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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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Mo Y, Lu J, Zhou J. Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter? J Diabetes Investig 2024; 15:5-14. [PMID: 37988220 PMCID: PMC10759720 DOI: 10.1111/jdi.14112] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/05/2023] [Accepted: 11/08/2023] [Indexed: 11/23/2023] Open
Abstract
Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.
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Affiliation(s)
- Yifei Mo
- Department of Endocrinology and MetabolismShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes MellitusShanghaiChina
| | - Jingyi Lu
- Department of Endocrinology and MetabolismShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes MellitusShanghaiChina
| | - Jian Zhou
- Department of Endocrinology and MetabolismShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes MellitusShanghaiChina
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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: 9] [Impact Index Per Article: 9.0] [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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Yoo JH, Kim JY, Kim JH. Association Between Continuous Glucose Monitoring-Derived Glycemia Risk Index and Albuminuria in Type 2 Diabetes. Diabetes Technol Ther 2023; 25:726-735. [PMID: 37335748 DOI: 10.1089/dia.2023.0165] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
Background: The glycemia risk index (GRI) is a new composite metric derived from continuous glucose monitoring (CGM) data to assess the quality of glycemia. This study investigates the association between the GRI and albuminuria. Methods: Professional CGM and urinary albumin-to-creatinine ratio (UACR) data from 866 individuals with type 2 diabetes were retrospectively reviewed. Albuminuria and macroalbuminuria were defined as one or more UACR measurements ≥30 and ≥300 mg/g, respectively. Results: The overall prevalence of albuminuria and macroalbuminuria was 36.6% and 13.9%, respectively. Participants with a higher UACR had a significantly higher hyperglycemia component and GRI score than those with a lower UACR (all P < 0.001), although the hypoglycemia component did not differ among the groups. Multiple logistic regression analyses that adjusted for various factors affecting albuminuria revealed that the odds ratio (OR) of albuminuria was 1.13 (95% confidence interval [CI]: 1.02-1.27, P = 0.039) per increase in the GRI zone. The results were similar for the risk of macroalbuminuria (OR: 1.42 [95% CI: 1.20-1.69], P < 0.001), and that association remained after adjusting for glycated hemoglobin (OR: 1.31 [95% CI: 1.10-1.58], P = 0.004). Conclusions: GRI is strongly associated with albuminuria, especially macroalbuminuria, in type 2 diabetes.
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Affiliation(s)
- Jee Hee Yoo
- Division of Endocrinology and Metabolism, Department of Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Ji Yoon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 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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Klonoff DC, Wang J, Rodbard D, Kohn MA, Li C, Liepmann D, Kerr D, Ahn D, Peters AL, Umpierrez GE, Seley JJ, Xu NY, Nguyen KT, Simonson G, Agus MSD, Al-Sofiani ME, Armaiz-Pena G, Bailey TS, Basu A, Battelino T, Bekele SY, Benhamou PY, Bequette BW, Blevins T, Breton MD, Castle JR, Chase JG, Chen KY, Choudhary P, Clements MA, Close KL, Cook CB, Danne T, Doyle FJ, Drincic A, Dungan KM, Edelman SV, Ejskjaer N, Espinoza JC, Fleming GA, Forlenza GP, Freckmann G, Galindo RJ, Gomez AM, Gutow HA, Heinemann L, Hirsch IB, Hoang TD, Hovorka R, Jendle JH, Ji L, Joshi SR, Joubert M, Koliwad SK, Lal RA, Lansang MC, Lee WA(A, Leelarathna L, Leiter LA, Lind M, Litchman ML, Mader JK, Mahoney KM, Mankovsky B, Masharani U, Mathioudakis NN, Mayorov A, Messler J, Miller JD, Mohan V, Nichols JH, Nørgaard K, O’Neal DN, Pasquel FJ, Philis-Tsimikas A, Pieber T, Phillip M, Polonsky WH, Pop-Busui R, Rayman G, Rhee EJ, Russell SJ, Shah VN, Sherr JL, Sode K, Spanakis EK, Wake DJ, Waki K, Wallia A, Weinberg ME, Wolpert H, Wright EE, Zilbermint M, Kovatchev B. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol 2023; 17:1226-1242. [PMID: 35348391 PMCID: PMC10563532 DOI: 10.1177/19322968221085273] [Citation(s) in RCA: 83] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
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Affiliation(s)
- David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
| | - Jing Wang
- Florida State University College of Nursing, Tallahassee, FL, USA
| | - David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, MD, USA
| | - Michael A. Kohn
- University of California, San Francisco, San Francisco, CA, USA
| | - Chengdong Li
- Florida State University College of Nursing, Tallahassee, FL, USA
| | | | - David Kerr
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - David Ahn
- Hoag Memorial Hospital Presbyterian, Newport Beach, CA, USA
| | | | | | | | - Nicole Y. Xu
- Diabetes Technology Society, Burlingame, CA, USA
| | | | | | | | | | | | | | - Ananda Basu
- University of Virginia, Charlottesville, VA, USA
| | - Tadej Battelino
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | | | | | | | | | | | | | - Kong Y. Chen
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | | | | | | | | | - Thomas Danne
- Diabetes Center Auf der Bult, Hannover Medical School, Hannover, Germany
| | | | | | | | | | - Niels Ejskjaer
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | | | | | | | | | | | | | | | | | - Thanh D. Hoang
- Walter Reed National Military Medical Center, Bethesda, MD, USA
| | | | | | - Linong Ji
- Peking University People’s Hospital, Peking University Diabetes Center, Beijing, China
| | | | | | | | | | - M. Cecilia Lansang
- Cleveland Clinic, Cleveland, OH, USA
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Wei-An (Andy) Lee
- LAC + USC Medical Center, Los Angeles County Department of Health Service, Los Angeles, CA, USA
| | - Lalantha Leelarathna
- Manchester University NHS Foundation Trust and The University of Manchester, Manchester, UK
| | - Lawrence A. Leiter
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital and University of Toronto, Toronto, ON, Canada
| | - Marcus Lind
- University of Gothenburg, Gothenburg, Sweden
| | | | | | | | | | - Umesh Masharani
- University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | | | - Viswanathan Mohan
- Dr. Mohan’s Diabetes Specialities Centre, Chennai, India
- Madras Diabetes Research Foundation, Chennai, India
| | | | | | | | | | | | | | - Moshe Phillip
- Institute for Endocrinology and Diabetes, Schneider Children’s Medical Center of Israel and Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | | | | | - Gerry Rayman
- Ipswich Hospital, East Suffolk and North Essex Foundation Trust and University of East Anglia, Ipswich, UK
| | - Eun-Jung Rhee
- Kangbuk Samsung Hospital, Sungkyunkwan University, Seoul, Korea
| | - Steven J. Russell
- Massachusetts General Hospital Diabetes Research Center, Boston, MA, USA
| | - Viral N. Shah
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO, USA
| | | | - Koji Sode
- The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- North Carolina State University, Raleigh, NC, USA
| | | | | | - Kayo Waki
- The University of Tokyo, Tokyo, Japan
| | | | | | | | | | - Mihail Zilbermint
- Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins Community Physicians, Bethesda, MD, USA
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6
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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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Szigeti M, Ferenci T, Kovács L. The Use of Extreme Value Statistics to Characterize Blood Glucose Curves and Patient Level Risk Assessment of Patients With Type I Diabetes. J Diabetes Sci Technol 2023; 17:400-408. [PMID: 34814774 PMCID: PMC10012361 DOI: 10.1177/19322968211059547] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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
OBJECTIVE Characterizing blood glucose curves and providing precise patient level risk assessment of hyperglycemia using extreme value statistics and comparing these assessments with traditional indicators of glycemic variability which are not designed to specifically capture the risk of hyperglycemia. RESEARCH DESIGN AND METHODS One year return level (blood glucose level exceeded exactly once every year on average) and probability of exceeding and expected time spent above certain thresholds (600 and 400 mg/dL) per year were calculated. As a comparison, traditional metrics for glycemic variability were determined too. The effect of body mass index on extremes was also investigated using non-stationary models. Metrics were calculated on a dataset containing 170.8 patient-years of measurements of 226 patients. RESULTS Nine high-risk patients were identified with the novel metrics: their estimated time spent above 600 mg/dL per year were above 2 hours. These patients were at moderate risk according to the traditional metrics. Higher body mass index was associated with more extreme glucose levels. CONCLUSIONS Through these estimates it is possible to assess each patient's individual clinical risk of hyperglycemia even beyond the observed blood glucose levels and detection limits. Additionally, it allows the assessment of the impact of clinical characteristics and treatments on blood glucose control in a novel, mathematically well-founded and potentially clinically more useful way than the already existing indicators.
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Affiliation(s)
- Mátyás Szigeti
- Imperial Clinical Trials Unit, Imperial
College London, London, UK
- Physiological Controls Research Center,
Budapest, Hungary
| | - Tamás Ferenci
- Physiological Controls Research Center,
Budapest, Hungary
- Department of Statistics, Corvinus
University of Budapest, Budapest, Hungary
| | - Levente Kovács
- Physiological Controls Research Center,
Budapest, Hungary
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Cho S, Vigers T, Pyle L, Franklin A, Sopfe J, Jeney F, Forlenza G. Composite Metric of Glycemic Control Q-Score Is Elevated in Pediatric and Adolescent/Young Adult Hematopoietic Stem Cell Transplant Recipients. Diabetes Technol Ther 2023; 25:116-121. [PMID: 36511871 PMCID: PMC9894599 DOI: 10.1089/dia.2022.0246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background: Malglycemia in pediatric, adolescent and young adult (AYA) patients who undergo hematopoietic stem cell transplant (HSCT) is associated with increased infection and mortality rate. Continuous glucose monitoring (CGM) has been safely used in pediatric/AYA HSCT recipients, but there is a need for a composite metric that can easily be used in clinical settings to assess the glycemic control and identify high-risk patients who needs therapeutic intervention. Composite metrics derived from CGM have not been studied in pediatric/AYA HSCT patients. Methods: Patients aged 2-30 years old who are admitted inpatient while undergoing HSCT at Children's Hospital Colorado underwent CGM using the Abbot Freestyle Libre Pro device from up to 7 days before and 60 days after HSCT. A composite metric Q-score, comprising five primary factors of CGM profiles (central tendency, hyperglycemia, hypoglycemia, intradaily variations, and interdaily variations), was calculated for each patient for the duration of CGM wear. Results: Twenty-nine patients received CGM for an average of 25 days per participant. The median Q-score was 10.2 (interquartile range [IQR]: 8.3, 14.3). Sixty-nine percent of patients had Q-scores that would be categorized into the Fair or Poor category. There was no difference in the Q-score by sources of stem cell, types of primary disease, types of preparative regimen, need for PICU admission, presence of documented infections, and total parenteral nutrition use in the peri-HSCT period. Conclusions: Most pediatric/AYA HSCT recipients have Q-scores indicating suboptimal glycemic control in the peri-HSCT period. Future study should focus on developing screening and treatment strategies to improve malglycemia and its associated adverse clinical outcomes. This study was registered at clinicaltrials.gov (NCT03482154).
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Affiliation(s)
- Soohee Cho
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Center for Cancer and Blood Disorder, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Tim Vigers
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, Auroa, Colorado, USA
| | - Laura Pyle
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, Auroa, Colorado, USA
| | - Anna Franklin
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Center for Cancer and Blood Disorder, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Jenna Sopfe
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Center for Cancer and Blood Disorder, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Frankie Jeney
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Gregory Forlenza
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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9
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Rodbard D. Continuous glucose monitoring metrics (Mean Glucose, time above range and time in range) are superior to glycated haemoglobin for assessment of therapeutic efficacy. Diabetes Obes Metab 2023; 25:596-601. [PMID: 36314133 DOI: 10.1111/dom.14906] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/13/2022] [Accepted: 10/25/2022] [Indexed: 02/02/2023]
Abstract
AIM To evaluate continuous glucose monitoring (CGM) metrics for use as alternatives to glycated haemoglobin (HbA1c) to evaluate therapeutic efficacy. METHODS We re-analysed correlations among CGM metrics from studies involving 545 people with type 1 diabetes (T1D), 5910 people with type 2 diabetes (T2D) and 98 people with T1D during pregnancy and the postpartum period. RESULTS Three CGM metrics, interstitial fluid Mean Glucose level, proportion of time above range (%TAR) and proportion of time in range (%TIR), were correlated with HbA1c and provided metrics that can be used to evaluate therapeutic efficacy. Mean Glucose showed the highest correlation with %TAR (r = 0.98 in T1D, 0.97 in T2D) but weaker correlations with %TIR (r = -0.92 in T1D, -0.83 in T2D) or with HbA1c (r = 0.78 in T1D). %TAR and %TIR were highly correlated (r = -0.96 in T1D, -0.91 in T2D). After 6 months of use of real-time CGM by people with T1D, changes in Mean Glucose level were more highly correlated with changes in %TAR (r = 0.95) than with changes in %TIR (r = -0.85) or with changes in HbA1c level (r = 0.52). These metrics can be combined with metrics of hypoglycaemia and/or glycaemic variability to provide a more comprehensive assessment of overall quality of glycaemic control. CONCLUSION The CGM metrics %TAR and %TIR show much higher correlations with Mean Glucose than with HbA1c and provide sensitive indicators of efficacy. Mean glucose may be the best metric and shows consistently higher correlations with %TAR than with %TIR.
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Affiliation(s)
- David Rodbard
- Clinical Biostatistics Department, Biomedical Informatics Consultants LLC, Potomac, Maryland, USA
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10
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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: 15] [Impact Index Per Article: 7.5] [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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Mo Y, Wang C, Lu J, Shen Y, Chen L, Zhang L, Lu W, Zhu W, Xia T, Zhou J. Impact of short-term glycemic variability on risk of all-cause mortality in type 2 diabetes patients with well-controlled glucose profile by continuous glucose monitoring: A prospective cohort study. Diabetes Res Clin Pract 2022; 189:109940. [PMID: 35662611 DOI: 10.1016/j.diabres.2022.109940] [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] [Received: 01/19/2022] [Revised: 05/12/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
AIMS To investigate the association between short-term glycemic variability (GV) and all-cause mortality in type 2 diabetes with well-controlled glucose profile by continuous glucose monitoring (CGM). METHODS In this prospective study, 1839 diabetes patients who reached percentage of time in the target glucose range of 3.9-10 mmol/L > 70%, percentage of time above range of 10 mmol/L < 25% and percentage of time below range of 3.9 mmol/L < 4% on CGM were enrolled and were classified into five groups by coefficient of variation for glucose (%CV) level: ≤20%, 20-25%, 25-30%, 30-35%, and > 35%. Cox proportional hazard models were used to estimate hazard ratios (HRs) of all-cause mortality risk associated with the different %CV categories. RESULTS At baseline, participants had mean age of 60.9 years and mean HbA1c of 7.3% (56 mmol/mol). A total of 165 deaths were identified during a median follow-up of 6.9 years. In multivariate Cox regression analysis, HRs associated with %CV categories were 1.00, 1.16 (95% CI 0.78-1.73), 1.38 (95% CI 0.89-2.15), 1.33 (95% CI 0.77-2.29) and 2.26 (95% CI 1.13-4.52) for all-cause mortality. CONCLUSIONS Greater %CV was associated with increased risk for all-cause mortality even among patients with seemingly well-controlled glucose status.
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Affiliation(s)
- Yifei Mo
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Chunfang Wang
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Yun Shen
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Lei Chen
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Lei Zhang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Wei Zhu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Tian Xia
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
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Arunachalum S, Velado K, Vigersky RA, Cordero TL. Glycemic Outcomes During Real-World Hybrid Closed-Loop System Use by Individuals With Type 1 Diabetes in the United States. J Diabetes Sci Technol 2022:19322968221088608. [PMID: 35414272 DOI: 10.1177/19322968221088608] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Glycemic outcomes during real-world hybrid closed-loop (HCL) system use by individuals with type 1 diabetes, in the United States, were retrospectively analyzed. METHODS Hybrid closed-loop system data voluntarily uploaded to Carelink™ personal software from March 2017 to November 2020 by individuals (aged ≥7 years) using the MiniMed™ 670G system and having ≥10 days of continuous glucose monitoring data after initiating Auto Mode were assessed. Glycemic outcomes including the mean glucose management indicator (GMI), sensor glucose (SG), percentage of time spent in (TIR), below (TBR), and above (TAR) target range (70-180 mg/dL) were analyzed. Outcomes were also analyzed in a subgroup of users per baseline GMI of <7% versus >8%. RESULTS The overall cohort (N = 123 355 users, with a mean of 87.9% of time in Auto Mode) had a GMI of 7.0% ± 0.4%, TIR of 70.4% ± 11.2%, TBR <70 mg/dL of 2.2% ± 2.1% and TAR>180 mg/dL of 27.5% ± 11.6%, post-Auto Mode initiation. Compared with pre-Auto Mode initiation, users (N = 52 941, 88.6% of time in Auto Mode) had a GMI that decreased from 7.3% ± 0.6% to 7.1% ± 0.5% (P < .001), TIR that increased from 61.5% ± 15.1% to 68.1% ± 11.9% (P < .001), TAR>180 mg/dL that decreased from 36.3% ± 15.7% to 29.8% ± 12.2% (P < .001) and TBR<70 mg/dL that decreased from 2.11 ± 2.4 to 2.07% ± 2.25% (P = .002). While all metrics statistically improved for the baseline GMI >8.0% group, the baseline GMI <7.0% group had unchanged TIR (77.4% ± 7.4% to 77.5% ± 8.0%, P = .456) and TAR>180 mg/dL that increased (19.2 ± 6.7 to 19.6 ± 7.9%, p < 0.001). CONCLUSION Real-world HCL system use in the U.S. demonstrated overall glycemic control that trended similarly with the system pivotal trial outcomes and previous real-world system use analyses.
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Affiliation(s)
- Klemen Dovc
- UMC-University Children's Hospital Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Bruce W Bode
- Atlanta Diabetes Associates and Emory University School of Medicine, Atlanta, GA
| | - Tadej Battelino
- Atlanta Diabetes Associates and Emory University School of Medicine, Atlanta, GA
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