1
|
Lee MH, Vogrin S, Jones TW, O'Neal DN. Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index. J Diabetes Sci Technol 2024; 18:764-770. [PMID: 38372246 DOI: 10.1177/19322968241231307] [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: 02/20/2024]
Abstract
BACKGROUND Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited. METHODS A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]). RESULTS GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference -22.8 [-27.2, -18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms (r = -.954; P = .001), and positively with TAR >250 mg/dL (r = .901; P = .001), TBR < 54 mg/dL (r = .416; P = .001), and glycemic variability (SD [r = .916] and CV [r = .732]; P = .001 for both). CONCLUSIONS Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.
Collapse
Affiliation(s)
- Melissa H Lee
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Sara Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
| | - Timothy W Jones
- Department of Endocrinology and Diabetes, Perth Children's Hospital, Perth, WA, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
- School of Paediatrics and Child Health, The University of Western Australia, Perth, WA, Australia
| | - David N O'Neal
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- The Australian Centre for Accelerating Diabetes Innovations, Melbourne, VIC, Australia
| |
Collapse
|
2
|
Shao X, Lu J, Tao R, Wu L, Wang Y, Lu W, Li H, Zhou J, Yu X. Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data. Diabetes Obes Metab 2024; 26:2082-2091. [PMID: 38409633 DOI: 10.1111/dom.15512] [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] [Received: 11/16/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/28/2024]
Abstract
AIM The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications. METHODS The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge-based temporal abstraction with numerical vectorization. The k-means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM. RESULTS By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic β-cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set. CONCLUSION Subtyping patients with T2DM using CGM data may help identify high-risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.
Collapse
Affiliation(s)
- Xiaopeng Shao
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Rui Tao
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Liang Wu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Yaxin Wang
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Hongru Li
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Xia Yu
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| |
Collapse
|
3
|
Augstein P, Heinke P, Nowak A, Salzsieder E, Kerner W. Q-Score Complements the Time in Range in the Evaluation of Short-Term Glycemic Control. J Diabetes Sci Technol 2024:19322968241246209. [PMID: 38641969 DOI: 10.1177/19322968241246209] [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: 04/21/2024]
Abstract
BACKGROUND AND AIMS The Q-Score is a single-number composite metric that is constructed based on the following components: central glycemic tendency, hyperglycemia, hypoglycemia, and intra- and interday variability. Herein, we refined the Q-Score for the screening and analysis of short-term glycemic control using continuous glucose monitoring (CGM) profiles. METHODS Continuous glucose monitoring profiles were obtained from noninterventional, retrospective cross-sectional studies. The upper limit of the Q-Score component hyperglycemia' that is, the time above target range (TAR), was adjusted from 8.9 to 10 mmol/L (n = 1562 three-day-sensor profiles). A total of 302 people with diabetes mellitus treated with intermittent CGM for ≥14 days were enrolled. The time to stability was determined via correlation-based analysis. RESULTS There was a strong correlation between the Q-Scores of the two TARs, that is, 8.9 and 10 mmol/L (Q-ScoreTAR10 = -0.03 + 1.00 Q-ScoreTAR8.9, r = .997, p < .001). The times to stability of the Q-Score and TIR were 10 and 12 days, respectively. The Q-Score was correlated with fructosamine concentrations, the glucose management indicator (GMI), the time in range (TIR), and the glycemic risk index (GRI) (r = .698, .887, -.874, and .941), respectively. The number of Q-Score components above the target increased as the TIR decreased, from two (1.7 ± 0.9) in CGM profiles with a TIR between 70% and 80% to four (3.9 ± 0.5) in the majority of the CGM profiles with a TIR below 50%. A conversion matrix between the Q-Score and glycemic indices was developed. CONCLUSIONS The Q-Score is a tool for assessing short-term glycemic control. The Q-Score can be translated into clinician opinion using the GRI.
Collapse
Affiliation(s)
- Petra Augstein
- Department for Diabetology, Heart and Diabetes Center Karlsburg, Klinikum Karlsburg, Karlsburg, Germany
| | - Peter Heinke
- Institute of Diabetes "Gerhardt Katsch," Karlsburg, Germany
| | - Alexandra Nowak
- Department for Diabetology, Heart and Diabetes Center Karlsburg, Klinikum Karlsburg, Karlsburg, Germany
| | | | - Wolfgang Kerner
- Department for Diabetology, Heart and Diabetes Center Karlsburg, Klinikum Karlsburg, Karlsburg, Germany
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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.
Collapse
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;
| |
Collapse
|
7
|
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).
Collapse
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
| |
Collapse
|
8
|
Augstein P, Heinke P, Vogt L, Kohnert KD, Salzsieder E. Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes. J Diabetes Sci Technol 2022; 16:1159-1166. [PMID: 34000840 PMCID: PMC9445344 DOI: 10.1177/19322968211008871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control. METHODS This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured. RESULTS At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters. CONCLUSIONS DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.
Collapse
Affiliation(s)
- Petra Augstein
- Institute of Diabetes “Gerhardt Katsch”, Karlsburg, Germany
- Department for Diabetology, Klinikum Karlsburg, Heart and Diabetes Center Karlsburg, Germany
- Petra Augstein, MD & Dsc, Department for Diabetology, Klinikum Karlsburg, Heart and Diabetes Center Karlsburg, Greifswalder Str. 11, Germany.
| | - Peter Heinke
- Institute of Diabetes “Gerhardt Katsch”, Karlsburg, Germany
| | - Lutz Vogt
- Diabetes Service Centre DCC, Karlsburg, Germany
| | | | | |
Collapse
|
9
|
Liu J, Wei Y, Zang P, Wang W, Feng Z, Yuan Y, Zhou H, Zhang Z, Lei H, Yang X, Liu J, Lu B, Shao J. Circulating osteocalcin is associated with time in range and other metrics assessed by continuous glucose monitoring in type 2 diabetes. Diabetol Metab Syndr 2022; 14:109. [PMID: 35927761 PMCID: PMC9351112 DOI: 10.1186/s13098-022-00863-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/13/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Osteocalcin, a protein secreted mainly by mature osteoblasts, has been shown to be involved in glucose metabolism through various pathways. However, few studies has explored the association between osteocalcin and Time in range (TIR). Continuous glucose monitoring (CGM) -derived metrics, such as TIR and other indexes have been gradually and widely used in clinical practice to assess glucose fluctuations. The main purpose of this study was to investigate the correlation between osteocalcin and indexes from CGM in patients with type 2 diabetes mellitus (T2DM). METHOD The total number of 376 patients with T2D were enrolled, all of them performed three consecutive days of monitoring. They were divided into four groups on account of the quartile of osteocalcin. Time in range, Time below range (TBR), Time above range(TAR) and measures of glycemic variability (GV) were assessed for analysing. After a 100 g standard steamed bread meal, blood glucose (Glu0h Glu0.5 h, Glu1h, Glu2h, GLu3h), C-peptide (Cp0h, Cp0.5 h, Cp1h, Cp2h, Cp3h), serum insulin (INS0h, INS0.5 h, INS1h, INS2h, INS3h) concentrations at different time points were obtained. HOMA-IS, HOMA-βwas calculated to evaluate insulin sensitivity and insulin secreting of the participants. RESULTS Patients with higher osteocalcin level had higher TIR (P < 0.05). Spearman correlation analysis showed that osteocalcin was positively correlated with TBR (although the P value for TBR was greater than 0.05) (r = 0.227, P < 0.001 r = 0.068, P = 0.189) and negatively correlated with TAR (- 0.229, P < 0.001). Similarly, there was a negative correlation between osteocalcin and glycemic variability (GV) indicators, including SD, MBG, MODD, ADDR, and MAGE (P value of MAGE > 0.05). Multiple stepwise regression showed that osteocalcin was an independent contributor to TIR, TAR and HOMA-IS. CONCLUSION Circulating osteocalcin is positively correlated with TIR and negatively correlated with MODD, ADDR, and MAGE. Osteocalcin may have a beneficial impact on glucose homeostasis in T2DM patients.
Collapse
Affiliation(s)
- Jun Liu
- Department of Endocrinology, Jinling Hospital, Southern Medical University, Nanjing, China
| | - Yinghua Wei
- Department of Endocrinology, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, China
| | - Pu Zang
- Department of Endocrinology, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, China
| | - Wei Wang
- Department of Endocrinology, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, China
| | - Zhouqin Feng
- Department of Endocrinology, Jinling Hospital, Southern Medical University, Nanjing, China
| | - Yanyu Yuan
- Department of Endocrinology, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Hui Zhou
- Department of Endocrinology, Jinling Hospital, Southern Medical University, Nanjing, China
| | - Zhen Zhang
- Department of Endocrinology, Jinling Hospital, Southern Medical University, Nanjing, China
| | - Haiyan Lei
- Department of Endocrinology, Jinling Hospital, Southern Medical University, Nanjing, China
| | - Xinyi Yang
- Department of Endocrinology, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, China
| | - Jun Liu
- Department of Endocrinology, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Bin Lu
- Department of Endocrinology, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, China.
| | - Jiaqing Shao
- Department of Endocrinology, Jinling Hospital, Southern Medical University, Nanjing, China.
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Nimri R, Phillip M, Kovatchev B. Decision Support Systems and Closed-Loop. Diabetes Technol Ther 2022; 24:S58-S75. [PMID: 35475696 DOI: 10.1089/dia.2022.2504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Revital Nimri
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Phillip
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Boris Kovatchev
- University of Virginia Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA
| |
Collapse
|
12
|
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.
Collapse
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC, Clinical Biostatistics Department, Potomac, Maryland, USA
| |
Collapse
|
13
|
Lobo B, Farhy L, Shafiei M, Kovatchev B. A data-driven approach to classifying daily continuous glucose monitoring (CGM) time series. IEEE Trans Biomed Eng 2021; 69:654-665. [PMID: 34375274 DOI: 10.1109/tbme.2021.3103127] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
According to the World Health Organization, about 422 million people worldwide have type 1 or type 2 diabetes (T1D, T2D), with the latter accounting for 90-95% of cases. Safe and effective treatment of patients with diabetes requires accurate and frequent monitoring of their blood sugar levels. Continuous glucose monitoring (CGM) is a monitoring technology developed to address this need, and its use among U.S. T1D patients has increased from 6% in 2011 to 38% in 2018 and continues to increase worldwide in both T1D and T2D. This paper presents a data-driven approach to determine Ω, a finite set of representative daily profiles (motifs) such that almost any daily CGM profile generated by a patient can be matched to one of the motifs in Ω. The training data set (9,741 profiles) was used to identify 8 candidate sets of motifs, while the validation data set (14,175 profiles) was used to select the final set Ω. The robustness of Ω was established by using it to successfully classify (match against a representative daily profile in Ω) 99.0% of 42,595 daily CGM profiles in the testing data set. All data sets contained daily CGM profiles from six studies involving T1D and T2D patients using a variety of treatment modes, including daily insulin injections, insulin pumps, or artificial pancreas (AP). The classified profiles can be used in predictive modeling, decision support, and automated control systems (e.g., AP).
Collapse
|
14
|
Hallström S, Hirsch IB, Ekelund M, Sofizadeh S, Albrektsson H, Dahlqvist S, Svensson AM, Lind M. Characteristics of Continuous Glucose Monitoring Metrics in Persons with Type 1 and Type 2 Diabetes Treated with Multiple Daily Insulin Injections. Diabetes Technol Ther 2021; 23:425-433. [PMID: 33416422 DOI: 10.1089/dia.2020.0577] [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: 12/26/2022]
Abstract
Background: Although guidelines advocate similar continuous glucose monitoring (CGM) targets for insulin-treated persons with type 1 diabetes (T1D) and type 2 diabetes (T2D), it is unclear how these persons differ with respect to hypoglycemia, glucose variability, and other CGM metrics in clinical practice. Methods: We used data from 2 multicenter randomized-controlled trials (GOLD and MDI-Liraglutide) where 161 persons with T1D and 124 persons with T2D treated with multiple daily injections were included and monitored with masked CGM. Results: Persons from both cohorts had similar mean glucose levels, 10.9 mmol/L (196 mg/dL) in persons with T1D and 10.8 mmol/L (194 mg/dL) in persons with T2D. Time in hypoglycemia (<3.9 mmol/L [70 mg/dL]) was 5.1% and 1.0% for persons with T1D and T2D, respectively (P < 0.001). Corresponding estimates for the standard deviations of mean glucose levels were 4.4 mmol/L (79 mg/dL) versus 3.0 (54 mg/dL) (P < 0.001), for coefficient of variation 41% versus 28% (P < 0.001), and for time in range 38.2% versus 45.3%, respectively (P = 0.004). Mean C-peptide levels were 0.05 nmol/L and 0.67 nmol/L (P < 0.001) for persons with T1D and T2D, respectively. Conclusions: Persons with T1D compared with persons with T2D treated with multiple daily insulin injections spend considerably more time in hypoglycemia, have higher glucose variability, and less "time in range." This needs to be taken into account in daily clinical care and in recommended targets for CGM metrics.
Collapse
Affiliation(s)
- Sara Hallström
- Department of Internal Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Irl B Hirsch
- Department of Medicine, Division of Metabolism, Endocrinology and Nutrition, University of Washington School of Medicine, Seattle, Washington, USA
| | - Magnus Ekelund
- Novo Nordisk A/S, Type 1 Diabetes & Functional Insulins, Soeborg, Denmark
| | | | | | | | - Ann-Marie Svensson
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
- Center of Registers in Region Västra Götaland, Gothenburg, Sweden
| | - Marcus Lind
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
- NU-Hospital Group, Uddevalla, Sweden
| |
Collapse
|
15
|
Grunberger G, Sherr J, Allende M, Blevins T, Bode B, Handelsman Y, Hellman R, Lajara R, Roberts VL, Rodbard D, Stec C, Unger J. American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus. Endocr Pract 2021; 27:505-537. [PMID: 34116789 DOI: 10.1016/j.eprac.2021.04.008] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 02/08/2023]
Abstract
OBJECTIVE To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders. METHODS The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development. MAIN OUTCOME MEASURES Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range. RESULTS This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base. RECOMMENDATIONS Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology. CONCLUSIONS Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.
Collapse
Affiliation(s)
| | - Jennifer Sherr
- Yale University School of Medicine, New Haven, Connecticut
| | - Myriam Allende
- University of Puerto Rico School of Medicine, San Juan, Puerto Rico
| | | | - Bruce Bode
- Atlanta Diabetes Associates, Atlanta, Georgia
| | | | - Richard Hellman
- University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | | | | | - David Rodbard
- Biomedical Informatics Consultants, LLC, Potomac, Maryland
| | - Carla Stec
- American Association of Clinical Endocrinology, Jacksonville, Florida
| | - Jeff Unger
- Unger Primary Care Concierge Medical Group, Rancho Cucamonga, California
| |
Collapse
|
16
|
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.
Collapse
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, Maryland, USA
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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.
Collapse
Affiliation(s)
- David Rodbard
- Department of Clinical Biostatistics, Biomedical Informatics Consultants LLC, Potomac, Maryland
| |
Collapse
|
21
|
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.
Collapse
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
| | | |
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Gabbay MAL, Rodacki M, Calliari LE, Vianna AGD, Krakauer M, Pinto MS, Reis JS, Puñales M, Miranda LG, Ramalho AC, Franco DR, Pedrosa HPC. Time in range: a new parameter to evaluate blood glucose control in patients with diabetes. Diabetol Metab Syndr 2020; 12:22. [PMID: 32190124 PMCID: PMC7076978 DOI: 10.1186/s13098-020-00529-z] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 03/07/2020] [Indexed: 01/17/2023] Open
Abstract
The International Consensus in Time in Range (TIR) was recently released and defined the concept of the time spent in the target range between 70 and 180 mg/dL while reducing time in hypoglycemia, for patients using Continuous Glucose Monitoring (CGM). TIR was validated as an outcome measures for clinical Trials complementing other components of glycemic control like Blood glucose and HbA1c. The challenge is to implement this practice more widely in countries with a limited health public and private budget as it occurs in Brazil. Could CGM be used intermittently? Could self-monitoring blood glucose obtained at different times of the day, with the amount of data high enough be used? More studies should be done, especially cost-effective studies to help understand the possibility of having sensors and include TIR evaluation in clinical practice nationwide.
Collapse
Affiliation(s)
| | - Melanie Rodacki
- Nutrology and Diabetes Section, Internal Medicine Department Federal University of Rio de Janeiro–UFRJ, Rio de Janeiro, Brazil
| | - Luis Eduardo Calliari
- Pediatric Endocrinology Unit, Pediatric Department, Santa Casa de São Paulo School of Medical Sciences, São Paulo, Brazil
| | - Andre Gustavo Daher Vianna
- Curitiba Diabetes Center, Department of Endocrine Diseases, Hospital Nossa Senhora das Graças, Curitiba, Brazil
| | | | - Mauro Scharf Pinto
- Curitiba Diabetes Center, Department of Endocrine Diseases, Hospital Nossa Senhora das Graças, Curitiba, Brazil
| | | | - Marcia Puñales
- Institute of Child with Diabetes, Conceição Children Hospital, Conceição Hospitalar Group, Porto Alegre, Brazil
| | - Leonardo Garcia Miranda
- Unit of Endocrinology and Research Center, Regional Hospital of Taguatinga, Secretariat of Health of the Federal District, Brasilia, Brazil
| | | | | | | |
Collapse
|
25
|
Saadane I, Ashraf T, Ali T, Lessan N. Diabetes and Ramadan: Utility of flash-glucose monitoring derived markers of glycaemic control and comparison with glycosylated haemoglobin. Diabetes Res Clin Pract 2019; 153:150-156. [PMID: 31150718 DOI: 10.1016/j.diabres.2019.05.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 05/20/2019] [Indexed: 01/20/2023]
Abstract
AIMS Flash glucose monitoring (FGM)-derived markers of glucose control and variability and laboratory measured HbA1c among patients with diabetes on insulin in context of Ramadan fasting (RF) were examined and compared. METHODS FGM data on insulin-treated patients (n = 20, age 42.3 ± 11.4 years; 18 male, 2 female; 13 with type 1 and 7 with type 2 diabetes) who fasted during Ramadan were used to calculate Q-score as an indicator of glycaemia before, during and after RF. Post-hoc analysis in a group of patients (n = 12) who had HbA1c available and appropriate for these periods was performed. Other relevant data were extracted from patient records. RESULTS Mean glucose (9.6 ± 1.32 v 10.78 ± 1.64 mmol/l; P < 0.0001) and Q-score increased significantly with Ramadan fasting and reduced after Ramadan. Post-hoc subgroup analysis showed a significant rise in eA1c (7.2 ± 0.4%; 55.0 ± 4.4 mmol/mol v 7.7 ± 0.5%; 61.0 ± 5.5 mmol/mol) but not in laboratory HbA1c with Ramadan fasting; eA1c reduced after Ramadan (P = 0.018). CONCLUSIONS Ramadan fasting was associated with a deterioration in overall glucose control and time in hyperglycaemia in insulin-treated patients. FGM-derived markers are useful and a preferable alternative to HbA1c in Ramadan studies.
Collapse
Affiliation(s)
- Ilham Saadane
- Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - Tanveer Ashraf
- Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - Tomader Ali
- Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - Nader Lessan
- Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| |
Collapse
|
26
|
Abstract
BACKGROUND There has been recent recognition of the limitations of hemoglobin A1C (HbA1C) in describing both short- and long-term glycemic control. Continuous glucose monitoring (CGM) provides robust data about short-term glycemic control and provides metrics such as percent time-in-range (%TIR) that are now routinely reported to describe the change in glycemic control after an intervention in a clinical study or a change in therapy in a patient's care. Recent studies have shown that %TIR may have similar associations with diabetes microvascular complications as does HbA1C. The relationship of %TIR to the long-standing metric of overall glycemic control has not been clearly defined to date. METHODS Articles that report paired HbA1C and %TIR metrics (n = 1137) or HbA1C and frequent self-monitoring of blood glucose (SMBG) (n = 1440) across a wide range of HbA1Cs, technologies, and subject demographics were reviewed to determine the correlation of these metrics. RESULTS Selected paired HbA1C and %TIR data from 18 articles were evaluated by linear regression analysis and Pearson's correlation coefficient. There was an excellent correlation between the two (R = -0.84; R2 = 0.71). This relationship did not change after excluding one study that used SMBG or six studies with ≤7 days of CGM. For every absolute 10% change in %TIR, there was a 0.8% (9 mmol/mol) change in HbA1C. CONCLUSIONS There is a good correlation between HbA1C and %TIR that may permit the transition to %TIR as the preferred metric for determining the outcome of clinical studies, predicting of the risk of diabetes complications, and assessing of an individual patient's glycemic control.
Collapse
Affiliation(s)
- Robert A Vigersky
- Medical Affairs and Data Science and Informatics, Medtronic Diabetes, Northridge, California
| | - Chantal McMahon
- Medical Affairs and Data Science and Informatics, Medtronic Diabetes, Northridge, California
| |
Collapse
|
27
|
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.
Collapse
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
| |
Collapse
|
28
|
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.
Collapse
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
| |
Collapse
|
29
|
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.
Collapse
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
| | | |
Collapse
|
30
|
Acciaroli G, Sparacino G, Hakaste L, Facchinetti A, Di Nunzio GM, Palombit A, Tuomi T, Gabriel R, Aranda J, Vega S, Cobelli C. Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data. J Diabetes Sci Technol 2018; 12:105-113. [PMID: 28569077 PMCID: PMC5761967 DOI: 10.1177/1932296817710478] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach. METHODS The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D. RESULTS Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy. CONCLUSIONS Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.
Collapse
Affiliation(s)
- Giada Acciaroli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Liisa Hakaste
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Folkhälsan Research Center, and Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | | | - Tiinamaija Tuomi
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Folkhälsan Research Center, and Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland
- Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Rafael Gabriel
- Escuela Nacional de Sanidad, Instituto de Salud Carlos III, Madrid, Spain
| | - Jaime Aranda
- Servicio de Endocrinologia Hospital General de Cuenca, Cuenca, Spain
| | | | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
- Claudio Cobelli, PhD, Department of Information Engineering, University of Padova, Via Gradenigo 6/B, Padova, PD 35131, Italy.
| |
Collapse
|
31
|
Vigersky RA, Shin J, Jiang B, Siegmund T, McMahon C, Thomas A. The Comprehensive Glucose Pentagon: A Glucose-Centric Composite Metric for Assessing Glycemic Control in Persons With Diabetes. J Diabetes Sci Technol 2018; 12:114-123. [PMID: 28748705 PMCID: PMC5761978 DOI: 10.1177/1932296817718561] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [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 Composite metrics have the potential to provide more complete and clinically useful information about glycemic control than traditional individual metrics such as hemoglobin A1C, %/time/area under curve of hypoglycemia and hyperglycemia. METHODS Using five key metrics that are derived from continuous glucose monitoring, we developed a new, multicomponent composite metric, the Comprehensive Glucose Pentagon (CGP) that demonstrates glycemic control both numerically and visually. Two of its axes are composite metrics-the intensity of hypoglycemia and intensity of hyperglycemia. This approach eliminates the use of the surrogate marker, hemoglobin A1C (A1C), and replaces it with glucose-centric metrics. RESULTS We reanalyzed the data from two randomized control trials, the STAR 3 and ASPIRE In-Home studies using the CGP. It provided new insights into the effect of sensor-augmented pumping (SAP) in the STAR 3 trial and sensor-integrated pumping with low-glucose threshold suspend (SIP+TS) in the ASPIRE In-Home trial. CONCLUSIONS The CGP has the potential to enable health care providers, investigators and patients to better understand the components of glycemic control and the effect of various interventions on the individual elements of that control. This can be done on a daily, weekly, or monthly basis. It also allows direct comparison of the effects on different interventions among clinical trials which is not possible using A1C alone. This new composite metric approach requires validation to determine if it provides a better predictor of long-term outcomes than A1C and/or better predictor of severe hypoglycemia than the low blood glucose index (LBGI).
Collapse
Affiliation(s)
| | - John Shin
- Medtronic Diabetes, Northridge, CA, USA
| | | | | | | | | |
Collapse
|
32
|
Peyser TA, Balo AK, Buckingham BA, Hirsch IB, Garcia A. Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data. Diabetes Technol Ther 2018; 20:6-16. [PMID: 29227755 PMCID: PMC5846572 DOI: 10.1089/dia.2017.0187] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability. METHOD We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children). RESULTS In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis. CONCLUSION A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.
Collapse
Affiliation(s)
| | | | - Bruce A. Buckingham
- Department of Pediatric Endocrinology, Stanford University, Stanford, California
| | - Irl B. Hirsch
- Department of Medicine, Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, Washington
| | | |
Collapse
|
33
|
Fico G, Hernández L, Cancela J, Isabel MM, Facchinetti A, Fabris C, Gabriel R, Cobelli C, Arredondo Waldmeyer MT. Exploring the Frequency Domain of Continuous Glucose Monitoring Signals to Improve Characterization of Glucose Variability and of Diabetic Profiles. J Diabetes Sci Technol 2017. [PMID: 28627250 PMCID: PMC5588824 DOI: 10.1177/1932296816685717] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability. METHODS The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients. RESULTS The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach. CONCLUSIONS Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.
Collapse
Affiliation(s)
- Giuseppe Fico
- Life Supporting Technologies, Departamento de Tecnología Fotónica y Bioingeniería, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid, Spain
- Giuseppe Fico, PhD, Life Supporting Technologies, Departamento de Tecnología Fotónica y Bioingeniería, Universidad Politécnica de Madrid, ETSI Telecomunicación, Ciudad Universitaria, Av, Complutense, 30, Madrid 28040, Spain.
| | - Liss Hernández
- Life Supporting Technologies, Departamento de Tecnología Fotónica y Bioingeniería, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid, Spain
| | - Jorge Cancela
- Life Supporting Technologies, Departamento de Tecnología Fotónica y Bioingeniería, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid, Spain
| | - Miguel María Isabel
- Life Supporting Technologies, Departamento de Tecnología Fotónica y Bioingeniería, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid, Spain
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Chiara Fabris
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Rafael Gabriel
- Asociación Española para el Desarrollo de la Epidemiología Clínica, Madrid, Spain
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - María Teresa Arredondo Waldmeyer
- Life Supporting Technologies, Departamento de Tecnología Fotónica y Bioingeniería, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid, Spain
| |
Collapse
|
34
|
Rodbard D. Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes. Diabetes Technol Ther 2017; 19:S25-S37. [PMID: 28585879 PMCID: PMC5467105 DOI: 10.1089/dia.2017.0035] [Citation(s) in RCA: 243] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Continuous Glucose Monitoring (CGM) has been demonstrated to be clinically valuable, reducing risks of hypoglycemia and hyperglycemia, glycemic variability (GV), and improving patient quality of life for a wide range of patient populations and clinical indications. Use of CGM can help reduce HbA1c and mean glucose. One CGM device, with accuracy (%MARD) of approximately 10%, has recently been approved for self-adjustment of insulin dosages (nonadjuvant use) and approved for reimbursement for therapeutic use in the United States. CGM had previously been used off-label for that purpose. CGM has been demonstrated to be clinically useful in both type 1 and type 2 diabetes for patients receiving a wide variety of treatment regimens. CGM is beneficial for people using either multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). CGM is used both in retrospective (professional, masked) and real-time (personal, unmasked) modes: both approaches can be beneficial. When CGM is used to suspend insulin infusion when hypoglycemia is detected until glucose returns to a safe level (low-glucose suspend), there are benefits beyond sensor-augmented pump (SAP), with greater reduction in the risk of hypoglycemia. Predictive low-glucose suspend provides greater benefits in this regard. Closed-loop control with insulin provides further improvement in quality of glycemic control. A hybrid closed-loop system has recently been approved by the U.S. FDA. Closed-loop control using both insulin and glucagon can reduce risk of hypoglycemia even more. CGM facilitates rigorous evaluation of new forms of therapy, characterizing pharmacodynamics, assessing frequency and severity of hypo- and hyperglycemia, and characterizing several aspects of GV.
Collapse
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
| |
Collapse
|
35
|
Abstract
Continuous glucose monitoring (CGM) provides information unattainable by intermittent capillary blood glucose, including instantaneous real-time display of glucose level and rate of change of glucose, alerts and alarms for actual or impending hypo- and hyperglycemia, "24/7" coverage, and the ability to characterize glycemic variability. Progressively more accurate and precise, reasonably unobtrusive, small, comfortable, user-friendly devices connect to the Internet to share information and are sine qua non for a closed-loop artificial pancreas. CGM can inform, educate, motivate, and alert people with diabetes. CGM is medically indicated for patients with frequent, severe, or nocturnal hypoglycemia, especially in the presence of hypoglycemia unawareness. Surprisingly, despite tremendous advances, utilization of CGM has remained fairly limited to date. Barriers to use have included the following: (1) lack of Food and Drug Administration approval, to date, for insulin dosing ("nonadjuvant use") in the United States and for use in hospital and intensive care unit settings; (2) cost and variable reimbursement; (3) need for recalibrations; (4) periodic replacement of sensors; (5) day-to-day variability in glycemic patterns, which can limit the predictability of findings based on retrospective, masked "professional" use; (6) time, implicit costs, and inconvenience for uploading of data for retrospective analysis; (7) lack of fair and reasonable reimbursement for physician time; (8) inexperience and lack of training of physicians and other healthcare professionals regarding interpretation of CGM results; (9) lack of standardization of software methods for analysis of CGM data; and (10) need for professional medical organizations to develop and disseminate additional clinical practice guidelines regarding the role of CGM. Ongoing advances in technology and clinical research have addressed several of these barriers. Use of CGM in conjunction with an insulin pump with automated suspension of insulin infusion in response to actual observed or predicted hypoglycemia, as well as progressive refinement of closed-loop systems, is expected to dramatically enhance the clinical utility and utilization of CGM.
Collapse
Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
| |
Collapse
|
36
|
Abstracts from ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes Milan, Italy-February 3-6, 2016. Diabetes Technol Ther 2016; 18 Suppl 1:A1-139. [PMID: 26836419 DOI: 10.1089/dia.2016.2525] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|