American Diabetes Association recommended A1C goal) the sensitivity was 100% (8/8 participants correctly diagnosed with T2D). Conclusions: Classifications based on the dCGM algorithm were in good agreement with traditional methods based on A1C. The dCGM algorithm may provide an alternative method for screening and diagnosing T2D, and warrants further investigation.
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Affiliation(s)
- Spencer Frank
- R&D Department, Dexcom, Inc., San Diego, California, USA
| | | | | | - Jee Hye Park
- R&D Department, Dexcom, Inc., San Diego, California, USA
| | - Chuck Stroyeck
- R&D Department, Dexcom, Inc., San Diego, California, USA
| | - David Price
- R&D Department, Dexcom, Inc., San Diego, California, USA
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9
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Montaser E, Fabris C, Kovatchev B. Essential Continuous Glucose Monitoring Metrics: The Principal Dimensions of Glycemic Control in Diabetes. Diabetes Technol Ther 2022; 24:797-804. [PMID: 35714355 DOI: 10.1089/dia.2022.0104] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Background: With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Methods: Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Results: Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Conclusion: Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control.
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Affiliation(s)
- Eslam Montaser
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Chiara Fabris
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Boris Kovatchev
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, Virginia, USA
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10
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Abstract
BACKGROUND With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. METHODS In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. RESULTS In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. CONCLUSIONS We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.
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Affiliation(s)
- Evan Olawsky
- Division of Biostatistics, School of
Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Yuan Zhang
- Division of Biostatistics, School of
Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Lynn E Eberly
- Division of Biostatistics, School of
Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Erika S Helgeson
- Division of Biostatistics, School of
Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Lisa S Chow
- Division of Diabetes, Endocrinology and
Metabolism, Department of Medicine, University of Minnesota, Minneapolis, MN,
USA
- Lisa S Chow, MD, MS, Division of Diabetes,
Endocrinology and Metabolism, Department of Medicine, University of Minnesota,
MMC 101, 420 Delaware St SE, Minneapolis, MN 55455, USA.
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11
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Cichosz SL, Hejlesen O. Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study. J Diabetes Sci Technol 2022; 16:1190-1195. [PMID: 33993744 PMCID: PMC9445338 DOI: 10.1177/19322968211015206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND OBJECTIVE Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups. METHODS Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation. RESULTS In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76. CONCLUSIONS Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Denmark
- Simon Lebech Cichosz, PhD, Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7D2, Aalborg DK-9220, Denmark.
| | - Ole Hejlesen
- Department of Health Science and Technology, Aalborg University, Denmark
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12
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Huang Y, Yue L, Qiu J, Gao M, Liu S, Wang J. Endothelial Dysfunction and Platelet Hyperactivation in Diabetic Complications Induced by Glycemic Variability. Horm Metab Res 2022; 54:419-428. [PMID: 35835141 PMCID: PMC9282943 DOI: 10.1055/a-1880-0978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The development and progression of the complications of chronic diabetes mellitus are attributed not only to increased blood glucose levels but also to glycemic variability. Therefore, a deeper understanding of the role of glycemic variability in the development of diabetic complications may provide more insight into targeted clinical treatment strategies in the future. Previously, the mechanisms implicated in glycemic variability-induced diabetic complications have been comprehensively discussed. However, endothelial dysfunction and platelet hyperactivation, which are two newly recognized critical pathogenic factors, have not been fully elucidated yet. In this review, we first evaluate the assessment of glycemic variability and then summarise the roles of endothelial dysfunction and platelet hyperactivation in glycemic variability-induced complications of diabetes, highlighting the molecular mechanisms involved and their interconnections.
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Affiliation(s)
- Ye Huang
- Emergency Department, China Academy of Chinese Medical Sciences Xiyuan
Hospital, Beijing, China
| | - Long Yue
- Emergency Department, China Academy of Chinese Medical Sciences Xiyuan
Hospital, Beijing, China
| | - Jiahuang Qiu
- Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences, Beijing, China
| | - Ming Gao
- Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences, Beijing, China
| | - Sijin Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences, Beijing, China
| | - Jingshang Wang
- Department of Traditional Chinese Medicine, Capital Medical University
Beijing Obstetrics and Gynecology Hospital, Beijing, China
- Correspondence Prof. Jingshang
Wang Capital Medical University Beijing Obstetrics and
Gynecology HospitalDepartment of Traditional Chinese
MedicineBeijingChina 18811213525
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13
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Xu NY, Nguyen KT, DuBord AY, Klonoff DC, Goldman JM, Shah SN, Spanakis EK, Madlock-Brown C, Sarlati S, Rafiq A, Wirth A, Kerr D, Khanna R, Weinstein S, Espinoza J. The Launch of the iCoDE Standard Project. J Diabetes Sci Technol 2022; 16:887-895. [PMID: 35533135 PMCID: PMC9264445 DOI: 10.1177/19322968221093662] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The first meeting of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) project, organized by Diabetes Technology Society, took place virtually on January 27, 2022. METHODS Clinicians, government officials, data aggregators, attorneys, and standards experts spoke in panels and breakout groups. Three themes were covered: 1) why digital health data integration into the electronic health record (EHR) is needed, 2) what integrated continuously monitored glucose data will look like, and 3) how this process can be achieved in a way that will satisfy clinicians, healthcare organizations, and regulatory experts. RESULTS The meeting themes were addressed within eight sessions: 1) What Do Inpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 2) What Do Outpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 3) Why Are Data Standards and Guidances Useful?, 4) What Value Can Data Integration Services Add?, 5) What Are Examples of Successful Integration?, 6) Which Privacy, Security, and Regulatory Issues Must Be Addressed to Integrate CGM Data into the EHR?, 7) Breakout Group Discussions, and 8) Presentation of Breakout Group Ideas. CONCLUSIONS Creation of data standards and workflow guidance are necessary components of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) standard project. This meeting, which launched iCoDE, will be followed by a set of working group meetings intended to create the needed standard.
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Affiliation(s)
- Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | | | | | - David C. Klonoff
- University of California, San
Francisco, San Francisco, CA, USA
- Mills-Peninsula Medical Center, San
Mateo, CA, USA
| | | | | | - Elias K. Spanakis
- Baltimore VA Medical Center, Baltimore,
MD, USA
- University of Maryland, Baltimore, MD,
USA
| | | | - Siavash Sarlati
- University of California, San
Francisco, San Francisco, CA, USA
- Anthem, Inc, Indianapolis, IN,
USA
| | - Azhar Rafiq
- National Aeronautics and Space
Administration, Washington, DC, USA
| | | | | | - Raman Khanna
- University of California, San
Francisco, San Francisco, CA, USA
| | | | - Juan Espinoza
- Division of General Pediatrics,
Department of Pediatrics, Children’s Hospital Los Angeles, University of Southern
California, Los Angeles, CA, USA
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14
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Dimova R, Chakarova N, Daniele G, Bianchi C, Dardano A, Del Prato S, Tankova T. Insulin secretion and action affect glucose variability in the early stages of glucose intolerance. Diabetes Metab Res Rev 2022; 38:e3531. [PMID: 35416379 DOI: 10.1002/dmrr.3531] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/10/2022] [Accepted: 02/24/2022] [Indexed: 11/10/2022]
Abstract
AIMS Since it is unknown whether glucose variability (GV) is increased and whether this is related to worsening of insulin secretion and action in prediabetes, we have assessed insulin secretion and sensitivity, and daily GV in early stages of dysglycemia. MATERIALS AND METHODS Twenty subjects with normal glucose tolerance (NGT; age 45.0 ± 9.5 years; BMI 31.1 ± 6.4 kg/m2), 25 with NGT and 1hrOGTT>8.6 mmol/L (1hrOGTT; 45.7 ± 8.5 years; 32.4 ± 7.0 kg/m2), and 59 with isolated impaired glucose tolerance (iIGT; 47.7 ± 11.2 years; 31.3 ± 6.1 kg/m2) underwent OGTT and MMTT. CGM was performed with blinded FreeStyle Libre Pro for 24 h under standard conditions. Parameters of beta-cell function, insulin sensitivity and GV were calculated. RESULTS Overall insulin secretion and action as well as GV progressively worsened across glucose tolerance categories. On a matrix analysis, GV parameters were inversely related to ISSI-2; r = -0.37 to -0.52; p < 0.0001; and IGI; r = -0.28 to -0.48; p < 0.0001 for CV, SD, J-index, LI, HBGI and MAGE. Insulin secretion (IGI) and b-cell function (ISSI-2) emerged as independent contributors to GV in early stage of dysglycemia accounting for about 16%-38% of its variability. CONCLUSIONS Our results show that daily GV worsens already with mild impairment of glucose tolerance. The increase in GV is inversely related to insulin secretion and action.
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Affiliation(s)
- Rumyana Dimova
- Division of Diabetology, Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria
| | - Nevena Chakarova
- Division of Diabetology, Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria
| | - Giuseppe Daniele
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Cristina Bianchi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Angela Dardano
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Tsvetalina Tankova
- Division of Diabetology, Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria
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15
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Valero P, Salas R, Pardo F, Cornejo M, Fuentes G, Vega S, Grismaldo A, Hillebrands JL, van der Beek EM, van Goor H, Sobrevia L. Glycaemia dynamics in gestational diabetes mellitus. Biochim Biophys Acta Gen Subj 2022; 1866:130134. [PMID: 35354078 DOI: 10.1016/j.bbagen.2022.130134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/14/2022] [Accepted: 03/24/2022] [Indexed: 12/19/2022]
Abstract
Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.
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Affiliation(s)
- Paola Valero
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile.
| | - Rodrigo Salas
- Biomedical Engineering School, Engineering Faculty, Universidad de Valparaíso, Valparaíso 2362905, Chile; Instituto Milenio Intelligent Healthcare Engineering, Chile
| | - Fabián Pardo
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Metabolic Diseases Research Laboratory, Interdisciplinary Centre of Territorial Health Research (CIISTe), Biomedical Research Center (CIB), San Felipe Campus, School of Medicine, Faculty of Medicine, Universidad de Valparaíso, San Felipe 2172972, Chile
| | - Marcelo Cornejo
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile; Faculty of Health Sciences, Universidad de Antofagasta, Antofagasta 02800, Chile; Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Gonzalo Fuentes
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile; Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Sofía Vega
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Medical School (Faculty of Medicine), Sao Paulo State University (UNESP), Brazil
| | - Adriana Grismaldo
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Department of Nutrition and Biochemistry, Faculty of Sciences, Pontificia Universidad Javeriana, Bogotá, DC, Colombia
| | - Jan-Luuk Hillebrands
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Eline M van der Beek
- Department of Pediatrics, University of Groningen, University Medical Center Groningen (UMCG), 9713GZ Groningen, the Netherlands; Nestlé Institute for Health Sciences, Nestlé Research, Societé des Produits de Nestlé, 1000 Lausanne 26, Switzerland
| | - Harry van Goor
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Luis Sobrevia
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Medical School (Faculty of Medicine), Sao Paulo State University (UNESP), Brazil; Department of Physiology, Faculty of Pharmacy, Universidad de Sevilla, Seville E-41012, Spain; University of Queensland Centre for Clinical Research (UQCCR), Faculty of Medicine and Biomedical Sciences, University of Queensland, Herston, QLD, 4029, Queensland, Australia; Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen (UMCG), 9713GZ Groningen, the Netherlands; Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico.
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16
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Shivaprasad C, Gautham K, Shah K, Gupta S, Palani P, Anupam B. Continuous Glucose Monitoring for the Detection of Hypoglycemia in Patients With Diabetes of the Exocrine Pancreas. J Diabetes Sci Technol 2021; 15:1313-1319. [PMID: 33322930 PMCID: PMC8655303 DOI: 10.1177/1932296820974748] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Detailed evaluations of hypoglycemia and associated indices based on continuous glucose monitoring (CGM) are limited in patients with diabetes of the exocrine pancreas. Our study sought to evaluate the frequency and pattern of hypoglycemic events and to investigate hypoglycemia-specific indices in this population. METHODS This was a cross-sectional study comprising 83 participants with diabetes of the exocrine pancreas. CGM and self-monitoring of blood glucose (SMBG) were performed on all participants for a minimum period of 72 hours. The frequency and pattern of hypoglycemic events, as well as hypoglycemia-related indices, were evaluated. RESULTS Hypoglycemia was detected in 90.4% of patients using CGM and 38.5% of patients using SMBG. Nocturnal hypoglycemic events were more frequent (1.9 episodes/patient) and prolonged (142 minutes) compared with day-time events (1.1 episodes/patient; 82.8 minutes, P < 0.05). The mean low blood glucose index was 2.1, and glycemic risk assessment diabetes equation hypoglycemia was 9.1%. The mean time spent below (TSB) <70 mg/dL was 9.2%, and TSB <54 mg/dL was 3.7%. The mean area under curve (AUC) <70 mg/dL was 1.7 ± 2.5 mg/dL/hour and AUC <54 mg/dL was 0.6 ± 1.3 mg/dL/hour. All of the CGM-derived hypoglycemic indices were significantly more deranged at night compared with during the day (P < 0.05). CONCLUSION Patients with diabetes of the exocrine pancreas have a high frequency of hypoglycemic episodes that are predominantly nocturnal. CGM is superior to SMBG in the detection of nocturnal and asymptomatic hypoglycemic episodes. CGM-derived hypoglycemic indices are beneficial in estimating the risk of hypoglycemia.
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Affiliation(s)
- Channabasappa Shivaprasad
- Department of Endocrinology, Sapthagiri
Institute of Medical Sciences and Research Centre (SIMS&RC), Bangalore,
India
- Channabasappa Shivaprasad, MD, DM,
Professor, Department of Endocrinology, Sapthagiri Institute of Medical Sciences
and Research Centre, 15, Hesarghatta Main Rd, Navy Layout, Chikkasandra,
Chikkabanavara, Bengaluru, Karnataka 560090, India.
| | - Kolla Gautham
- Department of Endocrinology, Vydehi
Institute of Medical Sciences and Research Centre (VIMS&RC), Bangalore,
India
| | - Kejal Shah
- Department of Internal Medicine, Vydehi
Institute of Medical Sciences and Research Centre, Bangalore, India
| | - Soumya Gupta
- Department of Internal Medicine, Vydehi
Institute of Medical Sciences and Research Centre, Bangalore, India
| | - Preethika Palani
- Department of Internal Medicine, Vydehi
Institute of Medical Sciences and Research Centre, Bangalore, India
| | - Biswas Anupam
- Department of Endocrinology, Vydehi
Institute of Medical Sciences and Research Centre (VIMS&RC), Bangalore,
India
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17
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Palaiodimou L, Lioutas VA, Lambadiari V, Theodorou A, Themistocleous M, Aponte L, Papagiannopoulou G, Foska A, Bakola E, Quispe R, Mendez L, Selim M, Novak V, Tzavellas E, Halvatsiotis P, Voumvourakis K, Tsivgoulis G. Glycemic variability of acute stroke patients and clinical outcomes: a continuous glucose monitoring study. Ther Adv Neurol Disord 2021; 14:17562864211045876. [PMID: 34589140 PMCID: PMC8474316 DOI: 10.1177/17562864211045876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/24/2021] [Indexed: 01/04/2023] Open
Abstract
Introduction: Glycemic variability (GV) has been associated with worse prognosis in
critically ill patients. We sought to evaluate the potential association
between GV indices and clinical outcomes in acute stroke patients. Methods: Consecutive diabetic and nondiabetic, acute ischemic or hemorrhagic stroke
patients underwent regular, standard-of-care finger-prick measurements and
continuous glucose monitoring (CGM) for up to 96 h. Thirteen GV indices were
obtained from CGM data. Clinical outcomes during hospitalization and
follow-up period (90 days) were recorded. Hypoglycemic episodes disclosed by
CGM but missed by finger-prick measurements were also documented. Results: A total of 62 acute stroke patients [48 ischemic and 14 hemorrhagic, median
NIHSS score: 9 (IQR: 3–16) points, mean age: 65 ± 10 years, women: 47%,
nondiabetic: 79%] were enrolled. GV expressed by higher mean absolute
glucose (MAG) values was associated with a lower likelihood of neurological
improvement during hospitalization before and after adjusting for potential
confounders (OR: 0.135, 95% CI: 0.024–0.751, p = 0.022).
There was no association of GV indices with 3-month clinical outcomes.
During CGM recording, 32 hypoglycemic episodes were detected in 17
nondiabetic patients. None of these episodes were identified by the periodic
blood glucose measurements and therefore they were not treated. Conclusions: Greater GV of acute stroke patients may be related to lower odds of
neurological improvement during hospitalization. No association was
disclosed between GV indices and 3-month clinical outcomes.
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Affiliation(s)
- Lina Palaiodimou
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | | | - Vaia Lambadiari
- Second Department of Internal Medicine-Propaedeutic and Diabetes Center, Medical School, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Aikaterini Theodorou
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Marios Themistocleous
- Department of Neurosurgery, Pediatric Hospital of Athens, Agia Sophia, Athens, Greece
| | - Laura Aponte
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georgia Papagiannopoulou
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Aikaterini Foska
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Eleni Bakola
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Rodrigo Quispe
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Laura Mendez
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Magdy Selim
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vera Novak
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Elias Tzavellas
- First Department of Psychiatry, Aiginition Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Panagiotis Halvatsiotis
- Second Department of Internal Medicine-Propaedeutic and Diabetes Center, Medical School, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos Voumvourakis
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios Tsivgoulis
- Second Department of Neurology, School of Medicine, University General Hospital 'Attikon', National and Kapodistrian University of Athens, Rimini 1, Chaidari, Athens 12462, Greece
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18
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Rodbard D. Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy. Diabetes Technol Ther 2021; 23:692-704. [PMID: 34086495 DOI: 10.1089/dia.2021.0115] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.
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Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC, Clinical Biostatistics Department, Potomac, Maryland, USA
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19
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Contador S, Velasco JM, Garnica O, Hidalgo JI. Glucose forecasting using genetic programming and latent glucose variability features. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Varghese JS, Ho JC, Anjana RM, Pradeepa R, Patel SA, Jebarani S, Baskar V, Narayan KV, Mohan V. Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data. Diabetes Technol Ther 2021; 23:555-564. [PMID: 33720761 PMCID: PMC9839354 DOI: 10.1089/dia.2020.0672] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Aims: To identify profiles of type 2 diabetes from continuous glucose monitoring (CGM) data using ambulatory glucose profile (AGP) indicators and examine the association with prevalent complications. Methods: Two weeks of CGM data, collected between 2015 and 2019, from 5901 adult type 2 diabetes patients were retrieved from a clinical database in Chennai, India. Non-negative matrix factorization was used to identify profiles as per AGP indicators. The association of profiles with existing complications was examined using multinomial and logistic regressions adjusted for glycated hemoglobin (HbA1c; %), sex, age at onset, and duration of diabetes. Results: Three profiles of glycemic variability (GV) were identified based on CGM data-Profile 1 ["TIR Profile"] (n = 2271), Profile 2 ["Hypo"] (n = 1471), and Profile 3 ["Hyper"] (n = 2159). Compared with time in range (TIR) profile, those belonging to Hyper had higher mean fasting plasma glucose (202.9 vs. 167.1, mg/dL), 2-h postprandial plasma glucose (302.1 vs. 255.6, mg/dL), and HbA1c (9.7 vs. 8.6; %). Both "Hypo profile" and "Hyper profile" had higher odds of nonproliferative diabetic retinopathy ("Hypo": 1.44, 1.20-1.73; "Hyper": 1.33, 1.11-1.58), macroalbuminuria ("Hypo": 1.58, 1.25-1.98; "Hyper": 1.37, 1.10-1.71), and diabetic kidney disease (DKD; "Hypo": 1.65, 1.18-2.31; "Hyper": 1.88, 1.37-2.58), compared with "TIR profile." Those in "Hypo profile" (vs. "TIR profile") had higher odds of proliferative diabetic retinopathy (PDR; 2.84, 1.65-2.88). Conclusions: We have identified three profiles of GV from CGM data. While both "Hypo profile" and "Hyper profile" had higher odds of prevalent DKD compared with "TIR profile," "Hypo profile" had higher odds of PDR. Our study emphasizes the clinical importance of recognizing and treating hypoglycemia (which is often unrecognized without CGM) in patients with type 2 Diabetes Mellitus.
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Affiliation(s)
- Jithin Sam Varghese
- Nutrition and Health Sciences Doctoral Program, Laney School of Graduate Studies, Emory University, Atlanta, Georgia, USA
| | - Joyce C. Ho
- Department of Computer Science, Emory University, Atlanta, Georgia, USA
| | - Ranjit Mohan Anjana
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Rajendra Pradeepa
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Shivani A. Patel
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Saravanan Jebarani
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Viswanathan Baskar
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - K.M. Venkat Narayan
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Viswanathan Mohan
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
- Address correspondence to: Viswanathan Mohan, MD, PhD, DSc, Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, ICMR Centre for Advanced Research on Diabetes, Chennai 600 086, India
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21
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Abstract
The ambulatory glucose profile (AGP) and the frequency distribution for glucose by ranges are well established as standard methods for display, analysis, and interpretation of glucose data arising from self-monitoring, continuous glucose monitoring, and automated insulin delivery systems. In this review, we consider several refinements that may further improve the utility of the AGP. These include (1) display of the AGP together with information regarding dietary intake, medication administration (e.g., insulin), glucose lowering (pharmacodynamic) activity of medications, and physical activity measured by accelerometers or heart rate; (2) display of average time below range (%TBR), time above range (%TAR), and time in range (%TIR) by time of day to indicate timing of hypoglycemic and hyperglycemic episodes; (3) detailed analysis of postprandial excursions for each of the major meals after synchronizing by onset of meals and adjusting for the premeal glucose levels, enabling comparisons of magnitude, shape, and patterns; (4) methods to characterize distinct patterns on different days of the week; (5) display of glucose on a nonlinear scale to improve the balance between deviations associated with hypoglycemia versus hyperglycemia; (6) use of time scales other than midnight-to-midnight to facilitate analysis of time segments of particular interest (e.g., overnight); (7) options to display individual glucose values to assist in the identification of dates and times of outliers and episodes of hypoglycemia and hyperglycemia; and (8) methods to compare AGPs obtained from different individuals or groups receiving alternative interventions in terms of therapy or technology. These refinements, individually or collectively, can potentially further enhance the effectiveness of the AGP for assessment of glucose levels, patterns, and variability. We discuss several questions regarding implementation and optimization of these methods.
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Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, Maryland, USA
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22
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Abstract
The hybrid closed-loop (HCL) system has been shown to improve glycemic control and reduce hypoglycemia. Optimization of HCL settings requires interpretation of the glucose, insulin, and factors affecting glucose such as food intake and exercise. To the best of our knowledge, there is no published guidance on the standardized reporting of HCL systems. Standardization of HCL reporting would make interpretation of data easy across different systems. We reviewed the literature on patient and provider perspectives on downloading and reporting glucose metric preferences. We also incorporated international consensus on standardized reporting for glucose metrics. We describe a single-page HCL data reporting, referred to here as "artificial pancreas (AP) Dashboard." We propose seven components in the AP Dashboard that can provide detailed information and visualization of glucose, insulin, and HCL-specific metrics. The seven components include (A) glucose metrics, (B) hypoglycemia, (C) insulin, (D) user experience, (E) hyperglycemia, (F) glucose modal-day profile, and (G) insight. A single-page report similar to an electrocardiogram can help providers and patients interpret HCL data easily and take the necessary steps to improve glycemic outcomes. We also describe the optimal sampling duration for HCL data download and color coding for visualization ease. We believe that this is a first step in creating a standardized HCL reporting, which may result in better uptake of the systems. For increased adoption, standardized reporting will require input from providers, patients, diabetes device manufacturers, and regulators.
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Affiliation(s)
- Viral N Shah
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Satish K Garg
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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Chrzanowski J, Michalak A, Łosiewicz A, Kuśmierczyk H, Mianowska B, Szadkowska A, Fendler W. Improved Estimation of Glycated Hemoglobin from Continuous Glucose Monitoring and Past Glycated Hemoglobin Data. Diabetes Technol Ther 2021; 23:293-305. [PMID: 33112161 DOI: 10.1089/dia.2020.0433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination (R2), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R2 = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. -0.152%, P < 0.0001], and overall 95% CI = -0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R2 = 0.83, bias = 0.026%, 95% CI = -0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients (R2 = 0.76, bias = -0.069%, 95% CI = -0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.
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Affiliation(s)
- Jędrzej Chrzanowski
- Department of Biostatistics and Translational Medicine, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Arkadiusz Michalak
- Department of Biostatistics and Translational Medicine, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
- Department of Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Aleksandra Łosiewicz
- Department of Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Hanna Kuśmierczyk
- Department of Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Beata Mianowska
- Department of Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Agnieszka Szadkowska
- Department of Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
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Abstract
Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variability. The package also allows one to visualize CGM data using time-series and lasagna plots. A distinct advantage of iglu is that it comes with a point-and-click graphical user interface (GUI) which makes the package widely accessible to users regardless of their programming experience. Thus, the open-source and easy to use iglu package will help advance CGM research and CGM data analyses. R package iglu is publicly available on CRAN and at https://github.com/irinagain/iglu.
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25
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Zou Y, Wang W, Zheng D, Hou X. Glycemic deviation index: a novel method of integrating glycemic numerical value and variability. BMC Endocr Disord 2021; 21:52. [PMID: 33736619 PMCID: PMC7976707 DOI: 10.1186/s12902-021-00691-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 01/27/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established. METHODS In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30). RESULTS Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels. CONCLUSIONS In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabetic patients undergoing CGM treatment.
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Affiliation(s)
- Yizhou Zou
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jing 5 Road, Jinan, 250021, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China
- Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
| | - Wanli Wang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Dongmei Zheng
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jing 5 Road, Jinan, 250021, China.
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China.
- Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China.
| | - Xu Hou
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jing 5 Road, Jinan, 250021, China.
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China.
- Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China.
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26
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Breyton AE, Lambert-Porcheron S, Laville M, Vinoy S, Nazare JA. CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review. Front Endocrinol (Lausanne) 2021; 12:666008. [PMID: 34566883 PMCID: PMC8458933 DOI: 10.3389/fendo.2021.666008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/08/2021] [Indexed: 12/23/2022] Open
Abstract
Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters' (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.
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Affiliation(s)
- Anne-Esther Breyton
- Centre de Recherche en Nutrition Humaine Rhône-Alpes, Univ-Lyon, CarMeN Laboratory, INSERM, INRA, INSA Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, F-CRIN/FORCE Network, Pierre Bénite, France
- Nutrition Research, Mondelez International, Saclay, France
| | - Stéphanie Lambert-Porcheron
- Centre de Recherche en Nutrition Humaine Rhône-Alpes, Univ-Lyon, CarMeN Laboratory, INSERM, INRA, INSA Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, F-CRIN/FORCE Network, Pierre Bénite, France
- Department of Endocrinology Diabetes and Nutrition, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Martine Laville
- Centre de Recherche en Nutrition Humaine Rhône-Alpes, Univ-Lyon, CarMeN Laboratory, INSERM, INRA, INSA Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, F-CRIN/FORCE Network, Pierre Bénite, France
- Department of Endocrinology Diabetes and Nutrition, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Sophie Vinoy
- Nutrition Research, Mondelez International, Saclay, France
| | - Julie-Anne Nazare
- Centre de Recherche en Nutrition Humaine Rhône-Alpes, Univ-Lyon, CarMeN Laboratory, INSERM, INRA, INSA Lyon, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, F-CRIN/FORCE Network, Pierre Bénite, France
- *Correspondence: Julie-Anne Nazare,
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27
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Shivaprasad C, Aiswarya Y, Kejal S, Sridevi A, Anupam B, Ramdas B, Gautham K, Aarudhra P. Comparison of CGM-Derived Measures of Glycemic Variability Between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus. J Diabetes Sci Technol 2021; 15:134-140. [PMID: 31282179 PMCID: PMC7782997 DOI: 10.1177/1932296819860133] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND To compare glycemic variability (GV) indices between patients with fibrocalculous pancreatic diabetes (FCPD) and type 2 diabetes mellitus (T2D) using continuous glucose monitoring (CGM). METHODS We measured GV indices using CGM (iPro™2 Professional CGM, Medtronic, USA) data in 61 patients each with FCPD and T2D who were matched for glycated hemoglobin A1c (HbA1c) and duration of diabetes. GlyCulator2 software was used to estimate the CGM-derived measures of GV (SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV]), hypoglycemia (time spent below 70 mg/dL, AUC below 70 mg/dL, glycemic risk assessment diabetes equation hypoglycemia, Low Blood Glucose Index), and hyperglycemia (time spent above 180 mg/dL at night [TSA > 180], AUC above 180 mg/dL [AUC > 180], glycemic risk assessment diabetes equation hyperglycemia, High Blood Glucose Index [HBGI], and J index). The correlation of GV indices with HbA1c, duration of diabetes, and demographic and biochemical parameters were also assessed. RESULTS All the CGM-derived measures of GV (SD, MAGE, CONGA, MODD, and %CV), except M value, were significantly higher in the FCPD group than in the T2D group (P < 0.05). Measures of hyperglycemia (TSA >180, AUC >180, HBGI, and J index) were significantly higher in the FCPD group than in the T2D group (P < 0.05). The measures of hypoglycemia were not significantly different between the two groups. All the hyperglycemia indices showed a positive correlation with HbA1c in both groups. CONCLUSIONS FCPD is associated with higher GV than is T2D. The findings of higher postprandial glycemic excursions in patients with FCPD could have potential therapeutic implications.
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Affiliation(s)
- Channabasappa Shivaprasad
- Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
- Channabasappa Shivaprasad, MD, DM, Professor, Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, #82, EPIP Area, Whitefield, Bangalore, Karnataka 560066, India.
| | - Yalamanchi Aiswarya
- Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
| | - Shah Kejal
- Department of Internal Medicine, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
| | - Atluri Sridevi
- Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
| | - Biswas Anupam
- Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
| | - Barure Ramdas
- Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
| | - Kolla Gautham
- Department of Endocrinology, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
| | - Premchander Aarudhra
- Department of Internal Medicine, Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India
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Moscardó V, Giménez M, Oliver N, Hill NR. Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes. Diabetes Technol Ther 2020; 22:701-708. [PMID: 32195607 PMCID: PMC7591379 DOI: 10.1089/dia.2019.0416] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.
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Affiliation(s)
- Vanessa Moscardó
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Marga Giménez
- Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic Universitari, IDIBAPS, Barcelona, Spain
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom
- Address correspondence to: Nick Oliver, FRCP, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG London, United Kingdom
| | - Nathan R. Hill
- Harris Manchester College, Mansfield Road, University of Oxford, Oxford, United Kingdom
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29
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Moscardó V, Herrero P, Reddy M, Hill NR, Georgiou P, Oliver N. Assessment of Glucose Control Metrics by Discriminant Ratio. Diabetes Technol Ther 2020; 22:719-726. [PMID: 32163723 PMCID: PMC7591377 DOI: 10.1089/dia.2019.0415] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Objective: Increasing use of continuous glucose monitoring (CGM) data has created an array of glucose metrics for glucose variability, temporal patterns, and times in ranges. However, a gold standard metric has not been defined. We assess the performance of multiple glucose metrics to determine their ability to detect intra- and interperson variability to determine a set of recommended metrics. Methods: The Juvenile Diabetes Research Foundation data set, a randomized controlled study of CGM and self-monitored blood glucose conducted in children and adults with type 1 diabetes (T1D), was used. To determine the ability of the evaluated glycemic metrics to discriminate between different subjects and attenuate the effect of within-subject variation, the discriminant ratio was calculated and compared for each metric. Then, the findings were confirmed using data from two other recent randomized clinical trials. Results: Mean absolute glucose (MAG) has the highest discriminant ratio value (2.98 [95% confidence interval {CI} 1.64-3.67]). In addition, low blood glucose index and index of glycemic control performed well (1.93 [95% CI 1.15-3.44] and 1.92 [95% CI 1.27-2.93], respectively). For percentage times in glucose target ranges, the optimal discriminator was percentage time in glucose target 70-180 mg/dL. Conclusions: MAG is the optimal index to differentiate glucose variability in people with T1D, and may be a complementary therapeutic monitoring tool in addition to glycated hemoglobin and a measure of hypoglycemia. Percentage time in glucose target 70-180 mg/dL is the optimal percentage time in range to report.
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Affiliation(s)
- Vanessa Moscardó
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Pau Herrero
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Monika Reddy
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Nathan R. Hill
- Harris Manchester College, Mansfield Road, University of Oxford, United Kingdom
| | - Pantelis Georgiou
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Nick Oliver
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
- Address correspondence to: Nick Oliver, FRCP, Division of Diabetes, Endocrinology and Metabolic Medicine, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, United Kingdom
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30
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Nguyen M, Han J, Spanakis EK, Kovatchev BP, Klonoff DC. A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control. Diabetes Technol Ther 2020; 22:613-622. [PMID: 32069094 PMCID: PMC7642748 DOI: 10.1089/dia.2019.0434] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
We performed a literature review of composite metrics for describing the quality of glycemic control, as measured by continuous glucose monitors (CGMs). Nine composite metrics that describe CGM data were identified. They are described in detail along with their advantages and disadvantages. The primary benefit to using composite metrics in clinical practice is to be able to quickly evaluate a patient's glycemic control in the form of a single number that accounts for multiple dimensions of glycemic control. Very little data exist about (1) how to select the optimal components of composite metrics for CGM; (2) how to best score individual components of composite metrics; and (3) how to correlate composite metric scores with empiric outcomes. Nevertheless, composite metrics are an attractive type of scoring system to present clinicians with a single number that accounts for many dimensions of their patients' glycemia. If a busy health care professional is looking for a single-number summary statistic to describe glucose levels monitored by a CGM, then a composite metric has many attractive features.
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Affiliation(s)
- Michelle Nguyen
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, California
| | - Julia Han
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, California
| | - Elias K. Spanakis
- Division of Endocrinology, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland
| | - Boris P. Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
| | - David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, California
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31
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Rodbard D. Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution. Diabetes Technol Ther 2020; 22:492-500. [PMID: 31886733 DOI: 10.1089/dia.2019.0440] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background: Examine the expected relationships between time in range (%TIR), time above range (%TAR), and time below range (%TBR) with median glucose (or %HbA1c) and %coefficient of variation (%CV) of glucose for various shapes of the glucose distribution. Methods: We considered several thresholds defining hypoglycemia and hyperglycemia and examined wide ranges of median glucose and %CV using three models for the glucose distribution: gaussian, log-gaussian, and a modified log-gaussian distribution. Results: There is a linear relationship between %TIR and median glucose for any specified %CV when median glucose is well removed from the threshold for hypoglycemia. %TIR reaches a peak when median glucose is close to 120 mg/dL and declines both at higher and lower median glucose values. There is a nearly linear relationship for %TAR and median glucose for a wider range of glucose (80-220 mg/dL). Risk of hypoglycemia is minimal when %CV is below 20%, but rises exponentially as %CV increases or as median glucose decreases. Similar results were obtained for a wide range of possible shapes of glucose distribution. These simulations are consistent with results from clinical studies. Conclusion: Both %TIR and %TAR are approximately linearly related to mean and median glucose (or %HbA1c). %TAR provides linearity over a wider range than %TIR. Risk of hypoglycemia (%TBR) is critically dependent on both glycemic variability (%CV) and mean or median glucose. These relationships support the use of %TIR, %TAR, and %TBR as metrics of quality of glycemic control for clinical, research, and regulatory purposes.
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Affiliation(s)
- David Rodbard
- Department of Clinical Biostatistics, Biomedical Informatics Consultants LLC, Potomac, Maryland
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32
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Rebesco DB, França SN, de Lima VA, Leite N, Smouter L, de Souza WC, Komatsu WR, Mascarenhas LPG. Different amounts of moderate to vigorous physical activity and change in glycemic variability in adolescents with type 1 diabetes: is there dose-response relationship? ARCHIVES OF ENDOCRINOLOGY AND METABOLISM 2020; 64:312-318. [PMID: 32555999 PMCID: PMC10522219 DOI: 10.20945/2359-3997000000254] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 04/10/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To identify the level of physical activity and glycemic variability of adolescents with type 1 diabetes mellitus and to compare glycemic variability on days with different amounts of moderate to vigorous physical activity (MVPA). SUBJECTS AND METHODS A sample of 34 subjects aged 10 to 15 years, 18 (52.94%) female; age: 13.04 ± 1.94; HbA1c: 9.76 ± 1.51. Physical activity was measured by wGT3X accelerometer. The glucose data were obtained using continuous glucose monitoring, and the following glycemic variability measures were calculated: standard deviation (SD), low blood glucose index (LBGI), high blood glucose index (HBGI), mean amplitude of glycemic excursions (MAGE), glycemic risk assessment in diabetes equation (GRADE) and coefficient of variation (CV). The most and least active days (the days with greater and lesser time dedicated to physical activities of moderate to vigorous intensity, respectively) were identified. In addition, based on the whole period of accelerometer use, daily means of time spent in MVPA were identified among participants, who were then divided into three groups: up to 100 minutes; from 101 to 200 minutes and above 201 minutes. Then, the measures of glycemic variability were compared among the most and least active days and among the groups too. RESULTS The amount of MVPA was significantly different between the days evaluated (237.49 ± 93.29 vs. 125.21 ± 58.10 minutes), but glycemic variability measures did not present a significant difference. CONCLUSION Despite the significant differences in the amount of MVPA between the two days evaluated, the glycemic variability did not change significantly. Arch Endocrinol Metab. 2020;64(3):312-8.
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Affiliation(s)
- Denise Barth Rebesco
- Programa de Pós-Graduação Interdisciplinar em Desenvolvimento ComunitárioDepartamento de Educação FísicaUniversidade Estadual do Centro-OesteIratiPRBrasilPrograma de Pós-Graduação Interdisciplinar em Desenvolvimento Comunitário. Departamento de Educação Física, Universidade Estadual do Centro-Oeste (Unicentro), Irati, PR, Brasil
| | - Suzana Nesi França
- Unidade de Endocrinologia PediátricaDepartamento de PediatriaUniversidade Federal do ParanáCuritibaPRBrasilUnidade de Endocrinologia Pediátrica, Departamento de Pediatria, Universidade Federal do Paraná (UFPR), Curitiba, PR, Brasil
| | - Valderi Abreu de Lima
- Departamento de Educação FísicaUniversidade Federal do ParanáCuritibaPRBrasilDepartamento de Educação Física, Universidade Federal do Paraná (UFPR), Curitiba, PR, Brasil
| | - Neiva Leite
- Departamento de Educação FísicaUniversidade Federal do ParanáCuritibaPRBrasilDepartamento de Educação Física, Universidade Federal do Paraná (UFPR), Curitiba, PR, Brasil
| | - Leandro Smouter
- Programa de Pós-Graduação Interdisciplinar em Desenvolvimento ComunitárioDepartamento de Educação FísicaUniversidade Estadual do Centro-OesteIratiPRBrasilPrograma de Pós-Graduação Interdisciplinar em Desenvolvimento Comunitário. Departamento de Educação Física, Universidade Estadual do Centro-Oeste (Unicentro), Irati, PR, Brasil
| | - William Cordeiro de Souza
- Prefeitura Municipal de Três BarrasTrês BarrasSCBrasilPrefeitura Municipal de Três Barras, Três Barras, SC, Brasil
| | - William Ricardo Komatsu
- Divisão de EndocrinologiaDepartamento de MedicinaUniversidade Federal de São PauloSão PauloSPBrasilDivisão de Endocrinologia, Departamento de Medicina, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brasil
| | - Luis Paulo Gomes Mascarenhas
- Programa de Pós-Graduação Interdisciplinar em Desenvolvimento ComunitárioDepartamento de Educação FísicaUniversidade Estadual do Centro-OesteIratiPRBrasilPrograma de Pós-Graduação Interdisciplinar em Desenvolvimento Comunitário. Departamento de Educação Física, Universidade Estadual do Centro-Oeste (Unicentro), Irati, PR, Brasil
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Zheng F, Jalbert M, Forbes F, Bonnet S, Wojtusciszyn A, Lablanche S, Benhamou PY. Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics. Diabetes Technol Ther 2020; 22:301-313. [PMID: 31657620 DOI: 10.1089/dia.2019.0250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Background: Glycemic variability (GV) is an important component of glycemic control for patients with type 1 diabetes (T1D). The inadequacy of existing measurements lies in the fact that they view the variability from different aspects, so that no consensus has been reached among physicians as to which metrics to use in practice. Moreover, although GV, from 1 day to another, can show very different patterns, few metrics have been dedicated to daily evaluations. Materials and Methods: A reference (stable glycemia) statistical model is built based on a combination of daily computed canonical glycemic control metrics including variability. The metrics are computed for subjects from the TRIMECO islet transplantation trial, selected when their β-score (composite score for grading success) is ≥6 after a transplantation. Then, for any new daily glycemia recording, its likelihood with respect to this reference model provides a multimetric score of daily GV severity. In addition, determining the likelihood value that best separates the daily glycemia with β-score = 0 from that with β-score ≥6, we propose an objective decision rule to classify daily glycemia into "stable" or "unstable." Results: The proposed characterization framework integrates multiple standard metrics and provides a comprehensive daily GV index, based on which, long-term variability evaluations and investigations on the implicit link between variability and β-score can be carried out. Evaluation, in a daily GV classification task, shows that the proposed method is highly concordant to the experience of diabetologists. Conclusion: A multivariate statistical model is proposed to characterize the daily GV of subjects with T1D. The model has the advantage to provide a single variability score that gathers the information power of a number of canonical scores, too partial to be used individually. A reliable decision rule to classify daily variability measurements into stable or unstable is also provided.
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Affiliation(s)
- Fei Zheng
- LJK, CNRS, Inria, Grenoble INP, University Grenoble Alpes, Grenoble, France
- CEA LETI, DTBS, University Grenoble Alpes, Grenoble, France
| | - Manon Jalbert
- Endocrinologie Diabétologie Nutrition, CHU Grenoble-Alpes, Grenoble, France
| | - Florence Forbes
- LJK, CNRS, Inria, Grenoble INP, University Grenoble Alpes, Grenoble, France
| | | | - Anne Wojtusciszyn
- Endocrinologie Diabétologie Nutrition, CHU Montpellier, Montpellier, France
| | - Sandrine Lablanche
- Endocrinologie Diabétologie Nutrition, CHU Grenoble-Alpes, Grenoble, France
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Longato E, Acciaroli G, Facchinetti A, Maran A, Sparacino G. Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices. J Diabetes Sci Technol 2020; 14:297-302. [PMID: 30931604 PMCID: PMC7196879 DOI: 10.1177/1932296819838856] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices. METHODS We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods. RESULTS The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%). CONCLUSION The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.
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Affiliation(s)
- Enrico Longato
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Giada Acciaroli
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Alberto Maran
- Department of Medicine, University of
Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering,
University of Padova, Padova, Italy
- Giovanni Sparacino, PhD, Department of
Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova,
Italy.
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Leelarathna L, Thabit H, Wilinska ME, Bally L, Mader JK, Pieber TR, Benesch C, Arnolds S, Johnson T, Heinemann L, Hermanns N, Evans ML, Hovorka R. Evaluating Glucose Control With a Novel Composite Continuous Glucose Monitoring Index. J Diabetes Sci Technol 2020; 14:277-283. [PMID: 30931606 PMCID: PMC7196869 DOI: 10.1177/1932296819838525] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE The objective was to describe a novel composite continuous glucose monitoring index (COGI) and to evaluate its utility, in adults with type 1 diabetes, during hybrid closed-loop (HCL) therapy and multiple daily injections (MDI) therapy combined with real-time continuous glucose monitoring (CGM). METHODS COGI consists of three key components of glucose control as assessed by CGM: Time in range (TIR), time below range (TBR), and glucose variability (GV) (weighted by 50%, 35% and 15%). COGI ranges from 0 to 100, where 1% increase of time <3.9 mmol/L (<70 mg/dl) is equivalent to 4.7% reduction of TIR between 3.9-10 mmol/L (70-180 mg/dl), and 0.5 mmol/L (9 mg/dl) increase in standard deviation is equivalent to 3% reduction in TIR. RESULTS Continuous subcutaneous insulin infusion (CSII) users with HbA1c >7.5-10%, had significantly higher COGI during 12 weeks of HCL compared to sensor-augmented pump therapy, mean (SD), 60.3 (8.6) versus 69.5 (6.9), P < .001. Similarly, in CSII users with HbA1c <7.5%, HCL improved COGI from 59.9 (11.2) to 74.8 (6.6), P < .001. In MDI users with HbA1c >7.5% to 9.9%, use of real-time CGM led to improved COGI, 49.8 (14.2) versus 58.2 (9.1), P < .0001. In MDI users with impaired awareness of hypoglycemia, use of real-time CGM led to improved COGI, 53.4 (12.2) versus 66.7 (11.1), P < .001. CONCLUSIONS COGI summarizes three key aspects of CGM data into a concise metric that could be utilized to evaluate the quality of glucose control and to demonstrate the incremental benefit of a wide range of treatment modalities.
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Affiliation(s)
- Lalantha Leelarathna
- Manchester Diabetes Centre, Manchester
University NHS Foundation Trust, Manchester Academic Health Science Centre,
Manchester, UK
- Division of Diabetes, Endocrinology and
Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester,
Manchester, UK
- Lalantha Leelarathna, PhD, Manchester
Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic
Health Science Centre, Manchester Royal Infirmary, Hathersage Rd, Manchester M13
9WL, UK.
| | - Hood Thabit
- Manchester Diabetes Centre, Manchester
University NHS Foundation Trust, Manchester Academic Health Science Centre,
Manchester, UK
- Division of Diabetes, Endocrinology and
Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester,
Manchester, UK
| | - Malgorzata E. Wilinska
- Wellcome Trust-MRC Institute of
Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Paediatrics, Cambridge
University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Lia Bally
- Wellcome Trust-MRC Institute of
Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Diabetes, Endocrinology,
Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital and
University of Bern, Bern, Switzerland
| | - Julia K. Mader
- Division of Endocrinology and
Diabetology, Department of Internal Medicine, Medical University of Graz, Graz,
Austria
| | - Thomas R. Pieber
- Division of Endocrinology and
Diabetology, Department of Internal Medicine, Medical University of Graz, Graz,
Austria
| | - Carsten Benesch
- Profil Institut für
Stoffwechselforschung GmbH, Neuss, Germany
| | - Sabine Arnolds
- Profil Institut für
Stoffwechselforschung GmbH, Neuss, Germany
| | | | - Lutz Heinemann
- Profil Institut für
Stoffwechselforschung GmbH, Neuss, Germany
- Science-Consulting in Diabetes GmBH,
Dusseldorf, Germany
| | - Norbert Hermanns
- Research Institute Diabetes of the
Diabetes Academy Mergentheim (FIDAM), Mergentheim, Germany
- Department of Clinical Psychology and
Psychotherapy, University of Bamberg, Bamberg, Germany
| | - Mark L. Evans
- Wellcome Trust-MRC Institute of
Metabolic Science, University of Cambridge, Cambridge, UK
- Wolfson Diabetes & Endocrinology
Clinic, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust,
Cambridge, UK
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of
Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Paediatrics, Cambridge
University Hospitals NHS Foundation Trust, Cambridge, UK
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Rama Chandran S, A Vigersky R, Thomas A, Lim LL, Ratnasingam J, Tan A, S L Gardner D. Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels. Diabetes Technol Ther 2020; 22:103-111. [PMID: 31502876 DOI: 10.1089/dia.2019.0277] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Complex changes of glycemia that occur in diabetes are not fully captured by any single measure. The Comprehensive Glucose Pentagon (CGP) measures multiple aspects of glycemia to generate the prognostic glycemic risk (PGR), which constitutes the relative risk of hypoglycemia combined with long-term complications. We compare the components of CGP and PGR across type 1 and type 2 diabetes. Methods: Participants: n = 60 type 1 and n = 100 type 2 who underwent continuous glucose monitoring (CGM). Mean glucose, coefficient of variation (%CV), intensity of hypoglycemia (INThypo), intensity of hyperglycemia (INThyper), time out-of-range (TOR <3.9 and >10 mmol/L), and PGR were calculated. PGR (median, interquartile ranges [IQR]) for diabetes types, and HbA1c classes were compared. Results: While HbA1c was lower in type 1 (type 1 vs. type 2: 8.0 ± 1.6 vs. 8.6 ± 1.7, P = 0.02), CGM-derived mean glucoses were similar across both groups (P > 0.05). TOR, %CV, INThypo, and INThyper were all higher in type 1 [type 1 vs. type 2: 665 (500, 863) vs. 535 (284, 823) min/day; 39% (33, 46) vs. 29% (24, 34); 905 (205, 2951) vs. 18 (0, 349) mg/dL × min2; 42,906 (23,482, 82,120) vs. 30,166 (10,276, 57,183) mg/dL × min2, respectively, all P < 0.05]. Across each HbA1c class, the PGR remained consistently and significantly higher in type 1. While mean glucose remained the same across HbA1c classes, %CV, TOR, INThyper, and INThypo were significantly higher for type 1. Even within the same HbA1c class, the variation (IQR) of each parameter in type 1 was wider. The PGR increased across diabetes groups; type 2 on orals versus type 2 on insulin versus type 1 (PGR: 1.6 vs. 2.2 vs. 2.9, respectively, P < 0.05). Conclusion: Composite indices such as the CGP capture significant differences in glycemia independent of HbA1c and mean glucose. The use of such indices must be explored in both the clinical and research settings.
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Affiliation(s)
| | | | | | - Lee Ling Lim
- Division of Endocrinology, Department of Internal Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Jeyakantha Ratnasingam
- Division of Endocrinology, Department of Internal Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Daphne S L Gardner
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
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Michalak A, Pagacz K, Młynarski W, Szadkowska A, Fendler W. Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes. Pediatr Diabetes 2019; 20:604-612. [PMID: 30945397 DOI: 10.1111/pedi.12854] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 02/21/2019] [Accepted: 03/20/2019] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE We aimed to compare glycemic control and variability parameters obtained from paired records of real-time continuous glucose monitoring (RT-CGM) and flash glucose monitoring (FGM). METHODS Ten Polish boys and 11 girls aged 15.3 ± 2.1 years with type 1 diabetes for 7.7 ± 4.5 years and glycated hemoglobin 7.35 ± 0.7% (57 ± 5 mmol/mol) were recruited between August 2017 and June 2018 and equipped with devices for RT-CGM (iPro2 system with Enlite electrodes) and FGM (FreeStyle Libre) for 1 week. Afterwards, Glyculator 2.0 software was used to calculate and compare key metrics of glycemic control listed in the International Consensus on Use of Continuous Glucose Monitoring, with distinction into all record/night-time/day-time blocks when appropriate. RESULTS Agreement between the two systems' measurements across patients ranged from poor (R2 = .39) to nearly perfect (R2 = .97). Significant differences between RT-CGM and FGM were observed in five important metrics: coefficient of variation (median difference: -4.12% [25%-75%: -7.50% to -2.96%], P = .0001), low blood glucose index (-0.88 [-1.88 to -0.18], P = .0004), % of time below 70 mg/dL (3.9 mmol/L) (-4.77 [-8.39 to -1.19], P = .0015) and 54 mg/dL (3 mmol/L) (-1.33 [-4.07 to 0.00], P = .0033) and primary time in range (TIR) 70-180 mg/dL (8.58 [1.31 to 12.66], P = .0006). CONCLUSIONS RT-CGM and FGM differ in their estimates of clinically important indices of glycemic control. Therefore, such metrics cannot be directly compared between people using different systems. Our result necessitates system-specific guidelines and targets if TIR and glycemic variability are to be used as an endpoint in clinical trials.
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Affiliation(s)
- Arkadiusz Michalak
- Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Konrad Pagacz
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland.,Postgraduate School of Molecular Medicine, Warsaw, Poland
| | - Wojciech Młynarski
- Department of Pediatrics, Oncology and Hematology, Medical, University of Lodz, Lodz, Poland
| | - Agnieszka Szadkowska
- Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland.,Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
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Abstract
BACKGROUND The evolution of glycemic changes after kidney transplantation has not been described. We prospectively examined glycemic control and variability over time from transplantation using continuous glucose monitoring (CGM). METHOD Continuous glucose monitoring devices were fitted for 3 to 5 days at time of transplant, month 3, and month 6 posttransplant. Indices of glucose control (mean glucose, percent time in hyperglycemic range, and Glycemic Risk Assessment Diabetes Equation score) and variability were calculated. An oral glucose tolerance test was performed at month 3. RESULTS Twenty-eight patients (mean age, 45 ± 15 years) were enrolled, 64% male, 75% white, receiving tacrolimus, mycophenolate, and prednisolone (93%). Of 24 patients with complete CGM data at month 0, 3 had prior diabetes and 6 (25%) developed new-onset diabetes after transplant (NODAT). Hyperglycemia (>11.1 mM) was evident in 79% during days 0 to 3 posttransplant, particularly between 1 and 9 PM. Compared with recipients without diabetes, recipients with prior diabetes had higher mean glucose (7.8 mM; 95% confidence interval [CI], 7.4-8.2 vs 9.9 mM; 95% CI, 8.9-10.8; P < 0.001), Glycemic Risk Assessment Diabetes Equation (GRADE) score (4.5; 95% CI, 3.7-5.4 vs 7.8; 95% CI, 5.6-10.4; P = 0.003) and percent time with hyperglycemia. Glycemic control was also inferior in those that subsequently developed NODAT (mean glucose, 8.8 mM; 95% CI, 8.2-9.4; P = 0.004, GRADE: 6.2, 95% CI, 5.2-7.7; P = 0.04 vs no diabetes). Glucose variability was increased in patients with prior diabetes (glucose standard deviation, 1.99; 95% CI, 1.72-2.27 vs 2.97; 95% CI, 2.27-3.67; P = 0.006) but not in NODAT. All measures of glucose control and variability significantly improved over time after transplantation (P < 0.001). CONCLUSIONS Dysglycemia is very common after renal transplantation, exhibiting a distinct diurnal pattern of afternoon and evening hyperglycemia. The magnitude of hyperglycemia and variability are maximal in recipients with preexisting diabetes and significant in those who go on to develop NODAT. Dysglycemia improves with time.
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Krhač M, Lovrenčić MV. Update on biomarkers of glycemic control. World J Diabetes 2019; 10:1-15. [PMID: 30697366 PMCID: PMC6347654 DOI: 10.4239/wjd.v10.i1.1] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 11/14/2018] [Accepted: 12/05/2018] [Indexed: 02/05/2023] Open
Abstract
Attaining and maintaining good glycemic control is a cornerstone of diabetes care. The monitoring of glycemic control is currently based on the self-monitoring of blood glucose (SMBG) and laboratory testing for hemoglobin A1c (HbA1c), which is a surrogate biochemical marker of the average glycemia level over the previous 2-3 mo period. Although hyperglycemia is a key biochemical feature of diabetes, both the level of and exposure to high glucose, as well as glycemic variability, contribute to the pathogenesis of diabetic complications and follow different patterns in type 1 and type 2 diabetes. HbA1c provides a valuable, standardized and evidence-based parameter that is relevant for clinical decision making, but several biological and analytical confounders limit its accuracy in reflecting true glycemia. It has become apparent in recent years that other glycated proteins such as fructosamine, glycated albumin, and the nutritional monosaccharide 1,5-anhydroglucitol, as well as integrated measures from direct glucose testing by an SMBG/continuous glucose monitoring system, may provide valuable complementary data, particularly in circumstances when HbA1c results may be unreliable or are insufficient to assess the risk of adverse outcomes. Long-term associations of these alternative biomarkers of glycemia with the risk of complications need to be investigated in order to provide clinically relevant cut-off values and to validate their utility in diverse populations of diabetes patients.
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Affiliation(s)
- Maja Krhač
- Division of Laboratory Medicine, Department of Medical Biochemistry and Laboratory Medicine, Merkur University Hospital, Zagreb 10000, Croatia
| | - Marijana Vučić Lovrenčić
- Division of Laboratory Medicine, Department of Medical Biochemistry and Laboratory Medicine, Merkur University Hospital, Zagreb 10000, Croatia
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40
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Jin HY, Lee KA, Kim YJ, Park TS, Lee S, Park SK, Hwang HP, Yang JD, Ahn SW, Yu HC. The Degree of Hyperglycemia Excursion in Patients of Kidney Transplantation (KT) or Liver Transplantation (LT) Assessed by Continuous Glucose Monitoring (CGM): Pilot Study. J Diabetes Res 2019; 2019:1757182. [PMID: 31886275 PMCID: PMC6900943 DOI: 10.1155/2019/1757182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/18/2019] [Accepted: 11/04/2019] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE This study used a continuous glucose monitoring system (CGMS) to investigate the glucose profiles and assess the degree of hyperglycemic excursion after kidney or liver transplantation during the early period after operation. METHODS Patients to whom a CGMS was attached during a postoperative period of approximately one month after transplantation were included. The CGM data of 31 patients including 24 with kidney transplantation (KT) and seven with liver transplantation (LT) were analyzed. RESULTS Hyperglycemia over 126 mg/dL (fasting) or 200 g/dL (postprandial) occurred in 42.1% (8/19) and 16.7% (1/6) of KT and LT patients, respectively, during this early period after transplantation, except for patients with preexisting diabetes (5 KT, 1 LT). The average mean amplitude of glycemic excursion (MAGE) and mean absolute glucose (MAG) levels were 91.18 ± 26.51 vs. 65.66 ± 22.55 (P < 0.05) and 24.62 ± 7.78 vs. 18.18 ± 7.07 (P < 0.05) in KT vs. LT patients, respectively, in patients without preexisting DM or PTDM patients who showed normal glucose levels. Average increase from the lowest level to the peak glucose value was higher in KT patients than LT patients (P < 0.05). Conclusions. The transplanted organ also needs to be considered as an important factor affecting glucose control and the occurrence of more severe glucose excursions in patients who receive transplantation although immunosuppression agents are well-known important factors; however, our study was limited to the early posttransplantation period. Further studies involving CGM follow-up at regular intervals based on the time since transplantation are needed.
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Affiliation(s)
- Heung Yong Jin
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Jeonbuk National University Medical School, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Kyung Ae Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Jeonbuk National University Medical School, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Yu Ji Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Jeonbuk National University Medical School, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Tae Sun Park
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Jeonbuk National University Medical School, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Sik Lee
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Jeonbuk National University Medical School, Republic of Korea
| | - Sung Kwang Park
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Jeonbuk National University Medical School, Republic of Korea
| | - Hong Pil Hwang
- Division of Surgery, Jeonbuk National University Medical School, Republic of Korea
| | - Jae Do Yang
- Division of Surgery, Jeonbuk National University Medical School, Republic of Korea
| | - Sung-Woo Ahn
- Division of Surgery, Jeonbuk National University Medical School, Republic of Korea
| | - Hee Chul Yu
- Research Institute of Clinical Medicine of Jeonbuk National University- - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
- Division of Surgery, Jeonbuk National University Medical School, Republic of Korea
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Souto DL, Dantas JR, Oliveira MMDS, Rosado EL, Luiz RR, Zajdenverg L, Rodacki M. Does sucrose affect the glucose variability in patients with type 1 diabetes? a pilot crossover clinical study. Nutrition 2018; 55-56:179-184. [PMID: 30086487 DOI: 10.1016/j.nut.2018.05.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 05/26/2018] [Accepted: 05/28/2018] [Indexed: 10/14/2022]
Abstract
OBJECTIVE The aim of this study was to compare the effects of a sucrose-free diet with a sucrose-added diet on glucose variability in patients with type 1 diabetes. METHODS This was a two-way crossover design study in which patients with type 1 diabetes were monitored by blinded continuous glucose monitoring and were selected to receive a sucrose-free diet (<30 g/d), followed by a sucrose-added diet (>80 g/d) for 2 d each. Intra-day glucose variability was assessed by the mean amplitude of glycemic excursions (MAGE), the M-value, J-index, glycemic risk assessment in diabetes equation (GRADE), and continuous overlapping net glycemic action (CONGA1-3). Between-day variability was determined by mean of daily difference (MODD). Statistical analyses were performed with a significance level of 5%. RESULTS Ten patients with type 1 diabetes were evaluated. The participants were a mean of 26.1 ± 7.1 y of age. The mean duration of disease was 16.5 ± 10.5 y, and patients' mean glycated hemoglobin was 7.4% ± 0.8%. The intra- and inter-day glucose variability indexes did not differ between the diet periods (MAGE: 10.2 ± 5.1 and 10.4 ± 6.8mmol/L, P = 0.98; M-value: 12.9 ± 2 and 15.6 ± 1.3mmol/L, P = 0.29; J-index: 50.9 ± 4.4 and 57.7 ± 3.3mmol/L, P = 0.41; GRADE: 7.2 ± 1 and 4.7 ± 5.3mmol/L, P = 0.07; and MODD: 3.9 ± 1 and 4.3 ± 1.5mmol/L, P = 0.28; for the sucrose-free and sucrose-added diets, respectively). CONGA1-3 were similar for both diet periods (P > 0.05). CONCLUSIONS The use of a moderate amount of sucrose, as part of a balanced diet, did not affect the glucose variability or insulin requirements in patients with type 1 diabetes.
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Affiliation(s)
- Débora Lopes Souto
- Department of Internal Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Institute of Nutrition Josué de Castro, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
| | - Joana Rodrigues Dantas
- Department of Internal Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Eliane Lopes Rosado
- Institute of Nutrition Josué de Castro, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ronir Raggio Luiz
- Institute of Public Health Studies, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Lenita Zajdenverg
- Department of Internal Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Melanie Rodacki
- Department of Internal Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
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Guilmin-Crépon S, Carel JC, Schroedt J, Scornet E, Alberti C, Tubiana-Rufi N. How Should We Assess Glycemic Variability in Type 1 Diabetes? Contribution of Principal Component Analysis for Interstitial Glucose Indices in 142 Children. Diabetes Technol Ther 2018; 20:440-447. [PMID: 29923773 DOI: 10.1089/dia.2017.0404] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Glycemic variability (GV) can be used to assess glycemic control in diabetes, but there is no clear consensus concerning the methods to use for its assessment. Methodological differences have resulted in differences in the outcome of GV metrics used in research studies, controversies over clinical impact, and an absence of integration into routine care. AIM To identify the indicators of GV most meaningful for clinicians, patients, and clinical researchers. MATERIALS AND METHODS Continuous glucose monitoring data were collected during the first 3 months of a pediatric diabetes clinical trial (Start-In!; n = 142). We used principal component analysis (PCA) to analyze weekly averages for 22 parameters relating to GV. RESULTS PCA identified five groups of parameters and three components explaining 85.7% of the variance. These components represented the amplitude, direction (hypoglycemia vs. hyperglycemia), and timing (within-day vs. between-days) of glucose excursions. CONCLUSIONS This study provides elements that could make GV parameters more useful in clinical practice and research. No single parameter was sufficient to represent the complexity of GV, but it was possible to restrict the number of indicators required. The five groups of parameters identified by PCA could facilitate the choice of the most relevant outcomes for GV analysis in pediatric diabetes according to the purpose of the analysis (e.g., exploration of GV associated with hypo- or hyperglycemia, with short- or long-term periodicity, or GV in its entirety).
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Affiliation(s)
- Sophie Guilmin-Crépon
- 1 AP-HP, Hôpital Universitaire Robert Debré , Departement of Pediatric Endocrinology and Diabetology and Centre de référence des Maladies Endocriniennes Rares de la Croissance, Paris, France
- 2 APHP, Hôpital Universitaire Robert Debré, Unit of Clinical Epidemiology , Paris, France
- 3 Inserm , UMR-S 1123 ECEVE and CIC-EC 1426, Paris, France
- 4 Univ Paris Diderot , Sorbonne Paris Cité, UMR-S 1123 ECEVE, Paris, France
| | - Jean-Claude Carel
- 1 AP-HP, Hôpital Universitaire Robert Debré , Departement of Pediatric Endocrinology and Diabetology and Centre de référence des Maladies Endocriniennes Rares de la Croissance, Paris, France
- 4 Univ Paris Diderot , Sorbonne Paris Cité, UMR-S 1123 ECEVE, Paris, France
- 5 Inserm, PROTECT, Université Paris Diderot , Sorbonne Paris Cité, Paris, France
| | - Julien Schroedt
- 2 APHP, Hôpital Universitaire Robert Debré, Unit of Clinical Epidemiology , Paris, France
- 3 Inserm , UMR-S 1123 ECEVE and CIC-EC 1426, Paris, France
| | - Erwan Scornet
- 2 APHP, Hôpital Universitaire Robert Debré, Unit of Clinical Epidemiology , Paris, France
| | - Corinne Alberti
- 2 APHP, Hôpital Universitaire Robert Debré, Unit of Clinical Epidemiology , Paris, France
- 3 Inserm , UMR-S 1123 ECEVE and CIC-EC 1426, Paris, France
- 4 Univ Paris Diderot , Sorbonne Paris Cité, UMR-S 1123 ECEVE, Paris, France
| | - Nadia Tubiana-Rufi
- 1 AP-HP, Hôpital Universitaire Robert Debré , Departement of Pediatric Endocrinology and Diabetology and Centre de référence des Maladies Endocriniennes Rares de la Croissance, Paris, France
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Abstract
Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.
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Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
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Rodbard D. Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with "Risk Indices". Diabetes Technol Ther 2018; 20:325-334. [PMID: 29792750 DOI: 10.1089/dia.2017.0416] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We sought to cross validate several metrics for quality of glycemic control, hypoglycemia, and hyperglycemia. RESEARCH DESIGN AND METHODS We analyzed the mathematical properties of several metrics for overall glycemic control, and for hypo- and hyperglycemia, to evaluate their similarities, differences, and interrelationships. We used linear regression to describe interrelationships and examined correlations between metrics within three conceptual groups. RESULTS There were consistently high correlations between %Time in range (%TIR) and previously described risk indices (M100, Blood Glucose Risk Index [BGRI], Glycemic Risk Assessment Diabetes Equation [GRADE], Index of Glycemic Control [IGC]), and with J-Index (J). There were also high correlations among %Hypoglycemia, Low Blood Glucose Index (LBGI), percentage of GRADE attributable to hypoglycemia (GRADE%Hypoglycemia), and Hypoglycemia Index, but negligible correlation with J. There were high correlations of percentage of time in hyperglycemic range (%Hyperglycemia) with High Blood Glucose Index (HBGI), percentage of GRADE attributable to hyperglycemia (GRADE%Hyperglycemia), Hyperglycemia Index, and J. %TIR is highly negatively correlated with %Hyperglycemia but very weakly correlated with %Hypoglycemia. By adjusting the parameters used in IGC, Hypoglycemia Index, Hyperglycemia Index, or in MR, one can more closely approximate the properties of BGRI, LBGI, or HBGI, and of GRADE, GRADE%Hypoglycemia, or GRADE%Hyperglycemia. CONCLUSIONS Simple readily understandable criteria such as %TIR, %Hypoglycemia, and %Hyperglycemia are highly correlated with and appear to be as informative as "risk indices." The J-Index is sensitive to hyperglycemia but insensitive to hypoglycemia.
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Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
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45
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Rama Chandran S, Tay WL, Lye WK, Lim LL, Ratnasingam J, Tan ATB, Gardner DSL. Beyond HbA1c: Comparing Glycemic Variability and Glycemic Indices in Predicting Hypoglycemia in Type 1 and Type 2 Diabetes. Diabetes Technol Ther 2018; 20:353-362. [PMID: 29688755 DOI: 10.1089/dia.2017.0388] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Hypoglycemia is the major impediment to therapy intensification in diabetes. Although higher individualized HbA1c targets are perceived to reduce the risk of hypoglycemia in those at risk of hypoglycemia, HbA1c itself is a poor predictor of hypoglycemia. We assessed the use of glycemic variability (GV) and glycemic indices as independent predictors of hypoglycemia. METHODS A retrospective observational study of 60 type 1 and 100 type 2 diabetes subjects. All underwent professional continuous glucose monitoring (CGM) for 3-6 days and recorded self-monitored blood glucose (SMBG). Indices were calculated from both CGM and SMBG. Statistical analyses included regression and area under receiver operator curve (AUC) analyses. RESULTS Hypoglycemia frequency (53.3% vs. 24%, P < 0.05) and %CV (40.1% ± 10% vs. 29.4% ± 7.8%, P < 0.001) were significantly higher in type 1 diabetes compared with type 2 diabetes. HbA1c was, at best, a weak predictor of hypoglycemia. %CVCGM, Low Blood Glucose Index (LBGI)CGM, Glycemic Risk Assessment Diabetes Equation (GRADE)HypoglycemiaCGM, and Hypoglycemia IndexCGM predicted hypoglycemia well. %CVCGM and %CVSMBG consistently remained a robust discriminator of hypoglycemia in type 1 diabetes (AUC 0.88). In type 2 diabetes, a combination of HbA1c and %CVSMBG or LBGISMBG could help discriminate hypoglycemia. CONCLUSION Assessment of glycemia should go beyond HbA1c and incorporate measures of GV and glycemic indices. %CVSMBG in type 1 diabetes and LBGISMBG or a combination of HbA1c and %CVSMBG in type 2 diabetes discriminated hypoglycemia well. In defining hypoglycemia risk using GV and glycemic indices, diabetes subtypes and data source (CGM vs. SMBG) must be considered.
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Affiliation(s)
| | - Wei Lin Tay
- 1 Department of Endocrinology, Singapore General Hospital , Singapore
| | - Weng Kit Lye
- 2 Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Medical School , Singapore
| | - Lee Ling Lim
- 3 Division of Endocrinology, Department of Internal Medicine, University of Malaya , Kuala Lumpur, Malaysia
| | - Jeyakantha Ratnasingam
- 3 Division of Endocrinology, Department of Internal Medicine, University of Malaya , Kuala Lumpur, Malaysia
| | - Alexander Tong Boon Tan
- 3 Division of Endocrinology, Department of Internal Medicine, University of Malaya , Kuala Lumpur, Malaysia
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Tong L, Chi C, Zhang Z. Association of various glycemic variability indices and vascular outcomes in type-2 diabetes patients: A retrospective study. Medicine (Baltimore) 2018; 97:e10860. [PMID: 29794785 PMCID: PMC6392700 DOI: 10.1097/md.0000000000010860] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Both blood glucose (BG) level and glycemic variability (GV) significantly associate with diabetes-related complications. However, the criterion standard in GV assessment is absent. We aimed to compare different GV indices in association of vascular outcomes.Ten commonly used GV indices based on self-monitored BG data were calculated, and their associations of vascular outcomes including coronary artery disease (CAD), stroke, and chronic kidney disease (CKD) were compared.In total, 288 type 2 diabetes patients (66.5 ± 11.1 years old) were included in present analysis. Spearman correlation analysis showed that only mean amplitude of glycemic excursions (MAGE) significantly correlated with both estimated glomerular filtration rate and urinary albumin creatinine ratio (P ≤ .03). In Cochran-Armitage trend test, vascular outcomes were significantly associated with the increment of BG risk index and MAGE (P ≤ .03). After adjustment for potential confounders, multiple logistic regression results suggested that BG risk index and MAGE still significantly associated with these three vascular outcomes (P ≤ .01), whereas the other GV indices did not. Receiver operating characteristic curve analysis showed that the abilities of BG risk index and MAGE were similar in identifying CAD, stroke, or CKD.BG risk index and MAGE were better associated with vascular outcomes than other GV indices in type 2 diabetes patients.
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Affiliation(s)
- Lei Tong
- Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, State Key Laboratory of Medical, Economics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
| | - Chen Chi
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhiguo Zhang
- Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, State Key Laboratory of Medical, Economics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
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Gaynanova I, Urbanek J, Punjabi NM. Corrections of Equations on Glycemic Variability and Quality of Glycemic Control. Diabetes Technol Ther 2018; 20:317. [PMID: 29664704 PMCID: PMC5910047 DOI: 10.1089/dia.2018.0057] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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48
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Rodbard D. Response to Letter to the Editor. Diabetes Technol Ther 2018; 20:318-319. [PMID: 29664707 DOI: 10.1089/dia.2018.0063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, Maryland
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49
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Longato E, Acciaroli G, Facchinetti A, Hakaste L, Tuomi T, Maran A, Sparacino G. Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data. Comput Biol Med 2018; 96:141-146. [PMID: 29573667 DOI: 10.1016/j.compbiomed.2018.03.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 03/10/2018] [Accepted: 03/10/2018] [Indexed: 11/17/2022]
Abstract
Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters-age, sex, BMI, and waist circumference-with an accuracy of 87.1%.
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Affiliation(s)
- Enrico Longato
- Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy.
| | - Giada Acciaroli
- Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy.
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy.
| | - Liisa Hakaste
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 8, FI-00014, Helsinki, Finland; Folkhälsan Research Center and Research Program for Diabetes and Obesity, University of Helsinki, Haartmaninkatu 8, FI-00014, Helsinki, Finland.
| | - Tiinamaija Tuomi
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 8, FI-00014, Helsinki, Finland; Folkhälsan Research Center and Research Program for Diabetes and Obesity, University of Helsinki, Haartmaninkatu 8, FI-00014, Helsinki, Finland; Finnish Institute for Molecular Medicine, University of Helsinki, Tukholmankatu 8, FI-00014, Helsinki, Finland.
| | - Alberto Maran
- Department of Medicine, University of Padova, Via Giustiniani 2, 35128, Padova, Italy.
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy.
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Calculating the Mean Amplitude of Glycemic Excursions from Continuous Glucose Data Using an Open-Code Programmable Algorithm Based on the Integer Nonlinear Method. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:6286893. [PMID: 29707038 PMCID: PMC5863323 DOI: 10.1155/2018/6286893] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 01/18/2018] [Accepted: 02/07/2018] [Indexed: 12/03/2022]
Abstract
The mean amplitude of glycemic excursions (MAGE) is an essential index for glycemic variability assessment, which is treated as a key reference for blood glucose controlling at clinic. However, the traditional “ruler and pencil” manual method for the calculation of MAGE is time-consuming and prone to error due to the huge data size, making the development of robust computer-aided program an urgent requirement. Although several software products are available instead of manual calculation, poor agreement among them is reported. Therefore, more studies are required in this field. In this paper, we developed a mathematical algorithm based on integer nonlinear programming. Following the proposed mathematical method, an open-code computer program named MAGECAA v1.0 was developed and validated. The results of the statistical analysis indicated that the developed program was robust compared to the manual method. The agreement among the developed program and currently available popular software is satisfied, indicating that the worry about the disagreement among different software products is not necessary. The open-code programmable algorithm is an extra resource for those peers who are interested in the related study on methodology in the future.
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