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Wang M, Liu J, Wang J, Jin Y, Zheng Z. Global, regional, and national burden of tracheal, bronchus, and lung cancers attributable to high fasting plasma glucose: A systematic analysis of global burden of disease 2019. J Diabetes 2024; 16:e13499. [PMID: 38009553 PMCID: PMC10925880 DOI: 10.1111/1753-0407.13499] [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: 05/25/2023] [Revised: 10/11/2023] [Accepted: 10/29/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Tracheal, bronchus, and lung (TBL) cancer is the third most common and lethal type of cancer worldwide. Glucose metabolism disorders, as represented by high fasting plasma glucose (HFPG), increase the risk of development and worsen the prognosis of TBL cancer. This study aimed to evaluate the global disease burden of TBL cancer attributable to HFPG. METHODS The TBL cancer burden attributable to HFPG was estimated based on a modeling strategy using the Global Burden of Disease Study 2019. The disease burden globally and by regions, countries, development levels, age groups, and sexes were also evaluated with the indicators of death, disability-adjusted life years, years of life lost, and years lived with disability. The estimated annual percentage change (EAPC) was calculated by regression model to show the temporal trend. RESULTS In 2019, approximately 8% of the total TBL cancer burden was attributable to HFPG. The HFPG-attributable TBL cancer burden increased globally from 1990 to 2019 with the EAPC of 0.98% per year. The burden was positively associated with social development levels, and the global burden was three times greater in men than in women. HFPG-attributable TBL cancer burden increased with age and peaked at above 70 years of age. CONCLUSIONS The findings highlight the effect and burden of glucose disorders, as represented by HFPG on TBL cancer burden. Integrated cancer prevention and control measures are needed, with control of glucose disorders as one of the key elements.
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Affiliation(s)
- Minmin Wang
- Department of Global Health, School of Public HealthPeking UniversityBeijingChina
- Institute for Global Health and DevelopmentPeking UniversityBeijingChina
| | - Jingyi Liu
- School of NursingPeking UniversityBeijingChina
| | - Jia Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing)Peking University Cancer Hospital & InstituteBeijingChina
| | - Yinzi Jin
- Department of Global Health, School of Public HealthPeking UniversityBeijingChina
- Institute for Global Health and DevelopmentPeking UniversityBeijingChina
| | - Zhi‐Jie Zheng
- Department of Global Health, School of Public HealthPeking UniversityBeijingChina
- Institute for Global Health and DevelopmentPeking UniversityBeijingChina
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2
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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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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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Asgari S, Masrouri S, Khalili D, Azizi F, Hadaegh F. Differences in the impact of impaired glucose status on clinical outcomes in younger and older adults: Over a decade of follow-up in the Tehran lipid and glucose study. Front Cardiovasc Med 2022; 9:1018403. [PMID: 36386371 PMCID: PMC9662168 DOI: 10.3389/fcvm.2022.1018403] [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: 08/13/2022] [Accepted: 09/28/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction Studies found that the impact of dysglycemia on microvascular, macrovascular events and mortality outcomes were different between the younger vs. older population. We aimed to investigate the age-specific association of prediabetes with clinical outcomes including type 2 diabetes (T2DM), hypertension, chronic kidney disease (CKD), cardiovascular disease (CVD), and mortality. Materials and methods A total of 5,970 Iranians (3,829 women) aged ≥30 years, without T2DM, were included. The age-specific (<60 and ≥60 years; minimum p-value for interaction = 0.001) multivariable-adjusted Cox regression was done to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) of the impaired glucose status including impaired fasting glucose (IFG) vs. normal fasting glucose (NFG), impaired glucose tolerance (IGT) vs. normal glucose tolerance (NGT), and IFG&IGT vs. NFG/NGT with each outcome. Results Among individuals aged ≥60 years, the prevalence of impaired glucose status (IFG, IGT, or both) was about 2 times higher compared to those aged <60. Age-specific association between prediabetes and incident hypertension was found for those aged <60 years; [HR (95% CI); IFG: 1.38 (1.16-1.65), IGT: 1.51 (1.26-1.81), and IFG&IGT: 1.62 (1.21-2.12)]. For CVD, in all impaired glycemic states, those aged <60 were at higher significant risk [IFG: 1.39 (1.09-1.77), IGT: 1.53 (1.19-1.97), and IFG&IGT: 1.60 (1.14-2.25)]. Stratified analyses showed similar associations for IFG and IGT with non-CV mortality 1.71 (1.04-2.80) and 2.12 (1.30-3.46), respectively, and for all-cause mortality among those aged <60 years [IFG: 1.63 (1.08-2.45) and IGT: 1.82 (1.20-2.76)]. In both age groups, all glycemic status groups were significantly associated with T2DM but not with CKD and CV mortality. Conclusions The high prevalence of prediabetes particularly among the elderly population, limited resources, and the observed significant age differences in the impact of prediabetes states on different clinical outcomes calls for multicomponent intervention strategies by policy health makers, including lifestyle and possible pharmacological therapy, with the priority for the young Iranian population.
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Affiliation(s)
- Samaneh Asgari
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroush Masrouri
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farzad Hadaegh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Association of Visit-to-Visit Variability in Fasting Plasma Glucose with Digestive Cancer Risk. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:4530894. [PMID: 35873802 PMCID: PMC9301759 DOI: 10.1155/2022/4530894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/23/2022] [Indexed: 11/17/2022]
Abstract
Background and Aims. The aim of this study is to investigate the association between visit-to-visit variability in fasting plasma glucose (FPG) and the risk of digestive cancers among individuals with and without diabetes. Methods. Using data from Kailuan cohort, a prospective population-based study, individuals who had at least two measurements of FPG between 2006 and 2012 without prior cancer were included in this study. Four indexes of variability were used, including standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average successive variability (ARV). Cox regression was used to evaluate the relationship between the quartiles of FPG variability and digestive cancers. Results. A total of 98,161 individuals were studied. Over a mean follow-up of
years, 1103 individuals developed incident digestive cancer (1.21 per 1000 person-years). Compared to the individuals in the lowest quartile, those in the highest quartile of FPG variability by SD had 38.7% higher risk of developing overall digestive cancers after adjusting for the significant confounders (hazard ratio, 1.387; 95% confidence interval, 1.160-1.659;
). Higher FPG variability was associated with significantly higher risks of colorectal cancer (fully adjusted HR 1.432, 95% CI [1.073-1.912],
) and pancreatic cancer (fully adjusted HR 2.105, 95% CI [1.024-4.329],
), but not liver cancer (fully adjusted HR 1.427, 95% CI [0.973-2.092],
) or esophageal and gastric cancer (fully adjusted HR 1.139, 95% CI [0.776-1.670],
). Subgroup analyses showed that individuals who were younger (<65 years), male, and those without diabetes experienced a predominantly higher risk of developing digestive cancers. Similar results were observed when using CV, VIM, and ARV. Conclusions. FPG variability was significantly associated with increasing risk of digestive cancers, especially for pancreatic and colorectal cancer. Our study suggested a potential role of FPG variability in risk stratification of digestive cancers. Approaches that reduce FPG variability may lower the risks of incident digestive cancers among the general population. This trial is registered with ChiCTR-TNRC-11001489.
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Ramdass V, Caskey E, Sklarz T, Ajmeri S, Patel V, Balogun A, Pomary V, Hall J, Qari O, Tripathi R, Hunter K, Roy S. Association Between Obesity and Cancer Mortality: An Internal Medicine Outpatient Clinic Perspective. J Clin Med Res 2021; 13:377-386. [PMID: 34394780 PMCID: PMC8336943 DOI: 10.14740/jocmr4543] [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: 06/17/2021] [Accepted: 07/02/2021] [Indexed: 11/11/2022] Open
Abstract
Background Obesity is one of the leading preventable causes of cancer that has a causal relationship with cancers of esophagus, breast and colon. Paradoxically, there are studies demonstrating that obesity is associated with improved survival in cancer patients. The aim of our study was to investigate the association of obesity and cancer mortality in adult patients. Methods Retrospective medical record review of 784 adult patients was performed who had a diagnosis of cancer and who were seen in our outpatient Internal Medicine Clinic between January 1, 2019 and December 31, 2019. Results Forty-three (5.2%) patients were cancer non-survivors and 741 (94.8%) were cancer survivors. The mean age of the cancer non-survivors group was significantly higher than that of the cancer survivors (78.7 vs. 68.0 years, respectively; P < 0.001). For every unit increase in age, there was 7.6% increased odds of cancer death (95% confidence interval (CI): 3-12%) (P = 0.001). Average body mass index (BMI) of the patients in the cancer non-survivors group was significantly lower than that of the cancer survivors group (25.0 vs. 28.1 kg/m2; P = 0.008). Non-obese patients had 4.9 times greater odds of cancer death (95% CI: 1.51 - 15.81) (P = 0.008). The mean glycosylated hemoglobin (HbA1c) was significantly higher in the cancer non-survivors group compared to the cancer survivors group (7.1% vs. 6.0%; P < 0.001), and for every unit increase in HbA1c there was 1.6 times greater odds of cancer death (95% CI: 1.14 - 2.23) (P = 0.006). Patients with peripheral artery disease (PAD) had 3.5 times greater odds of cancer death compared to those without PAD (95% CI: 1.18 - 10.19) (P = 0.023). Conclusions Non-obese patients with cancer had higher odds of cancer death. Rising HbA1c, increasing age, and presence of PAD were associated with increased cancer mortality.
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Affiliation(s)
- Vede Ramdass
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA.,Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Elizabeth Caskey
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA
| | - Tammarah Sklarz
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA
| | - Saaniya Ajmeri
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA
| | - Vaishali Patel
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA
| | | | - Victor Pomary
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA
| | - Jillian Hall
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Omar Qari
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Rahul Tripathi
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Krystal Hunter
- Cooper Research Institute, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Satyajeet Roy
- Department of Medicine, Cooper University Health Care, Camden, NJ, USA.,Cooper Medical School of Rowan University, Camden, NJ, USA
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Wu M, Lu J, Yang Z, Shen P, Yu Z, Tang M, Jin M, Lin H, Chen K, Wang J. Longitudinal changes in fasting plasma glucose are associated with risk of cancer mortality: A Chinese cohort study. Cancer Med 2021; 10:5321-5328. [PMID: 34152090 PMCID: PMC8335834 DOI: 10.1002/cam4.4070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/05/2021] [Accepted: 05/31/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Numerous studies have suggested that fasting plasma glucose (FPG) was associated with the risk of mortality. However, relationship on longitudinal changes of FPG with the risk of mortality remained inconsistent. METHODS We examined the association of FPG at baseline and its longitudinal changes with risk of mortality based on a cohort study in Yinzhou, China, during 2010-2018. Cox regression models and competing risk models were separately used to examine the association of FPG levels and long-term fluctuation with risk of total and cause-specific mortality. RESULTS Subjects who had an impaired fasting glucose or diabetes suffered a higher risk of total mortality than subjects who had a normal fasting glucose (HRs and 95% CIs: 1.17 [1.01-1.35], 1.30 [1.10-1.53], respectively). The HR for total mortality was 1.54 (95% CI: 1.29-1.84) and for cancer mortality was 1.41 (95% CI: 1.04-1.92) in the highest quartile of coefficient of variation of FPG. Trajectory analysis indicated that subjects with a significantly changed FPG suffered a higher risk of total mortality. CONCLUSION According to this cohort study, we found that long-term fluctuation of FPG was significantly associated with the risk of total and cancer mortality. Our findings suggest that long-term fluctuation of FPG could be used as an efficient indicator for predicting the subsequent risk of mortality.
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Affiliation(s)
- Mengyin Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou, China
| | - Jieming Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou, China
| | - Zongming Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou, China
| | - Peng Shen
- Department of Chronic Disease and Health Promotion, Yinzhou District Center for Disease Control and Prevention, Ningbo, China
| | - Zhebin Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou, China
| | - Mengling Tang
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou, China
| | - Mingjuan Jin
- Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongbo Lin
- Department of Chronic Disease and Health Promotion, Yinzhou District Center for Disease Control and Prevention, Ningbo, China
| | - Kun Chen
- Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianbing Wang
- Department of Epidemiology and Biostatistics, The Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
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