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Bai X, Li Z, Cai Z, Yao M, Chen L, Wang Y. Gender differences in risk factors for ischemic stroke: a longitudinal cohort study in East China. BMC Neurol 2024; 24:171. [PMID: 38783249 PMCID: PMC11112765 DOI: 10.1186/s12883-024-03678-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 05/17/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES Epidemiological studies of stroke and its risk factors can help develop strategies to prevent stroke. We aimed to explore the current gender-specific prevalence of stroke and associated risk factors. METHODS Data were collected using a structured precoded questionnaire designed by the Stroke Screening and Prevention Programme of the National Health and Wellness Commission Stroke Prevention and Control Project Committee, between June 2020 and November 2021. A total of 7394 residents took part in the study, 187 of whom had a stroke. The baseline information of each participant was obtained and included in this study. The chi-square test and Kruskal-Wallis tests were used to examine the relationship between these indicators and stroke, and then multivariate logistic regression was used to construct the prediction scale between different genders. RESULTS of 7394 participants,4571 (61.82%) were female. The overall prevalence of stroke patients in the study population was 2.53%, Multivariate analysis found that residence status (OR = 0.43, p = 0.002) 、HCY (OR = 0.962, p = 0.000)、Previous TIA (OR = 0.200, p = 0.002) 、Hypertension (OR = 0.33, p = 0.000) and Dyslipidemia (OR = 0.668, p = 0.028) were significant predictors of stroke. there are gender differences in the traditional risk factors for stroke, and women have more risk factors. ROC analysis confirmed the accuracy of the stroke risk model, and the AUC of the stroke risk model for the general population was 0.79 with p < 0.05. In the gender model, the female AUC was 0.796 (p < 0.05). and the male AUC was 0.786 with p < 0.05. CONCLUSION The prevalence of stroke in adults aged 40 years and above is high in eastern China were high. management of risk factors can effectively prevent the occurrence of most strokes. more attention should be paid to gender differences associated with stroke.
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Affiliation(s)
- Xinping Bai
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Zifeng Li
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Zhuo Cai
- Department of Neurology, Fuyang Hospital Affiliated to Bengbu Medical University, Anhui, 236000, People's Republic of China
| | - Mingren Yao
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Lin Chen
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Youmeng Wang
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China.
- Department of Neurology, Fuyang Hospital Affiliated to Bengbu Medical University, Anhui, 236000, People's Republic of China.
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Kim D, Kim HJ, Song TJ. Association of body composition indices with cardiovascular outcomes: a nationwide cohort study. Am J Clin Nutr 2024; 119:876-884. [PMID: 38408726 DOI: 10.1016/j.ajcnut.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 02/03/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Previous studies regarding BMI (kg/m2) and associated cardiovascular outcomes yield inconsistent results. OBJECTIVES We aimed to investigate the association between body composition and cardiovascular outcomes according to BMI categories in the Korean general population. METHODS A total of 2,604,401 participants were enrolled in this nationwide cohort study using the National Health Insurance Service-Health Checkup data set. Predicted lean BMI (pLBMI), body fat mass index (pBFMI), and appendicular skeletal muscle mass index (pASMMI) were calculated using validated anthropometric prediction equations. A multivariable time-dependent Cox regression analysis was conducted to assess the association with cardiovascular outcomes. The results were presented with adjusted hazard ratios (HRs) with 95% confidence intervals (CIs), considering BMI categories (BMI < 18.5, BMI 18.5-24.9, BMI 25-29.9, and BMI ≥ 30). RESULTS Higher pLBMI and pASMMI were correlated with a reduced risk of composite cardiovascular outcomes. For pLBMI, HR was 0.910 (95% CI: 0.908, 0.913, P < 0.001) for males and 0.905 (95% CI: 0.899, 0.910, P < 0.001) for females. For pASMMI, HR was 0.825 (95% CI: 0.820, 0.829, P < 0.001) for males and 0.788 (95% CI: 0.777, 0.800, P < 0.001) for females. Conversely, a higher pBFMI was associated with an increased risk, with HR of 1.082 (95% CI: 1.071, 1.093, P < 0.001) for males and 1.181 (95% CI: 1.170, 1.192, P < 0.001) for females. Subgroup analysis based on BMI categories revealed no significant risk association for pBFMI in the BMI < 18.5 group. In the group with BMI ≥ 30, neither pLBMI nor pASMMI demonstrated a significant risk association. CONCLUSIONS Our results highlight the value of pLBMI, pBFMI, and pASMMI as variables for assessing risk of composite cardiovascular outcomes. The significance of indicators may vary depending on BMI categories.
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Affiliation(s)
- Dongyeop Kim
- Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Hyung Jun Kim
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Tae-Jin Song
- Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
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Zhang Y, Sun Q, Yu C, Sun D, Pang Y, Pei P, Du H, Yang L, Chen Y, Yang X, Chen X, Chen J, Chen Z, Li L, Lv J. Associations of traditional cardiovascular risk factors with 15-year blood pressure change and trajectories in Chinese adults: a prospective cohort study. J Hypertens 2024:00004872-990000000-00438. [PMID: 38525868 DOI: 10.1097/hjh.0000000000003717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
OBJECTIVE How traditional cardiovascular disease (CVD) risk factors are related to long-term blood pressure change (BPC) or trajectories remain unclear. We aimed to examine the independent associations of these factors with 15-year BPC and trajectories in Chinese adults. METHODS We included 15 985 participants who had attended three surveys, including 2004-2008 baseline survey, and 2013-2014 and 2020-2021 resurveys, over 15 years in the China Kadoorie Biobank (CKB). We measured systolic and diastolic blood pressure (SBP and DBP), height, weight, and waist circumference (WC). We asked about the sociodemographic characteristics and lifestyle factors, including smoking, alcohol drinking, intake of fresh vegetables, fruits, and red meat, and physical activity, using a structured questionnaire. We calculated standard deviation (SD), cumulative blood pressure (cumBP), coefficient of variation (CV), and average real variability (ARV) as long-term BPC proxies. We identified blood pressure trajectories using the latent class growth model. RESULTS Most baseline sociodemographic and lifestyle characteristics were associated with cumBP. After adjusting for other characteristics, the cumSBP (mmHg × year) increased by 116.9 [95% confidence interval (CI): 111.0, 122.7] for every 10 years of age. The differences of cumSBP in heavy drinkers of ≥60 g pure alcohol per day and former drinkers were 86.7 (60.7, 112.6) and 48.9 (23.1, 74.8) compared with less than weekly drinkers. The cumSBP in participants who ate red meat less than weekly was 29.4 (12.0, 46.8) higher than those who ate red meat daily. The corresponding differences of cumSBP were 127.8 (120.7, 134.9) and 70.2 (65.0, 75.3) for BMI per 5 kg/m2 and WC per 10 cm. Most of the findings of other BPC measures by baseline characteristics were similar to the cumBP, but the differences between groups were somewhat weaker. Alcohol drinking was associated with several high-risk trajectories of SBP and DBP. Both BMI and WC were independently associated with all high-risk blood pressure trajectories. CONCLUSIONS Several traditional CVD risk factors were associated with unfavorable long-term BPC or blood pressure trajectories in Chinese adults.
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Affiliation(s)
- Yiqian Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
| | - Qiufen Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University
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Ma H, Sun J, Wu X, Mao J, Han Q. Percent body fat was negatively correlated with Testosterone levels in male. PLoS One 2024; 19:e0294567. [PMID: 38170701 PMCID: PMC10763932 DOI: 10.1371/journal.pone.0294567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/04/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Lower testosterone levels in men have been consistently associated with metabolic abnormalities, particularly obesity. This study aims to investigate the relationship between testosterone and obesity by analyzing the correlation between testosterone levels and body fat percentage using data from the NHANES (National Health and Nutrition Examination Survey) database. METHODS The study included a total of 5959 participants from the NHANES 2011-2016. Multivariable linear regression models were used to assess the association between testosterone levels and body composition parameters, including total percent fat (TPF), android percent fat (APF), gynoid percent fat (GPF), android to gynoid ratio (A/G), and lean mass percent (LMP). Subgroup analyses stratified by sex were conducted using multivariable linear regression. To account for potential non-linear relationships, fitted smoothing curves and generalized additive models were utilized. A separate analysis of participants with a BMI ≥ 30 kg/m2 was conducted to validate the conclusions. RESULT Among males, testosterone levels showed a significant negative correlation with TPF (β = -11.97, P <0.0001), APF (β = -9.36, P<0.0001), GPF (β = -10.29, P <0.0001), and A/G (β = -320.93, P<0.0001), while a positive correlation was observed between LMP and testosterone levels (β = 12.62, P<0.0001). In females, a contrasting pattern emerged in the relationship between testosterone and body fat, but no significant correlation was found between testosterone and body composition in obese women. CONCLUSIONS The findings of this study support a negative association between body fat and testosterone levels in males.
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Affiliation(s)
- Hailu Ma
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Juan Sun
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueyan Wu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangfeng Mao
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qin Han
- Beijing Key Laboratory, Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Center of Excellence in Tissue Engineering of Chinese Academy of Medical Sciences, Beijing, China
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Re F, Oguntade AS, Bohrmann B, Bragg F, Carter JL. Associations of general and central adiposity with hypertension and cardiovascular disease among South Asian populations: a systematic review and meta-analysis. BMJ Open 2023; 13:e074050. [PMID: 38110373 DOI: 10.1136/bmjopen-2023-074050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND The relevance of measures of general and central adiposity for cardiovascular disease (CVD) risks in populations of European descent is well established. However, it is less well characterised in South Asian populations, who characteristically manifest larger waist circumferences (WC) for equivalent body mass index (BMI). This systematic review and meta-analysis provide an overview of the literature on the association of different anthropometric measures with CVD risk among South Asians. METHODOLOGY MEDLINE and Embase were searched from 1990 to the present for studies in South Asian populations investigating associations of two or more adiposity measures with CVD. Random-effects meta-analyses were conducted on the associations of BMI, WC and waist-to-hip ratio (WHR) with blood pressure, hypertension and CVD. Quality assessment was performed using the Newcastle-Ottawa scale. RESULTS Titles and abstracts were screened for 7327 studies, yielding 147 full-text reviews. The final sample (n=30) included 2 prospective, 5 case-control and 23 cross-sectional studies. Studies reported generally higher risks of hypertension and CVD at higher adiposity levels. The pooled mean difference in systolic blood pressure (SBP) per 5 kg/m2 higher BMI was 3 mmHg (2.90 (95% CI 1.30 to 4.50)) and 6 mmHg (6.31 (95% CI 4.81 to 7.81) per 13 cm larger WC. The odds ratio (OR) of hypertension per 5 kg/m2 higher BMI was 1.33 (95% CI 1.18 to 1.51), 1.45 (95% CI 1.05 to 1.98) per 13 cm larger WC and 1.22 (95% CI 1.04 to 1.41) per 0.1-unit larger WHR. Pooled risk of CVD for BMI-defined overweight versus healthy-weight was 1.65 (95% CI 1.55 to 1.75) and 1.48 (95% CI 1.21 to 1.80) and 2.51 (95% CI 0.94 to 6.69) for normal versus large WC and WHR, respectively. Study quality was average with significant heterogeneity. CONCLUSIONS Measures of both general and central adiposity had similar, strong positive associations with the risk of CVD in South Asians. Larger prospective studies are required to clarify which measures of body composition are more informative for targeted CVD primary prevention in this population.
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Affiliation(s)
- Federica Re
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Ayodipupo S Oguntade
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bastian Bohrmann
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jennifer L Carter
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Lin W, Shi S, Huang H, Wen J, Chen G. Predicting risk of obesity in overweight adults using interpretable machine learning algorithms. Front Endocrinol (Lausanne) 2023; 14:1292167. [PMID: 38047114 PMCID: PMC10693451 DOI: 10.3389/fendo.2023.1292167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/02/2023] [Indexed: 12/05/2023] Open
Abstract
Objective To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm. Methods This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the individual level. Results Machine learning models for predicting obesity have demonstrated strong performance, with CatBoost emerging as the most effective in both model validity and net clinical benefit. Specifically, the CatBoost algorithm yielded the highest scores, registering 0.91 in the training set and an impressive 0.83 in the test set. This was further corroborated by the area under the curve (AUC) metrics, where CatBoost achieved 0.95 for the training set and 0.87 for the test set. In a rigorous five-fold cross-validation, the AUC for the CatBoost model ranged between 0.84 and 0.91, with an average AUC of ROC at 0.87 ± 0.022. Key predictors identified within these models included waist circumference, hip circumference, female gender, and systolic blood pressure. Conclusion CatBoost may be the best machine learning method for prediction. Combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.
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Affiliation(s)
- Wei Lin
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
| | - Songchang Shi
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital Jinshan Branch, Fujian Provincial Hospital, Fuzhou, China
| | - Huibin Huang
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
| | - Junping Wen
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
| | - Gang Chen
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
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Latief K, Nurrika D, Tsai MK, Gao W. Body Mass Index Asian populations category and stroke and heart disease in the adult population: a longitudinal study of the Indonesia Family Life Survey (IFLS) 2007 and 2014. BMC Public Health 2023; 23:2221. [PMID: 37950166 PMCID: PMC10636903 DOI: 10.1186/s12889-023-17126-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND A substantially elevated Body Mass Index (BMI) is one of the largest global modifiable risk factors for stroke and heart diseases. Most studies classify BMI according to the WHO BMI cut-off point in stroke and heart disease studies. However, there is a limited understanding of the association between the BMI cut-off point in the Asian population category and stroke and heart disease. This study aimed to investigate the incidence rate ratio of stroke and heart disease by BMI categories for the Asian population. METHODS A 7-year prospective longitudinal study (2007-2014) was conducted on 6,688 adult Indonesian individuals (≥ 35 years) residing across 13 different provinces in Indonesia during the survey periods. Data on BMI were collected in 2007. Information on stroke and heart disease was obtained in both 2007 and in 2014. A multivariate-adjusted Poisson regression model was used to estimate the incidence rate ratio (IRR) and 95% confidence intervals (CIs) of either stroke or heart disease or both stroke and heart disease by BMI. RESULTS Among the 6,688 eligible participants, 334 (5%) were judged as stroke and heart disease in 2014. The IRR (95% CI) of stroke and heart disease for participants with obesity was 2.57 (1.64-4.04) compared with those within normal weight. This incidence rate ratio was more pronounced among middle-aged adults (< 55 years) rather than the older adults (≥55 years).The IRR of stroke and heart disease among obese middle-aged adults was 4.18 (95% CI 2.10-8.31). CONCLUSIONS An association was observed between obesity and the risk of stroke and heart disease, especially in middle-aged adults. These findings suggest that lowering BMI through the adoption of healthy dietary habits and increasing physical activity, particularly among middle-aged adults with high education, occupational employment, and residence in either urban or rural areas, may be beneficial for preventing stroke and heart disease.
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Affiliation(s)
- Kamaluddin Latief
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- Center for Family Welfare, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia
| | - Dieta Nurrika
- Public Health Study Program, Banten School of Health Science, South Tangerang, 15318, Indonesia
- The Ministry of Education, Culture, Research, and Technology, Higher Education Service Institutions (LL-DIKTI) Region IV, Bandung, 40124, Indonesia
| | - Min-Kuang Tsai
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
| | - Wayne Gao
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan.
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Cui W, Xu L, Huang L, Tian Y, Yang Y, Li Y, Yu Q. Changes of gut microbiota in patients at different phases of stroke. CNS Neurosci Ther 2023; 29:3416-3429. [PMID: 37309276 PMCID: PMC10580337 DOI: 10.1111/cns.14271] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/06/2023] [Accepted: 05/08/2023] [Indexed: 06/14/2023] Open
Abstract
AIMS Gut dysbiosis appears rapidly after acute stroke and may affect the prognosis, whereas changes in gut microbiota with gradual recovery from stroke are unknown and rarely studied. The purpose of this study is to explore the characteristics of gut microbiota changes over time after stroke. METHODS Stroke patients and healthy subjects were selected to compare the clinical data and gut microbiota of the patient group in two phases with that of healthy subjects and 16S rRNA gene sequencing was used to search the differences of gut microbiota in subjects. RESULTS Compared with the healthy subjects, the subacute patients mainly decreased the abundance of some gut microbial communities, while the decreased communities reduced and more communities increased the abundance in the convalescent patients. The abundance of Lactobacillaceae increased in both phases in patient group, while Butyricimona, Peptostreptococaceae and Romboutsia decreased in both phases. Correlation analysis found that the MMSE scores of patients in the two phases had the greatest correlation with the gut microbiota. CONCLUSION Gut dysbiosis still existed in patients in the subacute phase and convalescent phase, and gradually improved with the recovery of stroke. Gut microbiota may affect the prognosis of stroke by affecting BMI and/or related indicators, and there is a strong correlation between gut microbiota and cognitive function after stroke.
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Affiliation(s)
- Wei Cui
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Li Xu
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Lin Huang
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Yang Tian
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Yan Yang
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Yamei Li
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Qian Yu
- Department of Rehabilitation MedicineSichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
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Yao P, Iona A, Kartsonaki C, Said S, Wright N, Lin K, Pozarickij A, Millwood I, Fry H, Mazidi M, Chen Y, Du H, Bennett D, Avery D, Schmidt D, Pei P, Lv J, Yu C, Hill M, Chen J, Peto R, Walters R, Collins R, Li L, Clarke R, Chen Z. Conventional and genetic associations of adiposity with 1463 proteins in relatively lean Chinese adults. Eur J Epidemiol 2023; 38:1089-1103. [PMID: 37676424 PMCID: PMC10570181 DOI: 10.1007/s10654-023-01038-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/28/2023] [Indexed: 09/08/2023]
Abstract
Adiposity is associated with multiple diseases and traits, but little is known about the causal relevance and mechanisms underlying these associations. Large-scale proteomic profiling, especially when integrated with genetic data, can clarify mechanisms linking adiposity with disease outcomes. We examined the associations of adiposity with plasma levels of 1463 proteins in 3977 Chinese adults, using measured and genetically-instrumented BMI. We further used two-sample bi-directional MR analyses to assess if certain proteins influenced adiposity, along with other (e.g. enrichment) analyses to clarify possible mechanisms underlying the observed associations. Overall, the mean (SD) baseline BMI was 23.9 (3.3) kg/m2, with only 6% being obese (i.e. BMI ≥ 30 kg/m2). Measured and genetically-instrumented BMI was significantly associated at FDR < 0.05 with levels of 1096 (positive/inverse: 826/270) and 307 (positive/inverse: 270/37) proteins, respectively, with FABP4, LEP, IL1RN, LSP1, GOLM2, TNFRSF6B, and ADAMTS15 showing the strongest positive and PON3, NCAN, LEPR, IGFBP2 and MOG showing the strongest inverse genetic associations. These associations were largely linear, in adiposity-to-protein direction, and replicated (> 90%) in Europeans of UKB (mean BMI 27.4 kg/m2). Enrichment analyses of the top > 50 BMI-associated proteins demonstrated their involvement in atherosclerosis, lipid metabolism, tumour progression and inflammation. Two-sample bi-directional MR analyses using cis-pQTLs identified in CKB GWAS found eight proteins (ITIH3, LRP11, SCAMP3, NUDT5, OGN, EFEMP1, TXNDC15, PRDX6) significantly affect levels of BMI, with NUDT5 also showing bi-directional association. The findings among relatively lean Chinese adults identified novel pathways by which adiposity may increase disease risks and novel potential targets for treatment of obesity and obesity-related diseases.
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Affiliation(s)
- Pang Yao
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Andri Iona
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Saredo Said
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Neil Wright
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Kuang Lin
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Alfred Pozarickij
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Iona Millwood
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mohsen Mazidi
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dan Schmidt
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Richard Peto
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Robin Walters
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK.
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK.
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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10
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Mouchti S, Orliacq J, Reeves G, Chen Z. Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies. BMC Med Imaging 2023; 23:127. [PMID: 37710156 PMCID: PMC10503139 DOI: 10.1186/s12880-023-01063-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/24/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND In studies of the association of adiposity with disease risk, widely used anthropometric measures of adiposity (e.g. body-mass-index [BMI], waist circumference [WC], waist-hip ratio [WHR]) are simple and inexpensive to implement at scale. In contrast, imaging-based techniques (e.g. magnetic resonance imaging [MRI] and dual x-ray absorptiometry [DXA]) are expensive and labour intensive, but can provide more accurate quantification of body fat composition. There is, however, limited evidence about the relationship between conventional and imaging-derived measures of adiposity. METHODS We searched Scopus and Web of Science for published reports in English of conventional versus imaging-derived measurements of adiposity. We identified 42 articles (MRI = 22; DXA = 20) that met selection criteria, involving 42,556 (MRI = 15,130; DXA = 27,426) individuals recruited from community or hospital settings. Study-specific correlation coefficients (r) were transformed using Fisher's Z transformation, and meta-analysed to yield weighted average correlations, both overall and by ancestry, sex and age, where feasible. Publication bias was investigated using funnel plots and Egger's test. RESULTS Overall, 98% of participants were 18 + years old, 85% male and 95% White. BMI and WC were most strongly correlated with imaging-derived total abdominal (MRI-derived: r = 0.88-; DXA-derived: 0.50-0.86) and subcutaneous abdominal fat (MRI-derived: 0.83-0.85), but were less strongly correlated with visceral abdominal fat (MRI-derived: 0.76-0.79; DXA-derived: 0.80) and with DXA-derived %body fat (0.76). WHR was, at best, strongly correlated with imaging-derived total abdominal (MRI-derived: 0.60; DXA-derived: 0.13), and visceral abdominal fat (MRI-derived: 0.67; DXA-derived: 0.65), and moderately with subcutaneous abdominal (MRI-derived: 0.54), and with DXA-derived %body fat (0.58). All conventional adiposity measures were at best moderately correlated with hepatic fat (MRI-derived: 0.36-0.43). In general, correlations were stronger in women than in men, in Whites than in non-Whites, and in those aged 18 + years. CONCLUSIONS In this meta-analysis, BMI and WC, but not WHR, were very strongly correlated with imaging-derived total and subcutaneous abdominal fat. By comparison, all three measures were moderately or strongly correlated with imaging-based visceral abdominal fat, with WC showing the greatest correlation. No anthropometric measure was substantially correlated with hepatic fat. Further larger studies are needed to compare these measures within the same study population, and to assess their relevance for disease risks in diverse populations.
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Affiliation(s)
- Sofia Mouchti
- Cancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK
| | - Josefina Orliacq
- Cancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Gillian Reeves
- Cancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Zhengming Chen
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK.
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11
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Carter JL, Abdullah N, Bragg F, Murad NAA, Taylor H, Fong CS, Lacey B, Sherliker P, Karpe F, Mustafa N, Lewington S, Jamal R. Body composition and risk factors for cardiovascular disease in global multi-ethnic populations. Int J Obes (Lond) 2023; 47:855-864. [PMID: 37460680 PMCID: PMC10439008 DOI: 10.1038/s41366-023-01339-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 06/21/2023] [Accepted: 07/04/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND No large-scale studies have compared associations between body composition and cardiovascular risk factors across multi-ethnic populations. METHODS Population-based surveys included 30,721 Malay, 10,865 Indian and 25,296 Chinese adults from The Malaysian Cohort, and 413,737 White adults from UK Biobank. Sex-specific linear regression models estimated associations of anthropometry and body composition (body mass index [BMI], waist circumference [WC], fat mass, appendicular lean mass) with systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), triglycerides and HbA1c. RESULTS Compared to Malay and Indian participants, Chinese adults had lower BMI and fat mass while White participants were taller with more appendicular lean mass. For BMI and fat mass, positive associations with SBP and HbA1c were strongest among the Chinese and Malay and weaker in White participants. Associations with triglycerides were considerably weaker in those of Indian ethnicity (eg 0.09 [0.02] mmol/L per 5 kg/m2 BMI in men, vs 0.38 [0.02] in Chinese). For appendicular lean mass, there were weak associations among men; but stronger positive associations with SBP, triglycerides, and HbA1c, and inverse associations with LDL-C, among Malay and Indian women. Associations between WC and risk factors were generally strongest in Chinese and weakest in Indian ethnicities, although this pattern was reversed for HbA1c. CONCLUSION There were distinct patterns of adiposity and body composition and cardiovascular risk factors across ethnic groups. We need to better understand the mechanisms relating body composition with cardiovascular risk to attenuate the increasing global burden of obesity-related disease.
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Affiliation(s)
- Jennifer L Carter
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Noraidatulakma Abdullah
- UKM Medical Molecular Biology Institute (UMBI), Jalan Yaacob Latiff, 56000 Cheras, Kuala Lumpur, Malaysia
| | - Fiona Bragg
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
- Medical Research Council, Population Health Research Unit, University of Oxford, Oxford, UK
| | - Nor Azian Abdul Murad
- UKM Medical Molecular Biology Institute (UMBI), Jalan Yaacob Latiff, 56000 Cheras, Kuala Lumpur, Malaysia
| | - Hannah Taylor
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Chin Siok Fong
- UKM Medical Molecular Biology Institute (UMBI), Jalan Yaacob Latiff, 56000 Cheras, Kuala Lumpur, Malaysia
| | - Benjamin Lacey
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Paul Sherliker
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, OX3 7LE, UK
| | - Norlaila Mustafa
- Department of Medicine, Faculty of Medicine, University Kebangsaan Malaysia, 56000 Cheras, Kuala Lumpur, Malaysia
| | - Sarah Lewington
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
- UKM Medical Molecular Biology Institute (UMBI), Jalan Yaacob Latiff, 56000 Cheras, Kuala Lumpur, Malaysia
- Medical Research Council, Population Health Research Unit, University of Oxford, Oxford, UK
| | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI), Jalan Yaacob Latiff, 56000 Cheras, Kuala Lumpur, Malaysia
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12
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Chae B, Ahn S, Kim YJ, Ryoo SM, Sohn CH, Seo DW, Kim WY. Modification of HEART Pathway for Patients With Chest Pain: A Korean Perspective. Korean Circ J 2023; 53:635-644. [PMID: 37653699 PMCID: PMC10475686 DOI: 10.4070/kcj.2022.0354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 04/30/2023] [Accepted: 05/24/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The History, Electrocardiography, Age, Risk factors, and Troponin (HEART) pathway was developed to identify patients at low risk of a major adverse cardiac event (MACE) among patients presenting with chest pain to the emergency department. METHODS We modified the HEART pathway by replacing the Korean cut-off of 25 kg/m² with the conventional threshold of 30 kg/m² in the definition of obesity among risk factors. The primary outcome was a MACE within 30 days, which included acute myocardial infarction, primary coronary intervention, coronary artery bypass grafting, and all-cause death. RESULTS Of the 1,304 patients prospectively enrolled, MACE occurred in 320 (24.5%). The modified HEART pathway identified 37.3% of patients as low-risk compared with 38.3% using the HEART pathway. Of the 500 patients classified as low-risk with HEART pathway, 8 (1.6%) experienced MACE, and of the 486 low-risk patients with modified HEART pathway, 4 (0.8%) experienced MACE. The modified HEART pathway had a sensitivity of 98.8%, a negative predictive value (NPV) of 99.2%, a specificity of 49.0%, and a positive predictive value (PPV) of 38.6%, compared with the original HEART pathway, with a sensitivity of 97.5%, a NPV of 98.4%, a specificity of 50.0%, and a PPV of 38.8%. CONCLUSIONS When applied to Korean population, modified HEART pathway could identify patients safe for early discharge more accurately by using body mass index cut-off levels suggested for Koreans.
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Affiliation(s)
- Bora Chae
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Shin Ahn
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Youn-Jung Kim
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Mok Ryoo
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Chang Hwan Sohn
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong-Woo Seo
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Won Young Kim
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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13
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Yang S, Sun Z, Sun D, Yu C, Guo Y, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Lu Y, Burgess S, Avery D, Clarke R, Chen J, Chen Z, Li L, Lv J. Associations of polygenic risk scores with risks of stroke and its subtypes in Chinese. Stroke Vasc Neurol 2023:svn-2023-002428. [PMID: 37640499 DOI: 10.1136/svn-2023-002428] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/11/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND AND PURPOSE Previous studies, mostly focusing on the European population, have reported polygenic risk scores (PRSs) might achieve risk stratification of stroke. We aimed to examine the association strengths of PRSs with risks of stroke and its subtypes in the Chinese population. METHODS Participants with genome-wide genotypic data in China Kadoorie Biobank were split into a potential training set (n=22 191) and a population-based testing set (n=72 150). Four previously developed PRSs were included, and new PRSs for stroke and its subtypes were developed. The PRSs showing the strongest association with risks of stroke or its subtypes in the training set were further evaluated in the testing set. Cox proportional hazards regression models were used to estimate the association strengths of different PRSs with risks of stroke and its subtypes (ischaemic stroke (IS), intracerebral haemorrhage (ICH) and subarachnoid haemorrhage (SAH)). RESULTS In the testing set, during 872 919 person-years of follow-up, 8514 incident stroke events were documented. The PRSs of any stroke (AS) and IS were both positively associated with risks of AS, IS and ICH (p<0.05). The HR for per SD increment (HRSD) of PRSAS was 1.10 (95% CI 1.07 to 1.12), 1.10 (95% CI 1.07 to 1.12) and 1.13 (95% CI 1.07 to 1.20) for AS, IS and ICH, respectively. The corresponding HRSD of PRSIS was 1.08 (95% CI 1.06 to 1.11), 1.08 (95% CI 1.06 to 1.11) and 1.09 (95% CI 1.03 to 1.15). PRSICH was positively associated with the risk of ICH (HRSD=1.07, 95% CI 1.01 to 1.14). PRSSAH was not associated with risks of stroke and its subtypes. The addition of current PRSs offered little to no improvement in stroke risk prediction and risk stratification. CONCLUSIONS In this Chinese population, the association strengths of current PRSs with risks of stroke and its subtypes were moderate, suggesting a limited value for improving risk prediction over traditional risk factors in the context of current genome-wide association study under-representing the East Asian population.
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Affiliation(s)
- Songchun Yang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhijia Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Dong Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yan Lu
- NCDs Prevention and Control Department, Suzhou CDC, Suzhou, Jiangsu, China
| | - Sushila Burgess
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
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14
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Gao S, Zhou Y, Yang R, Du C, Wu Y. Risk factors for postoperative cerebral infarction in patients after lung resection: a single-center case-control study. J Thorac Dis 2023; 15:376-385. [PMID: 36910048 PMCID: PMC9992601 DOI: 10.21037/jtd-22-1019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 11/15/2022] [Indexed: 03/06/2023]
Abstract
Background Patients who undergo lung resection are at risk of postoperative cerebral infarction, but the risk factors remain unclear, so the present study was a comprehensive investigation in patients who underwent lung resection for pulmonary nodules. Methods The clinical characteristics of patients with postoperative cerebral infarction and patients who underwent lung resection on the same day but did not develop cerebral infarction were retrospectively compared. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for cerebral infarction after lung resection. Results A total of 22 patients with postoperative cerebral infarction and 316 controls were included. Multivariate logistic regression analysis revealed that a history of cerebral infarction [odds ratio (OR), 7.289; P=0.030], activated partial thromboplastin time (APTT) <26.5 s (OR, 3.704; P=0.018), body mass index (BMI) ≥24.0 kg/m2 (OR, 3.656; P=0.015), and surgical method (P=0.005) were independent risk factors for cerebral infarction after lung resection. Compared with patients undergoing lobectomy, the risk for postoperative cerebral infarction was significantly increased in patients undergoing segmentectomy (OR, 24.322; P=0.001), wedge resection (OR, 6.992; P=0.018), or combined surgical approach (OR, 29.921; P=0.028). Conclusions A history of cerebral infarction, APTT <26.5 s, BMI ≥24.0 kg/m2, and surgical method were independent risk factors for cerebral infarction after lung resection. Strengthening thromboprophylaxis in patients with these risk factors may help to reduce the incidence of postoperative cerebral infarction.
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Affiliation(s)
- Shenhu Gao
- Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuwei Zhou
- Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Rong Yang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengli Du
- Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yihe Wu
- Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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15
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Pillay P, Lewington S, Taylor H, Lacey B, Carter J. Adiposity, Body Fat Distribution, and Risk of Major Stroke Types Among Adults in the United Kingdom. JAMA Netw Open 2022; 5:e2246613. [PMID: 36515951 PMCID: PMC9856404 DOI: 10.1001/jamanetworkopen.2022.46613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE Uncertainty persists regarding the independent association of general and central adiposity with major stroke types. OBJECTIVE To determine the independent associations of general and central adiposity with risk of ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage. DESIGN, SETTING, AND PARTICIPANTS Between 2006 and 2010, adults without prior stroke at recruitment in England, Scotland, and Wales were recruited into the UK Biobank prospective cohort study and were followed up through linkage with electronic medical records. Data were analyzed from September 2021 to September 2022. EXPOSURES General adiposity (body mass index [BMI] calculated as weight in kilograms divided by height in meters squared) and central adiposity (waist circumference). MAIN OUTCOMES AND MEASURES Incident ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage as recorded through record linkage with electronic hospital records. Cox regression estimated adjusted hazard ratios (HRs) by stroke type. RESULTS Among 490 071 participants, the mean (SD) age was 56.5 (8.1) years, 267 579 (54.6%) were female, and 461 647 (94.2%) were White. During a median (IQR) of 12 (11.2-12.7) years follow-up, 7117 incident ischemic strokes, 1391 intracerebral hemorrhages, and 834 subarachnoid hemorrhages were identified. After mutual adjustment for waist circumference, BMI had no independent association with ischemic stroke (HR per 5-unit higher BMI, 1.04; 95% CI, 0.97-1.11), but was inversely associated with intracerebral hemorrhage (HR, 0.85; 95% CI, 0.74-0.96) and subarachnoid hemorrhage (HR, 0.82; 95% CI, 0.69-0.96). Waist circumference (adjusted for BMI) was positively associated with ischemic stroke (HR per 10-cm higher waist circumference, 1.19; 95% CI, 1.13-1.25) and intracerebral hemorrhage (HR, 1.17; 95% CI, 1.05-1.30), but was not associated with subarachnoid hemorrhage (HR, 1.07; 95% CI, 0.93-1.22). CONCLUSIONS AND RELEVANCE In this large-scale prospective study, the independent and contrasting associations of BMI and waist circumference with stroke types suggests the importance of considering body fat distribution to stroke risk. Waist circumference was positively associated with the risk of ischemic stroke and intracerebral hemorrhage, while BMI was inversely associated with the risk of subarachnoid hemorrhage and intracerebral hemorrhage. This study also suggests that some adverse correlate of low BMI may be associated with an increased risk of intracerebral hemorrhage and subarachnoid hemorrhage, which warrants further investigation.
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Affiliation(s)
- Preyanka Pillay
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Sarah Lewington
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit, Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Hannah Taylor
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Ben Lacey
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Jennifer Carter
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
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16
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Fan G, Liu Q, Wu M, Bi J, Qin X, Fang Q, Wan Z, Lv Y, Wang Y, Song L. Exposure to Metal Mixtures and Overweight or Obesity Among Chinese Adults. Biol Trace Elem Res 2022:10.1007/s12011-022-03484-0. [PMID: 36383287 DOI: 10.1007/s12011-022-03484-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022]
Abstract
Previous research has investigated the association between individual metal exposure and overweight/obesity (OW/OB). However, there is limited data about metal mixture exposure and OW/OB. This study aimed to explore the individual and joint effects of 21 metals on OW/OB and its metabolic phenotypes. A total of 4042 participants were enrolled in our study, and 51.0% of them were overweight/obese. We quantified 21 metal levels in the urine sample. OW/OB was defined as BMI ≥ 24 kg/m2, while the metabolic phenotypes, including metabolic unhealthy overweight/obesity (MUOW/OB) and metabolic health overweight/obesity (MHOW/OB), were determined by BMI and metabolic state. We used logistic regression to analyze the effect of individual metal exposure on OW/OB and its metabolic phenotypes. Quantile g-computation was applied to evaluate the joint effect of metal exposure on OW/OB and its metabolic phenotypes. In logistic regression, zinc (Zn) was positively associated with OW/OB, with the odds ratio (OR) in the highest quartiles of 2.19 (95% confidence interval (CI), 1.74, 2.77; P trend < 0.001), while arsenic (As) and cadmium (Cd) were negatively associated with OW/OB (OR = 0.70 (0.56, 0.87) and 0.61 (0.48, 0.78), respectively). After adjustment for age, gender, education, cigarette smoking, alcohol drinking, physical activity, meat intake, and vegetable intake, Zn was positively associated with MUOW/OB, while As, Cd, nickel (Ni), and strontium (Sr) were negatively associated with MUOW/OB (all P trend < 0.05). Quantile g-computation showed a significantly negative association between metal mixture exposure and MUOW/OB. Our study suggested that metal mixture exposure might be negatively associated with OW/OB, particularly with MUOW/OB. Zn, As and Cd contributed most to the effect of the mixture. More prospective studies are warranted to confirm these findings and reveal the underlying mechanisms.
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Affiliation(s)
- Gaojie Fan
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing Liu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mingyang Wu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jianing Bi
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiya Qin
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing Fang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhengce Wan
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yongman Lv
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Youjie Wang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Lulu Song
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Brown PE, Izawa Y, Balakrishnan K, Fu SH, Chakma J, Menon G, Dikshit R, Dhaliwal RS, Rodriguez PS, Huang G, Begum R, Hu H, D'Souza G, Guleria R, Jha P. Mortality Associated with Ambient PM2.5 Exposure in India: Results from the Million Death Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:97004. [PMID: 36102642 PMCID: PMC9472672 DOI: 10.1289/ehp9538] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
BACKGROUND Studies on the extent to which long-term exposure to ambient particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5) contributes to adult mortality in India are few, despite over 99% of Indians being exposed to levels that the World Health Organization (WHO) considers unsafe. OBJECTIVE We conducted a retrospective cohort study within the Million Death Study (MDS) to provide the first-ever quantification of national mortality from exposure to PM2.5 in India from 1999 to 2014. METHODS We calculated relative risks (RRs) by linking a total of ten 3-y intervals of satellite-based estimated PM2.5 exposure to deaths 3 to 5 y later in over 7,400 small villages or urban blocks covering a total population of 6.8 million. We applied using a model-based geostatistical model, adjusted for individual age, sex, and year of death; smoking prevalence, rural/urban residency, area-level female illiteracy, languages, and spatial clustering and unit-level variation. RESULTS PM2.5 exposure levels increased from 1999 to 2014, particularly in central and eastern India. Among 212,573 deaths at ages 15-69 y, after spatial adjustment, we found a significant RR of 1.09 [95% credible interval (CI): 1.04, 1.14] for stroke deaths per 10-μg/m3 increase in PM2.5 exposure, but no significant excess for deaths from chronic respiratory disease and ischemic heart disease (IHD), all nonaccidental causes, and total mortality (after excluding stroke). Spatial adjustment attenuated the RRs for chronic respiratory disease and IHD but raised those for stroke. The RRs were consistent in various sensitivity analyses with spatial adjustment, including stratifying by levels of solid fuel exposure, by sex, and by age group, addition of climatic variables, and in supplementary case-control analyses using injury deaths as controls. DISCUSSION Direct epidemiological measurements, despite inherent limitations, yielded associations between mortality and long-term PM2.5 inconsistent with those reported in earlier models used by the WHO to derive estimates of PM2.5 mortality in India. The modest RRs in our study are consistent with near or null mortality effects. They suggest suitable caution in estimating deaths from PM2.5 exposure based on MDS results and even more caution in extrapolating model-based associations of risk derived mostly from high-income countries to India. https://doi.org/10.1289/EHP9538.
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Affiliation(s)
- Patrick E Brown
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
| | - Yurie Izawa
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
| | - Kalpana Balakrishnan
- Department of Environmental Health Engineering, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Sze Hang Fu
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
| | - Joy Chakma
- The Indian Council of Medical Research, New Delhi, India
| | - Geetha Menon
- The Indian Council of Medical Research, New Delhi, India
| | - Rajesh Dikshit
- Centre for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India
| | - R S Dhaliwal
- The Indian Council of Medical Research, New Delhi, India
| | - Peter S Rodriguez
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
| | - Guowen Huang
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
| | - Rehana Begum
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
| | - Howard Hu
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, USA
| | - George D'Souza
- St. John's Medical College, St. John's Research Institute, Bangalore, India
| | | | - Prabhat Jha
- Centre for Global Health Research (CGHR), St Michael's Hospital and Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada
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18
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Sun X, Yan AF, Shi Z, Zhao B, Yan N, Li K, Gao L, Xue H, Peng W, Cheskin LJ, Wang Y. Health consequences of obesity and projected future obesity health burden in China. Obesity (Silver Spring) 2022; 30:1724-1751. [PMID: 36000246 DOI: 10.1002/oby.23472] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 03/20/2022] [Accepted: 03/22/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE This study examined the effects of overweight/obesity on mortality and morbidity outcomes and the disparities, time trends, and projected future obesity health burden in China. METHODS Cohort studies that were conducted in China and published in English or Chinese between January 1, 1995, and July 31, 2021, were systematically searched. This study focused on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension, cardiovascular diseases, metabolic syndrome, cancers, and chronic kidney disease. RESULTS A total of 31 cohorts and 50 cohort studies reporting on mortality (n = 20) and morbidities (n = 30) associated with obesity met study inclusion criteria. Overall, BMI was nonlinearly (U-shaped) associated with all-cause mortality and linearly associated with risks of T2DM, cardiovascular diseases, hypertension, cancer, metabolic syndrome, and chronic kidney disease. In 2018, among adults, the prevalence of overweight/obesity, hypertension, and T2DM was 51.2%, 27.5%, and 12.4%, respectively. Their future projected prevalence would be 70.5%, 35.4%, and 18.5% in 2030, respectively. The projected number of adults having these conditions would be 810.65 million, 416.47 million, and 217.64 million, respectively. The urban-rural disparity in overweight/obesity prevalence was projected to shrink and then reverse over time. CONCLUSIONS The current health burden of obesity in China is high and it will sharply increase in coming years and affect population groups differently. China needs to implement vigorous interventions for obesity prevention and treatment.
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Affiliation(s)
- Xiaomin Sun
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Alice Fang Yan
- Center for Advancing Population Science, Division of Internal Medicine, Department of Medicine, Medical College of Wisconsin, Wauwatosa, Wisconsin, USA
| | - Zumin Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Bingtong Zhao
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Na Yan
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Ke Li
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Liwang Gao
- Center for Non-communicable Disease Management, Beijing Children's Hospital, National Center for Children's Health, Capital Medical University, Beijing, China
| | - Hong Xue
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, Virginia, USA
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University and Global Health Institute, Xi'an Jiaotong University, Xi'an, China
| | - Lawrence J Cheskin
- Department of Nutrition and Food Studies, College of Health and Human Services, George Mason University, Fairfax, Virginia, USA
- Department of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Youfa Wang
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
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Association between non-alcoholic fatty liver disease and metabolically healthy deterioration across different body shape phenotypes at baseline and change patterns. Sci Rep 2022; 12:14786. [PMID: 36042236 PMCID: PMC9427771 DOI: 10.1038/s41598-022-18988-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 08/23/2022] [Indexed: 11/29/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic syndrome (MetS), and the relationship between NAFLD and metabolic deterioration remains unclear. This study aimed to investigate dynamic changes in metabolically healthy phenotypes and to assess the impact of non-alcoholic fatty liver disease (NAFLD) on the conversion from metabolically healthy (MH) to metabolically unhealthy (MU) phenotypes across body shape phenotypes and phenotypic change patterns. We defined body shape phenotypes using both the body mass index (BMI) and waist circumference (WC) and defined metabolic health as individuals scoring ≤ 1 on the NCEP-ATP III criteria, excluding WC. A total of 12,910 Chinese participants who were MH at baseline were enrolled in 2013 and followed-up in 2019 or 2020. During a median follow-up of 6.9 years, 27.0% (n = 3,486) of the MH individuals developed an MU phenotype. According to the multivariate Cox analyses, NAFLD was a significant predictor of conversion from the MH to MU phenotype, independent of potential confounders (HR: 1.12; 95% confidence interval: 1.02–1.22). For the MH-normal weight group, the relative risk of NAFLD in phenotypic conversion was 1.21 (95% CI 1.03–1.41, P = 0.017), which was relatively higher than that of MH-overweight/obesity group (HR: 1.14, 95% CI 1.02–1.26, P = 0.013). Interestingly, the effect of NAFLD at baseline on MH deterioration was stronger in the “lean” phenotype group than in the “non-lean” phenotype group at baseline and in the “fluctuating non-lean” phenotype change pattern group than in the “stable non-lean” phenotype change pattern group during follow-up. In conclusion, lean NAFLD is not as benign as currently considered and requires more attention during metabolic status screening.
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Chun M, Clarke R, Zhu T, Clifton D, Bennett DA, Chen Y, Guo Y, Pei P, Lv J, Yu C, Yang L, Li L, Chen Z, Cairns BJ. Development, validation and comparison of multivariable risk scores for prediction of total stroke and stroke types in Chinese adults: a prospective study of 0.5 million adults. Stroke Vasc Neurol 2022; 7:328-336. [PMID: 35292536 PMCID: PMC9453839 DOI: 10.1136/svn-2021-001251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/11/2022] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND AND PURPOSE Low-income and middle-income countries have the greatest stroke burden, yet remain understudied. This study compared the utility of Framingham versus novel risk scores for prediction of total stroke and stroke types in Chinese adults. METHODS China Kadoorie Biobank (CKB) is a prospective study of 512 726 adults, aged 30-79 years, recruited from 10 areas in China in 2004-2008. By 1 January 2018, 43 234 incident first stroke cases (36 310 ischaemic stroke (IS); 8865 haemorrhagic stroke (HS)) were recorded in 503 842 participants with no history of stroke at baseline. We compared the predictive utility of the Framingham Stroke Risk Profile (FSRP) with novel CKB stroke risk scores and included recalibration, refitting, stratifying by study area and addition of other risk factors. Discrimination was assessed using area under the receiver operating characteristic curve (AUC) and calibration was assessed using Greenwood-Nam-D'Agostino χ2 statistics. RESULTS Incidence of total stroke varied fivefold by area in China. The FSRP had good discrimination for total stroke (AUC (95% CI); men: 0.78 (0.77 to 0.79), women: 0.77 (95% CI 0.76 to 0.78)), but poor calibration (χ2; men: 1,825, women: 3,053), substantially underestimating absolute risks. Recalibration reduced χ2 by >80%, but did not improve discrimination. Refitting the FSRP did not materially improve discrimination, but further improved calibration. Stratification by area improved discrimination (AUC; men: 0.82 (0.82 to 0.83); women: 0.82 (0.82 to 0.83)), but not calibration. Adding other risk factors yielded modest, but statistically significant, improvements in the AUCs. The findings for IS and HS were similar to those for total stroke. CONCLUSIONS The FSRP reliably differentiated Chinese adults with incident stroke, but substantially underestimated the absolute risks of stroke. Novel local risk prediction equations that took account of differences in stroke incidence within China enhanced risk prediction of total stroke and major stroke pathological types.
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Affiliation(s)
- Matthew Chun
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - David Clifton
- Department of Engineering Science, University of Oxford, Oxford, UK
- Department of Biomedical Engineering, Oxford-Suzhou Centre for Advanced Research, Suzhou, China
| | - Derrick A Bennett
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- CKB Project Department, Fuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China
| | - Pei Pei
- CKB Project Department, Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Epidemiology, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Epidemiology, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Epidemiology, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Zhengming Chen
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Benjamin J Cairns
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Yang S, Han Y, Yu C, Guo Y, Pang Y, Sun D, Pei P, Yang L, Chen Y, Du H, Wang H, Massa MS, Bennett D, Clarke R, Chen J, Chen Z, Lv J, Li L. Development of a Model to Predict 10-Year Risk of Ischemic and Hemorrhagic Stroke and Ischemic Heart Disease Using the China Kadoorie Biobank. Neurology 2022; 98:e2307-e2317. [PMID: 35410902 PMCID: PMC9202526 DOI: 10.1212/wnl.0000000000200139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 01/18/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Contemporary cardiovascular disease (CVD) risk prediction models are rarely applied in routine clinical practice in China due to substantial regional differences in absolute risks of major CVD types within China. Moreover, the inclusion of blood lipids in most risk prediction models also limits their use in the Chinese population. We developed 10-year CVD risk prediction models excluding blood lipids that may be applicable to diverse regions of China. METHODS We derived sex-specific models separately for ischemic heart disease (IHD), ischemic stroke (IS), and hemorrhagic stroke (HS) in addition to total CVD in the China Kadoorie Biobank. Participants were age 30-79 years without CVD at baseline. Predictors included age, systolic and diastolic blood pressure, use of blood pressure-lowering treatment, current daily smoking, diabetes, and waist circumference. Total CVD risks were combined in terms of conditional probability using the predicted risks of 3 submodels. Risk models were recalibrated in each region by 2 methods (practical and ideal) and risk prediction was estimated before and after recalibration. RESULTS Model derivation involved 489,596 individuals, including 45,947 IHD, 43,647 IS, and 11,168 HS cases during 11 years of follow-up. In women, the Harrell C was 0.732 (95% CI 0.706-0.758), 0.759 (0.738-0.779), and 0.803 (0.778-0.827) for IHD, IS, and HS, respectively. The Harrell C for total CVD was 0.734 (0.732-0.736), 0.754 (0.752-0.756), and 0.774 (0.772-0.776) for models before recalibration, after practical recalibration, and after ideal recalibration. The calibration performances improved after recalibration, with models after ideal recalibration showing the best model performances. The results for men were comparable to those for women. DISCUSSION Our CVD risk prediction models yielded good discrimination of IHD and stroke subtypes in addition to total CVD without including blood lipids. Flexible recalibration of our models for different regions could enable more widespread use using resident health records covering the overall Chinese population. CLASSIFICATION OF EVIDENCE This study provides Class I evidence that a prediction model incorporating accessible clinical variables predicts 10-year risk of IHD, IS, and HS in the Chinese population age 30-79 years.
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Affiliation(s)
- Songchun Yang
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Yuting Han
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Canqing Yu
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Yu Guo
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Pei Pei
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Ling Yang
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Yiping Chen
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Huaidong Du
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Hao Wang
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - M Sofia Massa
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Derrick Bennett
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Robert Clarke
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Junshi Chen
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Zhengming Chen
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Jun Lv
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
| | - Liming Li
- From the Department of Epidemiology & Biostatistics (S.Y., Y.H., C.Y., Y.P., D.S., J.L., L.L.), School of Public Health, Peking University; Peking University Center for Public Health and Epidemic Preparedness & Response (C.Y., J.L., L.L.); Fuwai Hospital Chinese Academy of Medical Sciences (Y.G.); Chinese Academy of Medical Sciences (P.P.), Beijing, China; Medical Research Council Population Health Research Unit at the University of Oxford (L.Y., Y.C., H.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (L.Y., Y.C., H.D., M.S.M., D.B., R.C., Z.C.), Nuffield Department of Population Health, University of Oxford, UK; NCDs Prevention and Control Department (H.W.), Zhejiang CDC, Hangzhou; China National Center for Food Safety Risk Assessment (J.C.); and Key Laboratory of Molecular Cardiovascular Sciences (Peking University) (J.L.), Ministry of Education, Beijing, China
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Effectiveness of a Worksite-Based Lifestyle Intervention on Employees' Obesity Control and Prevention in China: A Group Randomized Experimental Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116738. [PMID: 35682322 PMCID: PMC9180232 DOI: 10.3390/ijerph19116738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 02/05/2023]
Abstract
Background: This study was to culturally adapt a lifestyle intervention for employees’ obesity control and prevention using a participatory process, and evaluate the effectiveness of the project at worksites. Methods: A group randomized experimental study included four worksites (two intervention, two control) in the Yangtze River Delta in China was conducted. A total of 388 participants (216 in the intervention worksites and 172 in the control worksites) were finally recruited from 955 employees at the four worksites (464 in the intervention worksites and 491 in the control worksites). The final evaluation was completed by two hundred and seventy-eight employees (159 in the intervention worksites and 119 in the control worksites, respectively). Data of demographic information, weight, BMI, waist circumference, hip circumference and weight-related behaviors including diary behaviors and physical activities were collected before and after a 12-month intervention and analyzed using descriptive statistics, t-test, chi-square test, linear mixed regression and logistic mixed regression. Results: Although the intervention worksites had a reduction in body mass index (23.21 to 22.95, p < 0.01), hip circumference (95.97 to 95.28, p = 0.03) and waist-to-height ratio (0.49 to 0.48, p = 0.01), the differential changes compared to those of the control group were not statistically significant. The frequency of sweet beverages (−1.81, 95%CI: −0.52, −3.11), frequency of vegetable intake (5.66, 95%CI: 1.59, 9.74), daily servings of vegetables (0.53, 95%CI: 0.24, 0.82), frequency of fruit intake (3.68, 95%CI: 1.25, 6.12), daily servings of fruit (0.26, 95%CI: 0.44, 0.92), daily servings of vegetables and fruit (0.79, 95%CI: 0.43, 1.16), daily steps (863.19, 95%CI: 161.42, 1564.97) and self-efficacy to change physical activity (OR = 1.91, 95%CI: 1.02,3.60) were more improved in the intervention group than were those measures in the control group. Conclusions: The worksite-based lifestyle intervention project for obesity control and prevention improved several employees’ dietary behaviors and physical activities at worksites in China in a short time. Long-term intervention with larger samples in more worksites should be further examined.
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He L, Wu DF, Zhang JH, Zheng S, Li Y, He W. Factors affecting transtemporal window quality in transcranial sonography. Brain Behav 2022; 12:e2543. [PMID: 35238499 PMCID: PMC9015004 DOI: 10.1002/brb3.2543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/17/2022] [Accepted: 02/12/2022] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To assess the influencing factors of transtemporal window quality and identify patients suitable for transcranial sonography (TCS) examination in two-dimensional imaging. METHODS In this cross-sectional study, TCS was performed in 161 consecutive patients through the temporal bone window (TBW) in the neurology or neurosurgery department. Each patient's sex, age, height, weight, and temporal bone thickness (TBT) were collected. After examination, the patients were divided into two groups: TBW success and TBW failure. The data were statistically compared between the two groups. RESULTS Among the studied population, the total TBW success rate was 80.1% (95% confidence interval [CI]: 74-86). The TBW success rate was 91.4% (95% CI: 85-98) in males and 70.9% (95% CI: 61-81) in females (p = .001). Sex (p = .001), age (p = .002), height (p = .047), and TBT (p < .001) showed significant differences between the TBW success and failure groups. In males, only TBT (p = .001) showed a significant difference; in females, age (p < .001) and TBT (p = .003) showed a significant difference. The area under the receiver operating characteristic curve (AUC) of sex, age, and TBT and their combination was 0.686, 0.659, 0.842, and 0.922 (p < .001), respectively. The AUC of the combination of parameters was significantly greater than that of age and sex alone (p = .007; p = .0002) but not greater than that of TBT (p = .090). CONCLUSIONS The TBW success rate varied with sex, age, height, and TBT. Males, younger patients, taller patients, and patients with a thinner temporal bone tended to be more suitable for the examination by TCS.
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Affiliation(s)
- Lei He
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dong-Fang Wu
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing-Han Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuai Zheng
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yi Li
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wen He
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Tian S, Bi M, Bi Y, Che X, Liu Y. A Bayesian Network Analysis of the Probabilistic Relationships Between Various Obesity Phenotypes and Cardiovascular Disease Risk in Chinese Adults: Chinese Population-Based Observational Study. JMIR Med Inform 2022; 10:e33026. [PMID: 35234651 PMCID: PMC8928047 DOI: 10.2196/33026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/10/2022] [Accepted: 01/16/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) risk among individuals with different BMI levels might depend on their metabolic health. The extent to which metabolic health status and BMI affect CVD risk, either directly or through a mediator, in the Chinese population remains unclear. OBJECTIVE In this study, the Bayesian network (BN) perspective is adopted to characterize the multivariable probabilistic connections between CVD risk and metabolic health and obesity status and identify potential factors that influence these relationships among Chinese adults. METHODS The study population comprised 6276 Chinese adults aged 30 to 74 years who participated in the China Health and Nutrition Survey 2009. BMI was used to categorize participants as normal weight, overweight, or obese, and metabolic health was defined by the Adult Treatment Panel-3 criteria. Participants were categorized into 6 phenotypes according to their metabolic health and BMI categorization. The 10-year risk of CVD was determined using the Framingham Risk Score. BN modeling was used to identify the network structure of the variables and compute the conditional probability of CVD risk for the different metabolic obesity phenotypes with the given structure. RESULTS Of 6276 participants, 64.67% (n=4059), 20.37% (n=1279), and 14.95% (n=938) had a low, moderate, and high 10-year CVD risk. An averaged BN with a stable network structure was constructed by learning 300 bootstrapped networks from the data. Using BN reasoning, the conditional probability of high CVD risk increased as age progressed. The conditional probability of high CVD risk was 0.43% (95% CI 0.2%-0.87%) for the 30 to 40 years age group, 2.25% (95% CI 1.75%-2.88%) for the 40 to 50 years age group, 16.13% (95% CI 14.86%-17.5%) for the 50 to 60 years age group, and 52.02% (95% CI 47.62%-56.38%) for those aged ≥70 years. When metabolic health and BMI categories were instantiated to their different statuses, the conditional probability of high CVD risk increased from 7.01% (95% CI 6.27%-7.83%) for participants who were metabolically healthy normal weight to 10.47% (95% CI 7.63%-14.18%) for their metabolically healthy obese (MHO) counterparts and up to 21.74% and 34.48% among participants who were metabolically unhealthy normal weight and metabolically unhealthy obese (MUO), respectively. Sex was a significant modifier of the conditional probability distribution of metabolic obesity phenotypes and high CVD risk, with a conditional probability of high CVD risk of only 2.02% and 22.7% among MHO and MUO women, respectively, compared with 21.92% and 48.21% for their male MHO and MUO counterparts, respectively. CONCLUSIONS BN modeling was applied to investigate the relationship between CVD risk and metabolic health and obesity phenotypes in Chinese adults. The results suggest that both metabolic health and obesity status are important for CVD prevention; closer attention should be paid to BMI and metabolic status changes over time.
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Affiliation(s)
- Simiao Tian
- Department of Research, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Mei Bi
- Department of Clinical Nutrition and Metabolism, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yanhong Bi
- Department of Research, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Xiaoyu Che
- Department of Research, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yazhuo Liu
- Department of Clinical Nutrition and Metabolism, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
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Yang J, Du H, Guo Y, Bian Z, Yu C, Chen Y, Yang L, Liu J, Han X, Chen J, Lv J, Li L, Chen Z. Coarse Grain Consumption and Risk of Cardiometabolic Diseases: A Prospective Cohort Study of Chinese Adults. J Nutr 2022; 152:1476-1486. [PMID: 35234872 PMCID: PMC9178969 DOI: 10.1093/jn/nxac041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/24/2021] [Accepted: 02/17/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Lower consumption of whole grains is associated with higher risks of diabetes and coronary heart disease in Western populations, but evidence is still limited for stroke. Moreover, little is known in China, where the rates of cardiometabolic diseases are high and the grain types consumed are different from those in Western countries. OBJECTIVES To examine the associations between coarse-grain (e.g., millet, corn, and sorghum) consumption and incident cardiometabolic diseases among Chinese adults. METHODS The prospective China Kadoorie Biobank enrolled >0.5 million adults aged 30-79 years from 10 urban and rural areas during 2004-2008. At baseline, consumption frequencies (in 5 categories from "never" to "daily") of 12 major food groups, including coarse grains, were collected using a validated FFQ. After a median of 11 years of follow-up, 17,149 cases of diabetes, 29,876 ischemic strokes, 6097 hemorrhagic strokes, and 6704 major coronary events were recorded among 461,047 participants without a prevalence of major chronic diseases at baseline. Cox regression analyses were used to yield adjusted HRs for each disease associated with coarse-grain consumption. RESULTS Overall, 13.8% of participants reported regularly consuming (i.e., ≥4 days/week, regular consumers) and 29.4% reported never or rarely consuming coarse grains (i.e., nonconsumers) at baseline. Compared with nonconsumers, regular consumers had lower risks of diabetes (adjusted HR, 0.88; 95% CI, 0.78-0.98) and ischemic stroke (adjusted HR, 0.86; 95% CI, 0.81-0.93), but not hemorrhagic stroke (adjusted HR, 0.96; 95% CI, 0.76-1.20) or major coronary events (adjusted HR, 0.95; 95% CI, 0.81-1.12). For diabetes and ischemic stroke, each 100 g/day increase in the usual intake of coarse grains was associated with 14% (adjusted HR, 0.86; 95% CI, 0.76-0.97) and 13% (adjusted HR, 0.87; 95% CI, 0.81-0.94) lower risks, respectively, with similar results in various subgroups. CONCLUSIONS In Chinese adults, higher coarse-grain consumption is associated with lower risks of diabetes and ischemic stroke, supporting the promotion of coarse-grain consumption in China.
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Affiliation(s)
- Jiaomei Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | | | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jiben Liu
- Yongqinglu Community Health Service, Qingdao, Shandong Province, China
| | - Xianyong Han
- Yongqinglu Community Health Service, Qingdao, Shandong Province, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Zhengming Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Cong X, Liu S, Wang W, Ma J, Li J. Combined consideration of body mass index and waist circumference identifies obesity patterns associated with risk of stroke in a Chinese prospective cohort study. BMC Public Health 2022; 22:347. [PMID: 35180873 PMCID: PMC8855545 DOI: 10.1186/s12889-022-12756-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 02/08/2022] [Indexed: 12/24/2022] Open
Abstract
Background In China, few studies have examined the relationship between the combination of body mass index and waist circumference and the risk of stroke. Moreover, the relationship may also be different in different genders. Thus, we investigated the association between the combination of body mass index and waist circumference and the risk of stroke in Chinese. Methods This prospective cohort study included 36 632 participants aged 18 to 90 years. Participants were recruited from 60 surveillance sites (25 urban sites and 35 rural sites) across China in 2010 China Chronic Disease Risk Factor Surveillance, and followed up in 2016-2017. Incident cases of stroke were identified through questionnaires (including the basis of clinical diagnosis, imaging tests, time of diagnosis, diagnosis unit) and Cardiovascular Event Report System. Risk factors for stroke were collected at baseline using questionnaire, physical measurements and laboratory tests. Cox proportional hazards regression models were used to generate adjusted hazard ratios and 95%CI. All analyses were duplicated by gender stratification. Results During 6.42 ± 0.50 years of follow-up, 1 333 (597 males, 736 females) stroke events were observed among the 27 112 participants who did not have cardiovascular diseases at baseline. Compared with the general population who have normal weight or underweight with normal WC, those who have normal weight or underweight with abdominal obesity (adjusted hazard ratios 1.45, 95%CI 1.07-1.97 in males; 0.98, 95%CI 0.78-1.24 in females), overweight with abdominal obesity (1.41, 95%CI 1.14-1.75 in males; 1.33, 95%CI 1.10-1.61 in females), obesity with abdominal obesity (1.46, 95%CI 1.11-1.91 in males; 1.46, 95%CI 1.17-1.81 in females). Overweight with normal WC was found to be not statistically significant for both males and females (all P>0.05). Subgroup analysis found a multiplicative interaction between age and anthropometric group in females (P for interaction <0.05). Sensitivity analysis results did not change. In the subjects with CVD risk factors, we found a similar relationship as in the general population . Conclusions Combined assessment of body mass index and waist circumference identifies obesity patterns associated with stroke risk. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-12756-2.
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Affiliation(s)
- Xiangfeng Cong
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 100050, Beijing, China
| | - Shaobo Liu
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 100050, Beijing, China
| | - Wenjuan Wang
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 100050, Beijing, China
| | - Jixiang Ma
- Office of Non-Communicable Diseases and Ageing Health Management, Chinese Center for Disease Control and Prevention, 102206, Beijing, China
| | - Jianhong Li
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 100050, Beijing, China.
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Wang Y, Zhao X, Chen Y, Yao Y, Zhang Y, Wang N, Liu T, Fu C. Visceral adiposity measures are strongly associated with cardiovascular disease among female participants in Southwest China: A population-based prospective study. Front Endocrinol (Lausanne) 2022; 13:969753. [PMID: 36157470 PMCID: PMC9493204 DOI: 10.3389/fendo.2022.969753] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/11/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND AIMS Controversy remains regarding the prediction effects of different adiposity measure indicators for the risk of cardiovascular disease (CVD). Our study aimed to assess the associations of three traditional anthropometric indicators, namely, waist circumference (WC), waist-to-height ratio (WHtR), and body mass index (BMI) as well as three non-traditional anthropometric indicators, namely, the Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), and body shape index (ABSI), with the risk of CVD among Southwest Chinese population. METHODS Our study was based on the Guizhou Population Health Cohort Study (GPHCS) conducted from 2010 to 2020. A total of 9,280 participants were recruited from 12 areas in Guizhou Province, China, from November 2010 to December 2012, and followed up for major chronic diseases until December 2020. A total of 7,837 individuals with valid data were included in this analysis. The gender-specific associations of WC, WHtR, BMI, CVAI, LAP, and ABSI with CVD were evaluated using Cox proportional hazards models. Receiver operating characteristic (ROC) curve analysis was used to estimate the prediction powers of different indicators for CVD. RESULTS No association of six indicators with CVD was observed among male participants. Female participants with either WC-based central obesity (HR: 1.82, 95% CI: 1.12-2.97) or WHtR-based central obesity (HR: 1.68, 95% CI: 1.07-2.64) had a higher risk of CVD, after adjusted for age, area, ethnic group, smoking, alcohol drinking, MET, previous history of diabetes, hypertension and dyslipidemia, medication use, and nutraceutical intake. Compared with female participants in the lowest quartile (Q1), those in the highest quartile (Q4) of WHtR (HR: 2.24, 95% CI: 1.17-4.27), CVAI (HR: 3.98, 95% CI: 1.87-8.49), and ABSI (HR: 1.94, 95% CI: 1.06-3.52) had an increased risk for incident CVD. CAVI showed the maximum predictive power of CVD with the biggest AUC of 0.687 (95% CI: 0.654-0.720) compared to other indicators in female participants. CONCLUSIONS Visceral adiposity measures, especially CVAI, are stronger predictive indicators of CVD among female and not male participants in Southwest China. Different anthropometric indexes need to be combined to comprehensively assess health risks.
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Affiliation(s)
- Yingying Wang
- Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health, Fudan University, Shanghai, China
| | - Xiaodeng Zhao
- Guizhou Province Center for Disease Prevention and Control, Chronic Disease Prevention and Cure Research Institute, Guiyang, China
| | - Yun Chen
- Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health, Fudan University, Shanghai, China
| | - Yuntong Yao
- Guizhou Province Center for Disease Prevention and Control, Chronic Disease Prevention and Cure Research Institute, Guiyang, China
| | - Yixia Zhang
- Guizhou Province Center for Disease Prevention and Control, Chronic Disease Prevention and Cure Research Institute, Guiyang, China
| | - Na Wang
- Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health, Fudan University, Shanghai, China
- *Correspondence: Na Wang, ; Tao Liu,
| | - Tao Liu
- Guizhou Province Center for Disease Prevention and Control, Chronic Disease Prevention and Cure Research Institute, Guiyang, China
- *Correspondence: Na Wang, ; Tao Liu,
| | - Chaowei Fu
- Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health, Fudan University, Shanghai, China
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Iona A, Bragg F, Guo Y, Yang L, Chen Y, Pei P, Lv J, Yu C, Wang X, Zhou J, Chen J, Clarke R, Li L, Parish S, Chen Z. Adiposity and risks of vascular and non-vascular mortality among Chinese adults with type 2 diabetes: a 10-year prospective study. BMJ Open Diabetes Res Care 2022; 10:10/1/e002489. [PMID: 35042752 PMCID: PMC8768914 DOI: 10.1136/bmjdrc-2021-002489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 12/18/2021] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Among individuals with diabetes, high adiposity has been associated with lower cardiovascular disease (CVD) mortality (the so-called 'obesity paradox' phenomenon) in Western populations, for reasons that are still not fully elucidated. Moreover, little is known about such phenomena in Chinese adults with diabetes among whom very few were obese. We aimed to assess the associations of adiposity with vascular and non-vascular mortality among individuals with diabetes, and compare these with associations among individuals without diabetes. RESEARCH DESIGN AND METHODS In 2004-2008, the prospective China Kadoorie Biobank recruited >512 000 adults from 10 areas in China. After ~10 years of follow-up, 3509 deaths (1431 from CVD) were recorded among 23 842 individuals with diabetes but without prior major diseases at baseline. Cox regression yielded adjusted HRs associating adiposity with mortality. RESULTS Among people with diabetes, body mass index (BMI) (mean 25.0 kg/m2) was positively log linearly associated with CVD incidence (n=9943; HR=1.19 (95% CI 1.15 to 1.22) per 5 kg/m2), but showed U-shaped associations with CVD and overall mortality, with lowest risk at 22.5-24.9 kg/m2. At lower BMI, risk of death (n=671) within 28 days of CVD onset was particularly elevated, with an HR of 3.26 (95% CI 2.29 to 4.65) at <18.5 kg/m2 relative to 22.5-24.9 kg/m2, but no higher mortality risk at BMI ≥25.0 kg/m2. These associations were similar in self-reported and screen-detected diabetes, and persisted after extensive attempts to address reverse causality and confounding. Among individuals without diabetes (mean BMI 23.6 kg/m2; n=23 305 deaths), there were less extreme excess mortality risks at low BMI. CONCLUSIONS Among relatively lean Chinese adults with diabetes, there were contrasting associations of adiposity with CVD incidence and with mortality. The high mortality risk at low and high BMI levels highlights, if causal, the importance of maintaining normal weight among people with diabetes.
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Affiliation(s)
- Andri Iona
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Pei Pei
- Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Xiaohuan Wang
- NCDs Prevention and Control Department, Hainan Centre for Disease Control and Prevention, Haikou, Hainan, China
| | - Jinyi Zhou
- NCDs Prevention and Control Department, Jiangsu Centre for Disease Control and Prevention, Nanjing, Gulou District, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Sarah Parish
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Pathirana MM, Lassi Z, Ali A, Arstall M, Roberts CT, Andraweera PH. Cardiovascular risk factors in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. Rev Endocr Metab Disord 2021; 22:729-761. [PMID: 33106997 DOI: 10.1007/s11154-020-09587-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/26/2020] [Indexed: 12/16/2022]
Abstract
This systematic review and meta-analysis aimed to synthesize evidence on conventional cardiovascular disease (CVD) risk factors among women with previous Gestational Diabetes Mellitus (GDM). The review protocol is registered with PROSPERO (CRD42019118149). PubMed, CINAHL, SCOPUS, and EMBASE databases were searched. Studies reporting on CVD risk factors in women with previous GDM compared to women without previous GDM were selected. A total of 139 studies were eligible, of which 93 were included in the meta-analysis. Women with previous GDM have significantly higher systolic blood pressure (2.47 mmHg 95% CI 1.74 to 3.40, n = 48, 50,118 participants) diastolic blood pressure (1.89 mmHg 95% CI 1.32 to 2.46, n = 48, 49,495 participants), BMI (1.54 kg/m2 95% CI 1.32 to 2.46, n = 78, 255,308 participants), total cholesterol (0.26 SMD 95% CI 0.15 to 0.37, n = 48, 38,561 participants), LDL cholesterol (0.19 SMD 95% CI 0.08 to 0.30, n = 44, 16,980 participants), triglycerides (0.56 SMD 95% CI 0.42 to 0.70, n = 46, 13,175 participants), glucose (0.69 SMD 95% CI 0.56 to 0.81, n = 55, 127,900 participants), insulin (0.41 SMD 95% CI 0.23 to 0.59, n = 32, 8881 participants) and significantly lower HDL cholesterol (-0.28 SMD 95% CI -0.39 to -0.16, n = 56, 35,882 participants), compared to women without previous GDM. The increased blood pressure, total cholesterol, triglycerides and glucose are seen as early as <1 year post-partum.Women with previous GDM have a higher risk of CVD based on significant increases in conventional risk factors. Some risk factors are seen as early as <1 year post-partum. Women with GDM may benefit from early screening to identify modifiable CVD risk factors.
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Affiliation(s)
- Maleesa M Pathirana
- Adelaide Medical School and The Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Zohra Lassi
- Adelaide Medical School and The Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Anna Ali
- Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville, SA, Australia
- Adelaide G-TRAC Centre & CRE Frailty & Healthy Ageing Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Margaret Arstall
- Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Department of Cardiology, Lyell McEwin Hospital, Elizabeth Vale, SA, Australia
| | - Claire T Roberts
- Adelaide Medical School and The Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
| | - Prabha H Andraweera
- Adelaide Medical School and The Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia.
- Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.
- Department of Cardiology, Lyell McEwin Hospital, Elizabeth Vale, SA, Australia.
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Yao H, Zhang J, Wang Y, Wang Q, Zhao F, Zhang P. Stroke risk and its association with quality of life: a cross-sectional study among Chinese urban adults. Health Qual Life Outcomes 2021; 19:236. [PMID: 34627278 PMCID: PMC8501711 DOI: 10.1186/s12955-021-01868-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stroke is a leading cause of mortality and disability worldwide. Most stroke risk studies focused on more established biological and pathophysiological risk factors such as hypertension and smoking, psychosocial factors such as quality of life are often under-investigated and thus less reported. The current study aims to estimate stroke risk and explore the impact of quality of life on stroke risk among a community sample of urban residents in Shanghai. METHODS This cross-sectional study was conducted in Fengxian District of Shanghai City from December 2018 to April 2019. 4030 representative participants were recruited through a multistage, stratified, probability proportional to size sampling method and completed the study. Stroke risk was assessed using the Rapid Stroke Risk Screening Chart that included 8 risk factors for stroke. Quality of life was measured using the World Health Organization Quality of Life-brief version (WHOQOL-BREF). RESULTS One-third of residents were at risk for stroke, including 14.39% at high risk, and 18.68% at middle risk. The top three most commonly reported risk factors were physical inactivity (37.30%), hypertension (25.38%), and smoking (17.32%). Quality of life and its four domains were all independently and significantly associated with stroke risk. Multinominal logistic regressions showed that a one-unit increase in the quality of life was associated with a decreased relative risk for middle-risk relative to low-risk of stroke by a factor of 0.988 (95% CI:0.979, 0.997, P = 0.007), and a decreased relative risk for high-risk relative to low-risk of stroke by a factor of 0.975 (95% CI:0.966, 0.984, P < 0.001). CONCLUSIONS Our findings showed an alarmingly high prevalence of stroke risk among the sample, which may require future intervention programs to focus on improving both biological and behavioral risk factors such as increasing physical activity, early diagnosis and treatment of hypertension, and smoking cessation, as well as improving psychosocial factors such as quality of life.
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Affiliation(s)
- Huiqing Yao
- Clinical Trial Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing Key Laboratory of Drug Clinical Risk and Personalized Medication Evaluation, Beijing, 100730, People's Republic of China
| | - Juhua Zhang
- Fudan University, Shanghai, 200433, People's Republic of China.,Shanghai Pudong Health Development Research Institute, Shanghai, 200129, People's Republic of China.,Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, 201318, People's Republic of China
| | - Yanmei Wang
- Department of Nursing, Shanghai Gongli Hospital, Second Military Medical University, Shanghai, 200135, People's Republic of China
| | - Qingqing Wang
- Clinical Trial Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing Key Laboratory of Drug Clinical Risk and Personalized Medication Evaluation, Beijing, 100730, People's Republic of China
| | - Fei Zhao
- Clinical Trial Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing Key Laboratory of Drug Clinical Risk and Personalized Medication Evaluation, Beijing, 100730, People's Republic of China.
| | - Peng Zhang
- Department of Neurology, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201800, People's Republic of China. .,School of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, People's Republic of China.
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31
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Wang M, Huang Y, Song Y, Chen J, Liu X. Study on Environmental and Lifestyle Factors for the North-South Differential of Cardiovascular Disease in China. Front Public Health 2021; 9:615152. [PMID: 34336751 PMCID: PMC8322531 DOI: 10.3389/fpubh.2021.615152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
Human death and life span are closely related to the geographical environment and regional lifestyle. These factors considerably vary among counties and regions, leading to the geographical disparity of disease. Quantitative studies on this phenomenon are insufficient. Cerebrovascular and heart diseases are the leading causes of death. The mortality rate of cerebrovascular and heart diseases is statistically higher in northern China than in southern China; the p-value of t-test for cerebrovascular and heart diseases was 0.047 and 0.000, respectively. The population attribution fraction of 12 major risk factors for cardiovascular disease (CVD) in each province was calculated based on their exposure and relative risk. The results found that residents in northern China consume high sodium-containing food, fewer vegetables, and less sea food products, and tend to be overweight. Fine particulate matter is higher in northern China than in southern China. Cold temperatures also cause a greater number of deaths than hot temperatures. All these factors have resulted in a higher CVD mortality rate in northern China. The attributive differential for sodium, vegetable, fruit, smoking, PM2.5, omega-3, obesity, low temperature, and high temperature of heart disease between the two parts of China is 9.1, 0.7, -2.5, 0.1, 1.4, 1.3, 2.0, 4.7, and -2.1%, respectively. Furthermore, the attributive differential for the above factors of cerebrovascular disease between the two parts of China is 8.7, 0.0, -5.2, 0.1, 1.0, 0.0, 2.4, 4.7, and -2.1%. Diet high in sodium is the leading cause of the north-south differential in CVD, resulting in 0.71 less years of life expectancy in northern compared with that in southern China.
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Affiliation(s)
- Mengqi Wang
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Yi Huang
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Yanxin Song
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Jianwei Chen
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Xiaoxiao Liu
- School of Geographic Sciences, Nantong University, Nantong, China
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32
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Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol 2021; 9:446-461. [PMID: 34097869 DOI: 10.1016/s2213-8587(21)00118-2] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 12/16/2022]
Abstract
China has experienced many drastic social and economic changes and shifts in people's lifestyles since the 1990s, in parallel with the fast rising prevalence of obesity. About half of adults and a fifth of children have overweight or obesity according to the Chinese criteria, making China the country with the highest number of people with overweight or obesity in the world. Assuming that observed time trends would continue in the future, we projected the prevalence of and the number of people affected by overweight and obesity by 2030, and the associated medical costs. The rising incidence of obesity and number of people affected, as well as the related health and economic consequences, place a huge burden on China's health-care system. China has made many efforts to tackle obesity, including the implementation of relevant national policies and programmes. However, these measures are inadequate for controlling the obesity epidemic. In the past decade, China has attached great importance to public health, and the Healthy China 2030 national strategy initiated in 2016 provides a historical opportunity to establish comprehensive national strategies for tackling obesity. China is well positioned to explore an effective model to overcome the obesity epidemic; however, strong commitment and leadership from central and local governments are needed, as well as active participation of all related society sectors and individual citizens. TRANSLATION: For the Chinese translation of the paper see Supplementary Materials section.
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Affiliation(s)
- Youfa Wang
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China.
| | - Li Zhao
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Liwang Gao
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - An Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Xue
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA, USA
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33
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Mu L, Liu J, Zhou G, Wu C, Chen B, Lu Y, Lu J, Yan X, Zhu Z, Nasir K, Spatz ES, Krumholz HM, Zheng X. Obesity Prevalence and Risks Among Chinese Adults: Findings From the China PEACE Million Persons Project, 2014-2018. Circ Cardiovasc Qual Outcomes 2021; 14:e007292. [PMID: 34107739 PMCID: PMC8204767 DOI: 10.1161/circoutcomes.120.007292] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Supplemental Digital Content is available in the text. China has seen a burgeoning epidemic of obesity in recent decades, but few studies reported nationally on obesity across socio-demographic subgroups. We sought to assess the prevalence and socio-demographic associations of obesity nationwide.
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Affiliation(s)
- Lin Mu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.).,Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | | | - Guohai Zhou
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Chaoqun Wu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Bowang Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Yuan Lu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Jiapeng Lu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Xiaofang Yan
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Zhihong Zhu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Khurram Nasir
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Erica S Spatz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (H.M.K.).,Department of Health Policy and Management, Yale School of Public Health, New Haven, CT (H.M.K.)
| | - Xin Zheng
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
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34
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Liu K, Cao H, Guo C, Pan L, Cui Z, Sun J, Zhao W, Han X, Zhang H, Wang Z, Niu K, Tang N, Shan G, Zhang L. Environmental and Genetic Determinants of Major Chronic Disease in Beijing-Tianjin-Hebei Region: Protocol for a Community-Based Cohort Study. Front Public Health 2021; 9:659701. [PMID: 34150703 PMCID: PMC8212971 DOI: 10.3389/fpubh.2021.659701] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/31/2021] [Indexed: 01/23/2023] Open
Abstract
Introduction: Personal lifestyle and air pollution are potential risk factors for major non-communicable diseases (NCDs). However, these risk factors have experienced dramatic changes in the Beijing–Tianjin–Hebei (BTH) region in recent years, and few cohorts have focused on identifying risk factors for major NCDs in this specific region. The current study is a large, prospective, long-term, population-based cohort study that investigated environmental and genetic determinants of NCDs in BTH areas. The results of this study may provide scientific support for efforts to develop health recommendations for personalized prevention. Methods: About 36,000 participants 18 years or older would be obtained by multistage, stratified cluster sampling from five cities for the baseline assessment. Participants underwent seven examinations primarily targeting respiratory and circulatory system function and filled out questionnaires regarding lifestyle behavior, pollutant exposure, medical and family history, medication history, and psychological factors. Biochemistry indicators and inflammation markers were tested, and a biobank was established. Participants will be followed up every 2 years. Genetic determinants of NCDs will be demonstrated by using multiomics, and risk prediction models will be constructed using machine learning methods based on a multitude of environmental exposure, examination data, biomarkers, and psychosocial and behavioral assessments. Significant spatial and temporal differentiation is well-suited to demonstrating the health determinants of NCDs in the BTH region, which may facilitate public health strategies with respect to disease prevention and survivorship-related aspects.
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Affiliation(s)
- Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Han Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Chunyue Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Li Pan
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.,School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Ze Cui
- Department of Chronic and Noncommunicable Disease Prevention and Control, Hebei Provincial Center for Disease Prevention and Control, Shijiazhuang, China
| | - Jixin Sun
- Department of Chronic and Noncommunicable Disease Prevention and Control, Hebei Provincial Center for Disease Prevention and Control, Shijiazhuang, China
| | - Wei Zhao
- Department of Chronic and Noncommunicable Disease Prevention and Control, Chaoyang District Center for Disease Prevention and Control, Beijing, China
| | - Xiaoyan Han
- Department of Chronic and Noncommunicable Disease Prevention and Control, Chaoyang District Center for Disease Prevention and Control, Beijing, China
| | - Han Zhang
- Health Management Center, Beijing Aerospace General Hospital, Beijing, China
| | - Zhengfang Wang
- Health Management Center, Beijing Aerospace General Hospital, Beijing, China
| | - Kaijun Niu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Naijun Tang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Guangliang Shan
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.,School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
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Zeng Q, Li N, Pan XF, Chen L, Pan A. Clinical management and treatment of obesity in China. Lancet Diabetes Endocrinol 2021; 9:393-405. [PMID: 34022157 DOI: 10.1016/s2213-8587(21)00047-4] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 02/03/2021] [Accepted: 02/12/2021] [Indexed: 02/08/2023]
Abstract
China has one of the largest populations with obesity in the world, and obesity has become a major challenge for the country's health-care system. Current guidelines for obesity management are not adequately supported by evidence from clinical studies in Chinese populations. Effective lifestyle interventions suitable for Chinese populations are scarce, insufficient weight-loss medications have been approved by regulatory bodies, and there is low acceptance of non-lifestyle interventions (ie, medications and surgery) among both health-care providers and the general public. Large, well designed, and well implemented clinical trials are needed to strengthen the evidence base for the clinical management of obesity in China. Obesity management can be improved through use of a tiered system involving health management centres, integrated lifestyle interventions and medical treatments, strengthened obesity education and training, and use of advanced electronic health technologies. Resource mobilisation, support from major stakeholders for people with overweight or obesity, and education and changes to social norms among the wider public are also needed. National health policies should prioritise both obesity prevention and improvement of the treatment and management of obesity.
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Affiliation(s)
- Qiang Zeng
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Naishi Li
- Department of Endocrinology, Key Laboratory of Endocrinology of the National Health Commission, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiong-Fei Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
| | - An Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol 2021; 9:373-392. [PMID: 34022156 DOI: 10.1016/s2213-8587(21)00045-0] [Citation(s) in RCA: 566] [Impact Index Per Article: 188.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 01/15/2021] [Accepted: 02/12/2021] [Indexed: 12/11/2022]
Abstract
Obesity has become a major public health issue in China. Overweight and obesity have increased rapidly in the past four decades, and the latest national prevalence estimates for 2015-19, based on Chinese criteria, were 6·8% for overweight and 3·6% for obesity in children younger than 6 years, 11·1% for overweight and 7·9% for obesity in children and adolescents aged 6-17 years, and 34·3% for overweight and 16·4% for obesity in adults (≥18 years). Prevalence differed by sex, age group, and geographical location, but was substantial in all subpopulations. Strong evidence from prospective cohort studies has linked overweight and obesity to increased risks of major non-communicable diseases and premature mortality in Chinese populations. The growing burden of overweight and obesity could be driven by economic developments, sociocultural norms, and policies that have shaped individual-level risk factors for obesity through urbanisation, urban planning and built environments, and food systems and environments. Substantial changes in dietary patterns have occurred in China, with increased consumption of animal-source foods, refined grains, and highly processed, high-sugar, and high-fat foods, while physical activity levels in all major domains have decreased with increasing sedentary behaviours. The effects of dietary factors and physical inactivity intersect with other individual-level risk factors such as genetic susceptibility, psychosocial factors, obesogens, and in-utero and early-life exposures. In view of the scarcity of research around the individual and collective roles of these upstream and downstream factors, multidisciplinary and transdisciplinary studies are urgently needed to identify systemic approaches that target both the population-level determinants and individual-level risk factors for obesity in China.
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Affiliation(s)
- Xiong-Fei Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Limin Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - An Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Armas Rojas NB, Lacey B, Soni M, Charles S, Carter J, Varona-Pérez P, Burrett JA, Martínez MC, Lorenzo-Vázquez E, Constantén SB, Taylor H, Sherliker P, Rigau JMM, Ross S, Massa MS, López OJH, Islam N, Morales MÁM, Alomá IA, Estupiñan FA, González MD, Muñoz NR, Asencio MC, Díaz-Diaz O, Iglesias-Marichal I, Emberson J, Peto R, Lewington S. Body-mass index, blood pressure, diabetes and cardiovascular mortality in Cuba: prospective study of 146,556 participants. BMC Public Health 2021; 21:963. [PMID: 34039286 PMCID: PMC8157418 DOI: 10.1186/s12889-021-10911-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/26/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Cardiovascular disease accounts for about one-third of all premature deaths (ie, age < 70) in Cuba. Yet, the relevance of major risk factors, including systolic blood pressure (SBP), diabetes, and body-mass index (BMI), to cardiovascular mortality in this population remains unclear. METHODS In 1996-2002, 146,556 adults were recruited from the general population in five areas of Cuba. Participants were interviewed, measured (height, weight and blood pressure) and followed up by electronic linkage to national death registries until Jan 1, 2017; in 2006-08, 24,345 participants were resurveyed. After excluding all with missing data, cardiovascular disease at recruitment, and those who died in the first 5 years, Cox regression (adjusted for age, sex, education, smoking, alcohol and, where appropriate, BMI) was used to relate cardiovascular mortality rate ratios (RRs) at ages 35-79 years to SBP, diabetes and BMI; RR were corrected for regression dilution to give associations with long-term average (ie, 'usual') levels of SBP and BMI. RESULTS After exclusions, there were 125,939 participants (mean age 53 [SD12]; 55% women). Mean SBP was 124 mmHg (SD15), 5% had diabetes, and mean BMI was 24.2 kg/m2 (SD3.6); mean SBP and diabetes prevalence at recruitment were both strongly related to BMI. During follow-up, there were 4112 cardiovascular deaths (2032 ischaemic heart disease, 832 stroke, and 1248 other). Cardiovascular mortality was positively associated with SBP (>=120 mmHg), diabetes, and BMI (>=22.5 kg/m2): 20 mmHg higher usual SBP about doubled cardiovascular mortality (RR 2.02, 95%CI 1.88-2.18]), as did diabetes (2.15, 1.95-2.37), and 10 kg/m2 higher usual BMI (1.92, 1.64-2.25). RR were similar in men and in women. The association with BMI and cardiovascular mortality was almost completely attenuated following adjustment for the mediating effect of SBP. Elevated SBP (>=120 mmHg), diabetes and raised BMI (>=22.5 kg/m2) accounted for 27%, 14%, and 16% of cardiovascular deaths, respectively. CONCLUSIONS This large prospective study provides direct evidence for the effects of these major risk factors on cardiovascular mortality in Cuba. Despite comparatively low levels of these risk factors by international standards, the strength of their association with cardiovascular death means they nevertheless exert a substantial impact on premature mortality in Cuba.
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Affiliation(s)
| | - Ben Lacey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Monica Soni
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- Georgetown University School of Medicine, Washington, D.C, USA
| | - Shaquille Charles
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jennifer Carter
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Patricia Varona-Pérez
- Institute of Hygiene, Epidemiology and Microbiology, Ministry of Public Health, Havana, Cuba
| | - Julie Ann Burrett
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Marcy Calderón Martínez
- Institute of Hygiene, Epidemiology and Microbiology, Ministry of Public Health, Havana, Cuba
| | | | - Sonia Bess Constantén
- Directorate of Medical Records and Health Statistics, Ministry of Public Health, Havana, Cuba
| | - Hannah Taylor
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Paul Sherliker
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK
| | | | - Stephanie Ross
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - M Sofia Massa
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | | | - Nazrul Islam
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | | | - Ismell Alonso Alomá
- Directorate of Medical Records and Health Statistics, Ministry of Public Health, Havana, Cuba
| | | | - Mayda Díaz González
- Municipal Center of Hygiene, Epidemiology and Microbiology, Colón, Matanzas, Cuba
| | | | | | | | | | - Jonathan Emberson
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK
| | - Richard Peto
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Sarah Lewington
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK.
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK.
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
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Knowles R, Carter J, Jebb SA, Bennett D, Lewington S, Piernas C. Associations of Skeletal Muscle Mass and Fat Mass With Incident Cardiovascular Disease and All-Cause Mortality: A Prospective Cohort Study of UK Biobank Participants. J Am Heart Assoc 2021; 10:e019337. [PMID: 33870707 PMCID: PMC8200765 DOI: 10.1161/jaha.120.019337] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 03/01/2021] [Indexed: 02/06/2023]
Abstract
Background There is debate whether body mass index is a good predictor of health outcomes because different tissues, namely skeletal muscle mass (SMM) and fat mass (FM), may be differentially associated with risk. We investigated the association of appendicular SMM (aSMM) and FM with fatal and nonfatal cardiovascular disease (CVD) and all-cause mortality. We compared their prognostic value to that of body mass index. Methods and Results We studied 356 590 UK Biobank participants aged 40 to 69 years with bioimpedance analysis data for whole-body FM and predicted limb muscle mass (to calculate aSMM). Associations between aSMM and FM with CVD and all-cause mortality were examined using multivariable Cox proportional hazards models. Over 3 749 501 person-years of follow-up, there were 27 784 CVD events and 15 844 all-cause deaths. In men, aSMM was positively associated with CVD incidence (hazard ratio [HR] per 1 SD 1.07; 95% CI, 1.06-1.09) and there was a curvilinear association in women. There were stronger positive associations between FM and CVD with HRs per SD of 1.20 (95% CI, 1.19-1.22) and 1.25 (95% CI, 1.23-1.27) in men and women respectively. Within FM tertiles, the associations between aSMM and CVD risk largely persisted. There were J-shaped associations between aSMM and FM with all-cause mortality in both sexes. Body mass index was modestly better at discriminating CVD risk. Conclusions FM showed a strong positive association with CVD risk. The relationship of aSMM with CVD risk differed between sexes, and potential mechanisms need further investigation. Body fat and SMM bioimpedance measurements were not superior to body mass index in predicting population-level CVD incidence or all-cause mortality.
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Affiliation(s)
- Rebecca Knowles
- Nuffield Department of Population HealthUniversity of OxfordUnited Kingdom
| | - Jennifer Carter
- Nuffield Department of Population HealthUniversity of OxfordUnited Kingdom
| | - Susan A. Jebb
- Nuffield Department of Primary Care Health SciencesUniversity of OxfordUnited Kingdom
| | - Derrick Bennett
- Nuffield Department of Population HealthUniversity of OxfordUnited Kingdom
| | - Sarah Lewington
- Nuffield Department of Population HealthUniversity of OxfordUnited Kingdom
| | - Carmen Piernas
- Nuffield Department of Primary Care Health SciencesUniversity of OxfordUnited Kingdom
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Qi W, Ma J, Guan T, Zhao D, Abu‐Hanna A, Schut M, Chao B, Wang L, Liu Y. Risk Factors for Incident Stroke and Its Subtypes in China: A Prospective Study. J Am Heart Assoc 2020; 9:e016352. [PMID: 33103569 PMCID: PMC7763402 DOI: 10.1161/jaha.120.016352] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Background Managing risk factors is crucial to prevent stroke. However, few cohort studies have evaluated socioeconomic factors together with conventional factors affecting incident stroke and its subtypes in China. Methods and Results A 2014 to 2016 prospective study from the China National Stroke Screening and Intervention Program comprised 437 318 adults aged ≥40 years without stroke at baseline. There were 2429 cases of first‐ever stroke during a median follow‐up period of 2.1 years, including 2206 ischemic strokes and 237 hemorrhagic strokes. The multivariable Cox regression analysis indicated that age 50 to 59 years (versus 40–49 years), primary school or no formal education (versus middle school), having >1 child (versus 1 child), living in Northeast, Central, East, or North China (versus Southwest China), physical inactivity, hypertension, diabetes mellitus, and obesity were positively associated with the risk of total and ischemic stroke, whereas age 60 to 69 years and living with spouse or children (versus living alone) were negatively associated with the risk of total and ischemic stroke. Men, vegetable‐based diet, underweight, physical inactivity, hypertension, living in a high‐income region, having Urban Resident Basic Medical Insurance, and New Rural Cooperative Medical System were positively associated with the risk of hemorrhagic stroke, whereas age 60 to 69 years was negatively associated with the risk of hemorrhagic stroke. Conclusions We identified socioeconomic factors that complement traditional risk factors for incident stroke and its subtypes, allowing targeting these factors to reduce stroke burden.
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Affiliation(s)
- Wenwei Qi
- School of Health Policy and ManagementChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
- Tianjin Institute of CardiologySecond Hospital of Tianjin Medical UniversityTianjinChina
- Department of Medical InformaticsAmsterdam UMCAmsterdamThe Netherlands
| | - Jing Ma
- Brigham & Women’s HospitalHarvard Medical SchoolBostonMA
| | - Tianjia Guan
- School of Health Policy and ManagementChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
| | - Dongsheng Zhao
- Information CenterAcademy of Military Medical SciencesBeijingChina
| | - Ameen Abu‐Hanna
- Department of Medical InformaticsAmsterdam UMCAmsterdamThe Netherlands
| | - Martijn Schut
- Department of Medical InformaticsAmsterdam UMCAmsterdamThe Netherlands
| | - Baohua Chao
- National Health Commission of the People’s Republic of ChinaBeijingChina
| | - Longde Wang
- School of Public HealthPeking University Health Science CenterBeijingPeople’s Republic of China
| | - Yuanli Liu
- School of Health Policy and ManagementChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
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Gao M, Lv J, Yu C, Guo Y, Bian Z, Yang R, Du H, Yang L, Chen Y, Li Z, Zhang X, Chen J, Qi L, Chen Z, Huang T, Li L. Metabolically healthy obesity, transition to unhealthy metabolic status, and vascular disease in Chinese adults: A cohort study. PLoS Med 2020; 17:e1003351. [PMID: 33125374 PMCID: PMC7598496 DOI: 10.1371/journal.pmed.1003351] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 09/11/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Metabolically healthy obesity (MHO) and its transition to unhealthy metabolic status have been associated with risk of cardiovascular disease (CVD) in Western populations. However, it is unclear to what extent metabolic health changes over time and whether such transition affects risks of subtypes of CVD in Chinese adults. We aimed to examine the association of metabolic health status and its transition with risks of subtypes of vascular disease across body mass index (BMI) categories. METHODS AND FINDINGS The China Kadoorie Biobank was conducted during 25 June 2004 to 15 July 2008 in 5 urban (Harbin, Qingdao, Suzhou, Liuzhou, and Haikou) and 5 rural (Henan, Gansu, Sichuan, Zhejiang, and Hunan) regions across China. BMI and metabolic health information were collected. We classified participants into BMI categories: normal weight (BMI 18.5-23.9 kg/m²), overweight (BMI 24.0-27.9 kg/m²), and obese (BMI ≥ 28 kg/m²). Metabolic health was defined as meeting less than 2 of the following 4 criteria (elevated waist circumference, hypertension, elevated plasma glucose level, and dyslipidemia). The changes in obesity and metabolic health status were defined from baseline to the second resurvey with combination of overweight and obesity. Among the 458,246 participants with complete information and no history of CVD and cancer, the mean age at baseline was 50.9 (SD 10.4) years, and 40.8% were men, and 29.0% were current smokers. During a median 10.0 years of follow-up, 52,251 major vascular events (MVEs), including 7,326 major coronary events (MCEs), 37,992 ischemic heart disease (IHD), and 42,951 strokes were recorded. Compared with metabolically healthy normal weight (MHN), baseline MHO was associated with higher hazard ratios (HRs) for all types of CVD; however, almost 40% of those participants transitioned to metabolically unhealthy status. Stable metabolically unhealthy overweight or obesity (MUOO) (HR 2.22, 95% confidence interval [CI] 2.00-2.47, p < 0.001) and transition from metabolically healthy to unhealthy status (HR 1.53, 1.34-1.75, p < 0.001) were associated with higher risk for MVE, compared with stable healthy normal weight. Similar patterns were observed for MCE, IHD, and stroke. Limitations of the analysis included lack of measurement of lipid components, fasting plasma glucose, and visceral fat, and there might be possible misclassification. CONCLUSIONS Among Chinese adults, MHO individuals have increased risks of MVE. Obesity remains a risk factor for CVD independent of major metabolic factors. Our data further suggest that metabolic health is a transient state for a large proportion of Chinese adults, with the highest vascular risk among those remained MUOO.
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Affiliation(s)
- Meng Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
- Peking University Institute of Environmental Medicine, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Ruotong Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Zhongxiao Li
- Maiji Center for Disease Control and Prevention, Maiji, Gansu, China
| | - Xi Zhang
- Maiji Center for Disease Control and Prevention, Maiji, Gansu, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
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Abstract
A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.
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Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review. J Am Coll Cardiol 2020; 75:525-538. [PMID: 32029136 DOI: 10.1016/j.jacc.2019.11.044] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/06/2019] [Accepted: 11/17/2019] [Indexed: 02/07/2023]
Abstract
A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.
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Risk factors for cardiovascular disease in the Chinese population: recent progress and implications. GLOBAL HEALTH JOURNAL 2020. [DOI: 10.1016/j.glohj.2020.08.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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Effect of Post-Stroke Rehabilitation on Body Mass Composition in Relation to Socio-Demographic and Clinical Factors. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17145134. [PMID: 32708623 PMCID: PMC7400096 DOI: 10.3390/ijerph17145134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/09/2020] [Accepted: 07/14/2020] [Indexed: 12/15/2022]
Abstract
Background and objectives: Stroke is one of the leading causes of morbidity, mortality and long-term adult disability. The aim of this study was to assess the changes in body mass composition in patients after stroke in connection with selected socio-demographic and clinical factors (sex, age, type of stroke and time from the first symptoms) following the rehabilitation process. Materials and Methods: The study group consisted of 100 post-stroke subjects who participated in a comprehensive rehabilitation program for a duration of five weeks. The measurements of body composition by a Tanita MC 780 MA analyser were performed on the day of admission to hospital, on the day of discharge (after 5 weeks) and 12 weeks after discharge from hospital. Results: It was shown that before rehabilitation (Exam I) in the study group there were significant differences in body composition relative to sex, age and time from stroke. The rates of fat mass % and visceral fat level decreased after rehabilitation (Exam II) in both males and females. Exam II, at the end hospital rehabilitation, showed lower levels of fat mass %, visceral fat level, as well as fat-free mass % and higher values of total body water % and muscle mass %. In Exam III, i.e., 12 weeks after discharge, all of the parameters retained their values. Conclusions: The study shows an association between stroke risk factors (primarily age, sex and time from the onset of the first symptoms of stroke) and body mass composition resulting from rehabilitation. The type of stroke and the effects of rehabilitation on body mass components showed no differences. Comprehensive rehabilitation had a positive effect on the body mass components.
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Differences in Body Composition among Patientsafter Hemorrhagic and Ischemic Stroke. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17114170. [PMID: 32545352 PMCID: PMC7312185 DOI: 10.3390/ijerph17114170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 06/05/2020] [Accepted: 06/06/2020] [Indexed: 12/15/2022]
Abstract
The aim of the study was to assess differences in the body composition of patients after hemorrhagic and ischemic stroke. There were 74 male participants in the study, of which 13 (18%) experienced hemorrhagic stroke, while 61 (82%) were after ischemic stroke. Significantly (p < 0.05) higher values of body composition variables were noted for ischemic compared to hemorrhagic strokes, and concerned: body mass (BM) (kg), basal metabolic rate (BMR) (kJ), fat-free mass (FFM) (kg), total body water (TBW) (kg), muscle mass (MM) (kg), visceral fat level (VFL), bone mass (BoM) (kg), extracellular water(ECW) (kg),intracellular water (ICW) (kg), trunk fat-free mass (TFFM) (kg) and trunk muscle mass (TMM) (kg)in the paretic upper limb; FFM (kg) and MM (kg) in the non-paretic upper limb; FFM (kg) and MM (kg) in the paretic lower limbas well as FFM (kg) and MM (kg) in the non-paretic lower limb without paresis. Only for the variables fat mass (FM) (kg), body mass index (BMI), metabolic age (MA), trunk fat mass (TFM) (kg), and FM (kg) in the paretic upper limb and FM (kg) in the non-paretic upper limb were there no significant differences. Significant differences in body composition of patients after hemorrhagic and ischemic stroke have been demonstrated. Individuals after ischemic stroke had significantly worse body composition. Incorrect body composition is a significant risk factor, especially of ischemic stroke.
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Tu T, Peng J, Jiang Y. FNDC5/Irisin: A New Protagonist in Acute Brain Injury. Stem Cells Dev 2020; 29:533-543. [PMID: 31914844 DOI: 10.1089/scd.2019.0232] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Tianqi Tu
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jianhua Peng
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Neurosurgical Clinical Research Center of Sichuan Province, Luzhou, China
- Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yong Jiang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Neurosurgical Clinical Research Center of Sichuan Province, Luzhou, China
- Laboratory of Neurological Diseases and Brain Functions, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Systemic inflammation is associated with incident stroke and heart disease in East Asians. Sci Rep 2020; 10:5605. [PMID: 32221345 PMCID: PMC7101367 DOI: 10.1038/s41598-020-62391-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 03/12/2020] [Indexed: 12/24/2022] Open
Abstract
Systemic inflammation, reflected by increased plasma concentrations of C-reactive protein (CRP) and fibrinogen, is associated with increased risk of coronary heart disease, but its relevance for stroke types remains unclear. Moreover, evidence is limited in non-European populations. We investigated associations of CRP and fibrinogen with risks of incident major coronary events (MCE), ischemic stroke (IS) and intracerebral hemorrhage (ICH) in a cohort of Chinese adults. A nested case-control study within the prospective China Kadoorie Biobank included 1,508 incident MCE cases, 5,418 IS cases, 4,476 ICH cases, and 5,285 common controls, aged 30–79 years. High-sensitivity CRP and low-density lipoprotein cholesterol (LDL-C) were measured in baseline plasma samples from all participants, and fibrinogen in a subset (n = 9,380). Logistic regression yielded adjusted odds ratios (ORs) per SD higher usual levels of log-transformed CRP and fibrinogen. The overall mean (SD) baseline LDL-C was 91.6 mg/dL (24.0) and geometric mean (95% CI) CRP and fibrinogen were 0.90 mg/L (0.87–0.93) and 3.01 g/L (2.98–3.03), respectively. There were approximately log-linear positive associations of CRP with each outcome, which persisted after adjustment for LDL-C and other risk factors, with adjusted ORs (95% CI) per SD higher CRP of 1.67 (1.44–1.94) for MCE and 1.22 (1.10–1.36) for both IS and ICH. No associations of fibrinogen with MCE, IS, or ICH were identified. Adding CRP to prediction models based on established risk factors improved model fit for each of MCE, IS, and ICH, with small improvements in C-statistic and correct reclassification of controls to lower risk groups. Among Chinese adults, who have low mean LDL-C, CRP, but not fibrinogen, was independently associated with increased risks of MCE and stroke.
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Mechanick JI, Farkouh ME, Newman JD, Garvey WT. Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review. J Am Coll Cardiol 2020; 75:525-538. [PMID: 32029136 PMCID: PMC7187687 DOI: 10.1016/j.jacc.2019.11.044,+10.1016/s0735-1097(20)31152-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/06/2019] [Accepted: 11/17/2019] [Indexed: 02/01/2024]
Abstract
A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.
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Affiliation(s)
- Jeffrey I Mechanick
- Zena and Michael A. Wiener Cardiovascular Institute/Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Michael E Farkouh
- Peter Munk Cardiac Centre and the Heart and Stroke Richard Lewar Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jonathan D Newman
- Division of Cardiology and Center for the Prevention of Cardiovascular Disease, Department of Medicine, New York University Medical Center, New York, New York
| | - W Timothy Garvey
- Department of Nutrition Sciences and Diabetes Research Center, University of Alabama at Birmingham, Birmingham, Alabama; Geriatric Research Education and Clinical Center, Birmingham VA Medical Center, Birmingham, Alabama
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49
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Mechanick JI, Farkouh ME, Newman JD, Garvey WT. Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review. J Am Coll Cardiol 2020; 75. [PMID: 32029136 PMCID: PMC7187687 DOI: 10.1016/j.jacc.2019.11.044, 10.1016/s0735-1097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.
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Affiliation(s)
- Jeffrey I. Mechanick
- Zena and Michael A. Wiener Cardiovascular Institute/Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michael E. Farkouh
- Peter Munk Cardiac Centre and the Heart and Stroke Richard Lewar Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jonathan D. Newman
- Division of Cardiology and Center for the Prevention of Cardiovascular Disease, Department of Medicine, New York University Medical Center, New York, New York
| | - W. Timothy Garvey
- Department of Nutrition Sciences and Diabetes Research Center, University of Alabama at Birmingham, Birmingham, Alabama;,Geriatric Research Education and Clinical Center, Birmingham VA Medical Center, Birmingham, Alabama
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50
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Wang L, Xu F, Brickell A, Sun N, Mao X, Zhang Q, Wang G, Zhou Q, Yang B, Li F, Yue L, Zhang W, Hao Y, Sun C. Additional common loci associated with stroke and obesity identified using pleiotropic analytical approach. Mol Genet Genomics 2019; 295:439-451. [PMID: 31813042 DOI: 10.1007/s00438-019-01630-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 11/25/2019] [Indexed: 12/11/2022]
Abstract
Stroke is a complex disease with multiple etiologies. Numerous studies suggest an established association between obesity and stroke, which may partly arise from the shared genetic components between the two phenotypes. Despite genome-wide association studies (GWASs) have identified some loci associated with stroke and obesity individually, the estimated genetic variability explained by these loci is limited (especially for stroke) and the pleiotropic loci between them are largely unknown. In this study, we jointly applied the pleiotropy-informed conditional false discovery rate (cFDR) method and the genetic analysis incorporating pleiotropy and annotation (GPA) method on summary statistics of two large GWASs to detect the genetic overlap between stroke (n = 446,696) and obesity (n = 681,275). Stratified Q-Q and fold-enrichment plots showed strong pleiotropic enrichment between the two phenotypes. With cFDR < 0.05 and fdr.GPA < 0.2, we identified 24 (16 novel) stroke-associated SNPs and 12 (10 novel) of them to be potentially pleiotropic SNPs for both phenotypes. The corresponding genes were enriched in trait-associated gene ontology (GO) terms "brain development" and "negative regulation of transport". In conclusion, our study demonstrated the feasibility and effectivity of the two pleiotropic methods which successfully improved the genetic discovery by incorporating related GWAS datasets and validated the genetic intercommunity between stroke and obesity. The identification of pleiotropic loci may provide us any new insights into potential genetic and etiology mechanism between them for the further studies.
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Affiliation(s)
- Lianke Wang
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Fei Xu
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Anna Brickell
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Nan Sun
- Department of Management Information Systems, Terry College of Business, University of Georgia, Athens, GA, 30602, USA
| | - Xiangjie Mao
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Qiang Zhang
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Ganyi Wang
- Center for Food and Drug Reevaluation of Henan, No. 79 Xiongerhe Road, Jinshui District, Zhengzhou, 450000, Henan, People's Republic of China
| | - Qianyu Zhou
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Bin Yang
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Fangwei Li
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Limin Yue
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Weidong Zhang
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China
| | - Yibin Hao
- People's Hospital of Zhengzhou, No. 33 Huanghe Road, Jinshui District, Zhengzhou, 450000, Henan, People's Republic of China
| | - Changqing Sun
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China.
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