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Ke J, Ruan X, Liu W, Liu X, Wu K, Qiu H, Wang X, Ding Y, Tan X, Li Z, Cao G. Prospective cohort studies underscore the association of abnormal glycemic measures with all-cause and cause-specific mortalities. iScience 2024; 27:110233. [PMID: 39021808 PMCID: PMC11253504 DOI: 10.1016/j.isci.2024.110233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/10/2024] [Accepted: 06/06/2024] [Indexed: 07/20/2024] Open
Abstract
The role of fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and triglyceride-glucose index (TyG index) in predicting all-cause and cause-specific mortalities remains elusive. This study included 384,420 adults from the Shanghai cohort and the UK Biobank (UKB) cohort. After multivariable adjustment in the Cox models, FPG ≥7.0 mmol/L or HbA1c ≥ 6.5% increased the risk of all-cause mortality, FPG ≥5.6 mmol/L or HbA1c ≥ 6.5% increased CVD-related mortality, and higher or lower TyG index increased all-cause and CVD-related mortalities in the Shanghai cohort; FPG ≥5.6 mmol/L, HbA1c ≥ 5.7%, TyG index <8.31 or ≥9.08 increased the risks of all-cause, CVD-related, and cancer-related mortalities in the UKB cohort. FPG or HbA1c increased the discrimination of the conventional risk model in predicting all-cause and CVD-related mortalities in both cohorts. Thus, increased levels of FPG and HbA1c and U-shaped TyG index increase the risks of all-cause especially CVD-related mortalities.
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Affiliation(s)
- Juzhong Ke
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Xiaonan Ruan
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Wenbin Liu
- Department of Epidemiology, Second Military Medical University, Shanghai, P.R. China
- Shanghai Key Laboratory of Medical Bioprotection, Shanghai, P.R. China
- Key Laboratory of Biological Defense, Ministry of Education, Shanghai, P.R. China
| | - Xiaolin Liu
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Kang Wu
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Hua Qiu
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Xiaonan Wang
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Yibo Ding
- Department of Epidemiology, Second Military Medical University, Shanghai, P.R. China
- Shanghai Key Laboratory of Medical Bioprotection, Shanghai, P.R. China
- Key Laboratory of Biological Defense, Ministry of Education, Shanghai, P.R. China
| | - Xiaojie Tan
- Department of Epidemiology, Second Military Medical University, Shanghai, P.R. China
- Shanghai Key Laboratory of Medical Bioprotection, Shanghai, P.R. China
- Key Laboratory of Biological Defense, Ministry of Education, Shanghai, P.R. China
| | - Zhitao Li
- Center for Disease Control and Prevention of Pudong New Area, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, P.R. China
| | - Guangwen Cao
- Department of Epidemiology, Second Military Medical University, Shanghai, P.R. China
- Shanghai Key Laboratory of Medical Bioprotection, Shanghai, P.R. China
- Key Laboratory of Biological Defense, Ministry of Education, Shanghai, P.R. China
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Qiu S, Liu X, Lei L, Liang H, Li X, Wang Y, Yu C, Li X, Tang Y, Wu J, Wang Y, Zha D, Liu X, Xiao M, Xiu J. Association between the stress-hyperglycemia ratio and all-cause mortality in community-dwelling populations: An analysis of the National Health and Nutrition Examination Survey (NHANES) 1999-2014. J Diabetes 2024; 16:e13567. [PMID: 38769875 PMCID: PMC11106591 DOI: 10.1111/1753-0407.13567] [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: 04/05/2023] [Revised: 02/01/2024] [Accepted: 04/01/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Reportedly, the stress-hyperglycemia ratio (SHR) is closely associated with poor prognosis in patients with severe acute disease. However, the community-dwelling may also be in a state of stress due to environmental exposure. Our study aimed to explore the association between SHR and all-cause mortality in the community-dwelling population. METHODS A total of 18 480 participants were included out of 82 091 from the NHANES 1999-2014 survey. The Kaplan-Meier survival analyses were used to assess the disparities in survival rates based on SHR, and the log-rank test was employed to investigate the distinctions between groups. The multivariate Cox regression analysis and restricted cubic spline (RCS) analysis were performed to assess the association of SHR with all-cause mortality. A subgroup analysis was also conducted. RESULTS A total of 3188 deaths occurred during a median follow-up period of 11.0 (7.7; 15.4) years. The highest risk for all-cause mortality was observed when SHR≤ 0.843 or SHR ≥0.986 (log-rank p < .001). After adjusting for the confounding factors, compared with subjects in the second SHR quartile (Q2), participants in the highest (Q4, adjusted hazard ratio [HR] 1.49, 95% confidence interval [CI] 1.28-1.73) and lowest quartiles (Q1, adjusted HR 1.37, 95% CI 1.16-1.60) have a higher probability of all-cause death. The RCS observed a dose-response U-shaped association between SHR and all-cause mortality. The U-shaped association between SHR and all-cause mortality was similar across subgroup analysis. CONCLUSIONS The SHR was significantly associated with all-cause mortality in the community-dwelling population, and the relationship was U-shaped.
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Affiliation(s)
- Shifeng Qiu
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Xiaocong Liu
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Li Lei
- Department of CardiologyShenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology)ShenzhenChina
| | - Hongbin Liang
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Xue Li
- Department of GastroenterologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Yutian Wang
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Chen Yu
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Xiaobo Li
- Department of CardiologyXiangdong Hospital Affiliated to Hunan Normal UniversityZhuzhouChina
| | - Yongzhen Tang
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Juefei Wu
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Yuegang Wang
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Daogang Zha
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Department of General PracticeNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Xuewei Liu
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- The First School of Clinical MedicineSouthern Medical UniversityDongguanChina
| | - Min Xiao
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Jiancheng Xiu
- Department of CardiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Shock and MicrocirculationNanfang Hospital, Southern Medical UniversityGuangzhouChina
- State Key Laboratory of Organ Failure ResearchNanfang Hospital, Southern Medical UniversityGuangzhouChina
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Hong JY, Li YJ, Metcalfe RS, Chen YC. Effects of acute and chronic stair-climbing exercise on metabolic health: A systematic review. J Sports Sci 2024; 42:498-510. [PMID: 38695325 DOI: 10.1080/02640414.2024.2345414] [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: 11/15/2022] [Accepted: 04/12/2024] [Indexed: 05/15/2024]
Abstract
Stair climbing exercise (SE) provides a feasible approach to elevate physical activity, but the effects on metabolic health are unclear. We systematically reviewed the currently available evidence on the effects of SE on fasting and postprandial glycaemia and lipidaemia. Studies were included if they investigated the effects of acute or chronic (at least 2 weeks) SE on fasting and/or postprandial glycaemic (insulin and glucose) and lipidaemic (triacylglycerols and non-esterified fatty acids) responses in healthy, prediabetic or type 2 diabetic adult populations. PubMed, Web of Science and Scopus were searched for eligible studies until July 2022. A total of 25 studies (14 acute and 11 chronic) were eligible for review. Acute bout(s) of SE can reduce postprandial glycaemia in individuals with prediabetes and type 2 diabetes (8 of 9 studies), but not in normoglycemic individuals. The effects of acute SE on postprandial lipidaemic responses and SE training on both fasting and postprandial glycaemia/lipidaemia were unclear. Acute SE may reduce postprandial glucose concentrations in people with impaired glycaemic control, but high-quality studies are needed. More studies are needed to determine the effect of chronic SE training on postprandial glucose and lipid responses, and the acute effects of SE on lipid responses.
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Affiliation(s)
- Jing-Yuan Hong
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Yun-Jui Li
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Richard S Metcalfe
- Applied Sports Technology, Exercise and Medicine Research Centre (A-STEM), Swansea University, Swansea, UK
| | - Yung-Chih Chen
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
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Li W, Wen CP, Li W, Ying Z, Pan S, Li Y, Zhu Z, Yang M, Tu H, Guo Y, Song Z, Chu DTW, Wu X. 6-Year trajectory of fasting plasma glucose (FPG) and mortality risk among individuals with normal FPG at baseline: a prospective cohort study. Diabetol Metab Syndr 2023; 15:169. [PMID: 37574540 PMCID: PMC10424387 DOI: 10.1186/s13098-023-01146-2] [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: 05/11/2023] [Accepted: 08/03/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND Higher fasting plasma glucose (FPG) levels were associated with an increased risk of all-cause mortality; however, the associations between long-term FPG trajectory groups and mortality were unclear, especially among individuals with a normal FPG level at the beginning. The aims of this study were to examine the associations of FPG trajectories with the risk of mortality and identify modifiable lifestyle factors related to these trajectories. METHODS We enrolled 50,919 individuals aged ≥ 20 years old, who were free of diabetes at baseline, in the prospective MJ cohort. All participants completed at least four FPG measurements within 6 years after enrollment and were followed until December 2011. FPG trajectories were identified by group-based trajectory modeling. We used Cox proportional hazards models to examine the associations of FPG trajectories with mortality, adjusting for age, sex, marital status, education level, occupation, smoking, drinking, physical activity, body mass index, baseline FPG, hypertension, dyslipidemia, cardiovascular disease or stroke, and cancer. Associations between baseline lifestyle factors and FPG trajectories were evaluated using multinomial logistic regression. RESULTS We identified three FPG trajectories as stable (n = 32,481), low-increasing (n = 17,164), and high-increasing (n = 1274). Compared to the stable group, both the low-increasing and high-increasing groups had higher risks of all-cause mortality (hazard ratio (HR) = 1.18 (95% CI 0.99-1.40) and 1.52 (95% CI 1.09-2.13), respectively), especially among those with hypertension. Compared to participants with 0 to 1 healthy lifestyle factor, those with 6 healthy lifestyle factors were more likely to be in the stable group (ORlow-increasing = 0.61, 95% CI 0.51-0.73; ORhigh-increasing = 0.20, 95% CI 0.13-0.32). CONCLUSIONS Individuals with longitudinally increasing FPG had a higher risk of mortality even if they had a normal FPG at baseline. Adopting healthy lifestyles may prevent individuals from transitioning into increasing trajectories.
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Affiliation(s)
- Wanlu Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chi Pang Wen
- National Institute for Data Science in Health and Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, Zhejiang, China
| | - Wenyuan Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhijun Ying
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Sai Pan
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yizhan Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zecheng Zhu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Min Yang
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Nutrition and Food Hygiene School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Huakang Tu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Guo
- Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhenya Song
- Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | | | - Xifeng Wu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- National Institute for Data Science in Health and Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, Zhejiang, China.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China.
- School of Medicine and Health Science, George Washington University, Washington, DC, USA.
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Xu H, Cao L, Li J, Zhang F, Wang W, Liang T, Liu X, Fu C. Is Chinese Spring Festival a key point for glycemic control of patients with type 2 diabetes mellitus in China? Front Public Health 2022; 10:975544. [PMID: 36620247 PMCID: PMC9813744 DOI: 10.3389/fpubh.2022.975544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives This study aims to explore the long-term trend of fasting blood glucose (FBG) among urban patients with type 2 diabetes mellitus (T2DM) and the impacts of the Chinese Spring Festival on their glycemic control in urban China. Methods The general information and longitudinal monitoring data of patients with T2DM in Minhang District, Shanghai China from 15 December 2006 to 31 December 2015 were collected. The FBG records were grouped into three periods, namely, the preholiday period (2 months right before the Chinese Spring Festival), the holiday period (from 28 December to 7 January of the lunar calendar year), and the postholiday period (2 months after the Chinese Spring Festival). The Mann-Kendall trend test and Cochran-Armitage trend test were occupied to explore the long-term trend, and paired t-test and chi-square (χ2) test were used to determine the differences in glycemic level and control rate between the preholiday and postholiday periods, respectively. Results From 2007 to 2015, the glycemic control rate in patients with T2DM showed an upward trend (P < 0.001), and the FBG level showed a decreasing trend (P = 0.048). After the Chinese Spring Festival, the glycemic control rate decreased significantly (P < 0.001), and the FBG level increased significantly (P < 0.001) compared to those during the preholiday period. The incidence of hypoglycemia increased during holidays. Patients who were aged 60-69 years, overweight or obese, with hypertension, with a disease duration of <3 years, or with poor glycemic control in one previous year were more likely to be affected by the holiday. Conclusion Chinese Spring Festival is a key point for glycemic control of patients with T2DM in China. Intensive holiday-specific diabetic healthcare needs to be further improved, and community-based interventions should be developed and implemented to control the possible holiday effects.
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Affiliation(s)
- Huilin Xu
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Li Cao
- School of Public Health, Fudan University, Shanghai, China,NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China,Key Laboratory of Public Health Safety, Fudan University, Shanghai, China
| | - Jun Li
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Fen Zhang
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Weijie Wang
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Tongtong Liang
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Xiaohua Liu
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China,*Correspondence: Xiaohua Liu ✉
| | - Chaowei Fu
- School of Public Health, Fudan University, Shanghai, China,NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China,Key Laboratory of Public Health Safety, Fudan University, Shanghai, China,Chaowei Fu ✉
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Liu Y, Xu H, Li J, Yang Y, Zhang J, Liu X, Li J, Yu Y, Qin G. Separate and combined effect of visit-to-visit glycaemic variability and mean fasting blood glucose level on all-cause mortality in patients with type 2 diabetes: A population-based cohort study. Diabetes Obes Metab 2022; 24:2400-2410. [PMID: 35876225 DOI: 10.1111/dom.14826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/12/2022] [Accepted: 07/20/2022] [Indexed: 11/29/2022]
Abstract
AIMS To assess the independent and combined impacts of visit-to-visit fasting blood glucose variability (VVV-FBG) and mean fasting blood glucose level (M-FBG) on all-cause mortality. MATERIALS AND METHODS This prospective cohort study included 48 843 Chinese patients with type 2 diabetes. Cox proportional hazards regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) to evaluate the association of VVV-FBG and M-FBG with all-cause mortality. The potential nonlinear associations were examined using restricted cubic splines, and additive interaction was evaluated using relative excess risk due to interaction (RERI). Cox generalized additive models (CGAMs) and bivariate response surface models were further used to assess the combined effects of VVV-FBG and M-FBG. RESULTS A total of 4087 deaths were observed during a median follow-up of 6.99 years. Compared with patients with values at the 5th percentile of average real variability (ARV) and M-FBG, we observed a 23% and 38% increased risk of premature deaths among those with values at the 95th percentile of ARV (HR 1.23, 95% CI 1.10, 1.37) and M-FBG (HR 1.38, 95% CI 1.26, 1.51), respectively. The interaction between glycaemic variability (ARV) and M-FBG was significant on both the additive scale (RERI 0.80 [0.29, 1.32]) and the multiplicative scale (HR 1.90 [1.10, 3.28]). High VVV-FBG and high M-FBG conferred the highest risk of all-cause mortality (HR 1.89, 95% CI 1.64, 2.17), compared to low VVV-FBG and low M-FBG. The CGAMs showed significant synergistic effects between glycaemic variability and M-FBG (P < 0.05). Moreover, a bivariate surface plot showed that risk of death increased more rapidly in type 2 diabetes patients with lower M-FBG combined with lower VVV-FBG. CONCLUSIONS The coexistence of high glycaemic variability and high glucose level might exacerbate the independent risk of premature mortality in type 2 diabetes patients, highlighting the importance of achieving normal and stable glucose levels simultaneously in the management of glucose.
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Affiliation(s)
- Yahang Liu
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| | - Huilin Xu
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Jun Li
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, China
| | - Yating Yang
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| | - Jie Zhang
- Department of Public Health, Aarhus University, Aarhus, Denmark
- NCRR-National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Xiaoqin Liu
- NCRR-National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Jiong Li
- Department of Clinical Medicine-Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark
| | - Yongfu Yu
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China
| | - Guoyou Qin
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China
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Cui X, Li J, Yang Y, Wu J, Xu H, Yu Y, Qin G. Long-term fasting glucose variability and risk of cancer in patients with type 2 diabetes mellitus: A retrospective population-based cohort study in Shanghai. J Diabetes 2022; 14:727-738. [PMID: 36353746 PMCID: PMC9705804 DOI: 10.1111/1753-0407.13329] [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: 08/08/2022] [Revised: 09/17/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUNDS Fasting blood glucose (FBG) variability may make an impact on adverse events in patients with diabetes mellitus. However, the association between long-term changes in FBG and cancer remains unclear. We aimed to investigate this association in a large-scale longitudinal study. METHODS Data were collected from 46 761 patients with type 2 diabetes mellitus aged 20-80 years who participated in the Diabetes Standardized Management Program in Shanghai, China. We adopted four indicators, including standard deviation (SD), coefficient of variation (CV), variation independent of the mean (VIM), and average real variability (ARV) to describe FBG variability. Adjusted multivariable Cox regression analyses and restricted cubic splines were used to investigate the association between long-term FBG variability and cancer risk. We also determined the interactive effect of FBG variability with hypertension and FBG-mean with hypertension on cancer risk, respectively. RESULTS In this study, we confirmed 2218 cancer cases (51.1% male) over a median follow-up of 2.86 years. In the multivariable-adjusted models, participants in the highest quartile of FBG variability had an increased risk of cancer compared with those in the lowest quartile. The nonlinear association was found when using FBG-VIM, FBG-ARV, and FBG-SD in restricted cubic spline plots. There was a significant interaction effect of FBG variability with hypertension on cancer, whereas the effect of FBG-mean with hypertension did not attain significance. CONCLUSIONS Our retrospective cohort study demonstrated a positive association between the long-term changes in FBG and cancer risk in patients with type 2 diabetes mellitus. FBG variability may independently predict cancer incidence.
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Affiliation(s)
- Xiao‐rui Cui
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of EducationFudan UniversityShanghaiChina
| | - Jun Li
- Shanghai Minhang Center for Disease Control and PreventionShanghaiChina
| | - Ya‐ting Yang
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of EducationFudan UniversityShanghaiChina
| | - Jing‐yi Wu
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of EducationFudan UniversityShanghaiChina
| | - Hui‐lin Xu
- Shanghai Minhang Center for Disease Control and PreventionShanghaiChina
| | - Yong‐fu Yu
- Shanghai Institute of Infectious Disease and BiosecurityShanghaiChina
| | - Guo‐you Qin
- Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of EducationFudan UniversityShanghaiChina
- Shanghai Institute of Infectious Disease and BiosecurityShanghaiChina
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Xu H, Zhang F, Xu W, Li J, Zhu J, Zhang M, Wu Z, Qin G. Annual glycemic variations and risk of cancer among Chinese patients with type 2 diabetes mellitus: A population-based cohort study in Shanghai. Diabetes Res Clin Pract 2021; 171:108552. [PMID: 33242512 DOI: 10.1016/j.diabres.2020.108552] [Citation(s) in RCA: 2] [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] [Received: 08/06/2020] [Revised: 11/03/2020] [Accepted: 11/10/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND Long-term glycemic variation in diabetes patients may have contributed to cancer incidence. AIM In this study we aimed at the association between annual glycemic variation and the risk of cancer in Chinese patients with type 2 diabetes mellitus (T2DM). METHODS Subjects of this study were from an established population-based cohort of T2DM patients in Minhang District of Shanghai, China. Incident cancer were obtained from the Shanghai Cancer Registry. Glycemic variation was evaluated using the annual fasting glucose coefficient of variation (FG-CV), which was used as a time-dependent variable in a Cox regression model to estimate the associations with the cancer risk. Restricted cubic splines were used to explore potential non-linear associations. RESULTS A total of 2,140 incident cancers (1100 men and 1040 women) were identified from the 46,202 diabetes patients during 12-year follow-up. The annual FG-CV remained significantly associated with an increased risk of cancer, even after adjusting for the annual mean FG level. A significant non-linear association was found in male T2DM patients, and a significant linear association in female patients. CONCLUSIONS The positive association of the annual FG-CV with the risk of cancer in T2DM patients indicate the importance to stabilize the FG level.
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Affiliation(s)
- Huilin Xu
- Shanghai Minhang Center for Disease Control and Prevention, 965 Zhong Yi Road, Shanghai 201101, People's Republic of China
| | - Fen Zhang
- Shanghai Minhang Center for Disease Control and Prevention, 965 Zhong Yi Road, Shanghai 201101, People's Republic of China
| | - Wanghong Xu
- Department of Epidemiology, School of Public Health and Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, People's Republic of China
| | - Jun Li
- Shanghai Minhang Center for Disease Control and Prevention, 965 Zhong Yi Road, Shanghai 201101, People's Republic of China
| | - Jingjing Zhu
- Department of Biostatistics, School of Public Health and Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, People's Republic of China
| | - Minlu Zhang
- Shanghai Municipal Center for Disease Control and Prevention, 1380 West Zhong Shan Road, Shanghai 200336, People's Republic of China
| | - Zhenyu Wu
- Department of Biostatistics, School of Public Health and Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, People's Republic of China.
| | - Guoyou Qin
- Department of Biostatistics, School of Public Health and Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, People's Republic of China.
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Koraćević G, Mićić S, Stojanović M, Tomašević M, Kostić T, Koraćević M, Janković I. Single prognostic cut-off value for admission glycemia in acute myocardial infarction has been used although high-risk stems from hyperglycemia as well as from hypoglycemia (a narrative review). Prim Care Diabetes 2020; 14:594-604. [PMID: 32988774 DOI: 10.1016/j.pcd.2020.09.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/30/2020] [Accepted: 09/10/2020] [Indexed: 01/08/2023]
Abstract
All original articles and meta-analysis use the single cut-off value to distinguish high-risk hyperglycemic from other acute myocardial infarction (AMI) patients. The mortality rate is 3.9 times higher in non-diabetic AMI patients with admission glycemia ≥6.1mmol compared to normoglycemic non-diabetic AMI patients. On the other hand, admission hypoglycemia in AMI is an important predictor of mortality. Because both admission hypo- and hyperglycemia correspond to higher in-hospital mortality, this graph is recognized as "J or U shaped curve". The review suggests two cut-off values for admission glycemia for risk assessment in AMI instead of single one because hypoglycemia as well as hyperglycemia represents a high-risk factor.
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Affiliation(s)
- Goran Koraćević
- Department for Cardiovascular Diseases, Clinical Center Niš, Serbia; Faculty of Medicine, University of Niš, Serbia
| | | | | | - Miloje Tomašević
- Faculty of Medicine, University of Belgrade, Department of Cardiology, Clinical Center Serbia, Belgrade, Serbia
| | - Tomislav Kostić
- Department for Cardiovascular Diseases, Clinical Center Niš, Serbia; Faculty of Medicine, University of Niš, Serbia
| | - Maja Koraćević
- Faculty of Medicine, University of Niš, Serbia; Innovation Center, University of Niš, Serbia
| | - Irena Janković
- Faculty of Medicine, University of Niš, Serbia; Clinic of Plastic and Reconstructive Surgery, Clinical Center Niš, Serbia
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