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Barmanray RD, Kyi M, Worth LJ, Colman PG, Churilov L, Fazio TN, Rayman G, Gonzalez V, Hall C, Fourlanos S. Hyperglycemia in Hospital: An Independent Marker of Infection, Acute Kidney Injury, and Stroke for Hospital Inpatients. J Clin Endocrinol Metab 2024; 109:e2048-e2056. [PMID: 38279945 DOI: 10.1210/clinem/dgae051] [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: 10/22/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 01/29/2024]
Abstract
CONTEXT Hyperglycemia in hospital inpatients without pre-existing diabetes is associated with increased mortality. However, the independent contribution of hyperglycemia to health care-associated infection (HAI), acute kidney injury (AKI), and stroke is unclear. OBJECTIVE To investigate the relationship between hyperglycemia and adverse clinical outcomes in hospital for patients with and without diabetes. METHODS Diabetes IN-hospital: Glucose and Outcomes (DINGO) was a 26-week (October 2019-March 2020) prospective cohort study. Clinical and glucose data were collected up to the 14th day of admission. Primary stratification was by hyperglycemia, defined as ≥2 random capillary blood glucose (BG) measurements ≥11.1 mmol/L (≥200 mg/dL). Propensity weighting for 9 clinical characteristics was performed to allow interrogation of causality. To maintain the positivity assumption, patients with HbA1c >12.0% were excluded and prehospital treatment not adjusted for. The setting was the Royal Melbourne Hospital, a quaternary referral hospital in Melbourne, Australia. Admissions with at least 2 capillary glucose values and length of stay >24 hours were eligible, with half randomly sampled. Outcome measures were HAI, AKI, stroke, and mortality. RESULTS Of 2558 included admissions, 1147 (45%) experienced hyperglycemia in hospital. Following propensity-weighting and adjustment, hyperglycemia in hospital was found to, independently of 9 covariables, contribute an increased risk of in-hospital HAI (130 [11.3%] vs 100 [7.1%], adjusted odds ratio [aOR] 1.03, 95% CI 1.01-1.05, P = .003), AKI (120 [10.5%] vs 59 [4.2%], aOR 1.07, 95% CI 1.05-1.09, P < .001), and stroke (10 [0.9%] vs 1 [0.1%], aOR 1.05, 95% CI 1.04-1.06, P < .001). CONCLUSION In hospital inpatients (HbA1c ≤12.0%), irrespective of diabetes status and prehospital glycemia, hyperglycemia increases the risk of in-hospital HAI, AKI, and stroke compared with those not experiencing hyperglycemia.
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Affiliation(s)
- Rahul D Barmanray
- Department of Diabetes & Endocrinology, The Royal Melbourne Hospital, Melbourne 3000, Australia
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne 3000, Australia
- Australian Centre for Accelerating Diabetes Innovations (ACADI), The University of Melbourne, Melbourne 3000, Australia
| | - Mervyn Kyi
- Department of Diabetes & Endocrinology, The Royal Melbourne Hospital, Melbourne 3000, Australia
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne 3000, Australia
- Australian Centre for Accelerating Diabetes Innovations (ACADI), The University of Melbourne, Melbourne 3000, Australia
| | - Leon J Worth
- National Centre for Infections in Cancer (NCIC), Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne 3000, Australia
- Victorian Healthcare Associated Infection Surveillance System (VICNISS) Coordinating Centre, Doherty Institute, Melbourne 3000, Australia
| | - Peter G Colman
- Department of Diabetes & Endocrinology, The Royal Melbourne Hospital, Melbourne 3000, Australia
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne 3000, Australia
| | - Leonid Churilov
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne 3000, Australia
- Australian Centre for Accelerating Diabetes Innovations (ACADI), The University of Melbourne, Melbourne 3000, Australia
| | - Timothy N Fazio
- Health Intelligence Unit, The Royal Melbourne Hospital, Melbourne 3000, Australia
| | - Gerry Rayman
- Department of Diabetes and Endocrinology, Ipswich General Hospital NHS Trust, Ipswich IP4 5PD, UK
| | - Vicky Gonzalez
- Department of Diabetes & Endocrinology, The Royal Melbourne Hospital, Melbourne 3000, Australia
| | - Candice Hall
- Department of Diabetes & Endocrinology, The Royal Melbourne Hospital, Melbourne 3000, Australia
| | - Spiros Fourlanos
- Department of Diabetes & Endocrinology, The Royal Melbourne Hospital, Melbourne 3000, Australia
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne 3000, Australia
- Australian Centre for Accelerating Diabetes Innovations (ACADI), The University of Melbourne, Melbourne 3000, Australia
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Almagthali A, Alsohimi S, Alkhalaf A, Al Sulaiman K, Aljuhani O. Assessing glycemic variability in critically ill patients: A prospective cohort study comparing insulin infusion therapy with insulin sliding scale. Sci Rep 2024; 14:10128. [PMID: 38698018 PMCID: PMC11066101 DOI: 10.1038/s41598-024-57403-5] [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: 06/26/2023] [Accepted: 03/18/2024] [Indexed: 05/05/2024] Open
Abstract
Glycemic variability (GV) has been associated with an increased mortality rate among critically ill patients. The clinical outcomes of having less GV even with slight hyperglycemia are better than those having tight glycemic control but higher GV. Insulin infusion remains the preferred method to control stress hyperglycemia in critically ill patients. However, its impacts on GV and clinical outcomes in critically ill patients still need further investigation. This study intended to evaluate the impact of insulin infusion therapy (IIT) compared to the insulin sliding scale (ISS) on the extent of GV and explore its impact on the clinical outcomes for critically ill patients. A prospective, single-center observational cohort study was conducted at a tertiary academic hospital in Saudi Arabia between March 2021 and November 2021. The study included adult patients admitted to ICUs who received insulin for stress hyperglycemia management. Patients were categorized into two groups based on the regimen of insulin therapy during ICU stay (IIT versus ISS). The primary outcome was the GV between the two groups. Secondary outcomes were ICU mortality, the incidence of hypoglycemia, and ICU length of stay (LOS). A total of 381 patients were screened; out of them, eighty patients met the eligibility criteria. The distribution of patients having diabetes and a history of insulin use was similar between the two groups. The GV was lower in the IIT group compared to the ISS group using CONGA (- 0.65, 95% CI [- 1.16, - 0.14], p-value = 0.01). Compared with ISS, patients who received IIT had a lower incidence of hypoglycemia that required correction (6.8% vs 2.77%; p-value = 0.38). In contrast, there were no significant differences in ICU LOS and ICU mortality between the two groups. Our study demonstrated that the IIT is associated with decreased GV significantly in critically ill patients without increasing the incidence of severe hypoglycemia. There is no survival benefit with the use of the IIT. Further studies with larger sample size are required to confirm our findings and elaborate on IIT's potential effect in reducing ICU complications in critically ill patients.
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Affiliation(s)
- Alaa Almagthali
- Pharmaceutical Care Department, King Abdulaziz University Hospital, Jeddah, Saudi Arabia.
| | - Samiah Alsohimi
- Pharmaceutical Care Department, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
- Pharmaceutical Care Department, King Fahad Armed Forces hospital, Jeddah, Saudi Arabia
| | - Arwa Alkhalaf
- Measurement and Psychometrics, Psychology Department, Faculty of Graduate Educational Studies, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khalid Al Sulaiman
- Pharmaceutical Care Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia
- College of Pharmacy, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center-King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
- Saudi Critical Care Pharmacy Research (SCAPE) Platform, Riyadh, Saudi Arabia
- Saudi Society for Multidisciplinary Research Development and Education (SCAPE Society), Riyadh, Saudi Arabia
| | - Ohoud Aljuhani
- Department of Pharmacy Practice, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
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Association between visit-to-visit fasting glycemic variability and depression: a retrospective cohort study in a representative Korean population without diabetes. Sci Rep 2022; 12:18692. [PMID: 36333430 PMCID: PMC9636237 DOI: 10.1038/s41598-022-22302-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
Glycemic variability (GV) is a risk factor for depression in patients with diabetes. However, whether it is also a predictor of incident depression in people without diabetes remains unclear. We aimed to investigate the association between visit-to-visit variability in fasting serum glucose (FSG) levels and the incidence of depression among Koreans without diabetes. This retrospective cohort study included data of people without diabetes who did not have depression at baseline and had at least three FSG measurements (n = 264,480) extracted from the 2002-2007 Korean National Health Insurance Service-National Health Screening Cohort. GV was calculated as the average successive variability of FSG. Among 264,480 participants, 198,267 were observed during 2008-2013 and their hazard ratios (HR) of incident depression were calculated. Participants with the highest GV showed a higher risk of depression in fully adjusted models than those with the lowest GV (HR, 1.09; 95% CI, 1.02-1.16). The risk of incident depression heightened with increasing GV (p for trend < 0.001). Greater visit-to-visit GV may be associated with the risk of developing depression in people without diabetes. Conversely, maintaining steady FSG levels may reduce the risk of incident depression in people without diabetes.
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Mo Y, Wang C, Lu J, Shen Y, Chen L, Zhang L, Lu W, Zhu W, Xia T, Zhou J. Impact of short-term glycemic variability on risk of all-cause mortality in type 2 diabetes patients with well-controlled glucose profile by continuous glucose monitoring: A prospective cohort study. Diabetes Res Clin Pract 2022; 189:109940. [PMID: 35662611 DOI: 10.1016/j.diabres.2022.109940] [Citation(s) in RCA: 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] [Received: 01/19/2022] [Revised: 05/12/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
AIMS To investigate the association between short-term glycemic variability (GV) and all-cause mortality in type 2 diabetes with well-controlled glucose profile by continuous glucose monitoring (CGM). METHODS In this prospective study, 1839 diabetes patients who reached percentage of time in the target glucose range of 3.9-10 mmol/L > 70%, percentage of time above range of 10 mmol/L < 25% and percentage of time below range of 3.9 mmol/L < 4% on CGM were enrolled and were classified into five groups by coefficient of variation for glucose (%CV) level: ≤20%, 20-25%, 25-30%, 30-35%, and > 35%. Cox proportional hazard models were used to estimate hazard ratios (HRs) of all-cause mortality risk associated with the different %CV categories. RESULTS At baseline, participants had mean age of 60.9 years and mean HbA1c of 7.3% (56 mmol/mol). A total of 165 deaths were identified during a median follow-up of 6.9 years. In multivariate Cox regression analysis, HRs associated with %CV categories were 1.00, 1.16 (95% CI 0.78-1.73), 1.38 (95% CI 0.89-2.15), 1.33 (95% CI 0.77-2.29) and 2.26 (95% CI 1.13-4.52) for all-cause mortality. CONCLUSIONS Greater %CV was associated with increased risk for all-cause mortality even among patients with seemingly well-controlled glucose status.
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Affiliation(s)
- Yifei Mo
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Chunfang Wang
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Yun Shen
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Lei Chen
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Lei Zhang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Wei Zhu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China
| | - Tian Xia
- Vital Statistical Department, Institute of Health Information, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
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Oh S, Purja S, Shin H, Kim M, Kim E. Hypoglycemic agents and glycemic variability in individuals with type 2 diabetes: A systematic review and network meta-analysis. Diab Vasc Dis Res 2022; 19:14791641221106866. [PMID: 35686694 PMCID: PMC9189550 DOI: 10.1177/14791641221106866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
While hemoglobin A1c (HbA1c) is commonly used to monitor therapy response in type 2 diabetes (T2D), GV is emerging as an essential additional metric for optimizing glycemic control. Our goal was to learn more about the impact of hypoglycemic agents on HbA1c levels and GV in patients with T2D. A systematic review and network meta-analysis (NMA) of randomized controlled trials were performed to assess the effects of glucagon-like peptide 1 receptor agonists (GLP-1 RAs), sodium-glucose cotransporter (SGLT)-2 inhibitors, dipeptidyl peptidase (DPP)-4 inhibitors, sulfonylurea and thiazolidinediones on Mean Amplitude of Glycemic Excursions (MAGE) and HbA1c. Searches were performed using PubMed and EMBASE. A random-effect model was used in the NMA, and the surface under the cumulative ranking was used to rank comparisons. All studies were checked for quality according to their design and also for heterogeneity before inclusion in this NMA. The highest reduction in MAGE was achieved by GLP-1 RAs (SUCRA 0.83), followed by DPP-4 inhibitors (SUCRA: 0.72), and thiazolidinediones (SUCRA: 0.69). In terms of HbA1c reduction, GLP-1 RAs were the most effective (SUCRA 0.81), followed by DPP-4 inhibitors (SUCRA 0.72) and sulfonylurea (SUCRA 0.65). Our findings indicated that GLP-1 RAs have relatively high efficacy in terms of HbA1c and MAGE reduction when compared with other hypoglycemic agents and can thus have clinical application. Future studies with a larger sample size and appropriate subgroup analyses are warranted to completely understand the glycemic effects of these agents in various patients with T2D. The protocol for this systematic review was registered with the International Prospective Register of Systematic Reviews (CRD42021256363).
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Affiliation(s)
- SuA Oh
- Data Science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
| | - Sujata Purja
- Data Science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
- EunYoung Kim, Data science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea.
| | - Hocheol Shin
- Data Science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
| | - Minji Kim
- Data Science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
| | - Eunyoung Kim
- Data Science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
- EunYoung Kim, Data science, Evidence-Based and Clinical Research Laboratory, Department of Health, Social and Clinical Pharmacy, College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea.
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Lu Z, Tao G, Sun X, Zhang Y, Jiang M, Liu Y, Ling M, Zhang J, Xiao W, Hua T, Zhu H, Yang M. Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization. Front Public Health 2022; 10:857368. [PMID: 35570924 PMCID: PMC9099235 DOI: 10.3389/fpubh.2022.857368] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/25/2022] [Indexed: 01/13/2023] Open
Abstract
Background There was considerable debate regarding the effect of mean blood glucose (MBG) and glycemic variability (GV) on the mortality of septic patients. This retrospective cohort study aimed to assess the association between MBG and GV with ICU mortality of sepsis patients and to explore the optimal MBG range. Methods Sepsis patients were enrolled from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). MBG and glycemic coefficient of variation (GluCV) were, respectively, calculated to represent the overall glycemic status and GV during ICU stay. The associations between MBG, GluCV, and ICU mortality of the septic patients were assessed by using multivariate logistic regression in different subgroups and the severity of sepsis. Restricted cubic splines evaluated the optimal MBG target. Results A total of 7,104 adult sepsis patients were included. The multivariate logistic regression results showed that increased MBG and GluCV were significantly correlated with ICU mortality. The adjusted odds ratios were 1.14 (95% CI 1.09-1.20) and 1.05 (95% CI 1.00-1.12). However, there was no association between hyperglycemia and ICU mortality among diabetes, liver disease, immunosuppression, and hypoglycemia patients. And the impact of high GluCV on ICU mortality was not observed in those with diabetes, immunosuppression, liver disease, and non-septic shock. The ICU mortality risk of severe hyperglycemia (≧200 mg/dl) and high GluCV (>31.429%), respectively, elevated 2.30, 3.15, 3.06, and 2.37, 2.79, 3.14-folds in mild (SOFA ≦ 3), middle (SOFA 3-7), and severe group (SOFA ≧ 7). The MBG level was associated with the lowest risk of ICU mortality and hypoglycemia between 120 and 140 mg/dl in the subgroup without diabetes. For the diabetic subset, the incidence of hypoglycemia was significantly reduced when the MBG was 140-190 mg/dl, but a glycemic control target effectively reducing ICU mortality was not observed. Conclusion MBG and GluCV during the ICU stay were associated with all-cause ICU mortality in sepsis patients; however, their harms are not apparent in some particular subgroups. The impact of hyperglycemia and high GV on death increased with the severity of sepsis. The risk of ICU mortality and hypoglycemia in those with no pre-existing diabetes was lower when maintaining the MBG in the range of 120-140 mg/dl.
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Affiliation(s)
- Zongqing Lu
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Gan Tao
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoyu Sun
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yijun Zhang
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mengke Jiang
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yu Liu
- Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Ministry of Education, Hefei, China
| | - Meng Ling
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jin Zhang
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenyan Xiao
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tianfeng Hua
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Huaqing Zhu
- Laboratory of Molecular Biology and Department of Biochemistry, Anhui Medical University, Hefei, China
| | - Min Yang
- The Second Department of Intensive Care Unit, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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Perspectives of glycemic variability in diabetic neuropathy: a comprehensive review. Commun Biol 2021; 4:1366. [PMID: 34876671 PMCID: PMC8651799 DOI: 10.1038/s42003-021-02896-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/16/2021] [Indexed: 12/14/2022] Open
Abstract
Diabetic neuropathy is one of the most prevalent chronic complications of diabetes, and up to half of diabetic patients will develop diabetic neuropathy during their disease course. Notably, emerging evidence suggests that glycemic variability is associated with the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for diabetic neuropathy. In this review, we describe the commonly used metrics for evaluating glycemic variability in clinical practice and summarize the role and related mechanisms of glycemic variability in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. In addition, we also address the potential pharmacological and non-pharmacological treatment methods for diabetic neuropathy, aiming to provide ideas for the treatment of diabetic neuropathy. Zhang et al. describe metrics for evaluating glycaemic variability (GV) in clinical practice and summarize the role and related mechanisms of GV in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. They aim to stimulate ideas for the treatment of diabetic neuropathy.
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Zhou Z, Sun B, Huang S, Zhu C, Bian M. Glycemic variability: adverse clinical outcomes and how to improve it? Cardiovasc Diabetol 2020; 19:102. [PMID: 32622354 PMCID: PMC7335439 DOI: 10.1186/s12933-020-01085-6] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 07/02/2020] [Indexed: 12/26/2022] Open
Abstract
Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.
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Affiliation(s)
- Zheng Zhou
- Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China
| | - Bao Sun
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410000, China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, 410000, China
| | - Shiqiong Huang
- Department of Pharmacy, The First Hospital of Changsha, Changsha, 410005, China
| | - Chunsheng Zhu
- Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.
| | - Meng Bian
- Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.
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