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Bauhaus H, Erdogan P, Braun H, Thevis M. Continuous Glucose Monitoring (CGM) in Sports-A Comparison between a CGM Device and Lab-Based Glucose Analyser under Resting and Exercising Conditions in Athletes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6440. [PMID: 37568982 PMCID: PMC10418731 DOI: 10.3390/ijerph20156440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/12/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023]
Abstract
The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.
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Affiliation(s)
- Helen Bauhaus
- Institute of Biochemistry, German Sport University Cologne, 50933 Cologne, Germany
- German Research Centre of Elite Sports, German Sport University Cologne, 50933 Cologne, Germany;
| | - Pinar Erdogan
- Institute of Biochemistry, German Sport University Cologne, 50933 Cologne, Germany
- German Research Centre of Elite Sports, German Sport University Cologne, 50933 Cologne, Germany;
| | - Hans Braun
- German Research Centre of Elite Sports, German Sport University Cologne, 50933 Cologne, Germany;
- Manfred Donike Institute for Doping Analysis, 50933 Cologne, Germany
| | - Mario Thevis
- Institute of Biochemistry, German Sport University Cologne, 50933 Cologne, Germany
- German Research Centre of Elite Sports, German Sport University Cologne, 50933 Cologne, Germany;
- Manfred Donike Institute for Doping Analysis, 50933 Cologne, Germany
- Centre for Preventive Doping Research, German Sport University Cologne, 50933 Cologne, Germany
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Twigg S, Lim S, Yoo SH, Chen L, Bao Y, Kong A, Yeoh E, Chan SP, Robles J, Mohan V, Cohen N, McGill M, Ji L. Asia-Pacific Perspectives on the Role of Continuous Glucose Monitoring in Optimizing Diabetes Management. J Diabetes Sci Technol 2023:19322968231176533. [PMID: 37232515 DOI: 10.1177/19322968231176533] [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: 05/27/2023]
Abstract
Diabetes is prevalent, and it imposes a substantial public health burden globally and in the Asia-Pacific (APAC) region. The cornerstone for optimizing diabetes management and treatment outcomes is glucose monitoring, the techniques of which have evolved from self-monitoring of blood glucose (SMBG) to glycated hemoglobin (HbA1c), and to continuous glucose monitoring (CGM). Contextual differences with Western populations and limited regionally generated clinical evidence warrant regional standards of diabetes care, including glucose monitoring in APAC. Hence, the APAC Diabetes Care Advisory Board convened to gather insights into clinician-reported CGM utilization for optimized glucose monitoring and diabetes management in the region. We discuss the findings from a pre-meeting survey and an expert panel meeting regarding glucose monitoring patterns and influencing factors, patient profiles for CGM initiation and continuation, CGM benefits, and CGM optimization challenges and potential solutions in APAC. While CGM is becoming the new standard of care and a useful adjunct to HbA1c and SMBG globally, glucose monitoring type, timing, and frequency should be individualized according to local and patient-specific contexts. The results of this APAC survey guide methods for the formulation of future APAC-specific consensus guidelines for the application of CGM in people living with diabetes.
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Affiliation(s)
- Stephen Twigg
- Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - Soo Lim
- Department of Internal Medicine, Seoul National University Bundang Hospital, College of Medicine, Seoul National University, Seongnam, South Korea
| | - Seung-Hyun Yoo
- Department of Internal Medicine, Korea University Anam Hospital, Seoul, South Korea
| | - Liming Chen
- Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine, Affiliated Sixth People's Hospital, Shanghai, China
| | - Alice Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Ester Yeoh
- Diabetes Centre, Admiralty Medical Centre and Division of Endocrinology, Department of Medicine, Khoo Teck Puat Hospital, Singapore
| | - Siew Pheng Chan
- Department of Medicine, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Jeremyjones Robles
- Section of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Chong Hua Hospital, Cebu, Philippines
| | - Viswanathan Mohan
- Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Neale Cohen
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Margaret McGill
- Central Clinical School Faculty of Medicine and Health, Diabetes Centre, Royal Prince Alfred Hospital, The University of Sydney, Sydney, NSW, Australia
| | - Linong Ji
- Peking University Diabetes Center, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
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Chinese diabetes datasets for data-driven machine learning. Sci Data 2023; 10:35. [PMID: 36653358 PMCID: PMC9849330 DOI: 10.1038/s41597-023-01940-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023] Open
Abstract
Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM Datasets and made them publicly available for research purposes. This paper describes the datasets, which was acquired on Type 1 (n = 12) and Type 2 (n = 100) diabetic patients in Shanghai, China. The acquisition has been made in real-life conditions. The datasets contain the clinical characteristics, laboratory measurements and medications of the patients. Moreover, the continuous glucose monitoring readings with 3 to 14 days as a period together with the daily dietary information are also provided. The datasets can contribute to the development of data-driven algorithms/models and diabetes monitoring/managing technologies.
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Pan J, Yan X, Li F, Zhang Y, Jiang L, Wang C. Association of glycemic variability assessed by continuous glucose monitoring with subclinical diabetic polyneuropathy in type 2 diabetes patients. J Diabetes Investig 2022; 13:328-335. [PMID: 34455710 PMCID: PMC8847148 DOI: 10.1111/jdi.13652] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/17/2021] [Accepted: 08/25/2021] [Indexed: 02/06/2023] Open
Abstract
AIMS/INTRODUCTION Diabetic peripheral neuropathy is a common diabetes-related microvascular complication. The relationship between peripheral nerve function and glucose variability is unclear. We investigated the association of glucose variability with subclinical diabetic polyneuropathy in a large-scale sample of patients with type 2 diabetes. MATERIALS AND METHODS We enrolled 509 individuals with type 2 diabetes who were screened for diabetic peripheral neuropathy and monitored using a continuous glucose monitoring system. Multiple glycemic variability parameters, including the mean amplitude of glycemic excursions, glucose standard deviation (SDgluc ) and glucose coefficient of variation were calculated from 3-day glucose profiles obtained from continuous glucose monitoring. All participants underwent nerve conduction studies, and the composite Z-scores for nerve conduction parameters were calculated. RESULTS Multivariate logistic regression analyses showed that SDgluc and the conventional risk factor hemoglobin A1c (HbA1c) were independently associated with abnormal nerve function, and the corresponding odds ratios (95% confidence interval) were 1.198 (1.027-1.397, SDgluc ) and 1.182 (1.061-1.316, HbA1c), respectively. The composite Z-score of nerve conduction velocity and response amplitude obviously decreased with greater SDgluc , and the composite Z-score of distal latency significantly increased with increasing tertiles of SDgluc (all P trend <0.05). After adjusting for age, sex, body mass index, diabetes duration and HbA1c, SDgluc was independently associated with nerve conduction velocity (β = -0.124, P = 0.021). CONCLUSIONS The SDgluc is a significant independent contributor to subclinical diabetic polyneuropathy, in addition to conventional risk factors including diabetes duration and HbA1c.
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Affiliation(s)
- Jiemin Pan
- Department of Endocrinology and MetabolismShanghai Clinical Center for DiabetesShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
- Shanghai Key Laboratory of Diabetes MellitusShanghaiChina
| | - Xinfeng Yan
- Department of EndocrinologyShanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Fengwen Li
- Department of Endocrinology and MetabolismShanghai Clinical Center for DiabetesShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
- Shanghai Key Laboratory of Diabetes MellitusShanghaiChina
| | - Yinan Zhang
- Shanghai Key Laboratory of Diabetes MellitusShanghaiChina
- The Metabolic Diseases BiobankCenter for Translational MedicineShanghai JiaoTong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Lan Jiang
- Department of ElectrophysiologyShanghai JiaoTong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Congrong Wang
- Department of Endocrinology and MetabolismShanghai Fourth People's Hospital Affiliated to Tongji UniversityShanghaiChina
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Meng R, Gu T, Yang F, Liu J, Sun Q, Zhu D. Performance Evaluation of the Glunovo® Continuous Blood Glucose Monitoring System in Chinese Participants with Diabetes: A Multicenter, Self-Controlled Trial. Diabetes Ther 2021; 12:3153-3165. [PMID: 34704201 PMCID: PMC8586329 DOI: 10.1007/s13300-021-01171-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/07/2021] [Indexed: 11/26/2022] Open
Abstract
INTRODUCTION The present study was aimed to evaluate the performance and safety of the Glunovo® real-time continuous glucose monitoring system (CGMS) in monitoring interstitial fluid glucose in adult participants with diabetes (at least 18 years old) using venous blood glucose as control. METHODS This was a multicenter, self-controlled clinical trial, conducted in participants with diabetes from China, between March 2019 to October 2019. The CGMS was used by all the participants for a 14-day wear-in period. The real-time glucose values measured by Glunovo® CGMS were compared with venous blood glucose values measured by the Entwicklung, Konstruktion und Fertigung (EKF) blood glucose detector. The primary outcomes were the consistency rate of CGMS readings and venous blood glucose values (20/20% standard). RESULTS A total of 78 participants (41 men, 37 women) and 156 CGMS sensors were included in the study. Among the included participants, 25 and 53 participants had type 1 and type 2 diabetes, respectively, with median age of 52.50 years (range 32-62 years). The overall agreement rate (20/20%) was 89.71% (95% CI 89.18-90.24%). It was observed that 99.08% (95% CI 98.91-99.24%) and 99.82% (95% CI 99.74-99.89%) of the measuring points fell within the A + B zones of the Clarke error grid analysis and Parkes/consensus error grid analysis, respectively. The mean absolute relative difference was 10.30% ± 4.86%. The probability of a glucose measurement falling within a range, when stratified by venous glucose measurements, ranged from 7.14% for 19.44-22.22 mmol/L to 79.21% for 4.44-6.67 mmol/L. There were 73 (41.24%) and 27 (57.45%) successful CGMS alarms for hypoglycemic and hyperglycemic events, respectively. CONCLUSION From the results, Glunovo® CGMS had excellent accuracy and limited clinical risk compared with venous blood glucose in the range of 2.2-22.2 mmol/L over 14 days.
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Affiliation(s)
- Ran Meng
- Department of Endocrinology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, 210008, China
| | - Tianwei Gu
- Department of Endocrinology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, 210008, China
| | - Fan Yang
- Department of Endocrinology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, 210008, China
| | - Jie Liu
- Department of Endocrinology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, 210008, China
| | - Qichao Sun
- Department of Endocrinology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, 210008, China
| | - Dalong Zhu
- Department of Endocrinology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing, 210008, China.
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Shah NA, Levy CJ. Emerging technologies for the management of type 2 diabetes mellitus. J Diabetes 2021; 13:713-724. [PMID: 33909352 DOI: 10.1111/1753-0407.13188] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/19/2021] [Accepted: 04/25/2021] [Indexed: 01/02/2023] Open
Abstract
Diabetes mellitus is a global health problem affecting 422 million people worldwide, of which 34.2 million live in the United States alone. Complications due to diabetes can lead to considerable morbidity and mortality related to both microvascular and macrovascular disease. While glycosylated hemoglobin testing is the standard test utilized to evaluate glycemic control, emerging targets like "time in range" and "glycemic variability" often provide more accurate assessments of glycemic fluctuations and have implications for diabetes complications and quality of life. Patients with diabetes face considerable burdens of self-care including frequent glucose monitoring, multiple insulin injections, dietary management, and the need to track daily activities, all of which lead to reduced adherence and psychological burnout. From the provider perspective, limited patient data and access to self-management tools lead to treatment inertia and a reduced ability to help patients achieve and maintain their glycemic goals. In the past few decades, there have been considerable advances in treatment-based technology and technological applications designed to help reduce patient burden and provide tools for better self-management. These advances make real-time clinical data available for clinicians to make necessary changes in treatment regimens. In this review, we discuss the latest emerging technologies available for the management of people with type 2 diabetes mellitus.
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Affiliation(s)
- Nirali A Shah
- Division of Endocrinology, Diabetes and Bone Metabolism, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carol J Levy
- Division of Endocrinology, Diabetes and Bone Metabolism, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Shen Y, Fan X, Zhang L, Wang Y, Li C, Lu J, Zha B, Wu Y, Chen X, Zhou J, Jia W. Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring. Diabetes Care 2021; 44:976-982. [PMID: 33574126 PMCID: PMC7985431 DOI: 10.2337/dc20-1448] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 01/20/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Although elevated glucose levels are reported to be associated with adverse outcomes of coronavirus disease 2019 (COVID-19), the optimal range of glucose in patients with COVID-19 and diabetes remains unknown. This study aimed to investigate the threshold of glycemia and its association with the outcomes of COVID-19. RESEARCH DESIGN AND METHODS Glucose levels were assessed through intermittently scanned continuous glucose monitoring in 35 patients for an average period of 10.2 days. The percentages of time above range (TAR), time below range (TBR), time in range (TIR), and coefficient of variation (CV) were calculated. Composite adverse outcomes were defined as either the need for admission to the intensive care unit, need for mechanical ventilation, or morbidity with critical illness. RESULTS TARs using thresholds from 160 to 200 mg/dL were significantly associated with composite adverse outcomes after adjustment of covariates. Both TBR (<70 mg/dL) and TIR (70-160 mg/dL), but not mean sensor glucose level, were significantly associated with composite adverse outcomes and prolonged hospitalization. The multivariate-adjusted odds ratios of the CV of sensor glucose across tertiles for composite adverse outcomes of COVID-19 were 1.00, 1.18, and 25.2, respectively. CONCLUSIONS Patients with diabetes and COVID-19 have an increased risk of adverse outcomes with glucose levels >160 mg/dL and <70 mg/dL and a high CV. Therapies that improve these metrics of glycemic control may result in better prognoses for these patients.
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Affiliation(s)
- Yun Shen
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xiaohong Fan
- Department of Pulmonary Medicine and Critical Care, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Lei Zhang
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yaxin Wang
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Cheng Li
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Bingbing Zha
- Department of Endocrinology, Fifth People's Hospital of Shanghai Fudan University, Shanghai, China
| | - Yueyue Wu
- Department of Endocrinology, Fifth People's Hospital of Shanghai Fudan University, Shanghai, China
| | - Xiaohua Chen
- Department of Infectious Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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Gao F, Ma X, Peng J, Lu J, Lu W, Zhu W, Bao Y, Vigersky RA, Jia W, Zhou J. The Effect of Acarbose on Glycemic Variability in Patients with Type 2 Diabetes Mellitus Using Premixed Insulin Compared to Metformin (AIM): An Open-Label Randomized Trial. Diabetes Technol Ther 2020; 22:256-264. [PMID: 31638433 DOI: 10.1089/dia.2019.0290] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background: Acarbose (ACA) can effectively reduce the postprandial blood glucose and has similar antidiabetic effects as metformin (MET). To our knowledge, few studies have compared the effect of ACA or MET on glucose fluctuations. In the present study, we explored the effect of ACA or MET combined with premixed insulin (INS) on glycemic control and glycemic variability (GV). Methods: This was an open-label randomized trial that was conducted in type 2 diabetic patients taking premixed insulin. The patients were assigned to 12 weeks of MET (n = 62) or ACA (n = 62) treatment combined with INS. The main outcomes were changes in GV and glycosylated hemoglobin A1c (HbA1c) compared with baseline. Results: Compared with baseline, several GV indices (standard deviation [SD], mean amplitude of glycemic excursions [MAGE]) and blood glucose control indices (mean glucose [MG], time in range [TIR] and HbA1c) were both significantly improved in INS+ACA and INS+MET after 12-week therapy. However, coefficient of variation (CV) was significantly reduced in INS+ACA but not in INS+MET. Moreover, compared with INS+MET, INS+ACA led to a more pronounced percentage change from baseline in CV (26.3% [1.7%-44.6%] vs. 11.9% [-7.0% to 29.9%], P = 0.022), MAGE (40.5% [20.1%-60.5%] vs. 25.2% [-2.1% to 43.4%], P = 0.007) and SD (38.6% [25.2%-57.9%] vs. 30.1% [10.8%-46.5%], P = 0.041). Conclusion: Both MET and ACE combined with INS effectively reduced blood glucose. Compared with MET, ACA combined with INS reduced GV.
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Affiliation(s)
- Fei Gao
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xiaojing Ma
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jiahui Peng
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wei Zhu
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Robert A Vigersky
- Diabetes Institute of the Walter Reed National Military Medical Center, Bethesda, Maryland
- Medtronic Diabetes, Northridge, California
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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