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Wu X, Zhao Y, Zhou Q, Han M, Qie R, Qin P, Zhang Y, Huang Z, Liu J, Hu F, Luo X, Zhang M, Liu Y, Sun X, Hu D. All-cause mortality risk with different metabolic abdominal obesity phenotypes: the Rural Chinese Cohort Study. Br J Nutr 2023; 130:1637-1644. [PMID: 36924137 DOI: 10.1017/s0007114523000673] [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] [Indexed: 03/18/2023]
Abstract
We aimed to investigate the association of metabolic obesity phenotypes with all-cause mortality risk in a rural Chinese population. This prospective cohort study enrolled 15 704 Chinese adults (38·86 % men) with a median age of 51·00 (interquartile range: 41·00-60·00) at baseline (2007-2008) and followed up during 2013-2014. Obesity was defined by waist circumference (WC: ≥ 90 cm for men and ≥ 80 cm for women) or waist-to-height ratio (WHtR: ≥ 0·5). The hazard ratio (HR) and 95 % CI for the risk of all-cause mortality related to metabolic obesity phenotypes were calculated using the Cox hazards regression model. During a median follow-up of 6·01 years, 864 deaths were identified. When obesity was defined by WC, the prevalence of participants with metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically unhealthy obesity (MUO) at baseline was 12·12 %, 2·80 %, 41·93 % and 43·15 %, respectively. After adjusting for age, sex, alcohol drinking, smoking, physical activity and education, the risk of all-cause mortality was higher with both MUNO (HR = 1·20, 95 % CI 1·14, 1·26) and MUO (HR = 1·20, 95 % CI 1·13, 1·27) v. MHNO, but the risk was not statistically significant with MHO (HR = 0·99, 95 % CI 0·89, 1·10). This result remained consistent when stratified by sex. Defining obesity by WHtR gave similar results. MHO does not suggest a greater risk of all-cause mortality compared to MHNO, but participants with metabolic abnormality, with or without obesity, have a higher risk of all-cause mortality. These results should be cautiously interpreted as the representation of MHO is small.
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Affiliation(s)
- Xiaoyan Wu
- Department of Cardio-Cerebrovascular Disease and Diabetes Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, People's Republic of China
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Qionggui Zhou
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Minghui Han
- Department of Epidemiology and Health Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Ranran Qie
- Department of Epidemiology and Health Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Pei Qin
- Department of Medical Record Management, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Yanyan Zhang
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Zelin Huang
- Department of Biostatistics and Epidemiology, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Jiong Liu
- Department of Biostatistics and Epidemiology, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Xinping Luo
- Department of Biostatistics and Epidemiology, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of Biostatistics and Epidemiology, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Yu Liu
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Xizhuo Sun
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Dongsheng Hu
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
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Feng Y, Yang X, Li Y, Wu Y, Han M, Qie R, Huang S, Wu X, Zhang Y, Liu D, Hu F, Zhang M, Yang Y, Shi X, Lu J, Zhao Y, Hu D. Metabolic Score for Visceral Fat: A reliable indicator of visceral obesity for predicting risk for hypertension. Nutrition 2021; 93:111443. [PMID: 34563934 DOI: 10.1016/j.nut.2021.111443] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/24/2021] [Accepted: 07/28/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVES The aim of this study was to investigate the association of the Metabolic Score for Visceral Fat (METS-VF) with the risk for hypertension and to compare the ability of the METS-VF, the metabolic score for insulin resistance, visceral adiposity index, waist-to-height ratio, waist circumference, and body mass index to predict hypertension incidence based on a large prospective study of rural Chinese individuals. METHODS In all, 10 297 non-hypertensive adults (≥18 y of age) from a rural Chinese cohort study in 2007 and 2008 were included at baseline and followed up in 2013 and 2014. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between baseline METS-VF and hypertension risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the ability of METS-VF to predict hypertension incidence. RESULTS We identified 2071 hypertension cases during follow-up. After adjusting for multivariable confounding factors, the adjusted ORs (95% CIs) for the highest versus lowest METS-VF quartile overall and for men and women were 3.84 (3.23-4.56), 3.25 (2.48-4.24), and 4.14 (3.30-5.20), respectively. Also, per-SD increase in METS-VF was positively associated with hypertension risk overall and for men and women. Similar results were found in the sensitivity and subgroup analyses. Finally, the AUC value for hypertension was higher for METS-VF than the other five indices overall and for men and women. CONCLUSIONS The present study indicated that METS-VF was positively associated with hypertension incidence and performed better in predicting hypertension risk than five other indices, which suggests that METS-VF is a reliable predictor of hypertension in the Chinese population.
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Affiliation(s)
- Yifei Feng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xingjin Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yang Li
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Yuying Wu
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Minghui Han
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Ranran Qie
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Shengbing Huang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xiaoyan Wu
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Yanyan Zhang
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Dechen Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China; Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Fulan Hu
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Yongli Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xuezhong Shi
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Jie Lu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
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Zhang M, Zhao Y, Sun L, Xi Y, Zhang W, Lu J, Hu F, Shi X, Hu D. Cohort Profile: The Rural Chinese Cohort Study. Int J Epidemiol 2021; 50:723-724l. [PMID: 33367613 DOI: 10.1093/ije/dyaa204] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2020] [Indexed: 12/17/2022] Open
Affiliation(s)
- Ming Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
| | - Yang Zhao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Liang Sun
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuanlin Xi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Weidong Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Jie Lu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
| | - Xuezhong Shi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Dongsheng Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
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Zhao Y, Sun H, Zhang W, Xi Y, Shi X, Yang Y, Lu J, Zhang M, Sun L, Hu D. Elevated triglyceride-glucose index predicts risk of incident ischaemic stroke: The Rural Chinese cohort study. DIABETES & METABOLISM 2021; 47:101246. [PMID: 33722769 DOI: 10.1016/j.diabet.2021.101246] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/03/2021] [Accepted: 02/11/2021] [Indexed: 01/23/2023]
Abstract
AIM As the association between insulin resistance and ischaemic stroke is conflicting, our study aimed to examine the association between triglyceride-glucose (TyG), a surrogate marker of insulin resistance, and incident ischaemic stroke, and also to further assess the potential effect of modification by several known risk factors of stroke. METHODS The Rural Chinese Cohort Study enrolled 11,777 participants, aged ≥40 years, who were free of stroke and cardiovascular disease at baseline during 2007-2008, and who were then followed during 2013-2014. TyG was determined using the following formula: Ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The relative risk (RR) and 95% confidence interval (CI) of incident ischaemic stroke associated with TyG were estimated using modified Poisson regression models. RESULTS During a median follow-up duration of 6 years, 677 new ischaemic stroke cases were identified. After multivariate adjustment, RR (95% CI) values for ischaemic stroke were 1.33 (1.01-1.75), 1.57 (1.17-2.10) and 1.95 (1.34-2.82) in TyG quartile (Q) 2, 3 and 4 groups, respectively, compared with Q1. A significant interaction between TyG index and age for risk of ischaemic stroke (Pinteraction < 0.001) was also observed. However, no significant interaction was found between TyG index and other potential risk factors of risk for ischaemic stroke, although there were significant positive associations with female, non-smoker, non-drinker, low or moderate physical activity, non-obese and non-type 2 diabetes mellitus groups. CONCLUSION Elevated TyG index is an independent predictor of ischaemic stroke in the general population, and insulin resistance may be positively associated with future stroke risk.
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Affiliation(s)
- Yang Zhao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Haohang Sun
- Cardiovascular Department, Zhengzhou Yihe Hospital Affiliated to Henan University, Zhengzhou, Henan, People's Republic of China
| | - Weidong Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yuanlin Xi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xuezhong Shi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Jie Lu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Ming Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Liang Sun
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dongsheng Hu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China; Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
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5
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Guo C, Qin P, Li Q, Zhang D, Tian G, Liu D, Liu L, Cheng C, Chen X, Qie R, Han M, Huang S, Zhou Q, Liu F, Wu X, Zhao Y, Ren Y, Liu Y, Sun X, Li H, Wang B, Zhang M, Lu J, Hu D. Association between mean arterial pressure and risk of type 2 diabetes mellitus: The Rural Chinese Cohort Study. Prim Care Diabetes 2020; 14:448-454. [PMID: 32070665 DOI: 10.1016/j.pcd.2020.01.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 12/13/2019] [Accepted: 01/23/2020] [Indexed: 10/25/2022]
Abstract
AIMS Limited evidence is available on the association of mean arterial pressure and risk of type 2 diabetes mellitus (T2DM) among Chinese people. We aimed to investigate the association between MAP and risk of T2DM in rural Chinese adults. METHODS We performed a cohort study of 12,284 eligible participants (4668 men and 7616 women) without T2DM at baseline. Cox proportional-hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of MAP with risk of T2DM. Restricted cubic spline models were used to evaluate the dose-response association between MAP and risk of T2DM. RESULTS During a median of 6.01 years follow-up (73,403.52 person-years), T2DM developed in 847 participants (318 men and 529 women). In the multivariable-adjusted models, risk of T2DM was significantly higher for women with the third (90-100mmHg) and fourth MAP categories (≥100mmHg) than the first category (<80mmHg) after adjusting for confounders (HR=1.74 [95% CI 1.14-2.68] and 1.84 [1.20-2.83]). Restricted cubic spline analysis revealed increased risk of T2DM with increasing MAP for women. CONCLUSION High MAP was related to high incident T2DM among women in China.
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Affiliation(s)
- Chunmei Guo
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Pei Qin
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Quanman Li
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dongdong Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Gang Tian
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dechen Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Leilei Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Cheng Cheng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xu Chen
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Ranran Qie
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Minghui Han
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Shengbing Huang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Qionggui Zhou
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Feiyan Liu
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Xiaoyan Wu
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yongcheng Ren
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yu Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Honghui Li
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Bingyuan Wang
- The Central China Fuwai Cardiovascular Research Center, Zhengzhou, Henan, People's Republic of China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Jie Lu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
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Jia L, Lu H, Wu J, Wang X, Wang W, Du M, Wang P, Du S, Su Y, Zhang N. Association between diet quality and obesity indicators among the working-age adults in Inner Mongolia, Northern China: a cross-sectional study. BMC Public Health 2020; 20:1165. [PMID: 32711506 PMCID: PMC7382798 DOI: 10.1186/s12889-020-09281-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 07/20/2020] [Indexed: 02/08/2023] Open
Abstract
Background Obesity is a major risk factor for the global burden of disease in countries that are economically developed or not. This study aimed to investigate the association between diet quality and obesity indicators applying DASH and aMed. Methods This cross-sectional study on adult nutrition and chronic disease in Inner Mongolia (n = 1320). Dietary data were collected using 24-h diet recall for 3 consecutive days and weighing method. DASH and aMed were used to assess the dietary quality. WC, BMI and WC-BMI were used as obesity indicators. Logistic regression models were used to examine the associations between diet quality and obesity indicators. Results Higher diet quality, assessed by DASH, was only associated with WC. The odds ratio (OR) for abdominal obesity in the highest tertile of DASH scores compared with the lowest was 0.71 (95% confidence interval (CI) 0.53, 0.96; Ptrend = 0.03). Furthermore, aMed was inversely associated with obesity indicators. OR for abdominal obesity in the highest tertile of aMed score compared with the lowest were 0.63 (95% CI 0.47, 0.87; Ptrend = 0.005) and 0.57 (95% CI 0.41, 0.77; Ptrend = 0.02) for overweight and obesity, respectively, and 0.60 (95% CI 0.44, 0.81; Ptrend = 0.02) for high obesity risk. Conclusions Our findings suggest that dietary quality assessed using aMed is more closely associated with obesity than assessment using DASH in working-age adults in Inner Mongolia. The Mediterranean diet can be recommended as a healthy diet to control weight.
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Affiliation(s)
- Lu Jia
- Department of Health Statistics, School of Public Health, Inner Mongolia Medical University, Hohhot, 010110, China
| | - Haiwen Lu
- Department of Medical Imaging, Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia Medical University, Hohhot, 010050, China
| | - Jing Wu
- National Center for Chronic and Non-Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Xuemei Wang
- Department of Health Statistics, School of Public Health, Inner Mongolia Medical University, Hohhot, 010110, China.
| | - Wenrui Wang
- Department of Chronic Disease Control and Prevention, Inner Mongolia Center for Disease Control and Prevention, Hohhot, 010031, China
| | - Maolin Du
- Department of Health Statistics, School of Public Health, Inner Mongolia Medical University, Hohhot, 010110, China
| | - Peiyu Wang
- Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Sha Du
- Department of Health Statistics, School of Public Health, Inner Mongolia Medical University, Hohhot, 010110, China
| | - Yuenan Su
- Department of Health Statistics, School of Public Health, Inner Mongolia Medical University, Hohhot, 010110, China
| | - Nan Zhang
- Department of Hygienic Toxicology, School of Public Health, Inner Mongolia Medical University, Hohhot, 010110, China
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Deaths from total and premature cardiovascular disease associated with high normal blood pressure and hypertension in rural Chinese men and elderly people. J Hum Hypertens 2020; 35:741-750. [PMID: 32690863 DOI: 10.1038/s41371-020-0379-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/24/2020] [Accepted: 07/07/2020] [Indexed: 11/12/2022]
Abstract
To investigate the association of blood pressure (BP) categories with total and premature cardiovascular disease (CVD) mortality in rural Chinese. The study included 14,539 adults ≥18 years in rural China. Baseline study visits were conducted in 2007-2008, and follow-up visits in 2013-2014. Data were collected by face-to-face questionnaire interview, and anthropometric and laboratory measurements. A sub-distribution hazards model was used to calculate adjusted sub-distribution hazard ratios (aSHRs) and 95% confidence intervals (CIs). During the 6-year follow-up, 257 total and 209 premature CVD deaths occurred. As compared with normal BP (systolic BP/diastolic BP (SBP/DBP) < 120/80 mmHg), for men and people aged ≥60 years, hypertension (SBP/DBP ≥ 140/90 mmHg) associated with total CVD mortality (aSHR 3.57, 95% CI 2.06-6.17; aSHR 2.15, 1.29-3.56) and premature CVD mortality (aSHR 4.41, 2.37-8.21; aSHR 2.31, 1.27-4.19). Also, as compared with normal BP, for men and people aged ≥60 years with high normal BP (SBP/DBP 120-139/80-89 mmHg), risk of total CVD mortality increased (aSHR 1.85, 1.05-3.28; aSHR 1.78, 1.05-3.04), as was premature CVD mortality (aSHR 1.89, 0.99-3.64; aSHR 1.91, 1.03-3.54). Among men and people aged ≥60 years in rural China, risk of total and premature CVD mortality was increased for those with high normal BP and hypertension. Prevention and treatment strategies for additional CVD risk reduction targeting men and elderly people with hypertension or even high normal BP are needed to reduce CVD mortality risk.
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8
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Age and sex differences in the association between sleep duration and general and abdominal obesity at 6-year follow-up: the rural Chinese cohort study. Sleep Med 2020; 69:71-77. [DOI: 10.1016/j.sleep.2019.12.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 12/24/2019] [Accepted: 12/29/2019] [Indexed: 01/13/2023]
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9
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Qin P, Chen Q, Wang T, Chen X, Zhao Y, Li Q, Zhou Q, Guo C, Liu D, Tian G, Wu X, Qie R, Han M, Huang S, Liu L, Li Y, Hu D, Zhang M. Association of 6-year waist-circumference change with progression from prehypertension to hypertension: the Rural Chinese Cohort Study. J Hum Hypertens 2020; 35:215-225. [PMID: 32203072 DOI: 10.1038/s41371-020-0322-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/20/2020] [Accepted: 02/25/2020] [Indexed: 11/09/2022]
Abstract
Whether dynamic change in waist circumference is associated with progression from prehypertension to hypertension is not well understood. We explored this issue. A total of 4221 prehypertensive adults ≥18 years were enrolled during 2007-2008 and followed up during 2013-2014. Participants were classified by percentage waist-circumference change at follow-up: ≤-2.5, -2.5 to ≤2.5, 2.5 to ≤5.0, and >5.0%. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated with logistic regression models, with stable waist-circumference change (-2.5 to 2.5%) as the reference. During the 6 years of follow-up, 1464 prehypertensive patients (851 women) showed progression to hypertension, with an incidence rate of 32.7% for men and 36.3% for women. As compared with stable waist circumference, a waist-circumference gain > 5.0% was associated with increased hypertension risk: adjusted ORs (95% CI) were 1.08 (1.01-1.14) for men and 1.09 (1.04-1.15) for women. The risk also decreased significantly for men with ≥2.5% waist-circumference loss (OR = 0.94, 95% CI 0.88-1.00). We found a linear association between percentage waist-circumference gain and risk of progression from prehypertension to hypertension for both sexes by restricted cubic splines (pnonlinearity = 0.772 for men and 0.779 for women). For each 10% gain in waist circumference, the risk increased by 8% for men and 5% for women. The association remained significant for both sexes in a subgroup analysis by abdominal obesity at baseline. The long-term gain in waist circumference significantly increased the risk of progression from prehypertension to hypertension for both sexes in a rural Chinese population, regardless of abdominal obesity status at baseline.
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Affiliation(s)
- Pei Qin
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Qing Chen
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Tieqiang Wang
- Key Lab of Epidemiology, Department of Infectious Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, People's Republic of China
| | - Xiaoliang Chen
- Key Lab of Epidemiology, Department of Infectious Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Quanman Li
- Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Qionggui Zhou
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Chunmei Guo
- Key Lab of Epidemiology, Department of Infectious Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, People's Republic of China
| | - Dechen Liu
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.,Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China.,Key Lab of Epidemiology, Department of Infectious Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, People's Republic of China.,Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Gang Tian
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Xiaoyan Wu
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Ranran Qie
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Minghui Han
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Shengbing Huang
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Leilei Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yang Li
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Dongsheng Hu
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.,Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, People's Republic of China.,Key Lab of Epidemiology, Department of Infectious Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, People's Republic of China.,Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Ming Zhang
- School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
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10
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Metabolically healthy general and abdominal obesity are associated with increased risk of hypertension. Br J Nutr 2019; 123:583-591. [DOI: 10.1017/s0007114519003143] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
AbstractMetabolically healthy obesity refers to a subset of obese people with a normal metabolic profile. We aimed to explore the association between metabolically healthy and obesity status and risk of hypertension among Chinese adults from The Rural Chinese Cohort Study. This prospective cohort study enrolled 9137 Chinese adults without hypertension, type 2 diabetes or treatment for lipid abnormality at baseline (2007–2008) and followed up during 2013–2014. Modified Poisson regression models were used to examine the risk of hypertension by different metabolically healthy and obesity status, estimating relative risks (RR) and 95 % CI. During 6 years of follow-up, we identified 1734 new hypertension cases (721 men). After adjusting for age, sex, smoking and other confounding factors, risk of hypertension was increased with metabolically healthy general obesity (MHGO) defined by BMI (RR 1·75, 95 % CI 1·02, 3·00) and metabolically healthy abdominal obesity (MHAO) defined by waist circumference (RR 1·51, 95 % CI 1·12, 2·04) as compared with metabolically healthy non-obesity. The associations between metabolically healthy and obesity status and hypertension outcome were consistent after stratifying by sex, age, smoking, alcohol drinking and physical activity. Both MHGO and MHAO were associated with increased risk of hypertension. Obesity control programmes should be implemented to prevent or delay the development of hypertension in rural China.
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11
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Zhao Y, Zhang M, Liu Y, Sun H, Sun X, Yin Z, Li H, Ren Y, Liu D, Liu F, Chen X, Liu L, Cheng C, Zhou Q, Hu D. Adult height and risk of death from all-cause, cardiovascular, and cancer-specific disease: The Rural Chinese Cohort Study. Nutr Metab Cardiovasc Dis 2019; 29:1299-1307. [PMID: 31640891 DOI: 10.1016/j.numecd.2019.05.067] [Citation(s) in RCA: 4] [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: 03/02/2019] [Revised: 05/29/2019] [Accepted: 05/30/2019] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND AIMS We aimed to evaluate the sex-specific association of height and all-cause and cause-specific mortality in rural Chinese adults. METHODS AND RESULTS A total of 17,263 participants (10,448 women) ≥18 years old were randomly enrolled during 2007-2008 and followed up during 2013-2014. Sex-specific hazard ratios (HRs) for the height-mortality association, assessed in quintiles or 5 cm increments, were calculated by Cox proportional-hazards models. For both men and women, tall participants showed a baseline prevalence of high levels of socioeconomic factors including income and education but low systolic blood pressure and total cholesterol level. During a median of 6.01 years of follow-up, 620 men (in 39,993.45 person-years) and 490 women (in 61,590.10 person-years) died. With increasing height, the risk of all-cause mortality decreased in a curvilinear trend after adjustment for baseline age, socioeconomic and behavioral factors, and anthropometric and laboratory measurements. For men, height was inversely associated with all-cause mortality (HR per 5 cm increase: 0.89, 95% CI: 0.83-0.96) and cardiovascular mortality (HR per 5 cm increase: 0.81, 95% CI: 0.72-0.91). For women, height was inversely associated with all-cause mortality (HR per 5 cm increase: 0.88, 95% CI: 0.81-0.96) and other mortality (HR per 5 cm increase: 0.82, 95% CI: 0.71-0.96). CONCLUSIONS Our study demonstrated a sex-specific inverse effect of height on mortality from different major causes in rural Chinese adults.
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Affiliation(s)
- Yang Zhao
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Yu Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Haohang Sun
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Zhaoxia Yin
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Honghui Li
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Yongcheng Ren
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Dechen Liu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Feiyan Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Xu Chen
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Leilei Liu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Cheng Cheng
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Qionggui Zhou
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Dongsheng Hu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
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12
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Resting heart rate and its dynamic change and the risk of hypertension: The Rural Chinese Cohort Study. J Hum Hypertens 2019; 34:528-535. [DOI: 10.1038/s41371-019-0259-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 08/30/2019] [Accepted: 09/05/2019] [Indexed: 11/08/2022]
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13
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Muñoz-Torres FJ, Andriankaja OM, Ruiz JI, Joshipura KJ. Longitudinal association between adiposity and inter-arm blood pressure difference. J Clin Hypertens (Greenwich) 2019; 21:1519-1526. [PMID: 31490614 DOI: 10.1111/jch.13678] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/18/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
This is the first longitudinal study evaluating whether adiposity is associated with inter-arm blood pressure difference. We evaluated 714 overweight/obese individuals aged 40-65 years over a 3-year follow-up. Systolic and diastolic blood pressures were measured in both arms simultaneously using an automated machine. Linear regression assessed the associations of body mass index, fat %, waist, neck, thigh, and arm circumferences (cm), with absolute inter-arm differences in systolic (IAS) and diastolic (IAD) blood pressure (mm Hg). Poisson regression was used for binary outcomes (IAS and IAD ≥ 10 mm Hg). All models were adjusted for age, gender, smoking, physical activity, and HOMA-IR. Adiposity measures were associated with increased IAS and IAD (β range: 0.09-0.20 and 0.09-0.30). Neck circumference showed the strongest association with IAS (β = 0.20, 95% CI: 0.03, 0.37) and IAD (β = 0.30, 95% CI: 0.12, 0.47); arm circumference showed a similar association with IAS, but lower with IAD. Highest quartiles of BMI, thigh, and arm showed significant associations with IAS (IRR: 2.21, 2.46 and 2.70). Highest quartiles of BMI, waist, neck, and arm circumferences were significantly associated with IAD (IRR: 2.38, 2.68, 4.50 and 2.24). If the associations are corroborated in other populations, adiposity may be an important modifiable risk factor for inter-arm blood pressure difference with a large potential public health impact.
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Affiliation(s)
- Francisco J Muñoz-Torres
- Center for Clinical Research and Health Promotion, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Oelisoa M Andriankaja
- Center for Clinical Research and Health Promotion, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - José I Ruiz
- Center for Clinical Research and Health Promotion, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Kaumudi J Joshipura
- Center for Clinical Research and Health Promotion, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico.,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
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14
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Liu L, Chen X, Liu Y, Sun X, Yin Z, Li H, Zhang M, Wang B, Ren Y, Zhao Y, Liu D, Zhou J, Liu X, Zhang D, Cheng C, Liu F, Zhou Q, Xu Q, Xiong Y, Liu J, You Z, Hong S, Wang C, Hu D. The association between fasting plasma glucose and all-cause and cause-specific mortality by gender: The rural Chinese cohort study. Diabetes Metab Res Rev 2019; 35:e3129. [PMID: 30657630 DOI: 10.1002/dmrr.3129] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 01/12/2019] [Accepted: 01/14/2019] [Indexed: 11/10/2022]
Abstract
BACKGROUND To evaluate the association between fasting plasma glucose (FPG) and mortality by gender. METHODS A total of 17 248 eligible participants from a rural Chinese prospective cohort population were included. The same questionnaire interview and anthropometric and laboratory measurements were performed at both baseline (2007-2008) and follow-up (2013-2014). Participants were classified according to baseline FPG and diabetic status by sex. Restricted cubic splines and Cox proportional-hazards regression models, estimating hazard ratio (HR) and 95% confidence interval (CI), were used to assess the FPG-mortality relation. RESULTS During the 6-year follow-up, 618 men and 489 women died. The FPG-mortality relation was J shaped for both sexes. For men, risk of all-cause and noncardiovascular disease (CVD)/noncancer mortality was greater with low fasting glucose (LFG) than with normal fasting glucose (adjusted HR [aHR] 1.60; 95% CI, 1.05-2.43; and aHR 2.16; 95% CI, 1.15-4.05). Men with diabetes mellitus (DM) showed increased risk of all-cause (aHR 2.04; 95% CI, 1.60-2.60), CVD (aHR 1.98; 95% CI, 1.36-2.89), and non-CVD/noncancer mortality (aHR 2.62; 95% CI, 1.76-3.91). Men with impaired fasting glucose (IFG) had borderline risk of CVD mortality (aHR 1.34; 95% CI, 1.00-1.79). Women with LFG had increased risk of non-CVD/noncancer mortality (aHR 2.27; 95% CI, 1.04-4.95), and women with DM had increased risk of all-cause (aHR 1.73; 95% CI, 1.35-2.23), CVD (aHR 1.76; 95% CI, 1.24-2.50), and non-CVD/noncancer mortality (aHR 1.97; 95% CI, 1.27-3.08). CONCLUSIONS LFG is positively associated with all-cause mortality risk in rural Chinese men but not in women.
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Affiliation(s)
- Leilei Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Xu Chen
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yu Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, China
| | - Zhaoxia Yin
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, China
| | - Honghui Li
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Bingyuan Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Yongcheng Ren
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Dechen Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Junmei Zhou
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Xuejiao Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Dongdong Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Cheng Cheng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Feiyan Liu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Qionggui Zhou
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, China
| | - Qihuan Xu
- Department of Clinical Medicine, Shenzhen University, Shenzhen, China
| | - Yihan Xiong
- Department of Clinical Medicine, Shenzhen University, Shenzhen, China
| | - Jiali Liu
- Department of Clinical Medicine, Shenzhen University, Shenzhen, China
| | - Ziyang You
- Department of Clinical Medicine, Shenzhen University, Shenzhen, China
| | - Shihao Hong
- Department of Clinical Medicine, Shenzhen University, Shenzhen, China
| | - Chongjian Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
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15
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Zhao Y, Zhang M, Liu Y, Yin Z, Li H, Sun H, Wang C, Ren Y, Liu D, Cheng C, Liu F, Chen X, Liu L, Zhou Q, Xiong Y, Xu Q, Liu J, Hong S, You Z, Li J, Cao J, Huang J, Sun X, Hu D. 6-year change in resting heart rate is associated with incident type 2 diabetes mellitus. Nutr Metab Cardiovasc Dis 2019; 29:236-243. [PMID: 30718140 DOI: 10.1016/j.numecd.2018.12.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 11/13/2018] [Accepted: 12/07/2018] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND AIMS Elevated resting heart rate (RHR) is associated with risk of type 2 diabetes mellitus (T2DM). However, the association of change in RHR (ΔRHR) and incident T2DM is not fully elucidated. We aimed to assess the dose-response association between 6-year ΔRHR and T2DM. METHODS AND RESULTS A total of 12155 non-T2DM participants ≥18 years old were enrolled during 2007-2008 and followed up during 2013-2014. ΔRHR was calculated by subtracting the baseline RHR from the RHR value at 6-year follow-up. Age-, sex-, and RHR-specific relative risks (RRs) and 95% confidence intervals (CIs) for the effect of ΔRHR on incident T2DM were calculated by using modified Poisson regression models. As compared with ΔRHR of 0 beats/min, the adjusted risk of T2DM was significantly increased with RHR increment and reduced with RHR reduction. ΔRHR was positively associated with future risk of T2DM [RR per unit increase: 1.03 (1.03-1.04)]. As compared with stable change in RHR group (-5<ΔRHR<5 beats/min), for ΔRHR ≤ -10 beats/min, -10<ΔRHR ≤ -5 beats/min, 5≤ΔRHR<10 beats/min, and ΔRHR ≥10 beats/min groups, the pooled adjusted RR (95% CI) of T2DM was 0.69 (0.55-0.86), 0.90 (0.73-1.11), 1.31 (1.07-1.61), and 1.90 (1.59-2.26), respectively. This significant association still existed on subgroup analyses based on age, sex, and baseline RHR and sensitivity analyses. CONCLUSIONS Dynamic RHR change was significantly associated with incident T2DM. Our study suggests that RHR may be a non-invasive clinical indicator for interventions aiming to reduce incident T2DM in the general population.
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Affiliation(s)
- Y Zhao
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - M Zhang
- Guangdong Key Laboratory for Genome Stability & Disease Prevention, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Y Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Z Yin
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - H Li
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - H Sun
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, People's Republic of China
| | - C Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, People's Republic of China
| | - Y Ren
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Guangdong Key Laboratory for Genome Stability & Disease Prevention, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - D Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, People's Republic of China
| | - C Cheng
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, People's Republic of China
| | - F Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - X Chen
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - L Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Q Zhou
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Y Xiong
- Department of Clinical Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Q Xu
- Department of Clinical Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - J Liu
- Department of Clinical Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - S Hong
- Department of Clinical Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Z You
- Department of Clinical Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - J Li
- Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - J Cao
- Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - J Huang
- Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - X Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
| | - D Hu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China; Study Team of Shenzhen's Sanming Project, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
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16
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Zhao Y, Liu Y, Sun H, Sun X, Yin Z, Li H, Ren Y, Wang B, Zhang D, Liu X, Liu D, Zhang R, Liu F, Chen X, Liu L, Cheng C, Zhou Q, Hu D, Zhang M. Association of long-term dynamic change in body weight and incident hypertension: The Rural Chinese Cohort Study. Nutrition 2018; 54:76-82. [DOI: 10.1016/j.nut.2018.02.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 02/14/2018] [Accepted: 02/16/2018] [Indexed: 12/17/2022]
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17
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Wang B, Zhang M, Liu Y, Sun X, Zhang L, Wang C, Li L, Ren Y, Han C, Zhao Y, Zhou J, Pang C, Yin L, Feng T, Zhao J, Hu D. Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China. J Diabetes 2018; 10:641-652. [PMID: 29322661 DOI: 10.1111/1753-0407.12642] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 12/17/2017] [Accepted: 01/07/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Inexpensive and easily measured indices are needed for the early prediction of type 2 diabetes mellitus (T2DM) in rural areas of China. The aim of this study was to compare triglyceride glucose (TyG), visceral adiposity (VAI), and lipid accumulation product (LAP) with traditional individual measures and their ratios for predicting T2DM. METHODS Data for 11 113 people with baseline normal fasting glucose in a rural Chinese cohort were followed for a median of 6.0 years. Cox proportional hazards regression was used to calculate covariate-adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs) and receiver operating characteristic analysis was used to compare the ability of traditional measures and TyG, VAI, and LAP at baseline to predict T2DM at follow-up. RESULTS Among individual measures, fasting plasma glucose (FPG) and waist circumference (WC) were strongly associated with T2DM. Of all lipid ratios, an elevated triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ratio was associated the most with T2DM. Compared with the first quartiles of TyG, VAI, and LAP, their fourth quartiles were associated with T2DM for men (aHR 3.54 [95% CI 2.08-6.03], 2.89 [1.72-4.87], and 5.02 [2.85-8.85], respectively) and women (6.15 [3.48-10.85], 4.40 [2.61-7.42], and 6.49 [3.48-12.12], respectively). For predicting T2DM risk, TyG, VAI, and LAP were mostly superior to the TG: HDL-C ratio, but did not differ from FPG and WC. CONCLUSIONS Prediction of T2DM was not improved by TyG, VAI, and LAP versus FPG or WC alone. Therefore, TyG, VAI, and LAP may not be inexpensive tools for predicting T2DM in rural Chinese people.
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Affiliation(s)
- Bingyuan Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Yu Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Lu Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Chongjian Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Linlin Li
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yongcheng Ren
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Chengyi Han
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Junmei Zhou
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Chao Pang
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China
| | - Lei Yin
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China
| | - Tianping Feng
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China
| | - Jingzhi Zhao
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
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18
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Zhao Y, Liu Y, Sun H, Sun X, Yin Z, Li H, Ren Y, Wang B, Zhang D, Liu X, Liu D, Zhang R, Liu F, Chen X, Liu L, Cheng C, Zhou Q, Hu D, Zhang M. Body mass index and risk of all-cause mortality with normoglycemia, impaired fasting glucose and prevalent diabetes: results from the Rural Chinese Cohort Study. J Epidemiol Community Health 2018; 72:1052-1058. [PMID: 30042126 DOI: 10.1136/jech-2017-210277] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 06/18/2018] [Accepted: 07/08/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND Previous evidence of an association between body mass index (BMI) and mortality in patients with diabetes was inconsistent. The BMI-mortality association with normal fasting glucose (NFG), impaired fasting glucose (IFG) and prevalent diabetes is still unclear in the Chinese population. METHODS We analysed data for 17 252 adults from the Rural Chinese Cohort Study during 2007-2008 and followed for mortality during 2013-2014. Participants were classified with NFG, IFG and diabetes according to baseline measurement values of fasting glucose and self-reported diabetes. Multivariable Cox proportional hazard models were used to calculate HRs and 95% CIs across BMI categories by glycemic status. RESULTS During the 6-year follow-up, 1109 participants died (563/10 181 with NFG, 349/5572 with IFG and 197/1499 with diabetes). The BMI-mortality association was curvilinear, with low BMI (even in normal range) associated with increased mortality regardless of glycemic status. In adjusted Cox models, risk of mortality showed a decreasing trend with BMI≤18 kg/m2, 18<BMI≤20 kg/m2 and 20<BMI≤22 kg/m2 vs 22<BMI≤24 kg/m2: HR 2.83 (95% CI 1.78 to 4.51), 2.05 (1.46 to 2.87) and 1.45 (1.10 to 1.90), respectively, for NFG; 2.53 (1.25 to 5.14), 1.36 (0.86 to 2.14) and 1.09 (0.76 to 1.57), respectively, for IFG; and 4.03 (1.42 to 11.50), 2.00 (1.05 to 3.80) and 1.52 (0.88 to 2.60), respectively, for diabetes. The risk of mortality was lower for patients with diabetes who were overweight or obese versus normal weight. CONCLUSIONS Low BMI was associated with increased mortality regardless of glycemic status. Future studies are needed to explain the 'obesity paradox' in patients with diabetes.
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Affiliation(s)
- Yang Zhao
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Yu Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Haohang Sun
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Zhaoxia Yin
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Honghui Li
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Yongcheng Ren
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Bingyuan Wang
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Dongdong Zhang
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xuejiao Liu
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Dechen Liu
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Ruiyuan Zhang
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Feiyan Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Xu Chen
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Leilei Liu
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Cheng Cheng
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Qionggui Zhou
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Dongsheng Hu
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, People's Republic of China
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19
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Liu F, Zhang H, Liu Y, Sun X, Yin Z, Li H, Deng K, Zhao Y, Wang B, Ren Y, Zhang L, Zhou J, Han C, Liu X, Zhang D, Chen G, Hong S, Wang C, Hu D, Zhang M. Sleep Duration Interacts With Lifestyle Risk Factors and Health Status to Alter Risk of All-Cause Mortality: The Rural Chinese Cohort Study. J Clin Sleep Med 2018; 14:857-865. [PMID: 29734984 DOI: 10.5664/jcsm.7124] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 02/16/2018] [Indexed: 12/23/2022]
Abstract
STUDY OBJECTIVES Many studies suggest an association of both short and long sleep duration with all-cause mortality, but the effect of co-occurrence of sleep duration and other lifestyle risk factors or health status remains unclear. METHODS A total of 17,184 participants aged 18 years or older from rural areas of China were examined at baseline from 2007 to 2008 and followed up from 2013 to 2014. Cox proportional hazard models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). RESULTS During 6-year follow-up, we identified 1,101 deaths. The multivariable-adjusted mortality risk was significantly higher with short-duration sleepers (< 6.5 hours) (HR = 1.37, 95% CI 1.01-1.86) and long-duration sleepers (≥ 9.5 hours) (HR = 1.35, 95% CI 1.05-1.74) versus 6.5-7.5 hours. The multiplicative interaction of long sleep duration with some lifestyle risk factors and health statuses increased the mortality risk in men (low level of physical activity: HR = 1.03, 95% CI 1.02-1.04; hypertension: HR = 1.06, 95% CI 1.04-1.09; type 2 diabetes mellitus [T2DM]: HR = 1.07, 95% CI 1.04-1.11). Similar results were found in women (low level of physical activity: HR = 1.03, 95% CI 1.02-1.05; T2DM: HR = 1.07, 95% CI 1.05-1.10). CONCLUSIONS Sleep duration could be a predictor of all-cause mortality and its interaction with physical activity, hypertension, and T2DM may increase the risk of mortality.
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Affiliation(s)
- Feiyan Liu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,Guangdong Key Laboratory for Genome Stability and Disease Prevention, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Hongyan Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Yu Liu
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Zhaoxia Yin
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Honghui Li
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Kunpeng Deng
- Yantian Entry-exit Inspection and Quarantine Bureau, Shenzhen, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Bingyuan Wang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Yongcheng Ren
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Lu Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Junmei Zhou
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Chengyi Han
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Xuejiao Liu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Dongdong Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Guozhen Chen
- Department of Clinical Medicine, Shenzhen University, Shenzhen, Guangdong, People's Republic of China
| | - Shihao Hong
- Department of Clinical Medicine, Shenzhen University, Shenzhen, Guangdong, People's Republic of China
| | - Chongjian Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dongsheng Hu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,Guangdong Key Laboratory for Genome Stability and Disease Prevention, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.,The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
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20
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Wu O, Leng JH, Yang FF, Yang HM, Zhang H, Li ZF, Zhang XY, Yuan CD, Li JJ, Pan Q, Liu W, Ren YJ, Liu B, Liu QM, Cao CJ. A comparative research on obesity hypertension by the comparisons and associations between waist circumference, body mass index with systolic and diastolic blood pressure, and the clinical laboratory data between four special Chinese adult groups. Clin Exp Hypertens 2017; 40:16-21. [PMID: 29083240 DOI: 10.1080/10641963.2017.1281940] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The obesity-hypertension pathogenesis is complex. From the phenotype to molecular mechanism, there is a long way to clarify the mechanism. To explore the association between obesity and hypertension, we correlate the phenotypes such as the waist circumference (WC), body mass index (BMI), systolic blood pressure (SB), and diastolic blood pressure (DB) with the clinical laboratory data between four specific Chinese adult physical examination groups (newly diagnosed untreated just-obesity group, newly diagnosed untreated obesity-hypertension group, newly diagnosed untreated just-hypertension group, and normal healthy group), and the results may show something. OBJECTIVE To explore the mechanisms from obesity to hypertension by analyzing the correlations and differences between WC, BMI, SB, DB, and other clinical laboratory data indices in four specific Chinese adult physical examination groups. METHODS This cross-sectional study was conducted from September 2012 to July 2014, and 153 adult subjects, 34 women and 119 men, from 21 to 69 years, were taken from four characteristic Chinese adult physical examination groups (newly diagnosed untreated just-obesity group, newly diagnosed untreated obesity-hypertension group, newly diagnosed untreated just-hypertension group, and normal healthy group). The study was approved by the ethics committee of Hangzhou Center for Disease Control and Prevention. WC, BMI, SB, DB, and other clinical laboratory data were collected and analyzed by SPSS. RESULTS Serum levels of albumin (ALB),alanine aminotransferase (ALT), low density lipoprotein cholesterol (LDLC), triglyceride (TG), high density lipoprotein cholesterol (HDLC), alkaline phosphatase (ALP), uric acid (Ua), and TC/HDLC (odds ratio) were statistically significantly different between the four groups. WC statistically significantly positively correlated with BMI, ALT, Ua, and serum levels of glucose (GLU), and TC/HDLC, and negatively with ALB, HDLC, and serum levels of conjugated bilirubin (CB). BMI was statistically significantly positively related to ALT, Ua, LDLC, WC, and TC/HDLC, and negatively to ALB, HDLC, and CB. DB statistically significantly positively correlated with ALP, BMI, and WC. SB was statistically significantly positively related to LDLC, GLU, serum levels of fructosamine (FA), serum levels of the total protein (TC), BMI, and WC. CONCLUSION The negative body effects of obesity are comprehensive. Obesity may lead to hypertension through multiple ways by different percents. GGT, serum levels of gamma glutamyltransferase; ALB, serum levels of albumin; ALT, serum levels of alanine aminotransferase; LDLC, serum levels of low density lipoprotein cholesterol; TG, serum levels of triglyceride; HDLC, serum levels of high density lipoprotein cholesterol; FA, serum levels of fructosamine; S.C.R, serum levels of creatinine; IB, serum levels of indirect bilirubin; ALP, serum levels of alkaline phosphatase; CB, serum levels of conjugated bilirubin; UREA, Urea; Ua, serum levels of uric acid; GLU, serum levels of glucose; TC, serum levels of the total cholesterol; TB, serum levels of the total bilirubin; TP, serum levels of the total protein; TC/HDLC, TC/HDLC ratio.
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Affiliation(s)
- Ou Wu
- a Department of Chronic and Non-communicable Disease Control and Prevention , Hangzhou Center for Disease Control and Prevention , Zhejiang , P.R. China
| | - Jian-Hang Leng
- b Department of Central Laboratory/Medical examination center of Hangzhou , The Frist People's Hospital of Hangzhou , Zhejiang , P.R. China
| | - Fen-Fang Yang
- b Department of Central Laboratory/Medical examination center of Hangzhou , The Frist People's Hospital of Hangzhou , Zhejiang , P.R. China
| | - Hai-Ming Yang
- b Department of Central Laboratory/Medical examination center of Hangzhou , The Frist People's Hospital of Hangzhou , Zhejiang , P.R. China
| | - Hu Zhang
- c Department of Thoracic Surgery , Sir Run Run Shaw Hospital Affiliated with Medical College of Zhejiang University , Zhejiang , P.R. China
| | - Zeng-Fang Li
- b Department of Central Laboratory/Medical examination center of Hangzhou , The Frist People's Hospital of Hangzhou , Zhejiang , P.R. China
| | - Xing-Yu Zhang
- d Department of Anatomy with Radiology , University of Auckland , Auckland , New Zealand
| | - Cheng-Da Yuan
- e Dermatological department of Zhejiang Chinese Medical University Affiliated Hospital , Kwong Hing/Hangzhou Municipal TCM Hospital , Hangzhou , P.R. China
| | - Jia-Jia Li
- f Department of Central Laboratory , The First Affiliated Hospital of Anhui Medical University , Anhui , P.R. China
| | - Qi Pan
- g Department of Neurosurgery , The Affiliated Hospital of Hainan Medical College , Hainan, P.R. China
| | - Wei Liu
- a Department of Chronic and Non-communicable Disease Control and Prevention , Hangzhou Center for Disease Control and Prevention , Zhejiang , P.R. China
| | - Yan-Jun Ren
- a Department of Chronic and Non-communicable Disease Control and Prevention , Hangzhou Center for Disease Control and Prevention , Zhejiang , P.R. China
| | - Bing Liu
- a Department of Chronic and Non-communicable Disease Control and Prevention , Hangzhou Center for Disease Control and Prevention , Zhejiang , P.R. China
| | - Qing-Min Liu
- a Department of Chronic and Non-communicable Disease Control and Prevention , Hangzhou Center for Disease Control and Prevention , Zhejiang , P.R. China
| | - Cheng-Jian Cao
- h Director Office of Hangzhou hospital for the prevention and treatment of occupational diseases
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21
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Zhang M, Zhao Y, Sun H, Luo X, Wang C, Li L, Zhang L, Wang B, Ren Y, Zhou J, Han C, Zhang H, Yang X, Pang C, Yin L, Feng T, Zhao J, Hu D. Effect of dynamic change in body mass index on the risk of hypertension: Results from the Rural Chinese Cohort Study. Int J Cardiol 2017; 238:117-122. [DOI: 10.1016/j.ijcard.2017.03.025] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 03/03/2017] [Accepted: 03/08/2017] [Indexed: 02/03/2023]
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22
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Zhao Y, Sun H, Wang B, Zhang M, Luo X, Ren Y, Zhou J, Han C, Wang C, Li L, Zhang L, Pang C, Yin L, Feng T, Zhao J, Hu D. Impaired fasting glucose predicts the development of hypertension over 6years in female adults: Results from the rural Chinese cohort study. J Diabetes Complications 2017; 31:1090-1095. [PMID: 28433447 DOI: 10.1016/j.jdiacomp.2017.04.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 03/17/2017] [Accepted: 04/06/2017] [Indexed: 12/28/2022]
Abstract
AIMS To investigate whether impaired fasting glucose (IFG) is an independent risk factor for incident hypertension in a rural Chinese population. METHODS We selected 9583 eligible participants 18 to 75years old, who were without hypertension and diabetes at baseline (from 2007 to 2008) and were from a rural area in the middle of China. Concentration of fasting glucose at baseline was assessed in quartiles to predict hypertension risk by gender. Odds ratios (ORs) and 95% confidence intervals (CIs) for IFG (fasting glucose of 100 to 125mg/dl) associated with hypertension were estimated by logistic regression models. RESULTS Risk of hypertension was increased for females with glucose levels in quartile 2 (90-96mg/dl), quartile 3 (96-102mg/dl), and quartile 4 (102-125mg/dl) versus quartile 1 (<90mg/dl): OR=1.27 (95% CI=1.01-1.60), 1.30 (1.04-1.63), and 1.55 (1.24-1.93), respectively. During the 6-year follow-up, the cumulative incidence of hypertension was greater for people with IFG than normal fasting glucose (NFG) at baseline (23.9% vs 18.4%, p<0.001 for males and 23.8% vs 16.4%, p<0.001 for females). Risk of incident hypertension was significantly increased for females with IFG versus NFG (OR=1.23 95% CI=1.05-1.45). CONCLUSIONS IFG may be an independent risk factor for hypertension in normotensive nondiabetic Chinese females.
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Affiliation(s)
- Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Haohang Sun
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Bingyuan Wang
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Xinping Luo
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Yongcheng Ren
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Junmei Zhou
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Chengyi Han
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Chongjian Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Linlin Li
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Lu Zhang
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China; Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
| | - Chao Pang
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
| | - Lei Yin
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
| | - Tianping Feng
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
| | - Jingzhi Zhao
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
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23
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Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. Cardiovasc Diabetol 2017; 16:30. [PMID: 28249577 PMCID: PMC5333419 DOI: 10.1186/s12933-017-0514-x] [Citation(s) in RCA: 174] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Accepted: 02/22/2017] [Indexed: 12/15/2022] Open
Abstract
Background Risk of type 2 diabetes mellitus (T2DM) is increased in metabolically obese but normal-weight people. However, we have limited knowledge of how to prevent T2DM in normal-weight people. We aimed to evaluate the association between triglyceride glucose (TyG) index and incident T2DM among normal-weight people in rural China. Methods We included data from 5706 people with normal body mass index (BMI) (18.5–23.9 kg/m2) without baseline T2DM in a rural Chinese cohort followed for a median of 6.0 years. A Cox proportional-hazard model was used to assess the risk of incident T2DM by quartiles of TyG index and difference in TyG index between follow-up and baseline (TyG-D), estimating hazard ratios (HRs) and 95% confidence intervals (CIs). A generalized additive plot was used to show the nonparametric smoothed exposure–response association between risk of T2DM and TyG index as a continuous variable. TyG was calculated as ln [fasting triglyceride level (mg/dl) × fasting plasma glucose level (mg/dl)/2]. Results Risk of incident T2DM was increased with quartiles 2, 3 and 4 versus quartile 1 of TyG index (adjusted HR [aHR] 2.48 [95% CI 1.20–5.11], 3.77 [1.83–7.79], and 5.30 [2.21–12.71], Ptrend < 0.001 across quartiles of TyG index). Risk of incident T2DM was increased with quartile 4 versus quartile 1 of TyG-D (aHR 3.91 [2.22–6.87]). The results were consistent when analyses were restricted to participants without baseline metabolic syndrome and impaired fasting glucose level. The generalized additive plot showed cumulative increased risk of T2DM with increasing TyG index. Conclusions Risk of incident T2DM is increased with increasing TyG index among rural Chinese people, so the index might be an important indicator for identifying people at high risk of T2DM.
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Body mass index and waist circumference combined predicts obesity-related hypertension better than either alone in a rural Chinese population. Sci Rep 2016; 6:31935. [PMID: 27545898 PMCID: PMC4992958 DOI: 10.1038/srep31935] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Accepted: 07/29/2016] [Indexed: 12/25/2022] Open
Abstract
Limited information is available on the association of obesity defined by both body mass index (BMI) and waist circumference (WC) with incident hypertension in rural China. A total of 9,174 participants ≥18 years old from rural areas in middle of China, free of hypertension, diabetes, myocardial infarction and stroke, were selected in this cohort study. Questionnaire interview and anthropometric and laboratory measurements were performed at baseline (2007–2008) and follow-up (2013–2014). During the 6 years of follow-up, hypertension developed in 733/3,620 men and 1,051/5,554 women. After controlling for age, education level, smoking, drinking, physical activity, and family history of hypertension, the relative risk of hypertension was lower for participants with high BMI but normal WC than those with both BMI and WC obesity for men 18–39 and 40–59 years old. Women 18–39 years old with normal BMI but high WC showed a 1.96-fold risk of hypertension, and being female with age 40–59 years and high BMI but normal WC was independently associated with hypertension incidence as compared with both normal BMI and WC. BMI is more associated with hypertension as compared with WC in both genders. High WC tends to add additional risk of hypertension in young women.
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