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Hjort A, Iggman D, Rosqvist F. Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis. Clin Nutr 2024; 43:915-925. [PMID: 38401227 DOI: 10.1016/j.clnu.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND & AIMS Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes. METHODS Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome. RESULTS Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease. CONCLUSION GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.
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Affiliation(s)
- Anna Hjort
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Kemivägen 10, 41296 Gothenburg, Sweden.
| | - David Iggman
- Center for Clinical Research Dalarna, Uppsala University, Nissers väg 3, 79182 Falun, Sweden; Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Husargatan 3, BMC, Box 564, 75122 Uppsala, Sweden.
| | - Fredrik Rosqvist
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Husargatan 3, BMC, Box 564, 75122 Uppsala, Sweden.
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Gupta MK, Gouda G, Sultana S, Punekar SM, Vadde R, Ravikiran T. Structure-related relationship: Plant-derived antidiabetic compounds. STUDIES IN NATURAL PRODUCTS CHEMISTRY 2023:241-295. [DOI: 10.1016/b978-0-323-91294-5.00008-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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Li J, Lu J, Tobore I, Liu Y, Kandwal A, Wang L, Ma X, Lu W, Bao Y, Zhou J, Nie Z. Gradient variability coefficient: a novel method for assessing glycemic variability and risk of hypoglycemia. Endocrine 2022; 76:29-35. [PMID: 35066742 DOI: 10.1007/s12020-021-02950-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/15/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Despite the clinical importance of glycemic variability and hypoglycemia, thus far, there is no consensus on the optimum method for assessing glycemic variability and risk of hypoglycemia simultaneously. RESEARCH DESIGN AND METHODS A novel metric, the gradient variability coefficient (GVC), was proposed for characterizing glycemic variability and risk of hypoglycemia. A total of 208 daily records of CGM encompassing 104 patients with T1DM and 2380 daily records from 1190 patients with T2DM were obtained in our study. Simulated CGM waveforms were used to assess the ability of GVC and other metrics to capture the amplitude and frequency of glucose fluctuations. In addition, the association between GVC and the risk of hypoglycemia was evaluated by receiver operating characteristic (ROC) curve. RESULTS The results of simulated CGM waveforms indicated that, compared with the widely used metrics of glycemic variability including standard deviation of sensor glucose (SD), coefficient of variation (CV), and mean amplitude of glycemic excursion (MAGE), GVC could reflect both the amplitude and frequency of glucose oscillations. In addition, the area under the curve (AUC) of ROC was 0.827 in T1DM and 0.873 in T2DM, indicating good performance in predicting hypoglycemia. CONCLUSIONS The proposed GVC might be a clinically useful tool in characterizing glycemic variability and the assessment of hypoglycemia risk in patients with diabetes.
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Affiliation(s)
- Jingzhen Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Igbe Tobore
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Yuhang Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Abhishek Kandwal
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Lei Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Xiaojing Ma
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.
| | - Zedong Nie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
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