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Song S, Lv J, Li L, Pang Y. Editorial: Exploring the influence of diet on later-onset ulcerative colitis-Are eggs and spicy foods the key factors in Asia? Authors' reply. Aliment Pharmacol Ther 2024; 59:1453-1454. [PMID: 38711361 DOI: 10.1111/apt.17999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
LINKED CONTENTThis article is linked to Song et al papers. To view these articles, visit https://doi.org/10.1111/apt.17963 and https://doi.org/10.1111/apt.17983
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Affiliation(s)
- Shuyao Song
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 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
| | - 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
| | - 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
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Song S, Wu Z, Lv J, Yu C, Sun D, Pei P, Pan L, Yang L, Chen Y, Du H, Chen L, Schmidt D, Avery D, Duan L, Chen J, Chen Z, Li L, Pang Y. Dietary factors and patterns in relation to risk of later-onset ulcerative colitis in Chinese: A prospective study of 0.5 million people. Aliment Pharmacol Ther 2024; 59:1425-1434. [PMID: 38654428 DOI: 10.1111/apt.17963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/27/2024] [Accepted: 03/10/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND There is limited evidence on the associations of dietary factors and patterns with risk of later-onset ulcerative colitis (UC) in Chinese adults. AIMS To investigate the associations of dietary factors and patterns with risk of later-onset UC in Chinese. METHODS The prospective China Kadoorie Biobank cohort study recruited 512,726 participants aged 30-79. Dietary habits were assessed using food frequency questionnaires. Dietary patterns were derived by factor analysis with a principal component method. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS During a median follow-up of 12.1 years, 312 cases of newly diagnosed UC were documented (median age of diagnosis 60.1 years). Egg consumption was associated with higher risk of UC (HR for daily vs. never or rarely: 2.29 [95% CI: 1.26-4.16]), while spicy food consumption was inversely associated with risk of UC (HR: 0.63 [0.45-0.88]). The traditional northern dietary pattern, characterised by high intake of wheat and low intake of rice, was associated with higher risk of UC (HR for highest vs. lowest quartile of score: 2.79 [1.93-4.05]). The modern dietary pattern, characterised by high intake of animal-origin foods and fruits, was associated with higher risk of UC (HR: 2.48 [1.63-3.78]). Population attributable fraction was 13.04% (7.71%-19.11%) for daily/almost daily consumption of eggs and 9.87% (1.94%-18.22%) for never/rarely consumption of spicy food. CONCLUSIONS The findings highlight the importance of evaluating dietary factors and patterns in the primary prevention of later-onset UC in Chinese adults.
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Affiliation(s)
- Shuyao Song
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhiyu Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 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
| | - 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
| | - 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
| | - Pei Pei
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Lang Pan
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- 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
| | - 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
| | - Lingli Chen
- Tongxiang Center for Disease Control and Prevention, Tongxiang, China
| | - Dan Schmidt
- 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
| | - Liping Duan
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
| | - 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
| | - 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
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Pang Y, Åberg F, Chen Z, Li L, Kartsonaki C. Predicting risk of chronic liver disease in Chinese adults: External validation of the CLivD score. J Hepatol 2024; 80:e264-e266. [PMID: 38181826 DOI: 10.1016/j.jhep.2023.12.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024]
Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, China.
| | - Fredrik Åberg
- Transplantation and Liver Surgery, HUCH Meilahti Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
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Weng C, Shao Z, Xiao M, Song M, Zhao Y, Li A, Pang Y, Huang T, Yu C, Lv J, Li L, Sun D. Association of sex hormones with non-alcoholic fatty liver disease: An observational and Mendelian randomization study. Liver Int 2024; 44:1154-1166. [PMID: 38345150 DOI: 10.1111/liv.15866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/20/2024] [Accepted: 01/28/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND AND AIMS Sex-specific associations of sex hormone-binding globulin (SHBG) and bioavailable testosterone (BAT) with NAFLD remain indeterminate. We aimed to explore observational and genetically determined relationships between each hormone and NAFLD. METHODS We included 187 395 men and 170 193 women from the UK Biobank. Linear and nonlinear Cox regression models and Mendelian randomization (MR) analysis were used to test the associations. RESULTS During 12.49 years of follow-up, 2209 male and 1886 female NAFLD cases were documented. Elevated SHBG levels were linearly associated with a lower risk of NAFLD in women (HR (95% CI), .71 (.63, .79)), but not in men (a "U" shape, pnon-linear < .001). Higher BAT levels were associated with a lower NAFLD risk in men (HR (95% CI), .81 (.71, .93)) but a higher risk in women (HR (95% CI): 1.25 (1.15, 1.36)). Genetically determined SHBG and BAT levels were linearly associated with NAFLD risk in women (OR (95% CI): .57 (.38, .87) and 2.21 (1.41, 3.26) respectively); in men, an "L-shaped" MR association between SHBG levels and NAFLD risk was found (pnon-linear = .016). The bidirectional MR analysis further revealed the effect of NAFLD on SHBG and BAT levels in both sexes. CONCLUSIONS Consistently, linear associations of lower SHBG and higher BAT levels with increased NAFLD risk were both conventionally and genetically found in women, while in men, SHBG acts in a nonlinear manner. In addition, NAFLD may affect SHBG and BAT levels.
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Affiliation(s)
- Chenghao Weng
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Zilun Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Meng Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Mingyu Song
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yuxuan Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Aolin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Tao Huang
- 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
- 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 and Biostatistics, School of Public Health, Peking University Health Science Center, 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
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 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
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 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
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Miao K, Hong X, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Hu R, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association between epigenetic age and type 2 diabetes mellitus or glycemic traits: A longitudinal twin study. Aging Cell 2024:e14175. [PMID: 38660768 DOI: 10.1111/acel.14175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/18/2024] [Accepted: 04/04/2024] [Indexed: 04/26/2024] Open
Abstract
Epigenetic clocks based on DNA methylation have been known as biomarkers of aging, including principal component (PC) clocks representing the degree of aging and DunedinPACE representing the pace of aging. Prior studies have shown the associations between epigenetic aging and T2DM, but the results vary by epigenetic age metrics and people. This study explored the associations between epigenetic age metrics and T2DM or glycemic traits, based on 1070 twins (535 twin pairs) from the Chinese National Twin Registry. It also explored the temporal relationships of epigenetic age metrics and glycemic traits in 314 twins (157 twin pairs) who participated in baseline and follow-up visits after a mean of 4.6 years. DNA methylation data were used to calculate epigenetic age metrics, including PCGrimAge acceleration (PCGrimAA), PCPhenoAge acceleration (PCPhenoAA), DunedinPACE, and the longitudinal change rate of PCGrimAge/PCPhenoAge. Mixed-effects and cross-lagged modelling assessed the cross-sectional and temporal relationships between epigenetic age metrics and T2DM or glycemic traits, respectively. In the cross-sectional analysis, positive associations were identified between DunedinPACE and glycemic traits, as well as between PCPhenoAA and fasting plasma glucose, which may be not confounded by shared genetic factors. Cross-lagged models revealed that glycemic traits (fasting plasma glucose, HbA1c, and TyG index) preceded DunedinPACE increases, and TyG index preceded PCGrimAA increases. Glycemic traits are positively associated with epigenetic age metrics, especially DunedinPACE. Glycemic traits preceded the increases in DunedinPACE and PCGrimAA. Lowering the levels of glycemic traits may reduce DunedinPACE and PCGrimAA, thereby mitigating age-related comorbidities.
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Affiliation(s)
- Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Runhua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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Wu Z, Jia X, Lu L, Xu C, Pang Y, Peng S, Liu M, Wu Y. Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00119-5. [PMID: 38631974 DOI: 10.1016/j.clon.2024.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/22/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024]
Abstract
AIMS Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. MATERIALS AND METHODS We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. RESULTS The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. CONCLUSION AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.
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Affiliation(s)
- Z Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China
| | - X Jia
- Department of Radiotherapy, The Ninth People's Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - L Lu
- Department of Radiotherapy, Tongling People's Hospital, Anhui, PR China
| | - C Xu
- Department of Radiotherapy, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, PR China
| | - Y Pang
- Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China
| | - S Peng
- Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China
| | - M Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China.
| | - Y Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Tong J, Li SS, Pang Y, Gao MQ. [Advances in the co-signal molecular function of Mtb-specific T lymphocytes]. Zhonghua Jie He He Hu Xi Za Zhi 2024; 47:275-281. [PMID: 38448182 DOI: 10.3760/cma.j.cn112147-20230823-00099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Costimulatory and co-inhibitory receptors on T lymphocytes play an essential role in the immune response. There is increasing evidence that the expression of co-signal molecules on T cells is altered in infection, tumor, autoimmunity, and other diseases, and that intervention of co-signal molecules can be used in the immunotherapy. This paper reviewed the costimulatory and coinhibitory receptors on Mtb-specific T lymphocytes and further explained the mechanism of co-signal molecules in the progression of tuberculosis, to provide a reference for future research and clinical application.
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Affiliation(s)
- J Tong
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, China
| | - S S Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, China
| | - Y Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, China
| | - M Q Gao
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, China
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Wang Y, Hong X, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Age effect on the shared etiology of glycemic traits and serum lipids: evidence from a Chinese twin study. J Endocrinol Invest 2024; 47:535-546. [PMID: 37524979 DOI: 10.1007/s40618-023-02164-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 07/24/2023] [Indexed: 08/02/2023]
Abstract
PURPOSE Diabetes and dyslipidemia are among the most common chronic diseases with increasing global disease burdens, and they frequently occur together. The study aimed to investigate differences in the heritability of glycemic traits and serum lipid indicators and differences in overlapping genetic and environmental influences between them across age groups. METHODS This study included 1189 twin pairs from the Chinese National Twin Registry and divided them into three groups: aged ≤ 40, 41-50, and > 50 years old. Univariate and bivariate structural equation models (SEMs) were conducted on glycemic indicators and serum lipid indicators, including blood glucose (GLU), glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), in the total sample and three age groups. RESULTS All phenotypes showed moderate to high heritability (0.37-0.64). The heritability of HbA1c demonstrated a downward trend with age (HbA1c: 0.50-0.79), while others remained relatively stable (GLU: 0.55-0.62, TC: 0.58-0.66, TG: 0.50-0.63, LDL-C: 0.24-0.58, HDL-C: 0.31-0.57). The bivariate SEMs demonstrated that GLU and HbA1c were correlated with each serum lipid indicator (0.10-0.17), except HDL-C. Except for HbA1c and LDL-C, as well as HbA1c and HDL-C, differences in genetic correlations underlying glycemic traits and serum lipids between age groups were observed, with the youngest group showing a significantly higher genetic correlation than the oldest group. CONCLUSION Across the whole adulthood, genetic influences were consistently important for GLU, TC, TG, LDL-C and HDL-C, and age may affect the shared genetic influences between glycemic traits and serum lipids. Further studies are needed to elucidate the role of age in the interactions of genes related to glycemic traits and serum lipids.
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Affiliation(s)
- Y Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - X Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - W Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - J Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - C Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - T Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - D Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - C Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Y Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Z Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - M Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - H Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - X Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Y Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - W Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
| | - L Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
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10
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Pang Y, Liu X, Liu G, Lv M, Lu M, Wu J, Huang Y. Corrigendum to "Effectiveness of influenza vaccination on in-hospital death and recurrent hospitalization in older adults with cardiovascular diseases" [International Journal of Infectious Diseases, Volume 122 (2022). Pages 162-168]. Int J Infect Dis 2024; 141:106939. [PMID: 38412654 DOI: 10.1016/j.ijid.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xiaofan Liu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Guangqi Liu
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Min Lv
- Institute for Immunization and Prevention, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Ming Lu
- Department of Biomedical Informatics, School of Basic Medicine, Peking University, Beijing, 100191, China
| | - Jiang Wu
- Institute for Immunization and Prevention, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yangmu Huang
- Department of Global Health, School of Public Health, Peking University, Beijing, China.
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11
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Li H, Pang Y. Response to: Immunodiagnosis of Mycobacterium tuberculosis. QJM 2024; 117:156-157. [PMID: 37930881 DOI: 10.1093/qjmed/hcad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Indexed: 11/08/2023] Open
Affiliation(s)
- H Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, People's Republic of China
| | - Y Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, People's Republic of China
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12
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Zhang TH, Ma ZC, Liu RM, Shang YY, Ma LP, Han M, Pang Y. [Evaluation of the efficacy of urine-based lipoarabinomannan antigen test in the diagnosis of pulmonary tuberculosis]. Zhonghua Jie He He Hu Xi Za Zhi 2024; 47:132-136. [PMID: 38309962 DOI: 10.3760/cma.j.cn112147-20230814-00074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/05/2024]
Abstract
Objective: To analyze the diagnostic efficacy of urinary lipoarabinomannan (LAM) antigen detection method in tuberculosis patients, and to provide an experimental basis for the clinical application of urinary LAM kit in China. Methods: From March to May 2023, 228 patients with lung diseases [134 male, 94 female, age 20-82 (44.8±16.7) years] were prospectively collected in Beijing Chest Hospital, Capital Medical University, including 143 pulmonary tuberculosis patients and 85 non-tuberculosis patients. Urine and sputum samples from patients were collected for traditional etiological detection and urinary LAM antigen detection. The screening results of each positive detection combination were analyzed, and the difference analysis and regression analysis were performed. Results: The detection sensitivity and specificity of the urinary LAM kit were 46.2% (95%CI: 37.9%-54.7%) and 96.5% (95%CI: 89.3%-99.1%), respectively, with an overall coincidence rate of 64.9%. The detection rate of LAM antigen detection and GeneXpert MTB/RIF (Xpert) combined (60.8%, 87/143) was significantly higher than that of Xpert alone (49.7%, 71/143), and the difference was statistically significant (P<0.05). The results of risk factor analysis showed that the risk of negative urinary LAM antigen test results increased significantly as the bacterial load decreased. Conclusions: Urine LAM antigen detection method has a high specificity and can be combined with traditional methods to effectively improve the detection rate. Urinary LAM antigen detection method still has limitations, such as the influence of bacterial load and the inability to distinguish nontuberculosis mycobacteria samples, which needs further experimental verification.
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Affiliation(s)
- T H Zhang
- First Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Z C Ma
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - R M Liu
- First Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Y Y Shang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - L P Ma
- First Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - M Han
- First Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Y Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
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13
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Fang W, Guo W, Lu R, Yan Y, Liu X, Wu D, Li FM, Zhou Y, He C, Xia C, Niu H, Wang S, Liu Y, Mao Y, Zhang C, You B, Pang Y, Duan L, Yang X, Song F, Zhai T, Wang G, Guo X, Tan B, Yao T, Wang Z, Xia BY. Durable CO 2 conversion in the proton-exchange membrane system. Nature 2024; 626:86-91. [PMID: 38297172 DOI: 10.1038/s41586-023-06917-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 11/30/2023] [Indexed: 02/02/2024]
Abstract
Electrolysis that reduces carbon dioxide (CO2) to useful chemicals can, in principle, contribute to a more sustainable and carbon-neutral future1-6. However, it remains challenging to develop this into a robust process because efficient conversion typically requires alkaline conditions in which CO2 precipitates as carbonate, and this limits carbon utilization and the stability of the system7-12. Strategies such as physical washing, pulsed operation and the use of dipolar membranes can partially alleviate these problems but do not fully resolve them11,13-15. CO2 electrolysis in acid electrolyte, where carbonate does not form, has therefore been explored as an ultimately more workable solution16-18. Herein we develop a proton-exchange membrane system that reduces CO2 to formic acid at a catalyst that is derived from waste lead-acid batteries and in which a lattice carbon activation mechanism contributes. When coupling CO2 reduction with hydrogen oxidation, formic acid is produced with over 93% Faradaic efficiency. The system is compatible with start-up/shut-down processes, achieves nearly 91% single-pass conversion efficiency for CO2 at a current density of 600 mA cm-2 and cell voltage of 2.2 V and is shown to operate continuously for more than 5,200 h. We expect that this exceptional performance, enabled by the use of a robust and efficient catalyst, stable three-phase interface and durable membrane, will help advance the development of carbon-neutral technologies.
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Affiliation(s)
- Wensheng Fang
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Guo
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Ruihu Lu
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Ya Yan
- CAS Key Laboratory of Materials for Energy Conversion, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China
| | - Xiaokang Liu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
| | - Dan Wu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
| | - Fu Min Li
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Yansong Zhou
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Chaohui He
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Chenfeng Xia
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Huiting Niu
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Sicong Wang
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
| | - Youwen Liu
- State Key Laboratory of Materials Processing and Die & Mould Technology and School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Mao
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Chengyi Zhang
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Bo You
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanjie Pang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Lele Duan
- Department of Chemistry and Shenzhen Grubbs Institute, Southern University of Science and Technology, Shenzhen, China
| | - Xuan Yang
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Fei Song
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die & Mould Technology and School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Guoxiong Wang
- State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Xingpeng Guo
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Bien Tan
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Yao
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China.
| | - Ziyun Wang
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand.
| | - Bao Yu Xia
- School of Chemistry and Chemical Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
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14
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Pang Y, Lv J, Wu T, Yu C, Guo Y, Chen Y, Yang L, Millwood IY, Walters RG, Yang X, Stevens R, Clarke R, Chen J, Li L, Chen Z, Kartsonaki C. Associations of diabetes, circulating protein biomarkers, and risk of pancreatic cancer. Br J Cancer 2024; 130:504-510. [PMID: 38129526 PMCID: PMC10844301 DOI: 10.1038/s41416-023-02533-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is associated with higher risk of pancreatic cancer (PC), but the underlying mechanisms are not fully understood. METHODS We conducted a case-subcohort study involving 610 PC cases and 623 subcohort participants with 92 protein biomarkers measured in baseline plasma samples. Genetically-instrumented T2D was derived using 86 single-nucleotide polymorphisms (SNPs), including insulin resistance (IR) SNPs. RESULTS In observational analyses of 623 subcohort participants (mean age, 52 years; 61% women), T2D was positively associated with 13 proteins (SD difference: IL6: 0.52 [0.23-0.81]; IL10: 0.41 [0.12-0.70]), of which 8 were nominally associated with incident PC. The 8 proteins potentially mediated 36.9% (18.7-75.0%) of the association between T2D and PC. In MR, no associations were observed for genetically-determined T2D with proteins, but there were positive associations of genetically-determined IR with IL6 and IL10 (SD difference: 1.23 [0.05-2.41] and 1.28 [0.31-2.24]). In two-sample MR, fasting insulin was associated with both IL6 and PC, but no association was observed between IL6 and PC. CONCLUSIONS Proteomics were likely to explain the association between T2D and PC, but were not causal mediators. Elevated fasting insulin driven by insulin resistance might explain the associations of T2D, proteomics, and PC.
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Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, 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, 38 Xueyuan Road, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ting Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Yu Guo
- National Clinical Research Center of Cardiovascular Diseases, National Center for Cardiovascular Diseases, Beijing, China
- Fuwai Hospital, Chinese Academy of Medical Sciences, 167 Beilishi Road, Xicheng District, 100037, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 37 Guangqu Road, 100021, Beijing, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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15
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Hong X, Miao K, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Hu R, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association of psychological distress and DNA methylation: A 5-year longitudinal population-based twin study. Psychiatry Clin Neurosci 2024; 78:51-59. [PMID: 37793011 DOI: 10.1111/pcn.13606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/06/2023]
Abstract
AIM To identify the psychological distress (PD)-associated 5'-cytosine-phosphate-guanine-3' sites (CpGs), and investigate the temporal relationship between dynamic changes in DNA methylation (DNAm) and PD. METHODS This study included 1084 twins from the Chinese National Twin Register (CNTR). The CNTR conducted epidemiological investigations and blood withdrawal twice in 2013 and 2018. These included twins were used to perform epigenome-wide association studies (EWASs) and to validate the previously reported PD-associated CpGs selected from previous EWASs in PubMed, Embase, and the EWAS catalog. Next, a cross-lagged study was performed to examine the temporality between changes in DNAm and PD in 308 twins who completed both 2013 and 2018 surveys. RESULTS The EWAS analysis of our study identified 25 CpGs. In the validation analysis, 741 CpGs from 29 previous EWASs on PD were selected for validation, and 101 CpGs were validated to be significant at a false discovery rate <0.05. The cross-lagged analysis found a unidirectional path from PD to DNAm at 14 CpGs, while no sites showed significance from DNAm to PD. CONCLUSIONS This study identified and validated PD-related CpGs in a Chinese twin population, and suggested that PD may be the cause of changes in DNAm over time. The findings provide new insights into the molecular mechanisms underlying PD pathophysiology.
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Affiliation(s)
- Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Runhua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
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16
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Xiao M, Li A, Wang Y, Yu C, Pang Y, Pei P, Yang L, Chen Y, Du H, Schmidt D, Avery D, Sun Q, Chen J, Chen Z, Li L, Lv J, Sun D. A wide landscape of morbidity and mortality risk associated with marital status in 0.5 million Chinese men and women: a prospective cohort study. Lancet Reg Health West Pac 2024; 42:100948. [PMID: 38357394 PMCID: PMC10865043 DOI: 10.1016/j.lanwpc.2023.100948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/26/2023] [Accepted: 10/16/2023] [Indexed: 02/16/2024]
Abstract
Background A comprehensive depiction of long-term health impacts of marital status is lacking. Methods Sex-stratified phenome-wide association analyses (PheWAS) of marital status (living with vs. without a spouse) were performed using baseline (2004-2008) and follow-up information (ICD10-coded events till Dec 31, 2017) from the China Kadoorie Biobank (CKB). We estimated adjusted hazard ratios (aHRs) to evaluate the associations of marital status with morbidity risks of phenome-wide significant diseases or sex-specific top-10 death causes in China documented in 2017. Additionally, the association between marital status and mortality risks among participants with major chronic diseases at baseline was assessed. Findings During up to 11.1 years of the median follow-up period, 1,946,380 incident health events were recorded among 210,202 men and 302,521 women aged 30-79. Marital status was found to have phenome-wide significant associations with thirteen diseases among men (p < 9.92 × 10-5) and nine diseases among women (p < 9.33 × 10-5), respectively. After adjusting for all disease-specific covariates in the final model, participants living without a spouse showed increased risks of schizophrenia, schizotypal and delusional disorders (aHR [95% CI]: 2.55, [1.83-3.56] for men; 1.49, [1.13-1.97] for women) compared with their counterparts. Additional higher risks in overall mental and behavioural disorder (1.31, 1.13-1.53), cardiovascular disease (1.07, 1.04-1.10) and cancer (1.06, 1.00-1.12) were only observed among men without a spouse, whereas women living without a spouse were at lower risks of developing genitourinary diseases (0.89, 0.85-0.93) and injury & poisoning (0.93, 0.88-0.97). Among 282,810 participants with major chronic diseases at baseline, 39,166 deaths were recorded. Increased mortality risks for those without a spouse were observed in 12 of 21 diseases among male patients and one of 23 among female patients. For patients with any self-reported disease at baseline, compared with those living with a spouse, the aHRs (95% CIs) of mortality risk were 1.29 (1.24-1.34) and 1.04 (1.00-1.07) among men and women without a spouse (pinteraction<0.0001), respectively. Interpretation Long-term associations of marital status with morbidity and mortality risks are diverse among middle-aged Chinese adults, and the adverse impacts due to living without a spouse are more profound among men. Marital status may be an influential factor for health needs. Funding The National Natural Science Foundation of China, the Kadoorie Charitable Foundation, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, and the UK Wellcome Trust.
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Affiliation(s)
- Meng Xiao
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Aolin Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yueqing Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Dan Schmidt
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Qiang Sun
- NCDs Prevention and Control Department, Pengzhou CDC, Pengzhou, Sichuan, 611930, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
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Li H, Ren W, Liang Q, Zhang X, Li Q, Shang Y, Ma L, Li S, Pang Y. A novel chemokine biomarker to distinguish active tuberculosis from latent tuberculosis: a cohort study. QJM 2023; 116:1002-1009. [PMID: 37740371 PMCID: PMC10753411 DOI: 10.1093/qjmed/hcad214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/12/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND Interferon-γ release assays (IGRAs), which are widely used to diagnose tuberculosis (TB), cannot effectively discriminate latent TB infection (LTBI) from active TB (ATB). This study aimed to identify potential antigen-specific biomarkers for differentiating LTBI cases from ATB cases. METHODS Ongoing recruitment was conducted of individuals meeting study inclusion criteria at Beijing Chest Hospital from May 2020 to April 2022; 208 participants were enrolled and assigned to three groups: HC (60 healthy controls), LTBI (52 subjects with LTBI) and ATB (96 ATB patients). After participants were assigned to the discovery cohort (20 or 21 subjects/group), all others were assigned to the verification cohort. Discovery cohort blood levels of 40 chemokines were measured using Luminex assays to identify chemokines that could be used to discriminate LTBI cases from ATB cases; candidate biomarkers were verified using enzyme-linked immunosorbent assay-based testing of validation cohort samples. RESULTS Luminex results revealed highest ATB group levels of numerous cytokines, growth factors and chemokines. Receiving operating characteristic curve-based analysis of 40 biomarkers revealed CCL8 (AUC = 0.890) and CXCL9 (AUC = 0.883) effectively discriminated between LTBI and TB cases; greatest diagnostic efficiency was obtained using both markers together (AUC = 0.929). Interpretation of CCL8 and CXCL9 levels for validation cohort IGRA-positive subjects (based on a 0.658-ng/ml cutoff) revealed ATB group CCL8-based sensitivity and specificity rates approaching 90.79% and 100.00%, respectively. CONCLUSION TB-specific chemokines hold promise as ATB diagnostic biomarkers. Additional laboratory confirmation is needed to establish whether CCL8-based assays can differentiate between ATB and LTBI cases, especially for bacteriologically unconfirmed TB cases.
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Affiliation(s)
- H Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No. 9, Beiguan Street, Tongzhou District, Beijing 101149, People’s Republic of China
| | - W Ren
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No. 9, Beiguan Street, Tongzhou District, Beijing 101149, People’s Republic of China
| | - Q Liang
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, People’s Republic of China
| | - X Zhang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No. 9, Beiguan Street, Tongzhou District, Beijing 101149, People’s Republic of China
| | - Q Li
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, People’s Republic of China
| | - Y Shang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No. 9, Beiguan Street, Tongzhou District, Beijing 101149, People’s Republic of China
| | - L Ma
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, People’s Republic of China
| | - S Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No. 9, Beiguan Street, Tongzhou District, Beijing 101149, People’s Republic of China
| | - Y Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Postal No. 9, Beiguan Street, Tongzhou District, Beijing 101149, People’s Republic of China
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18
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Wang Z, Zhou Y, Qiu P, Xia C, Fang W, Jin J, Huang L, Deng P, Su Y, Crespo-Otero R, Tian X, You B, Guo W, Di Tommaso D, Pang Y, Ding S, Xia BY. Advanced Catalyst Design and Reactor Configuration Upgrade in Electrochemical Carbon Dioxide Conversion. Adv Mater 2023; 35:e2303052. [PMID: 37589167 DOI: 10.1002/adma.202303052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/28/2023] [Indexed: 08/18/2023]
Abstract
Electrochemical carbon dioxide reduction reaction (CO2 RR) driven by renewable energy shows great promise in mitigating and potentially reversing the devastating effects of anthropogenic climate change and environmental degradation. The simultaneous synthesis of energy-dense chemicals can meet global energy demand while decoupling emissions from economic growth. However, the development of CO2 RR technology faces challenges in catalyst discovery and device optimization that hinder their industrial implementation. In this contribution, a comprehensive overview of the current state of CO2 RR research is provided, starting with the background and motivation for this technology, followed by the fundamentals and evaluated metrics. Then the underlying design principles of electrocatalysts are discussed, emphasizing their structure-performance correlations and advanced electrochemical assembly cells that can increase CO2 RR selectivity and throughput. Finally, the review looks to the future and identifies opportunities for innovation in mechanism discovery, material screening strategies, and device assemblies to move toward a carbon-neutral society.
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Affiliation(s)
- Zhitong Wang
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
- School of Marine Science and Engineering, Hainan Provincial Key Lab of Fine Chemistry, School of Chemistry and Chemical Engineering, Hainan University, Haikou, 570228, China
| | - Yansong Zhou
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
| | - Peng Qiu
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, 430074, China
| | - Chenfeng Xia
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
| | - Wensheng Fang
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
| | - Jian Jin
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, 430074, China
| | - Lei Huang
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
| | - Peilin Deng
- School of Marine Science and Engineering, Hainan Provincial Key Lab of Fine Chemistry, School of Chemistry and Chemical Engineering, Hainan University, Haikou, 570228, China
| | - Yaqiong Su
- School of Chemistry, Xi'an Jiaotong University, 28 Xianning West Rd, Xi'an, 710049, China
| | - Rachel Crespo-Otero
- Department of Chemistry, University of College London, 20 Gordon Street, London, WC1H 0AJ, UK
| | - Xinlong Tian
- School of Marine Science and Engineering, Hainan Provincial Key Lab of Fine Chemistry, School of Chemistry and Chemical Engineering, Hainan University, Haikou, 570228, China
| | - Bo You
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
| | - Wei Guo
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
| | - Devis Di Tommaso
- School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Yuanjie Pang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, 430074, China
| | - Shujiang Ding
- School of Chemistry, Xi'an Jiaotong University, 28 Xianning West Rd, Xi'an, 710049, China
| | - Bao Yu Xia
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Key Laboratory of Material Chemistry and Service Failure, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Rd, Wuhan, 430074, China
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19
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Hamilton EM, Yang L, Wright N, Turnbull I, Mentzer AJ, Matthews PC, Chen Y, Du H, Kartsonaki C, Pang Y, Pei P, Tian H, Yang X, Avery D, Yu C, Lv J, Clarke R, Li L, Millwood IY, Chen Z. Chronic Hepatitis B Virus Infection and Risk of Stroke Types: A Prospective Cohort Study of 500 000 Chinese Adults. Stroke 2023; 54:3046-3053. [PMID: 37942646 PMCID: PMC10664797 DOI: 10.1161/strokeaha.123.043327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/12/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Stroke is a leading cause of mortality and permanent disability in China, with large and unexplained geographic variations in rates of different stroke types. Chronic hepatitis B virus infection is prevalent among Chinese adults and may play a role in stroke cause. METHODS The prospective China Kadoorie Biobank included >500 000 adults aged 30 to 79 years who were recruited from 10 (5 urban and 5 rural) geographically diverse areas of China from 2004 to 2008, with determination of hepatitis B surface antigen (HBsAg) positivity at baseline. During 11 years of follow-up, a total of 59 117 incident stroke cases occurred, including 11 318 intracerebral hemorrhage (ICH), 49 971 ischemic stroke, 995 subarachnoid hemorrhage, and 3036 other/unspecified stroke. Cox regression models were used to estimate adjusted hazard ratios (HRs) for risk of stroke types associated with HBsAg positivity. In a subset of 17 833 participants, liver enzymes and lipids levels were measured and compared by HBsAg status. RESULTS Overall, 3.0% of participants were positive for HBsAg. HBsAg positivity was associated with an increased risk of ICH (adjusted HR, 1.29 [95% CI, 1.16-1.44]), similarly for fatal (n=5982; adjusted HR, 1.36 [95% CI, 1.16-1.59]) and nonfatal (n=5336; adjusted HR, 1.23 [95% CI, 1.06-1.44]) ICH. There were no significant associations of HBsAg positivity with risks of ischemic stroke (adjusted HR, 0.97 [95% CI, 0.92-1.03]), subarachnoid hemorrhage (adjusted HR, 0.87 [95% CI, 0.57-1.33]), or other/unspecified stroke (adjusted HR, 1.12 [95% CI, 0.89-1.42]). Compared with HBsAg-negative counterparts, HBsAg-positive individuals had lower lipid and albumin levels and higher liver enzyme levels. After adjustment for liver enzymes and albumin, the association with ICH from HBsAg positivity attenuated to 1.15 (0.90-1.48), suggesting possible mediation by abnormal liver function. CONCLUSIONS Among Chinese adults, chronic hepatitis B virus infection is associated with an increased risk of ICH but not other stroke types, which may be mediated through liver dysfunction and altered lipid metabolism.
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Affiliation(s)
- Elizabeth M. Hamilton
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Neil Wright
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Iain Turnbull
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Alexander J. Mentzer
- Wellcome Centre for Human Genetics (A.J.M.), University of Oxford, United Kingdom
| | - Philippa C. Matthews
- Division of Infection and Immunity, University College London, United Kingdom (P.C.M.)
- Matthews lab HBV Elimination Laboratory, The Francis Crick Institute, London, United Kingdom (P.C.M.)
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Huizi Tian
- Non-Communicable Diseases Prevention and Control Department, Henan, China (H.T.)
| | - Xiaoming Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Iona Y. Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
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20
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Wang X, Wu Z, Lv J, Yu C, Sun D, Pei P, Yang L, Millwood IY, Walters R, Chen Y, Du H, Yuan M, Schmidt D, Barnard M, Chen J, Chen Z, Li L, Pang Y. Life-course adiposity and severe liver disease: a Mendelian randomization analysis. Obesity (Silver Spring) 2023; 31:3077-3085. [PMID: 37869961 DOI: 10.1002/oby.23913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 10/24/2023]
Abstract
OBJECTIVE There is little evidence on the genetic associations between life-course adiposity (including birth weight, childhood BMI, and adulthood BMI) and severe liver disease (SLD; including cirrhosis and liver cancer). The current study aimed to examine and contrast these associations. METHODS Genetic variants were obtained from genome-wide association studies. Two-sample Mendelian randomization (MR) analyses were performed to assess the genetic associations of life-course adiposity with SLD and liver biomarkers. Cox regression was used to estimate adjusted hazard ratios for SLD associated with genetic risk scores of life-course adiposity and adulthood weight change in the China Kadoorie Biobank. RESULTS In observational analyses, genetic predispositions to childhood adiposity and adulthood adiposity were each associated with SLD. There was a U-shaped association between adulthood weight change and risk of SLD. In meta-analyses of MR results, genetically predicted 1-standard deviation increase in birth weight was inversely associated with SLD at a marginal significance (odds ratio: 0.81 [95% CI: 0.65-1.00]), whereas genetically predicted 1-standard deviation higher childhood BMI and adulthood BMI were positively associated with SLD (odds ratio: 1.27 [95% CI: 1.05-1.55] and 1.79 [95% CI: 1.59-2.01], respectively). The results of liver biomarkers mirrored those of SLD. CONCLUSIONS The current study provided genetic evidence on the associations between life-course adiposity and SLD.
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Affiliation(s)
- Xinyu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhiyu Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and 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
| | - Canqing Yu
- Department of Epidemiology and 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
| | - Dianjianyi Sun
- Department of Epidemiology and 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
| | - 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 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
| | - Mingqiang Yuan
- Pengzhou Center for Disease Control and Prevention, Pengzhou, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Maxim Barnard
- 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 and 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 and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
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21
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Yang S, Sun D, Sun Z, Yu C, Guo Y, Si J, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Pang Z, Schmidt D, Stevens R, Clarke R, Chen J, Chen Z, Lv J, Li L. Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores: evidence from the China Kadoorie Biobank. Chin Med J (Engl) 2023; 136:2476-2483. [PMID: 37200020 PMCID: PMC10586831 DOI: 10.1097/cm9.0000000000002694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population. METHODS Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately. RESULTS In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model. CONCLUSIONS In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.
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Affiliation(s)
- Songchun Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Dong Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Zhijia Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Jiahui Si
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Iona Y. Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Robin G. Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Zengchang Pang
- Qingdao Center of Disease Control and Prevention, Qingdao, Shandong 266033, China
| | - Dan Schmidt
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing 100738, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
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22
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Sun Q, Hu Y, Yu C, Guo Y, Pei P, Yang L, Chen Y, Du H, Sun D, Pang Y, Burgess S, Sansome S, Ning F, Chen J, Chen Z, Li L, Lv J. Healthy lifestyle and life expectancy free of major chronic diseases at age 40 in China. Nat Hum Behav 2023; 7:1542-1550. [PMID: 37430072 PMCID: PMC7615116 DOI: 10.1038/s41562-023-01624-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/27/2023] [Indexed: 07/12/2023]
Abstract
Whether a healthy lifestyle helps achieve gains in life expectancy (LE) free of major non-communicable diseases and its share of total LE in Chinese adults remains unknown. We considered five low-risk lifestyle factors: never smoking or quitting for reasons other than illness, no excessive alcohol use, being physically active, healthy eating habits and healthy body fat levels. Here we show that after a median follow-up of 11.1 years for 451,233 Chinese adults, the LE free of cardiovascular diseases, cancer and chronic respiratory diseases (95% confidence interval) at age 40 years for individuals with all five low-risk factors was on average 6.3 (5.1-7.5) years (men) and 4.2 (3.6-5.4) years (women) longer than those with 0-1 low-risk factors. Correspondingly, the proportion of disease-free LE to total LE increased from 73.1% to 76.3% for men and from 67.6% to 68.4% for women. Our findings suggest that promoting healthy lifestyles could be associated with gains in disease-free LE in the Chinese population.
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23
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Pang Y, Lv J, Kartsonaki C, Yu C, Guo Y, Chen Y, Yang L, Millwood IY, Walters RG, Lv S, Burgess S, Sansome S, Chen J, Chen Z, Li L. Genetic and healthy lifestyle factors in relation to the incidence and prognosis of severe liver disease in the Chinese population. Chin Med J (Engl) 2023; 136:1929-1936. [PMID: 37476847 PMCID: PMC10431401 DOI: 10.1097/cm9.0000000000002754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Severe liver disease (SLD), including cirrhosis and liver cancer, constitutes a major disease burden in China. We aimed to examine the association of genetic and healthy lifestyle factors with the incidence and prognosis of SLD. METHODS The study population included 504,009 participants from the prospective China Kadoorie Biobank aged 30-79 years. The individuals were from 10 diverse areas in China without a history of cancer or liver disease at baseline. Cox regression was used to estimate adjusted hazard ratios (HRs) for incident SLD and death after SLD diagnosis associated with healthy lifestyle factors (smoking, alcohol, physical activity, and central adiposity). Additionally, the contribution of genetic risk for hepatitis B virus (HBV, assessed by genetic variants in major histocompatibility complex, class II, DP/DQ [ HLA - DP / DQ ] genes) was also estimated. RESULTS Compared with those with 0-1 healthy lifestyle factor, participants with 2, 3, and 4 factors had 12% (HR 0.88 [95% confidence interval [CI] 0.85, 0.92]), 26% (HR 0.74 [95%CI: 0.69, 0.79]), and 44% (HR 0.56 [95%CI: 0.48, 0.65]) lower risks of SLD, respectively. Inverse associations were observed among participants with both low and high genetic risks (HR per 1-point increase 0.83 [95%CI: 0.74, 0.94] and 0.91 [95%CI: 0.82, 1.02], respectively; Pinteraction = 0.51), although with a non-significant trend among those with a high genetic risk. Inverse associations were also observed between healthy lifestyle factors and liver biomarkers regardless of the genetic risk. Despite the limited power, healthy lifestyle factors were associated with a lower risk of death after incident SLD among participants with a low genetic risk (HR 0.59 [95%CI: 0.37, 0.96]). CONCLUSIONS Lifestyle modification may be beneficial in terms of lowering the risk of SLD regardless of the genetic risk. Moreover, it is also important for improving the prognosis of SLD in individuals with a low genetic risk. Future studies are warranted to examine the impact of healthy lifestyles on SLD prognosis, particularly among individuals with a high genetic risk.
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Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Christiana Kartsonaki
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
- Nuffield Department of Population Health, Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, University of Oxford, Oxford OX37LF, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Yu Guo
- National Clinical Research Center of Cardiovascular Diseases, National Center for Cardiovascular Diseases, Beijing 100730, China
- Fuwai Hospital, Chinese Academy of Medical Sciences, 167 Beilishi Road, Xicheng District, Beijing 100037, China
| | - Yiping Chen
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
- Nuffield Department of Population Health, Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, University of Oxford, Oxford OX37LF, UK
| | - Ling Yang
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
- Nuffield Department of Population Health, Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, University of Oxford, Oxford OX37LF, UK
| | - Iona Y. Millwood
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
- Nuffield Department of Population Health, Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, University of Oxford, Oxford OX37LF, UK
| | - Robin G. Walters
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
- Nuffield Department of Population Health, Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, University of Oxford, Oxford OX37LF, UK
| | - Silu Lv
- Licang Center for Disease Prevention and Control, Licang District, Qingdao, Shandong 266041, China
| | - Sushila Burgess
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
| | - Sam Sansome
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, Beijing 100021, China
| | - Zhengming Chen
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford OX37LF, UK
- Nuffield Department of Population Health, Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, University of Oxford, Oxford OX37LF, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
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25
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Wang X, Zhang R, Man S, Lv J, Yu C, Yin J, Wang X, Deng Y, Wang B, Li L, Pang Y. Metabolic-associated fatty liver disease in relation to site-specific and multiple-site subclinical atherosclerosis. Liver Int 2023; 43:1691-1698. [PMID: 37337780 DOI: 10.1111/liv.15591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/19/2023] [Accepted: 04/15/2023] [Indexed: 06/21/2023]
Abstract
BACKGROUND & AIMS Non-alcoholic fatty liver disease (NAFLD) and the newly proposed metabolic-associated fatty liver disease (MAFLD) were each associated with subclinical atherosclerosis. However, there is limited evidence on risk of atherosclerosis in individuals who meet the criteria for one but not the other. We aimed to investigate the associations of MAFLD or NAFLD status with site-specific and multiple-site atherosclerosis. METHODS This is a prospective cohort study involving 4524 adults within the MJ health check-up cohort. Logistic regression model was used to estimate odds ratios (ORs) and confidence intervals (CIs) for subclinical atherosclerosis (elevated carotid intima-media thickness [CIMT], carotid plaque [CP], coronary artery calcification [CAC] and retinal atherosclerosis [RA]) associated with MAFLD or NAFLD status, MAFLD subtypes and fibrosis status. RESULTS MAFLD was associated with higher risks of elevated CIMT, CP, CAC and RA (OR: 1.41 [95% CI 1.18-1.68], 1.23 [1.02-1.48], 1.60 [1.24-2.08], and 1.79 [1.28-2.52], respectively), whereas NAFLD per se did not increase risk of atherosclerosis except for elevated CIMT. Individuals who met both definitions or the definition for MAFLD but not NAFLD had higher risk of subclinical atherosclerosis. Among MAFLD subtypes, MAFLD with diabetes had the highest risk of subclinical atherosclerosis, but the associations did not differ by fibrosis status. Stronger positive associations were observed of MAFLD with multiple-site than single-site atherosclerosis. CONCLUSIONS In Chinese adults, MAFLD was associated with subclinical atherosclerosis, with stronger associations for multiple-site atherosclerosis. More attention should be paid to MAFLD with diabetes, and MAFLD might be a better predictor for atherosclerotic disease than NAFLD.
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Affiliation(s)
- Xinyu Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Ruosu Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Sailimai Man
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Institute of Health, Beijing, China
- Peking University Health Science Center, Meinian Public Health Institute, 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 and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, 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 and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | | | - Xiaona Wang
- Beijing MJ Health Check-up Center, Beijing, China
| | - Yuhan Deng
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Bo Wang
- Meinian Institute of Health, Beijing, China
- Peking University Health Science Center, Meinian Public Health Institute, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
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26
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Sun D, Liu C, Zhu Y, Yu C, Guo Y, Sun D, Pang Y, Pei P, Du H, Yang L, Chen Y, Meng X, Liu Y, Zhang J, Schmidt D, Avery D, Chen J, Chen Z, Lv J, Kan H, Li L. Long-Term Exposure to Fine Particulate Matter and Incidence of Esophageal Cancer: A Prospective Study of 0.5 Million Chinese Adults. Gastroenterology 2023; 165:61-70.e5. [PMID: 37059339 PMCID: PMC7615725 DOI: 10.1053/j.gastro.2023.03.233] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/22/2023] [Accepted: 03/02/2023] [Indexed: 04/16/2023]
Abstract
BACKGROUND & AIMS Evidence is sparse and inconclusive on the association between long-term fine (≤2.5 μm) particulate matter (PM2.5) exposure and esophageal cancer. We aimed to assess the association of PM2.5 with esophageal cancer risk and compared the esophageal cancer risk attributable to PM2.5 exposure and other established risk factors. METHODS This study included 510,125 participants without esophageal cancer at baseline from China Kadoorie Biobank. A high-resolution (1 × 1 km) satellite-based model was used to estimate PM2.5 exposure during the study period. Hazard ratios (HR) and 95% CIs of PM2.5 with esophageal cancer incidence were estimated using Cox proportional hazard model. Population attributable fractions for PM2.5 and other established risk factors were estimated. RESULTS There was a linear concentration-response relationship between long-term PM2.5 exposure and esophageal cancer. For each 10-μg/m3 increase in PM2.5, the HR was 1.16 (95% CI, 1.04-1.30) for esophageal cancer incidence. Compared with the first quarter of PM2.5 exposure, participants in the highest quarter had a 1.32-fold higher risk for esophageal cancer, with an HR of 1.32 (95% CI, 1.01-1.72). The population attributable risk because of annual average PM2.5 concentration ≥35 μg/m3 was 23.3% (95% CI, 6.6%-40.0%), higher than the risks attributable to lifestyle risk factors. CONCLUSIONS This large prospective cohort study of Chinese adults found that long-term exposure to PM2.5 was associated with an elevated risk of esophageal cancer. With stringent air pollution mitigation measures in China, a large reduction in the esophageal cancer disease burden can be expected.
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Affiliation(s)
- Dong Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Cong Liu
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, National Health Commission Key Laboratory of Health Technology Assessment, Integrated Research on Disaster Risk, International Centers of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
| | - Yunqing Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and 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 and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and 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 and Response, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the 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 at the 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
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the 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
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, National Health Commission Key Laboratory of Health Technology Assessment, Integrated Research on Disaster Risk, International Centers of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Jun Zhang
- Suzhou Center for Disease Prevention and Control, Suzhou, China
| | - Dan Schmidt
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit, 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 and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Haidong Kan
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, National Health Commission Key Laboratory of Health Technology Assessment, Integrated Research on Disaster Risk, International Centers of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
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Han Y, Hu Y, Yu C, Sun D, Pang Y, Pei P, Yang L, Chen Y, Du H, Liu J, Schmidt D, Avery D, Chen J, Chen Z, Li L, Lv J. Duration-dependent impact of cardiometabolic diseases and multimorbidity on all-cause and cause-specific mortality: a prospective cohort study of 0.5 million participants. Cardiovasc Diabetol 2023; 22:135. [PMID: 37308998 DOI: 10.1186/s12933-023-01858-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/12/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND The association of incident cardiometabolic multimorbidity (CMM) with mortality risk is rarely studied, and neither are the durations of cardiometabolic diseases (CMDs). Whether the association patterns of CMD durations with mortality change as individuals progress from one CMD to CMM is unclear. METHODS Data from China Kadoorie Biobank of 512,720 participants aged 30-79 was used. CMM was defined as the simultaneous presence of two or more CMDs of interest, including diabetes, ischemic heart disease, and stroke. Cox regression was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the duration-dependent associations of CMDs and CMM with all-cause and cause-specific mortality. All information on exposures of interest was updated during follow-up. RESULTS During a median follow-up of 12.1 years, 99,770 participants experienced at least one incident CMD, and 56,549 deaths were documented. Among 463,178 participants free of three CMDs at baseline, compared with no CMD during follow-up, the adjusted HRs (95% CIs) between CMM and all-cause mortality, mortality from circulatory system diseases, respiratory system diseases, cancer, and other causes were 2.93 (2.80-3.07), 5.05 (4.74-5.37), 2.72 (2.35-3.14), 1.30 (1.16-1.45), and 2.30 (2.02-2.61), respectively. All CMDs exhibited a high mortality risk in the first year of diagnosis. Subsequently, with prolonged disease duration, mortality risk increased for diabetes, decreased for IHD, and sustained at a high level for stroke. With the presence of CMM, the above association estimates inflated, but the pattern of which remained. CONCLUSION Among Chinese adults, mortality risk increased with the number of the CMDs and changed with prolonged disease duration, the patterns of which varied among the three CMDs.
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Affiliation(s)
- Yuting Han
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yizhen Hu
- 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
| | - 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
| | - 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
| | - Jingchao Liu
- NCDs Prevention and Control Department, Wuzhong CDC, Suzhou, Jiangsu, China
| | - Dan Schmidt
- 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
| | - 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.
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Miao K, Wang Y, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Hu R, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Genetic and Environmental Influences on Blood Pressure and Serum Lipids Across Age-Groups. Twin Res Hum Genet 2023; 26:223-230. [PMID: 37650338 DOI: 10.1017/thg.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37-0.59) and moderate unique environmental variance (0.30-0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from -0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.
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Affiliation(s)
- Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Runhua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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29
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Cheng Z, Tan Z, Zhou L, Li L, Xu X, Yuen MF, Li L, Pang Y, Debecker DP, Ma R, Wang C. Engineering Amorphous/Crystalline Ru(OH) 3/CoFe-Layered Double Hydroxide for Hydrogen Evolution at 1000 mA cm -2. Inorg Chem 2023; 62:7424-7433. [PMID: 37141089 DOI: 10.1021/acs.inorgchem.3c00686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
For large-scale industrial applications, it is highly desirable to create effective, economical electrocatalysts with long-term stability for the hydrogen evolution reaction (HER) at a large current density. Herein, we report a unique motif with crystalline CoFe-layered hydroxide (CoFe-LDH) nanosheets enclosed by amorphous ruthenium hydroxide (a-Ru(OH)3/CoFe-LDH) to realize the efficient hydrogen production at 1000 mA cm-2, with a low overpotential of 178 mV in alkaline media. During the continuous HER process for 40 h at such a large current density, the potential remains almost constant with only slight fluctuations, indicating good long-term stability. The remarkable HER performance can be attributed to the charge redistribution caused by abundant oxygen vacancies in a-Ru(OH)3/CoFe-LDH. The increased electron density of states lowers the charge-transfer resistance and promotes the formation and release of H2 molecules. The water-splitting electrolyzer with a-Ru(OH)3/CoFe-LDH as both an anode and a cathode in 1.0 M KOH demonstrates stable hydrogen production and a 100% faradic efficiency. The design strategy of interface engineering in this work will inspire the design of practical electrocatalysts for water splitting on an industrial scale.
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Affiliation(s)
- Zhuoer Cheng
- School of Pharmaceutical Sciences, South-Central MinZu University, Wuhan 430074, P. R. China
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Zhanming Tan
- College of Horticulture and Forestry, Tarim University, Alar 843300, P. R. China
| | - Li Zhou
- School of Pharmaceutical Sciences, South-Central MinZu University, Wuhan 430074, P. R. China
| | - Linfeng Li
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Xuefei Xu
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Muk Fung Yuen
- The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, P. R. China
| | - Ligui Li
- New Energy Research Institute, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, P. R. China
| | - Yuanjie Pang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Damien P Debecker
- Institute of Condensed Matter and Nanoscience (IMCN), UCLouvain, Louvain-La-Neuve 1348, Belgium
| | - Ruguang Ma
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, P. R. China
| | - Chundong Wang
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
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30
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Jin J, Wicks J, Min Q, Li J, Hu Y, Ma J, Wang Y, Jiang Z, Xu Y, Lu R, Si G, Papangelakis P, Shakouri M, Xiao Q, Ou P, Wang X, Chen Z, Zhang W, Yu K, Song J, Jiang X, Qiu P, Lou Y, Wu D, Mao Y, Ozden A, Wang C, Xia BY, Hu X, Dravid VP, Yiu YM, Sham TK, Wang Z, Sinton D, Mai L, Sargent EH, Pang Y. Constrained C 2 adsorbate orientation enables CO-to-acetate electroreduction. Nature 2023; 617:724-729. [PMID: 37138081 DOI: 10.1038/s41586-023-05918-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/02/2023] [Indexed: 05/05/2023]
Abstract
The carbon dioxide and carbon monoxide electroreduction reactions, when powered using low-carbon electricity, offer pathways to the decarbonization of chemical manufacture1,2. Copper (Cu) is relied on today for carbon-carbon coupling, in which it produces mixtures of more than ten C2+ chemicals3-6: a long-standing challenge lies in achieving selectivity to a single principal C2+ product7-9. Acetate is one such C2 compound on the path to the large but fossil-derived acetic acid market. Here we pursued dispersing a low concentration of Cu atoms in a host metal to favour the stabilization of ketenes10-chemical intermediates that are bound in monodentate fashion to the electrocatalyst. We synthesize Cu-in-Ag dilute (about 1 atomic per cent of Cu) alloy materials that we find to be highly selective for acetate electrosynthesis from CO at high *CO coverage, implemented at 10 atm pressure. Operando X-ray absorption spectroscopy indicates in situ-generated Cu clusters consisting of <4 atoms as active sites. We report a 12:1 ratio, an order of magnitude increase compared to the best previous reports, in the selectivity for acetate relative to all other products observed from the carbon monoxide electroreduction reaction. Combining catalyst design and reactor engineering, we achieve a CO-to-acetate Faradaic efficiency of 91% and report a Faradaic efficiency of 85% with an 820-h operating time. High selectivity benefits energy efficiency and downstream separation across all carbon-based electrochemical transformations, highlighting the importance of maximizing the Faradaic efficiency towards a single C2+ product11.
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Affiliation(s)
- Jian Jin
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Joshua Wicks
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Qiuhong Min
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Li
- Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai, China
| | - Yongfeng Hu
- Department of Chemical & Biological Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jingyuan Ma
- Shanghai Synchrotron Radiation Facility, Zhangjiang National Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
| | - Yu Wang
- Shanghai Synchrotron Radiation Facility, Zhangjiang National Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
| | - Zheng Jiang
- Shanghai Synchrotron Radiation Facility, Zhangjiang National Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
| | - Yi Xu
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Ruihu Lu
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
| | - Gangzheng Si
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Panagiotis Papangelakis
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Mohsen Shakouri
- Canadian Light Source, Inc., University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Qunfeng Xiao
- Canadian Light Source, Inc., University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Pengfei Ou
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Xue Wang
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Zhu Chen
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Wei Zhang
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, School of Materials Science and Engineering, Wuhan University of Technology, Wuhan, China
| | - Kesong Yu
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, School of Materials Science and Engineering, Wuhan University of Technology, Wuhan, China
| | - Jiayang Song
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohang Jiang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Qiu
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanhao Lou
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Wu
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Mao
- School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
| | - Adnan Ozden
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Chundong Wang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Bao Yu Xia
- Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Hu
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- The NUANCE Center, Northwestern University, Evanston, IL, USA
| | - Vinayak P Dravid
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- The NUANCE Center, Northwestern University, Evanston, IL, USA
| | - Yun-Mui Yiu
- Department of Chemistry, Western University, London, ON, Canada
| | - Tsun-Kong Sham
- Department of Chemistry, Western University, London, ON, Canada
| | - Ziyun Wang
- School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
| | - David Sinton
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Liqiang Mai
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, School of Materials Science and Engineering, Wuhan University of Technology, Wuhan, China.
| | - Edward H Sargent
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
| | - Yuanjie Pang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
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31
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Ma Y, Zou L, Liang Y, Liu Q, Sun Q, Pang Y, Lin H, Deng X, Tang S. [Rapid detection and genotyping of SARS-CoV-2 Omicron BA.4/5 variants using a RT-PCR and CRISPR-Cas12a-based assay]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:516-526. [PMID: 37202186 DOI: 10.12122/j.issn.1673-4254.2023.04.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To establish a rapid detection and genotyping method for SARS-CoV-2 Omicron BA.4/5 variants using CRISPPR-Cas12a gene editing technology. METHODS We combined reverse transcription-polymerase chain reaction (RT-PCR) and CRISPR gene editing technology and designed a specific CRISPPR RNA (crRNA) with suboptimal protospacer adjacent motifs (PAM) for rapid detection and genotyping of SARS- CoV-2 Omicron BA.4/5 variants. The performance of this RT- PCR/ CRISPPR-Cas12a assay was evaluated using 43 clinical samples of patients infected by wild-type SARS-CoV-2 and the Alpha, Beta, Delta, Omicron BA. 1 and BA. 4/5 variants and 20 SARS- CoV- 2-negative clinical samples infected with 11 respiratory pathogens. With Sanger sequencing method as the gold standard, the specificity, sensitivity, concordance (Kappa) and area under the ROC curve (AUC) of RT-PCR/CRISPPR-Cas12a assay were calculated. RESULTS This assay was capable of rapid and specific detection of SARS- CoV-2 Omicron BA.4/5 variant within 30 min with the lowest detection limit of 10 copies/μL, and no cross-reaction was observed in SARS-CoV-2-negative clinical samples infected with 11 common respiratory pathogens. The two Omicron BA.4/5 specific crRNAs (crRNA-1 and crRNA-2) allowed the assay to accurately distinguish Omicron BA.4/5 from BA.1 sublineage and other major SARS-CoV-2 variants of concern. For detection of SARS-CoV-2 Omicron BA.4/5 variants, the sensitivity of the established assay using crRNA-1 and crRNA-2 was 97.83% and 100% with specificity of 100% and AUC of 0.998 and 1.000, respectively, and their concordance rate with Sanger sequencing method was 92.83% and 96.41%, respectively. CONCLUSION By combining RT-PCR and CRISPPR-Cas12a gene editing technology, we successfully developed a new method for rapid detection and identification of SARS-CoV-2 Omicron BA.4/5 variants with a high sensitivity, specificity and reproducibility, which allows rapid detection and genotyping of SARS- CoV-2 variants and monitoring of the emerging variants and their dissemination.
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Affiliation(s)
- Y Ma
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - L Zou
- Institute of Pathogenic Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Guangzhou 511430, China
| | - Y Liang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Q Liu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Q Sun
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y Pang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - H Lin
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X Deng
- Institute of Pathogenic Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Guangzhou 511430, China
| | - S Tang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
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Li L, Liu YY, Yuan JF, Peng X, Pang Y, Lu J, Tang SJ. [Effect of Mycobacterium tuberculosis protein Rv0309 on intracellular survival of Mycobacterium smegmatis by inhibiting macrophage autophagy via protein STUB1]. Zhonghua Jie He He Hu Xi Za Zhi 2023; 46:396-403. [PMID: 36990704 DOI: 10.3760/cma.j.cn112147-20221125-00928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Objective: To investigate the molecular regulatory mechanism of Mycobacterium tuberculosis (MTB) protein Rv0309 to promote the survival of Mycobacterium smegmatis (Ms) in macrophages. Methods: Using Ms as a model to study Mycobacterium tuberculosis, recombinant Ms transfected with pMV261 and PMV261-RV0309 in the control group and RAW264.7 cells were constructed. The effect of Rv0309 protein on intracellular survival of Ms was investigated by counting colony forming units (CFUs). Mass spectrometry was used to screen proteins interacting with host protein Rv0309, and immunocoprecipitate (Co-IP) was used to verify that host protein STUB1 could interact with host protein Rv0309. STUB1 gene knock-out RAW264.7 cells were infected with Ms, and CFUs were counted to explore the effect of protein Rv0309 on intracellular survival of Ms after STUB1 gene knock-out. STUB1 gene knock-out RAW264.7 cells were infected with Ms, and after obtaining samples, Western blotting assay was performed to explore the effect of protein Rv0309 on autophagy function of macrophages after STUB1 gene knock-out. Statistical analysis was performed using GraphPad Prism 8 software. T-test was selected for analysis in this experiment, with P<0.05 was considered statistically significant. Results: Western blotting showed that Rv0309 was expressed in M. smegmatis and secreted extracellularly. The CFUs of the Ms-Rv0309 group was higher than that of Ms-pMV261 group at 24 h after THP-1 macrophage infection, and the difference was statistically significant (P<0.05). The trend of infected RAW264.7 macrophages was the same as that of infected THP-1 macrophages. The Co-IP results showed that the corresponding Flag and HA bands appeared in the results of immunoprecipitation (IP):Flag and IP: HA. The level of CFUs in the experimental group with STUB1 deletion was significantly higher than that in the control group without STUB1 deletion. Compared with Ms-pMV261, the CFUs in the Ms-Rv0309 group was significantly higher than that in the Ms-pMV261 group. The gray scale of LC3Ⅱ bands of Ms-Rv0309 in experimental group was lighter than that of Ms-pMV261 in the control group at the corresponding time point, and the result was most significant at 8 h (LC3Ⅱ/β-actin: 0.76±0.05 vs 0.47±0.07), the difference being statistically significant (P<0.05). After STUB1 genome knock-out, the gray level of LC3Ⅱ bands at the corresponding time was lighter than that without STUB1 genome knock-out. Comparison of the results of Ms-pMV261 and Ms-Rv0309 strains revealed that LC3Ⅱ band gray Rv0309 group was lighter at the corresponding time compared with pMV261 group. Conclusions: MTB protein Rv0309 can be successfully expressed in M. smegmatis and secreted extracellularly, which can inhibit the autophagy process of macrophages. Protein Rv0309 interacts with host protein STUB1 to inhibit macrophage autophagy and promote intracellular survival of Ms.
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Affiliation(s)
- L Li
- Beijing Institute of Tuberculosis and Chest Cancer, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Y Y Liu
- Department of Otolaryngology, National Children's Medical Center, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - J F Yuan
- Beijing Institute of Tuberculosis and Chest Cancer, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - X Peng
- Department of Otolaryngology, National Children's Medical Center, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - Y Pang
- Beijing Institute of Tuberculosis and Chest Cancer, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - J Lu
- Department of Otolaryngology, National Children's Medical Center, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - S J Tang
- Beijing Institute of Tuberculosis and Chest Cancer, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
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Pan L, Shi K, Lv J, Pang Y, Guo Y, Pei P, Du H, Millwood I, Yang L, Chen Y, Gao R, Yang X, Avery D, Chen J, Yu C, Chen Z, Li L. Association of dietary patterns, circulating lipid profile, and risk of obesity. Obesity (Silver Spring) 2023; 31:1445-1454. [PMID: 37037666 DOI: 10.1002/oby.23720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/08/2022] [Accepted: 01/01/2023] [Indexed: 04/12/2023]
Abstract
OBJECTIVE The aim of this study was to simultaneously explore the associations of major dietary patterns (DP) with lipid profiles and the associations of these profiles with general and central obesity risks and to evaluate the extent to which the metabolites mediate such associations. METHODS Habitual food consumption of 4778 participants with an average age of 47.0 from the China Kadoorie Biobank was collected using a 12-item food frequency questionnaire. Plasma samples were analyzed via targeted nuclear magnetic resonance (NMR) spectroscopy to quantify 129 lipid-related metabolites. Anthropometric information was measured by trained staff. RESULTS Two DPs were derived by factor analysis. The newly affluent southern pattern was characterized by high intakes of rice, meat, poultry, and fish, whereas the balanced pattern was characterized by consuming meat, poultry, fish, fresh fruit, fresh vegetables, dairy, eggs, and soybean. The newly affluent southern pattern was positively associated with 45 metabolites, which were positively associated with risks of obesity at the same time. The global lipid profile potentially explained 30.9%, 34.7%, and 53.1% of the effects of this DP on general obesity, waist circumference-defined central obesity, and waist-hip ratio-defined central obesity, respectively. CONCLUSIONS The newly affluent southern pattern points to an altered lipid profile, which showed higher general and central obesity risks. These findings partly suggest the biological mechanism for the obesogenic effects of this DP.
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Affiliation(s)
- Lang Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Kexiang Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China
| | - 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, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona 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
| | - 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
| | - 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
| | - Ruqin Gao
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | - Xiaoming Yang
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | - Daniel Avery
- 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
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 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 and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
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Yang S, Ding Y, Yu C, Guo Y, Pang Y, Sun D, Pei P, Yang L, Chen Y, Du H, Schmidt D, Stevens R, Bennett D, Clarke R, Chen J, Chen Z, Li L, Lv J. WHO cardiovascular disease risk prediction model performance in 10 regions, China. Bull World Health Organ 2023; 101:238-247. [PMID: 37008262 PMCID: PMC10042093 DOI: 10.2471/blt.22.288645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/11/2022] [Accepted: 12/07/2022] [Indexed: 04/04/2023] Open
Abstract
Objective To validate the World Health Organization (WHO) non-laboratory-based cardiovascular disease risk prediction model in regions of China. Methods We performed an external validation of the WHO model for East Asia using the data set of China Kadoorie Biobank, an ongoing cohort study with 512 725 participants recruited from 10 regions of China from 2004-2008. We also recalculated the recalibration parameters for the WHO model in each region and evaluated the predictive performance of the model before and after recalibration. We assessed discrimination performance by Harrell's C index. Findings We included 412 225 participants aged 40-79 years. During a median follow-up of 11 years, 58 035 and 41 262 incident cardiovascular disease cases were recorded in women and men, respectively. Harrell's C of the WHO model was 0.682 in women and 0.700 in men but varied among regions. The WHO model underestimated the 10-year cardiovascular disease risk in most regions. After recalibration in each region, discrimination and calibration were both improved in the overall population. Harrell's C increased from 0.674 to 0.749 in women and from 0.698 to 0.753 in men. The ratios of predicted to observed cases before and after recalibration were 0.189 and 1.027 in women and 0.543 and 1.089 in men. Conclusion The WHO model for East Asia yielded moderate discrimination for cardiovascular disease in the Chinese population and had limited prediction for cardiovascular disease risk in different regions in China. Recalibration for diverse regions greatly improved discrimination and calibration in the overall population.
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Affiliation(s)
- Songchun Yang
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
| | - Yinqi Ding
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, England
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, England
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, England
| | - Dan Schmidt
- Nuffield Department of Population Health, University of Oxford, Oxford, England
| | - Rebecca Stevens
- Nuffield Department of Population Health, University of Oxford, Oxford, England
| | - Derrick Bennett
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, England
| | - Robert Clarke
- Nuffield Department of Population Health, University of Oxford, Oxford, England
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, England
| | - Liming Li
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing100191, China
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Sun D, Liu C, Ding Y, Yu C, Guo Y, Sun D, Pang Y, Pei P, Du H, Yang L, Chen Y, Meng X, Liu Y, Liu J, Sohoni R, Sansome G, Chen J, Chen Z, Lv J, Kan H, Li L. Long-term exposure to ambient PM 2·5, active commuting, and farming activity and cardiovascular disease risk in adults in China: a prospective cohort study. Lancet Planet Health 2023; 7:e304-e312. [PMID: 37019571 PMCID: PMC10104773 DOI: 10.1016/s2542-5196(23)00047-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/12/2023] [Accepted: 02/15/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Increased physical activity is associated with a reduced risk of cardiovascular disease, but outdoor physical activity can be accompanied by increased inhalation of fine particulate matter (PM2·5). The extent to which long-term exposure to PM2·5 can offset the cardiovascular benefits of physical activity is unknown. We aimed to evaluate whether the associations between active commuting or farming activity and incident risks of cerebrovascular disease and ischaemic heart disease were consistent between populations with different ambient PM2·5 exposures. METHODS We did a prospective cohort study using data from people aged 30-79 years without cardiovascular disease at baseline from the China Kadoorie Biobank (CKB). Active commuting and farming activity were assessed at baseline using questionnaires. A high-resolution (1 × 1 km) satellite-based model was used to estimate annual average PM2·5 exposure during the study period. Participants were stratified according to PM2·5 exposure (54 μg/m3 or greater vs less than 54 μg/m3). Hazard ratios (HRs) and 95% CIs for incident cerebrovascular disease and ischaemic heart disease by active commuting and farming activity were estimated using Cox proportional hazard models. Effect modifications by PM2·5 exposure were tested by likelihood ratio tests. Analyses were restricted to the period from Jan 1, 2005, to Dec 31, 2017. FINDINGS Between June 25, 2004, and July 15, 2008, 512 725 people were enrolled in the CKB cohort. 322 399 eligible participants completed the baseline survey and were included in the analysis of active commuting (118 274 non-farmers and 204 125 farmers). Among 204 125 farmers, 2985 reported no farming time and 201 140 were included in the farming activity analysis. During a median follow-up of 11 years, 39 514 cerebrovascular disease cases and 22 313 ischaemic heart disease cases were newly identified. Among non-farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting was associated with lower risks of cerebrovascular disease (highest active commuting vs lowest active commuting HR 0·70, 95% CI 0·65-0·76) and ischaemic heart disease (0·60, 0·54-0·66). However, among non-farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, there was no association between active commuting and cerebrovascular disease or ischaemic heart disease. Among farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting (highest active commuting vs lowest active commuting HR 0·77, 95% CI 0·63-0·93) and increased farming activity (highest activity vs lowest activity HR 0·85, 95% CI 0·79-0·92) were both associated with a lower cerebrovascular disease risk. However, among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, increases in active commuting (highest active commuting vs lowest active commuting HR 1·12, 95% CI 1·05-1·19) and farming activity (highest activity vs lowest activity HR 1·18, 95% CI 1·09-1·28) were associated with an elevated cerebrovascular disease risk. The above associations differed significantly between PM2·5 strata (all interaction p values <0·0001). INTERPRETATION For participants with long-term exposure to higher ambient PM2·5 concentrations, the cardiovascular benefits of active commuting and farming activity were significantly attenuated. Higher levels of active commuting and farming activity even increased the cerebrovascular disease risk among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater. FUNDING National Natural Science Foundation of China, National Key Research and Development Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust.
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Affiliation(s)
- Dong Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Cong Liu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, NHC 12 Key Lab of Health Technology Assessment, IRDR ICoE on Risk Interconnectivity and 13 Governance on Weather or Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
| | - Yinqi Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Pei Pei
- Chinese Academy of Medical Sciences, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xia Meng
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, NHC 12 Key Lab of Health Technology Assessment, IRDR ICoE on Risk Interconnectivity and 13 Governance on Weather or Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jiben Liu
- Prevention and Health Department, Yongqinglu Community Health Service, Qingdao, China
| | - Rajani Sohoni
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Gary Sansome
- Clinical Trial Service Unit and Epidemiological Studies Unit, 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 and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China.
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, NHC 12 Key Lab of Health Technology Assessment, IRDR ICoE on Risk Interconnectivity and 13 Governance on Weather or Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China.
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Si J, Chen L, Yu C, Guo Y, Sun D, Pang Y, Millwood IY, Walters RG, Yang L, Chen Y, Du H, Feng S, Yang X, Avery D, Chen J, Chen Z, Liang L, Li L, Lv J. Healthy lifestyle, DNA methylation age acceleration, and incident risk of coronary heart disease. Clin Epigenetics 2023; 15:52. [PMID: 36978155 PMCID: PMC10045869 DOI: 10.1186/s13148-023-01464-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/12/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND DNA methylation clocks emerged as a tool to determine biological aging and have been related to mortality and age-related diseases. Little is known about the association of DNA methylation age (DNAm age) with coronary heart disease (CHD), especially in the Asian population. RESULTS Methylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip for 491 incident CHD cases and 489 controls in the prospective China Kadoorie Biobank. We calculated the methylation age using a prediction model developed among Chinese. The correlation between chronological age and DNAm age was 0.90. DNA methylation age acceleration (Δage) was defined as the residual of regressing DNA methylation age on the chronological age. After adjustment for multiple risk factors of CHD and cell type proportion, compared with participants in the bottom quartile of Δage, the OR (95% CI) for CHD was 1.84 (1.17, 2.89) for participants in the top quartile. One SD increment in Δage was associated with 30% increased risk of CHD (OR = 1.30; 95% CI 1.09, 1.56; Ptrend = 0.003). The average number of cigarette equivalents consumed per day and waist-to-hip ratio were positively associated with Δage; red meat consumption was negatively associated with Δage, characterized by accelerated aging in those who never or rarely consumed red meat (all P < 0.05). Further mediation analysis revealed that 10%, 5% and 18% of the CHD risk related to smoking, waist-to-hip ratio and never or rarely red meat consumption was mediated through methylation aging, respectively (all P for mediation effect < 0.05). CONCLUSIONS We first identified the association between DNAm age acceleration and incident CHD in the Asian population, and provided evidence that unfavorable lifestyle-induced epigenetic aging may play an important part in the underlying pathway to CHD.
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Affiliation(s)
- Jiahui Si
- Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
- National Institute of Health Data Science at Peking University, Peking University, Beijing, China
| | - Lu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit and 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 and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit and 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 and 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 and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Shixian Feng
- NCDs Prevention and Control Department, Henan CDC, Zhengzhou, Henan, China
| | - Xiaoming Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit and 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
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Liang
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China.
- Center for Public Health and Epidemic Preparedness & Response, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China.
- Center for Public Health and Epidemic Preparedness & Response, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
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Han Y, Zhu X, Hu Y, Yu C, Guo Y, Hang D, Pang Y, Pei P, Ma H, Sun D, Yang L, Chen Y, Du H, Yu M, Chen J, Chen Z, Huo D, Jin G, Lv J, Hu Z, Shen H, Li L. Electronic Health Record-Based Absolute Risk Prediction Model for Esophageal Cancer in the Chinese Population: Model Development and External Validation. JMIR Public Health Surveill 2023; 9:e43725. [PMID: 36781293 PMCID: PMC10132027 DOI: 10.2196/43725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/09/2023] [Accepted: 02/03/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND China has the largest burden of esophageal cancer (EC). Prediction models can be used to identify high-risk individuals for intensive lifestyle interventions and endoscopy screening. However, the current prediction models are limited by small sample size and a lack of external validation, and none of them can be embedded into the booming electronic health records (EHRs) in China. OBJECTIVE This study aims to develop and validate absolute risk prediction models for EC in the Chinese population. In particular, we assessed whether models that contain only EHR-available predictors performed well. METHODS A prospective cohort recruiting 510,145 participants free of cancer from both high EC-risk and low EC-risk areas in China was used to develop EC models. Another prospective cohort of 18,441 participants was used for validation. A flexible parametric model was used to develop a 10-year absolute risk model by considering the competing risks (full model). The full model was then abbreviated by keeping only EHR-available predictors. We internally and externally validated the models by using the area under the receiver operating characteristic curve (AUC) and calibration plots and compared them based on classification measures. RESULTS During a median of 11.1 years of follow-up, we observed 2550 EC incident cases. The models consisted of age, sex, regional EC-risk level (high-risk areas: 2 study regions; low-risk areas: 8 regions), education, family history of cancer (simple model), smoking, alcohol use, BMI (intermediate model), physical activity, hot tea consumption, and fresh fruit consumption (full model). The performance was only slightly compromised after the abbreviation. The simple and intermediate models showed good calibration and excellent discriminating ability with AUCs (95% CIs) of 0.822 (0.783-0.861) and 0.830 (0.792-0.867) in the external validation and 0.871 (0.858-0.884) and 0.879 (0.867-0.892) in the internal validation, respectively. CONCLUSIONS Three nested 10-year EC absolute risk prediction models for Chinese adults aged 30-79 years were developed and validated, which may be particularly useful for populations in low EC-risk areas. Even the simple model with only 5 predictors available from EHRs had excellent discrimination and good calibration, indicating its potential for broader use in tailored EC prevention. The simple and intermediate models have the potential to be widely used for both primary and secondary prevention of EC.
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Affiliation(s)
- Yuting Han
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xia Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yizhen Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Dong Hang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Pei Pei
- Chinese Academy of Medical Sciences, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit, 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
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, 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
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, 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
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, United States
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
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Chen L, Zhang Y, Yu C, Guo Y, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Liu Y, Burgess S, Stevens R, Chen J, Chen Z, Li L, Lv J. Modeling biological age using blood biomarkers and physical measurements in Chinese adults. EBioMedicine 2023; 89:104458. [PMID: 36758480 PMCID: PMC9941058 DOI: 10.1016/j.ebiom.2023.104458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/15/2023] [Accepted: 01/17/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND This study aimed to: 1) assess the associations of biological age acceleration based on Klemera and Doubal's method (KDM-AA) with long-term risk of all-cause mortality; and 2) compare the association of KDM-AA with all-cause mortality among participants potentially at different stages of the cardiovascular disease (CVD) continuum. METHODS The present study was based on a subpopulation of the China Kadoorie Biobank, with baseline survey during 2004-08. A total of 12,377 participants free of ischemic heart disease, stroke, or cancer at baseline were included, in which 8180 participants were identified to develop major coronary event (MCE), ischemic stroke (IS), intracerebral hemorrhage (ICH) or subarachnoid hemorrhage (SAH), and 4197 remained free of these cardiovascular diseases before 1 January 2014. These participants were followed up until 1 Jan 2018. KDM-AA was calculated by regressing biological age measurement, which was constructed based on baseline 16 physical and 9 biochemical markers using Klemera and Doubal's method, on chronological age. We estimated the associations of KDM-AA with the mortality risk using the hazard ratio (HR) and 95% confidence interval (CI) from Cox proportional hazard models. We assessed discrimination performance by Harrell's C-index and net reclassification index (NRI). FINDINGS The participants who developed MCE (mean KDM-AA = 0.1 year, standard deviation [SD] = 1.6 years) or ICH/SAH (0.3 ± 1.5 years) during subsequent follow-up showed accelerated aging at baseline compared to those of IS (0.0 ± 1.2 years) and control (-0.3 ± 1.3 years) groups. The KDM-AA was positively associated with long-term risk of all-cause mortality (HR = 1.20; 95% CI: 1.17, 1.23), and the association was robust for participants potentially at different stages of the CVD continuum. Adding KDM-AA improved mortality prediction compared to the model only with sociodemographic and lifestyle factors in whole participants, with the Harrell's C-index increasing from 0.813 (0.807, 0.819) to 0.821 (0.815, 0.826) (NRI = 0.011; 95% CI: 0.003, 0.019). INTERPRETATION In this middle-aged and elderly Chinese population, the KDM-AA is a promising measurement for biological age, and can capture the difference in cardiovascular health and predict the risk of all-cause mortality over a decade. FUNDING This work was supported by National Natural Science Foundation of China (82192904, 82192901, 82192900, 81941018). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), grants (2016YFC0900500) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 91846303), and Chinese Ministry of Science and Technology (2011BAI09B01).
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Affiliation(s)
- Lu Chen
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yiqian Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, 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, 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - 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, United Kingdom
| | - Iona Y Millwood
- 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, United Kingdom
| | - Robin G Walters
- 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, 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, United Kingdom
| | - 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, United Kingdom
| | - Yongmei Liu
- Qingdao Centers for Disease Control and Prevention (CDC), Qingdao, China
| | - Sushila Burgess
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of 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, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China.
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Liu K, Zhang Z, Hou Q, Pang Y, Nouzi F, Yaghmai V, Zhang Z, Eresen A. Abstract No. 51 Differentiation of Irreversible Electroporation Regions in Normal and Tumor Tissues Using MRI Textures. J Vasc Interv Radiol 2023. [DOI: 10.1016/j.jvir.2022.12.094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
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40
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Wang X, Wang A, Zhang R, Cheng S, Pang Y. Life's Essential 8 and MAFLD in the United States. J Hepatol 2023; 78:e61-e63. [PMID: 36306872 DOI: 10.1016/j.jhep.2022.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Xinyu Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Aruna Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Ruosu Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Si Cheng
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China.
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41
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Pang Y, Kukull W, Sano M, Albin RL, Shen C, Zhou J, Dodge HH. Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer's Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator. J Prev Alzheimers Dis 2023; 10:301-313. [PMID: 36946457 PMCID: PMC10033942 DOI: 10.14283/jpad.2023.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Clinical trials are increasingly focused on pre-manifest and early Alzheimer's disease (AD). Accurately predicting clinical progressions from normal to MCI or from MCI to dementia/AD versus non-progression is challenging. Accurate identification of symptomatic progressors is important to avoid unnecessary treatment and improve trial efficiency. Due to large inter-individual variability, biomarker positivity and comorbidity information are often insufficient to identify those destined to have symptomatic progressions. Using only clinical variables, we aimed to predict clinical progressions, estimating probabilities of progressions with a small set of variables selected by machine learning approaches. This work updates our previous work that was applied to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set Version 2 (V2), by using the most recent version (V3) with additional analyses. We generated a user-friendly conversion probability calculator which can be used for effectively pre-screening trial participants.
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Affiliation(s)
- Y Pang
- Hiroko H. Dodge, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA,
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Hong X, Miao K, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association Between DNA Methylation and Blood Pressure: A 5-Year Longitudinal Twin Study. Hypertension 2023; 80:169-181. [PMID: 36345830 DOI: 10.1161/hypertensionaha.122.19953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous EWASs (Epigenome-Wide Association Studies) have reported hundreds of blood pressure (BP) associated 5'-cytosine-phosphate-guanine-3' (CpG) sites. However, their results were inconsistent. Longitudinal observations on the temporal relationship between DNA methylation and BP are lacking. METHODS A candidate CpG site association study for BP was conducted on 1072 twins in the Chinese National Twin Registry. PubMed and EMBASE were searched for candidate CpG sites. Cross-lagged models were used to assess the temporal relationship between BP and DNA methylation in 308 twins who completed 2 surveys in 2013 and 2018. Then, the significant cross-lagged associations were validated by adopting the Inference About Causation From Examination of Familial Confounding approach. Finally, to evaluate the cumulative effects of DNA methylation on the progression of hypertension, we established methylation risk scores based on BP-associated CpG sites and performed Markov multistate models. RESULTS 16 and 20 CpG sites were validated to be associated with systolic BP and diastolic BP, respectively. In the cross-lagged analysis, we detected that methylation of 2 CpG sites could predict subsequent systolic BP, and systolic BP predicted methylation at another 3 CpG sites. For diastolic BP, methylation at 3 CpG sites had significant cross-lagged effects for predicting diastolic BP levels, while the prediction from the opposite direction was observed at one site. Among these, 3 associations were validated in the Inference About Causation From Examination of Familial Confounding analysis. Using the Markov multistate model, we observed that methylation risk scores were associated with the development of hypertension. CONCLUSIONS Our findings suggest the significance of DNA methylation in the development of hypertension.
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Affiliation(s)
- Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, China (Z.P.)
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China (M.Y.)
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China (H.W.)
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China (X.W.)
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China (Y.L.)
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
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Liu Q, Qin G, Xiang T, Xiao W, Zhao Y, Pang Y. Laparoscopic radical resection for rectal cancer in a patient with uncorrected truncus arteriosus type IV: A case report. Rev Esp Anestesiol Reanim (Engl Ed) 2023; 70:56-59. [PMID: 36621567 DOI: 10.1016/j.redare.2021.05.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/23/2021] [Indexed: 01/07/2023]
Abstract
Persistent truncus arteriosus is a rare congenital heart malformation which if not corrected, results in the death of about 50% of the patients, while fewer than 20% of the patients survive the first year of life. Here, we report the successful anesthetic management of an adult patient with uncorrected truncus arteriosus who presented for the laparoscopic radical resection of rectal cancer.
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Affiliation(s)
- Q Liu
- Department of Anesthesiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
| | - G Qin
- Department of Anesthesiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - T Xiang
- Department of Anesthesiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - W Xiao
- Department of Anesthesiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Y Zhao
- Department of Anesthesiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Y Pang
- Department of Anesthesiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
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Hu Y, Sun Z, Yu C, Guo Y, Pei P, Yang L, Chen Y, Du H, Sun D, Pang Y, Tian X, Gilbert S, Avery D, Chen J, Chen Z, Li L, Lv J. Association between pneumonia hospitalisation and long-term risk of cardiovascular disease in Chinese adults: A prospective cohort study. EClinicalMedicine 2023; 55:101761. [PMID: 36483267 PMCID: PMC9722470 DOI: 10.1016/j.eclinm.2022.101761] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/06/2022] [Accepted: 11/09/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Lower respiratory tract infections, including pneumonia, have been associated with short-term increased risk of cardiovascular disease (CVD). However, there is only limited evidence about the long-term impact of pneumonia on the cardiovascular system beyond one year. METHODS We conducted a prospective matched cohort study based on data from the China Kadoorie Biobank study of 482,017 adults who were enrolled between June 25, 2004, and July 15, 2008, and were free of CVD at baseline and before pneumonia hospitalization. A total of 24,060 patients hospitalised with pneumonia were identified until December 31, 2018, and were matched on age, sex, urban or rural areas, and decile of the frailty index to 223,875 controls. We used the piecewise Cox proportional hazards model to estimate the hazard ratios (HRs) and their 95% confidence intervals (CIs) for pre-specified incident CVD outcomes, including ischaemic heart disease (IHD), arrhythmia, heart failure (HF), ischaemic stroke (IS), and hemorrhagic stroke (HS), at various time intervals through 10 years after pneumonia hospitalization. FINDINGS Of the 247,935 pneumonia cases and controls included, the mean age (standard deviation) was 53.5 (10.4), and 40.8% (101,159) were men. During follow-up, 2389 (9.9%) pneumonia cases developed IHD, 489 (2.0%) cases developed arrhythmia, 545 (2.3%) cases developed HF, 1764 (7.3%) cases developed IS, and 348 (1.4%) cases developed HS. After adjustment for sociodemographic characteristics, lifestyle factors, health status and medication, underlying conditions, and family history of CVD, the elevated CVD risk was highest within the first 30 days after pneumonia hospitalisation, with subsequent risk reductions varying by subtypes. The elevated risk remained until the eighth year after pneumonia hospitalisation for IHD, arrhythmia, and HF, with HRs (95% CIs) of 1.48 (1.13-1.93), 2.69 (1.70-4.25), and 4.36 (2.86-6.64), respectively. The risk of stroke associated with pneumonia hospitalisation remained elevated until the seventh year for IS (HR = 1.30; 95% CI: 1.04-1.63), and until the second year for HS (1.39; 1.07-1.80). The above associations were consistently observed across various characteristics of the participants. INTERPRETATION In middle-aged and older Chinese adults, pneumonia hospitalisation was associated with short- and long-term CVD risk, with the elevated risk of certain CVD outcomes persisting for up to 8 years. FUNDING National Natural Science Foundation of China, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, the Kadoorie Charitable Foundation in Hong Kong, the UK Wellcome Trust.
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Affiliation(s)
- Yizhen Hu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
| | - Zhijia Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
| | - Xiaocao Tian
- NCDs Prevention and Control Department, Qingdao Center for Disease Control and Prevention, Shandong 266033, China
| | - Simon Gilbert
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
- Corresponding author. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.
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Wang Y, Liu L, Chen D, Pang Y, Xu X, Liu J, Li M, Guan X. Development and validation of a novel nomogram predicting pseudohypoxia type pheochromocytomas and paragangliomas. J Endocrinol Invest 2022:10.1007/s40618-022-01984-3. [PMID: 36508127 DOI: 10.1007/s40618-022-01984-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE Pseudohypoxia type (PHT) pheochromocytomas and paragangliomas (PPGLs) are more likely to metastasize and have a poor prognosis. However, application of genetic tests has many restrictions. The study aims to establish a novel nomogram for predicting the risk of PHT PPGLs. METHODS This retrospective cross-sectional study included 242 patients with pathology confirmed PPGLs in one tertiary care center in China in 2010-2021. Clinical and biochemical characteristics were collected. Next-generation sequencing was performed in all PPGLs patients for detection of mutation. Univariate and multivariable logistic regression analyses were used to select risk factors for constructing the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination of the nomogram and the calibration curve was performed. RESULTS Four variables including age ≤ 35 years, hypertension, 24 h urinary output of urinary vanillylmandelic acid (VMA) ≥ 100 umol/24 h and urinary 17-ketosteroide (17 KS) ≤ 50 umol/24 h levels were independently associated with PHT PPGLs in the logistic regression analysis and were included in the nomogram. The nomogram showed a good discrimination performance with AUC of 0.829 [95% confidence interval (CI), 0.767-0.891] in the training set and 0.797 (95%CI, 0.659-0.935) in the validation set, respectively. The calibration curve showed a bias-corrected AUC of 0.809 vs. 0.795, and a Hosmer-Lemeshow (H-L) test yielded a p value of 0.801 vs. 0.885, indicating the nomogram's good ability to distinguish PHT PPGLs from non-PHT PPGLs. CONCLUSION Our study has proposed a novel nomogram for individualized prediction of the PHT PPGLs, which may make contributions to guide the patients' personalized management, follow-up, and treatment.
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Affiliation(s)
- Y Wang
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - L Liu
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - D Chen
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - Y Pang
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - X Xu
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - J Liu
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - M Li
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - X Guan
- Department of Urology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China.
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Hong X, Wu Z, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Cong L, Wang H, Wu X, Liu Y, Gao W, Li L. Longitudinal Association of DNA Methylation With Type 2 Diabetes and Glycemic Traits: A 5-Year Cross-Lagged Twin Study. Diabetes 2022; 71:2804-2817. [PMID: 36170668 DOI: 10.2337/db22-0513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/20/2022] [Indexed: 01/11/2023]
Abstract
Investigators of previous cross-sectional epigenome-wide association studies (EWAS) in adults have reported hundreds of 5'-cytosine-phosphate-guanine-3' (CpG) sites associated with type 2 diabetes mellitus (T2DM) and glycemic traits. However, the results from EWAS have been inconsistent, and longitudinal observations of these associations are scarce. Furthermore, few studies have investigated whether DNA methylation (DNAm) could be modified by smoking, drinking, and glycemic traits, which have broad impacts on genome-wide DNAm and result in altering the risk of T2DM. Twin studies provide a valuable tool for epigenetic studies, as twins are naturally matched for genetic information. In this study, we conducted a systematic literature search in PubMed and Embase for EWAS, and 214, 33, and 117 candidate CpG sites were selected for T2DM, HbA1c, and fasting blood glucose (FBG). Based on 1,070 twins from the Chinese National Twin Registry, 67, 17, and 16 CpG sites from previous studies were validated for T2DM, HbA1c, and FBG. Longitudinal review and blood sampling for phenotypic information and DNAm were conducted twice in 2013 and 2018 for 308 twins. A cross-lagged analysis was performed to examine the temporal relationship between DNAm and T2DM or glycemic traits in the longitudinal data. A total of 11 significant paths from T2DM to subsequent DNAm and 15 paths from DNAm to subsequent T2DM were detected, suggesting both directions of associations. For glycemic traits, we detected 17 cross-lagged associations from baseline glycemic traits to subsequent DNAm, and none were from the other cross-lagged direction, indicating that CpG sites may be the consequences, not the causes, of glycemic traits. Finally, a longitudinal mediation analysis was performed to explore the mediation effects of DNAm on the associations of smoking, drinking, and glycemic traits with T2DM. No significant mediations of DNAm in the associations linking smoking and drinking with T2DM were found. In contrast, our study suggested a potential role of DNAm of cg19693031, cg00574958, and cg04816311 in mediating the effect of altered glycemic traits on T2DM.
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Affiliation(s)
- Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhiyu Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Liming Cong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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Zhang R, Pang Y, Wan S, Lu M, Lv M, Wu J, Huang Y. Effectiveness of influenza vaccination on in-hospital death in older adults with respiratory diseases. Hum Vaccin Immunother 2022; 18:2117967. [PMID: 36094827 DOI: 10.1080/21645515.2022.2117967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Influenza vaccination is associated with lower risk of hospitalization outcomes among older adults with respiratory diseases, but there is limited evidence by disease subtypes and patients' characteristics. This study included patients aged ≥60 years hospitalized for respiratory diseases from the Beijing Urban Employee Basic Medical Insurance database during 6 influenza seasons. Vaccination status was assessed by linking with the Beijing Elderly Influenza Vaccination database. Multi-variable logistic regression was performed to calculate effect estimates. After adjusting for measured and unmeasured confounders, influenza vaccination was associated with a lower risk of in-hospital death among older adults hospitalized for respiratory diseases (odds ratio [95% confidence interval], 0.70 [0.62-0.80]). The protective association was observed among patients with chronic obstructive pulmonary disease (0.67 [0.47-0.98]) as well as those with pneumonia or influenza (0.77 [0.64-0.93]). The protective association was stronger in younger patients (0.59 [0.43-0.81] for <75 and 0.72 [0.63-0.83] for ≥75) and those with fewer comorbidities (0.49 [0.16-1.62] for 0, 0.65 [0.50-0.86] for 1-2, and 0.72 [0.63-0.83] for ≥3 comorbidities). Influenza vaccination was associated with lower risk of in-hospital death among older patients hospitalized for respiratory diseases, with stronger associations in patients with younger age and fewer comorbidities.
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Affiliation(s)
- Ruosu Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Shiyu Wan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Ming Lu
- Department of Biomedical Informatics, School of Basic Medicine, Peking University, Beijing, China
| | - Min Lv
- Institute for Immunization and Prevention, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Jiang Wu
- Institute for Immunization and Prevention, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yangmu Huang
- Department of Global Health, School of Public Health, Peking University, Beijing, China
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Pang Y, Han Y, Yu C, Kartsonaki C, Guo Y, Chen Y, Yang L, Du H, Hou W, Schmidt D, Stevens R, Chen J, Chen Z, Lv J, Li L. The role of lifestyle factors on comorbidity of chronic liver disease and cardiometabolic disease in Chinese population: A prospective cohort study. Lancet Reg Health West Pac 2022; 28:100564. [PMID: 35991535 PMCID: PMC9386629 DOI: 10.1016/j.lanwpc.2022.100564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Lifestyle factors are associated with chronic liver disease (CLD) and death after CLD diagnosis. However, their associations with pathways of CLD progression have been unclear, particularly transition to cardiometabolic disease (CMD), a major comorbid condition with CLD. We assessed the associations of lifestyle factors with CLD progression. METHODS The study population involved 486,828 participants of the prospective China Kadoorie Biobank (CKB) aged 30-79 years without a history of cardiovascular disease, diabetes, CLD, or cancer at baseline. Liver-cardiometabolic comorbidity (LCC) was defined as developing CMD subsequently after first CLD (FCLD) in an individual. A multi-state model was used to estimate the associations of high-risk lifestyle factors (smoking, alcohol, physical inactivity, and central adiposity) with CLD progression from healthy to FCLD, subsequently to LCC, and further to death. FINDINGS During a median follow-up of 11 years, 5046 participants developed FCLD, 519 developed LCC, and 157 died afterwards. There were positive associations between the number of high-risk lifestyle factors and risks of all transitions. The hazard ratios (95% CIs) per 1-factor increase were 1.30 (1.25-1.35) for transitions from baseline to FCLD, 1.21 (1.09-1.34) for FCLD to LCC, 1.20 (1.17-1.23) for baseline to death, 1.15 (1.09-1.22) for FCLD to death, and 1.17 (1.06-1.31) for LCC to death. For CLD subtypes, lifestyle factors showed different associations with disease-specific transitions even within the same transition stage. INTERPRETATION High-risk lifestyle factors played a key role in all disease transition stages from healthy to FCLD, subsequently to LCC, and then to death, with different magnitude of associations. FUNDING Kadoorie Charitable Foundation, Chinese MoST and NSFC.
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Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuting Han
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Beijing 100191, China
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Yu Guo
- Chinese Academy of Medical Sciences, 9 Dongdan San Tiao, Beijing 100730, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Wei Hou
- Licang Center for Disease Prevention and Control, 20 Yongnian Road, Licang District, Qingdao 266041, China
| | - Danile Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 37 Guangqu Road, Beijing 100021, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Beijing 100191, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, 38 Xueyuan Road, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Beijing 100191, China
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Wu Z, Chen L, Hong X, Si J, Cao W, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Cong L, Wang H, Wu X, Liu Y, Guo Y, Chen Z, Lv J, Gao W, Li L. Temporal associations between leukocytes DNA methylation and blood lipids: a longitudinal study. Clin Epigenetics 2022; 14:132. [PMID: 36274151 PMCID: PMC9588246 DOI: 10.1186/s13148-022-01356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/13/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The associations between blood lipids and DNA methylation have been investigated in epigenome-wide association studies mainly among European ancestry populations. Several studies have explored the direction of the association using cross-sectional data, while evidence of longitudinal data is still lacking. RESULTS We tested the associations between peripheral blood leukocytes DNA methylation and four lipid measures from Illumina 450 K or EPIC arrays in 1084 participants from the Chinese National Twin Registry and replicated the result in 988 participants from the China Kadoorie Biobank. A total of 23 associations of 19 CpG sites were identified, with 4 CpG sites located in or adjacent to 3 genes (TMEM49, SNX5/SNORD17 and CCDC7) being novel. Among the validated associations, we conducted a cross-lagged analysis to explore the temporal sequence and found temporal associations of methylation levels of 2 CpG sites with triglyceride and 2 CpG sites with high-density lipoprotein-cholesterol (HDL-C) in all twins. In addition, methylation levels of cg11024682 located in SREBF1 at baseline were temporally associated with triglyceride at follow-up in only monozygotic twins. We then performed a mediation analysis with the longitudinal data and the result showed that the association between body mass index and HDL-C was partially mediated by the methylation level of cg06500161 (ABCG1), with a mediation proportion of 10.1%. CONCLUSIONS Our study indicated that the DNA methylation levels of ABCG1, AKAP1 and SREBF1 may be involved in lipid metabolism and provided evidence for elucidating the regulatory mechanism of lipid homeostasis.
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Affiliation(s)
- Zhiyu Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Lu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jiahui Si
- National Institute of Health Data Science at Peking University, Peking University, Beijing, 100191, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, 100191, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, 266033, China
| | - Liming Cong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, 210008, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, 610041, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, 150090, China
| | - Yu Guo
- Fuwai hospital Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, 100191, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, 100191, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, 100191, China.
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50
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He Y, Pang Y, Su Z, Zhou Y, Wang Y, Lu Y, Jiang Y, Han X, Song L, Wang L, Li Z, Lv X, Wang Y, Yao J, Liu X, Zhou X, He S, Zhang Y, Song L, Li J, Wang B, Tang L. Symptom burden, psychological distress, and symptom management status in hospitalized patients with advanced cancer: a multicenter study in China. ESMO Open 2022; 7:100595. [PMID: 36252435 PMCID: PMC9808454 DOI: 10.1016/j.esmoop.2022.100595] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The management of physical symptoms and psychological distress of cancer patients is an important component of cancer care. The purpose of this study was to evaluate the symptom burden, psychological distress, and management status of hospitalized patients with advanced cancer in China and explore the potential influencing factors of undertreatment and non-treatment of symptoms. PATIENTS AND METHODS A total of 2930 hospitalized patients with advanced cancer (top six types of cancer in China) were recruited from 10 centers all over China. Patient-reported MD Anderson Symptom Inventory, Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9) scales and symptom management-related information were collected and linked with the patient's clinical data. The proportion of patients reporting moderate-to-severe (MS) symptoms and whether they were currently well managed were examined. Multivariable logistic regression models were applied to explore the factors correlated to undertreatment and non-treatment of symptoms. RESULTS About 27% of patients reported over three MS symptoms, 16% reported over five, and 9% reported over seven. Regarding psychological distress, the prevalence of HADS-anxiety was 29% and that of PHQ-9 depression was 11%. Sixty-one percent of patients have at least one MS symptom without any treatment. Sex [odds ratio (OR) = 2.238, 95% confidence interval (95% CI) 1.502-3.336], Eastern Cooperative Oncology Group (ECOG; OR = 0.404, 95% CI 0.241-0.676), and whether currently undergoing anticancer treatment (OR = 0.667, 95% CI 0.503-0.886) are the main factors correlated with the undertreatment of symptoms. Age (OR = 1.972, 95% CI 1.263-3.336), sex (OR = 0.626, 95% CI 0.414-0.948), ECOG (OR = 0.266, 95% CI 0.175-0.403), whether currently undergoing anticancer treatment (OR = 0.356, 95% CI 0.249-0.509), and comorbidity (OR = 0.713, 95% CI 0.526-0.966) are the main factors correlated with the non-treatment of symptoms. CONCLUSIONS This study shows that hospitalized patients with advanced cancer had a variety of physical and psychological symptoms but lacked adequate management and suggests that a complete symptom screening and management system is needed to deal with this complex problem.
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Affiliation(s)
- Y. He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Pang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Z. Su
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Zhou
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Wang
- Department of Breast Cancer Radiotherapy, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Y. Lu
- The Fifth Department of Chemotherapy, The Affiliated Cancer Hospital of Guangxi Medical University, Guangxi Zhuang Autonomous Region, Nanning, China
| | - Y. Jiang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - X. Han
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L. Song
- Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - L. Wang
- Department of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Z. Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - X. Lv
- Department of Oncology, Xiamen Humanity Hospital, Xiamen, China
| | - Y. Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - J. Yao
- Department of Integrated Chinese and Western Medicine, Shaanxi Provincial Cancer Hospital Affiliated to Medical College of Xi'an Jiaotong University, Xi'an, China
| | - X. Liu
- Department of Clinical Spiritual Care, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - X. Zhou
- Radiotherapy Center, Hubei Cancer Hospital, Wuhan, China
| | - S. He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L. Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - J. Li
- Department of Psycho-oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - B. Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L. Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China,Correspondence to: Dr Lili Tang, Fu-Cheng Road 52, Hai-Dian District, Beijing 100142, China. Tel: +86-1088196648
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