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Wang Y, Li S, Lu J, Feng K, Huang X, Hu F, Sun M, Zou Y, Li Y, Huang W, Zhou J. The complexity of glucose time series is associated with short- and long-term mortality in critically ill adults: a multi-center, prospective, observational study. J Endocrinol Invest 2024:10.1007/s40618-024-02393-4. [PMID: 38762634 DOI: 10.1007/s40618-024-02393-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/11/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND The wealth of data taken from continuous glucose monitoring (CGM) remains to be fully used. We aimed to evaluate the relationship between a promising new CGM metric, complexity of glucose time series index (CGI), and mortality in critically ill patients. METHODS A total of 293 patients admitted to mixed medical/surgical intensive care units from 5 medical centers in Shanghai were prospectively included between May 2020 and November 2021. CGI was assessed using intermittently scanned CGM, with a median monitoring period of 12.0 days. Outcome measures included short- and long-term mortality. RESULTS During a median follow-up period of 1.7 years, a total of 139 (47.4%) deaths were identified, of which 73 (24.9%) occurred within the first 30 days after ICU admission, and 103 (35.2%) within 90 days. The multivariable-adjusted HRs for 30-day mortality across ascending tertiles of CGI were 1.00 (reference), 0.68 (95% CI 0.38-1.22) and 0.36 (95% CI 0.19-0.70), respectively. For per 1-SD increase in CGI, the risk of 30-day mortality was decreased by 51% (HR 0.49, 95% CI 0.35-0.69). Further adjustment for HbA1c, mean glucose during hospitalization and glucose variability partially attenuated these associations, although the link between CGI and 30-day mortality remained significant (per 1-SD increase: HR 0.57, 95% CI 0.40-0.83). Similar results were observed when 90-day mortality was considered as the outcome. Furthermore, CGI was also significantly and independently associated with long-term mortality (per 1-SD increase: HR 0.77, 95% CI 0.61-0.97). CONCLUSIONS In critically ill patients, CGI is significantly associated with short- and long-term mortality.
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Affiliation(s)
- Y 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, 600 Yishan Road, Shanghai, 200233, China
| | - S Li
- Department of Anesthesiology, Tongji University Affiliated Shanghai Tenth People's Hospital, Shanghai, China
- Department of Critical Care Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - J 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, 600 Yishan Road, Shanghai, 200233, China
| | - K Feng
- Department of Critical Care Medicine, Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai, China
| | - X Huang
- Department of Critical Care Medicine, Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai, China
| | - F Hu
- Department of Critical Care Medicine, Shanghai Fengxian District Central Hospital, Shanghai, China
| | - M Sun
- Department of Critical Care Medicine, Shanghai Eighth People's Hospital, Shanghai, China
| | - Y Zou
- Department of Critical Care Medicine, Shanghai Sixth People's Hospital East Campus, Shanghai, China
| | - Y Li
- Department of Critical Care Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
- Department of Critical Care Medicine, Tongji University Affiliated Shanghai Tenth People's Hospital, 301 Yanan Middle Road, Shanghai, 200040, China.
| | - W Huang
- Department of Critical Care Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
- Department of Critical Care Medicine, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, 966 Huaihai Middle Road, Shanghai, 200031, China.
| | - J 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, 600 Yishan Road, Shanghai, 200233, China.
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Yu C, Wang Y, Zhang B, Xu X, Zhang W, Ding Q, Miao Y, Hou Y, Ma X, Wu T, Yang S, Fu L, Zhang Z, Zhou J, Bi Y. Associations between complexity of glucose time series and cognitive function in adults with type 2 diabetes. Diabetes Obes Metab 2024; 26:840-850. [PMID: 37994378 DOI: 10.1111/dom.15376] [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: 09/13/2023] [Revised: 10/27/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023]
Abstract
AIMS To characterize the comparative contributions of different glycaemic indicators to cognitive dysfunction, and further investigate the associations between the most significant indicator and cognitive function, along with related cerebral alterations. MATERIALS AND METHODS We performed a cross-sectional study in 449 subjects with type 2 diabetes who completed continuous glucose monitoring and cognitive assessments. Of these, 139 underwent functional magnetic resonance imaging to evaluate cerebral structure and olfactory neural circuit alterations. Relative weight and Sobol's sensitivity analyses were employed to characterize the comparative contributions of different glycaemic indicators to cognitive dysfunction. RESULTS Complexity of glucose time series index (CGI) was found to have a more pronounced association with mild cognitive impairment (MCI) compared to glycated haemoglobin, time in range, and standard deviation. The proportion and multivariable-adjusted odds ratios (ORs) for MCI increased with descending CGI tertile (Tertile 1: reference group [≥4.0]; Tertile 2 [3.6-4.0] OR 1.23, 95% confidence interval [CI] 0.68-2.24; Tertile 3 [<3.6] OR 2.27, 95% CI 1.29-4.00). Decreased CGI was associated with cognitive decline in executive function and attention. Furthermore, individuals with decreased CGI displayed reduced olfactory activation in the left orbitofrontal cortex (OFC) and disrupted functional connectivity between the left OFC and right posterior cingulate gyrus. Mediation analysis demonstrated that the left OFC activation partially mediated the associations between CGI and executive function. CONCLUSION Decreased glucose complexity closely relates to cognitive dysfunction and olfactory brain activation abnormalities in diabetes.
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Affiliation(s)
- Congcong Yu
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, 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
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xiang Xu
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Wen Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qun Ding
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Yingwen Miao
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Yinjiao Hou
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Xuelin Ma
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Tianyu Wu
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Sijue Yang
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Linqing Fu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhou Zhang
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, 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
| | - Yan Bi
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
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Li C, Ma X, Lu J, Tao R, Yu X, Mo Y, Lu W, Bao Y, Zhou J, Jia W. Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation. Front Med 2022; 17:68-74. [PMID: 36562949 DOI: 10.1007/s11684-022-0955-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022]
Abstract
Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual's health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique "continuous glucose monitoring" (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.
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Affiliation(s)
- Cheng Li
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Xiaojing Ma
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Rui Tao
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Xia Yu
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Yifei Mo
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China.
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China.
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Cai J, Yang Q, Lu J, Shen Y, Wang C, Chen L, Zhang L, Lu W, Zhu W, Xia T, Zhou J. Impact of the complexity of glucose time series on all-cause mortality in patients with type 2 diabetes. J Clin Endocrinol Metab 2022; 108:1093-1100. [PMID: 36458883 PMCID: PMC10099164 DOI: 10.1210/clinem/dgac692] [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] [Received: 08/28/2022] [Revised: 11/09/2022] [Accepted: 11/29/2022] [Indexed: 12/04/2022]
Abstract
CONTEXT Previous studies suggest that the complexity of glucose time series may serve as a novel marker of glucose homeostasis. OBJECTIVE We aimed to investigate the relationship between the complexity of glucose time series and all-cause mortality in patients with type 2 diabetes. METHODS Prospective data of 6000 adult inpatients with type 2 diabetes from a single center was analyzed. The complexity of glucose time series index (CGI) based on continuous glucose monitoring (CGM) was measured at baseline with refined composite multi-scale entropy. Participants were stratified by the tertiles of CGI: < 2.15, 2.15-2.99, and ≥ 3.00. Cox proportional hazards regression models were used to assess the relationship between CGI and all-cause mortality. RESULTS During a median follow-up of 9.4 years, 1217 deaths were identified. A significant interaction between glycated hemoglobin A1c (HbA1c) and CGI in relation to all-cause mortality was noted (P for interaction = 0.016). The multivariable-adjusted hazard ratios for all-cause mortality at different CGI levels [≥ 3.00 (reference group), 2.15-2.99, and < 2.15] were 1.00, 0.76 (95% CI 0.52-1.12), and 1.47 (95% CI 1.03-2.09) in patients with HbA1c < 7.0%, while the association was nonsignificant in those with HbA1c ≥ 7.0%. The restricted cubic spline regression revealed a non-linear (P for non-linearity = 0.041) relationship between CGI and all-cause mortality in subjects with HbA1c < 7.0% only. CONCLUSIONS Lower CGI is associated with an increased risk of all-cause mortality among patients with type 2 diabetes achieving the HbA1c target. CGI may be a new indicator for the identification of residual risk of death in well-controlled type 2 diabetes.
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Affiliation(s)
- Jinghao Cai
- 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
| | - Qing Yang
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai 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
| | - Yun Shen
- 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
| | - Chunfang Wang
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Lei Chen
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Lei Zhang
- Department of Endocrinology and Metabolism, Shanghai 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
| | - Wei Zhu
- 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
| | - Tian Xia
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai 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
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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.
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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
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Valero P, Salas R, Pardo F, Cornejo M, Fuentes G, Vega S, Grismaldo A, Hillebrands JL, van der Beek EM, van Goor H, Sobrevia L. Glycaemia dynamics in gestational diabetes mellitus. Biochim Biophys Acta Gen Subj 2022; 1866:130134. [PMID: 35354078 DOI: 10.1016/j.bbagen.2022.130134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/14/2022] [Accepted: 03/24/2022] [Indexed: 12/19/2022]
Abstract
Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.
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Affiliation(s)
- Paola Valero
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile.
| | - Rodrigo Salas
- Biomedical Engineering School, Engineering Faculty, Universidad de Valparaíso, Valparaíso 2362905, Chile; Instituto Milenio Intelligent Healthcare Engineering, Chile
| | - Fabián Pardo
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Metabolic Diseases Research Laboratory, Interdisciplinary Centre of Territorial Health Research (CIISTe), Biomedical Research Center (CIB), San Felipe Campus, School of Medicine, Faculty of Medicine, Universidad de Valparaíso, San Felipe 2172972, Chile
| | - Marcelo Cornejo
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile; Faculty of Health Sciences, Universidad de Antofagasta, Antofagasta 02800, Chile; Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Gonzalo Fuentes
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile; Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Sofía Vega
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Medical School (Faculty of Medicine), Sao Paulo State University (UNESP), Brazil
| | - Adriana Grismaldo
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Department of Nutrition and Biochemistry, Faculty of Sciences, Pontificia Universidad Javeriana, Bogotá, DC, Colombia
| | - Jan-Luuk Hillebrands
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Eline M van der Beek
- Department of Pediatrics, University of Groningen, University Medical Center Groningen (UMCG), 9713GZ Groningen, the Netherlands; Nestlé Institute for Health Sciences, Nestlé Research, Societé des Produits de Nestlé, 1000 Lausanne 26, Switzerland
| | - Harry van Goor
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico
| | - Luis Sobrevia
- Cellular and Molecular Physiology Laboratory (CMPL), Department of Obstetrics, Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile; Medical School (Faculty of Medicine), Sao Paulo State University (UNESP), Brazil; Department of Physiology, Faculty of Pharmacy, Universidad de Sevilla, Seville E-41012, Spain; University of Queensland Centre for Clinical Research (UQCCR), Faculty of Medicine and Biomedical Sciences, University of Queensland, Herston, QLD, 4029, Queensland, Australia; Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen (UMCG), 9713GZ Groningen, the Netherlands; Tecnologico de Monterrey, Eutra, The Institute for Obesity Research (IOR), School of Medicine and Health Sciences, Monterrey, Nuevo León. Mexico.
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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.
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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
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Dynamic properties of glucose complexity during the course of critical illness: a pilot study. J Clin Monit Comput 2020; 34:361-370. [PMID: 30888595 DOI: 10.1007/s10877-019-00299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
Abstract
Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.
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Joshi A, Mitra A, Anjum N, Shrivastava N, Khadanga S, Pakhare A, Joshi R. Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program. Med Sci (Basel) 2019; 7:medsci7030052. [PMID: 30934620 PMCID: PMC6473237 DOI: 10.3390/medsci7030052] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 03/14/2019] [Accepted: 03/19/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Variations in blood glucose levels over a given time interval is termed as glycemic variability (GV). Higher GV is associated with higher diabetes-related complications. The current study was done with the aim of detecting the sensitivity of various GV indices among individuals with type 2 diabetes mellitus of different glycemic control status. Methods: We performed a longitudinal study among individuals with type 2 diabetes mellitus (T2DM) who were participating in a two-week diabetes self-management education (DSME) program. Participants were categorized by their HbA1c as poor (≥8%), acceptable (7%–8%), and optimal control (<7%). Continuous glucose monitoring (CGM) sensors recorded interstitial glucose every 15 min from day 1. The evaluated GV measures include standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), continuous overlapping net glycemic action (CONGA), mean of daily difference for inter-day variation (MODD), high blood glucose index (HBGI), and low blood glucose index (LBGI). Results: A total of 41 study participants with 46347 CGM values were available for analysis. Of 41 participants, 20 (48.7%) were in the poor, 10 (24.3%) in the acceptable, and 11 (26.8%) in the optimal control group. The GV indices (SD; CV; MODD; MAGE; CONGA; HBGI) of poorly controlled (77.43; 38.02; 45.82; 216.63; 14.10; 16.62) were higher than acceptable (50.02; 39.32; 30.79; 138.01; 8.87; 5.56) and optimal (34.15; 29.46; 24.56; 126.15; 8.67; 3.13) control group. Glycemic variability was reduced in the poorly and acceptably controlled groups by the end of the 2-week period. There was a rise in LBGI in the optimally controlled group, indicating pitfalls of tight glycemic control. Conclusion: Indices of glycemic variability are useful complements, and changes in it can be demonstrated within short periods.
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Affiliation(s)
- Ankur Joshi
- Department of Community and Family Medicine, All India Institute of Medical Sciences (AIIMS), Bhopal 462020 India.
| | - Arun Mitra
- Department of Community and Family Medicine, All India Institute of Medical Sciences (AIIMS), Bhopal 462020 India.
| | - Nikhat Anjum
- Hospital Services, All India Institute of Medical Sciences, Bhopal 462020, India.
| | - Neelesh Shrivastava
- Department of Medicine, All India Institute of Medical Sciences, Bhopal 462020, India.
| | - Sagar Khadanga
- Department of Medicine, All India Institute of Medical Sciences, Bhopal 462020, India.
| | - Abhijit Pakhare
- Department of Community and Family Medicine, All India Institute of Medical Sciences (AIIMS), Bhopal 462020 India.
| | - Rajnish Joshi
- Department of Medicine, All India Institute of Medical Sciences, Bhopal 462020, India.
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Dong X, Chen C, Geng Q, Cao Z, Chen X, Lin J, Jin Y, Zhang Z, Shi Y, Zhang XD. An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals. ENTROPY 2019; 21:e21030274. [PMID: 33266989 PMCID: PMC7514754 DOI: 10.3390/e21030274] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/08/2019] [Accepted: 03/09/2019] [Indexed: 11/17/2022]
Abstract
Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.
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Affiliation(s)
- Xinzheng Dong
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China;
- Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China
| | - Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
| | - Qingshan Geng
- Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou 510080, China;
| | - Zhixin Cao
- Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China; (Z.C.); (Y.S.)
| | - Xiaoyan Chen
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; (X.C.); (J.L.)
| | - Jinxiang Lin
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; (X.C.); (J.L.)
| | - Yu Jin
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
| | - Zhaozhi Zhang
- School of Law, Washington University, St. Louis, MO 63130, USA;
| | - Yan Shi
- Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China; (Z.C.); (Y.S.)
- Department of Mechanical and Electronic Engineering, Beihang University, Beijing 100191, China
| | - Xiaohua Douglas Zhang
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
- Correspondence: ; Tel: +853-8822-4813
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11
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Decreased complexity of glucose dynamics in diabetes in rhesus monkeys. Sci Rep 2019; 9:1438. [PMID: 30723274 PMCID: PMC6363759 DOI: 10.1038/s41598-018-36776-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/26/2018] [Indexed: 11/08/2022] Open
Abstract
Until recently, preclinical and clinical work on diabetes has focused on the understanding of blood glucose elevation and its detrimental metabolic sequelae. The advent of continuous glucose monitoring (CGM) technology now allows real time monitoring of blood glucose levels as a time series, and thus the exploration of glucose dynamics at short time scales. Previous work has shown decreases in the complexity of glucose dynamics, as measured by multiscale entropy (MSE) analysis, in diabetes in humans, mice, and rats. Analyses for non-human primates (NHP) have not been reported, nor is it known if anti-diabetes compounds affect complexity of glucose dynamics. We instrumented four healthy and six diabetic rhesus monkeys with CGM probes in the carotid artery and collected glucose values at a frequency of one data point per second for the duration of the sensors' life span. Sensors lasted between 45 and 78 days. Five of the diabetic rhesus monkeys were also administered the anti-diabetic drug liraglutide daily beginning at day 39 of the CGM monitoring period. Glucose levels fluctuated during the day in both healthy and diabetic rhesus monkeys, peaking between 12 noon - 6 pm. MSE analysis showed reduced complexity of glucose dynamics in diabetic monkeys compared to healthy animals. Although liraglutide decreased glucose levels, it did not restore complexity in diabetic monkeys consistently. Complexity varied by time of day, more strongly for healthy animals than for diabetic animals. And by dividing the monitoring period into 3-day or 1-week subperiods, we were able to estimate within-animal variability of MSE curves. Our data reveal that decreased complexity of glucose dynamics is a conserved feature of diabetes from rodents to NHPs to man.
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12
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Kohnert KD, Heinke P, Vogt L, Augstein P, Salzsieder E. Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients. Front Physiol 2018; 9:1257. [PMID: 30237767 PMCID: PMC6136234 DOI: 10.3389/fphys.2018.01257] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 08/20/2018] [Indexed: 02/05/2023] Open
Abstract
Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (α1, α2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and α exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol2/l2): 21.71 ± 9.82 vs. 7.25 ± 5.92, and α1: 1.94 ± 0.12 vs. 1.75 ± 0.12, α2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol2/l2), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents α1 and α2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (β = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though α1 and MSE failed. In the regression models, including SFE, AFE, and α2 (β = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.
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Affiliation(s)
| | - Peter Heinke
- Institute of Diabetes "Gerhardt Katsch", Karlsburg, Germany
| | - Lutz Vogt
- Diabetes Service Center, Karlsburg, Germany
| | - Petra Augstein
- Institute of Diabetes "Gerhardt Katsch", Karlsburg, Germany.,Heart and Diabetes Medical Center, Karlsburg, Germany
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Reiterer F, Reiter M, del Re L, Bechmann Christensen M, Nørgaard K. Analyzing the Potential of Advanced Insulin Dosing Strategies in Patients With Type 2 Diabetes: Results From a Hybrid In Silico Study. J Diabetes Sci Technol 2018; 12:1029-1040. [PMID: 29681172 PMCID: PMC6134623 DOI: 10.1177/1932296818770694] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND The ongoing improvement of continuous glucose monitoring (CGM) sensors and of insulin pumps are paving the way for a fast implementation of artificial pancreas (AP) for type 1 diabetes (T1D) patients. The case for type 2 diabetes (T2D) patients is less obvious since usually some residual beta cell function allows for simpler therapy approaches, and even multiple daily injections (MDI) therapy is not very widespread. However, the number of insulin dependent T2D patients is vastly increasing and therefore a need for understanding chances and challenges of an automated insulin therapy arises. Based on this background, this article analyzes conditions under which the use of more advanced therapeutic approaches, particularly AP, could bring a substantial improvement and should be considered as a viable therapy option. METHOD Data of 14 insulin-treated T2D patients on MDI wearing a CGM device and deviation analysis methods were used to estimate the expected improvements in the clinical outcome by using self-monitoring of blood glucose (SMBG) with advanced carbohydrate counting, a full AP or intermediate approaches, either CGM measurements with MDI therapy or SMBG with insulin pump. HbA1C and time in range (70-140 mg/dl, 70-180 mg/dl, respectively) were used as a performance measure. Outcome measures beyond glycemic control (eg, compliance, patient acceptance) have not been analyzed in this study. RESULTS AP has the potential to improve the condition of many poorly controlled insulin-treated T2D patients. However, as the interpatient variability is much higher than in T1D, a prescreening is recommended to select suitable patients. CONCLUSIONS Clinical criteria need to be developed for inclusion/exclusion of T2D patients for AP related therapies.
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Affiliation(s)
- Florian Reiterer
- Institute for Design and Control of Mechatronical Systems, Johannes Kepler University, Linz, Austria
- Florian Reiterer, PhD, Institute for Design and Control of Mechatronical Systems, Johannes Kepler University, Altenberger Straße 69, Linz, 4040, Austria.
| | - Matthias Reiter
- Institute for Design and Control of Mechatronical Systems, Johannes Kepler University, Linz, Austria
| | - Luigi del Re
- Institute for Design and Control of Mechatronical Systems, Johannes Kepler University, Linz, Austria
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Rodbard D. Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with "Risk Indices". Diabetes Technol Ther 2018; 20:325-334. [PMID: 29792750 DOI: 10.1089/dia.2017.0416] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We sought to cross validate several metrics for quality of glycemic control, hypoglycemia, and hyperglycemia. RESEARCH DESIGN AND METHODS We analyzed the mathematical properties of several metrics for overall glycemic control, and for hypo- and hyperglycemia, to evaluate their similarities, differences, and interrelationships. We used linear regression to describe interrelationships and examined correlations between metrics within three conceptual groups. RESULTS There were consistently high correlations between %Time in range (%TIR) and previously described risk indices (M100, Blood Glucose Risk Index [BGRI], Glycemic Risk Assessment Diabetes Equation [GRADE], Index of Glycemic Control [IGC]), and with J-Index (J). There were also high correlations among %Hypoglycemia, Low Blood Glucose Index (LBGI), percentage of GRADE attributable to hypoglycemia (GRADE%Hypoglycemia), and Hypoglycemia Index, but negligible correlation with J. There were high correlations of percentage of time in hyperglycemic range (%Hyperglycemia) with High Blood Glucose Index (HBGI), percentage of GRADE attributable to hyperglycemia (GRADE%Hyperglycemia), Hyperglycemia Index, and J. %TIR is highly negatively correlated with %Hyperglycemia but very weakly correlated with %Hypoglycemia. By adjusting the parameters used in IGC, Hypoglycemia Index, Hyperglycemia Index, or in MR, one can more closely approximate the properties of BGRI, LBGI, or HBGI, and of GRADE, GRADE%Hypoglycemia, or GRADE%Hyperglycemia. CONCLUSIONS Simple readily understandable criteria such as %TIR, %Hypoglycemia, and %Hyperglycemia are highly correlated with and appear to be as informative as "risk indices." The J-Index is sensitive to hyperglycemia but insensitive to hypoglycemia.
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Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
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