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Sun X, Ma S, Guo Y, Chen C, Pan L, Cui Y, Chen Z, Dijkhuizen RM, Zhou Y, Boltze J, Yu Z, Li P. The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 281:116638. [PMID: 38944013 DOI: 10.1016/j.ecoenv.2024.116638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024]
Abstract
Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40-69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5-10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1-T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4-28.7) μg/m3; NOx, 41.3 (36.2-46.7) μg/m3; PM10, 15.9 (15.4-16.4) μg/m3; PM2.5, 9.9 (9.5-10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0-1.2) per metre; and PM2.5-10, 6.1 (5.9-6.3) μg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5-10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5-10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants.
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Affiliation(s)
- Xiaowei Sun
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Shiyang Ma
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yunlu Guo
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Caiyang Chen
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lijun Pan
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yidan Cui
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zengai Chen
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Johannes Boltze
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Zhangsheng Yu
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Peiying Li
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Outcomes Research Consortium, Cleveland, OH, United States.
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Sun D, Ding Y, Yu C, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Du H, Chen X, Schmidt D, Stevens R, Chen J, Chen Z, Li L, Lv J. Joint impact of polygenic risk score and lifestyles on early- and late-onset cardiovascular diseases. Nat Hum Behav 2024:10.1038/s41562-024-01923-7. [PMID: 38987358 DOI: 10.1038/s41562-024-01923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Understanding the interactions between genetic risk and lifestyles on different types and age onsets of cardiovascular disease (CVD) risk can help identify individuals for whom lifestyle changes would be beneficial. Here we developed three polygenic risk scores, called MetaPRSs, for coronary artery disease, ischaemic stroke and intracerebral haemorrhage by combining PRSs for CVD and CVD-related risk factors in 96,400 participants from the prospective China Kadoorie Biobank. Genetic and lifestyle risks were categorized by the disease-specific MetaPRSs and the number of unfavourable lifestyles. High genetic risk and unfavourable lifestyles were found to be more strongly associated with early than late onset of CVD outcomes in men and women. Change from unfavourable to favourable lifestyles resulted in 14.7-, 2.5- and 2.6-fold greater reductions in incidence rates of early-onset coronary artery disease and ischaemic stroke and late-onset coronary artery disease in high than low genetic risk group. Young adults at high genetic risk may have larger benefits in preventing CVD from lifestyle improvements.
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Pathan N, Kharod MK, Nawab S, Di Scipio M, Paré G, Chong M. Genetic Determinants of Vascular Dementia. Can J Cardiol 2024:S0828-282X(24)00293-9. [PMID: 38579965 DOI: 10.1016/j.cjca.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/20/2024] [Accepted: 03/29/2024] [Indexed: 04/07/2024] Open
Abstract
Vascular dementia (VaD) is a prevalent form of cognitive impairment with underlying vascular etiology. In this review, we examine recent genetic advancements in our understanding of VaD, encompassing a range of methodologies including genome-wide association studies, polygenic risk scores, heritability estimates, and family studies for monogenic disorders revealing the complex and heterogeneous nature of the disease. We report well known genetic associations and highlight potential pathways and mechanisms implicated in VaD and its pathological risk factors, including stroke, cerebral small vessel disease, and cerebral amyloid angiopathy. Moreover, we discuss important modifiable risk factors such as hypertension, diabetes, and dyslipidemia, emphasizing the importance of a multifactorial approach in prevention, treatment, and understanding the genetic basis of VaD. Last, we outline several areas of scientific advancements to improve clinical care, highlighting that large-scale collaborative efforts, together with an integromics approach can enhance the robustness of genetic discoveries. Indeed, understanding the genetics of VaD and its pathophysiological risk factors hold the potential to redefine VaD on the basis of molecular mechanisms and to generate novel diagnostic, prognostic, and therapeutic tools.
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Affiliation(s)
- Nazia Pathan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Muskaan Kaur Kharod
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Sajjha Nawab
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada; Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada; Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada; Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.
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Diao T, Liu K, Lyu J, Zhou L, Yuan Y, Yang H, Wu T, Zhang X. Changes in Sleep Patterns, Genetic Susceptibility, and Incident Cardiovascular Disease in China. JAMA Netw Open 2024; 7:e247974. [PMID: 38652473 DOI: 10.1001/jamanetworkopen.2024.7974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Importance The associations of changes in sleep patterns with incident cardiovascular disease (CVD) are not fully elucidated, and whether these associations are modified by genetic susceptibility remains unknown. Objectives To investigate the associations of 5-year changes in sleep patterns with incident CVD and whether genetic susceptibility modifies these associations. Design, Setting, and Participants This prospective cohort study of the Dongfeng-Tongji cohort was conducted from 2008 to 2018 in China. Eligible participants included those with complete sleep information at baseline survey (2008-2010) and the first follow-up survey (2013); participants who had no CVD or cancer in 2013 were prospectively assessed until 2018. Statistical analysis was performed in November 2023. Exposures Five-year changes in sleep patterns (determined by bedtime, sleep duration, sleep quality, and midday napping) between 2008 and 2013, and polygenic risk scores (PRS) for coronary heart disease (CHD) and stroke. Main Outcomes and Measures Incident CVD, CHD, and stroke were identified from 2013 to 2018. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) and 95% CIs. Results Among 15 306 individuals (mean [SD] age, 65.8 [7.4] years; 8858 [57.9%] female and 6448 male [42.1%]), 5474 (35.78%) had persistent unfavorable sleep patterns and 3946 (25.8%) had persistent favorable sleep patterns. A total of 3669 incident CVD cases were documented, including 2986 CHD cases and 683 stroke cases, over a mean (SD) follow-up of 4.9 (1.5) years. Compared with those with persistent unfavorable sleep patterns, individuals with persistent favorable sleep patterns over 5 years had lower risks of incident CVD (HR, 0.80; 95% CI, 0.73-0.87), CHD (HR, 0.84; 95% CI, 0.76-0.92), and stroke (HR, 0.66; 95% CI, 0.54-0.82) in the subsequent 5-year period. No significant effect modification by PRS was observed for sleep pattern change and CHD or stroke risk. However, sleep pattern changes and PRS were jointly associated with the CHD and stroke risk in a dose-dependent manner, with the lowest risk being among those with persistent favorable sleep patterns combined with low PRS (HR for CHD, 0.65; 95% CI, 0.52-0.82 and HR for stroke, 0.48; 95% CI, 0.29-0.79). Conclusions and Relevance In this cohort study of middle-aged and older Chinese adults, individuals with persistent favorable sleep patterns had a lower CVD risk, even among those with higher genetic risk. These findings highlight the importance of maintaining favorable sleep patterns for CVD prevention.
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Affiliation(s)
- Tingyue Diao
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kang Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Public Health, Guangzhou Medical University, Guangzhou, China
| | - Junrui Lyu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lue Zhou
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Yuan
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Handong Yang
- Department of Cardiovascular Diseases, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Park YS, Jang HM, Park JH, Kim BJ, Park HY, Kim YJ. Evaluating cardiovascular disease risk stratification using multiple-polygenic risk scores and pooled cohort equations: insights from a 17-year longitudinal Korean cohort study. Front Genet 2024; 15:1364993. [PMID: 38606355 PMCID: PMC11007088 DOI: 10.3389/fgene.2024.1364993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, caused by a complex interplay of genetic and environmental factors. This study aimed to evaluate the combined efficacy of multi-polygenic risk scores and pooled cohort equations (PCE) for predicting future CVD risks in the Korean population. In this longitudinal study, 7,612 individuals from the Ansan and Ansung cohorts were analyzed over a 17-year follow-up period. The participants were genotyped using the Korea Biobank Array, and quality-controlled genetic data were subjected to imputation analysis. The weighted sum of the PRSs (wPRSsum) was calculated using PRS-CS with summary statistics from myocardial infarction, ischemic stroke, coronary artery disease, and hypertension genome-wide association studies. The recalibrated PCE was used to assess clinical risk, and the participants were stratified into risk groups based on the wPRSsum and PCE. Associations between these risk scores and incident CVD were evaluated using Cox proportional hazards models and Kaplan-Meier analysis. The wPRSsum approach showed a significant association with incident CVD (HR = 1.15, p = 7.49 × 10-5), and the top 20% high-risk genetic group had an HR of 1.50 (p = 5.04 × 10-4). The recalibrated PCE effectively differentiated between the low and high 10-year CVD risk groups, with a marked difference in survival rates. The predictive models constructed using the wPRSsum and PCE demonstrated a slight improvement in prediction accuracy, particularly among males aged <55 years (C-index = 0.640). We demonstrated that while the integration of wPRSsum with PCE did not significantly outperform the PCE-only model (C-index: 0.703 for combined and 0.704 for PCE-only), it provided enhanced stratification of CVD risk. The highest risk group, identified through the combination of high wPRSsum and PCE scores, exhibited an HR of 4.99 for incident CVD (p = 1.45 × 10-15). These findings highlight the potential of integrating genetic risk assessments with traditional clinical tools for effective CVD risk stratification. Although the addition of wPRSsum to the PCE provided a marginal predictive improvement, it proved valuable in identifying high-risk individuals and supporting personalized treatment strategies. This study reinforces the utility of multi-PRS in conjunction with clinical risk assessment tools, paving the way for more tailored approaches for CVD prevention and management in diverse populations.
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Affiliation(s)
- Yi Seul Park
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Ji Hye Park
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Hyun-Young Park
- National Institute of Health, Cheongju-si, Republic of Korea
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
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Xia C, Xu Y, Li H, He S, Chen W. Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 44:101012. [PMID: 38304718 PMCID: PMC10832505 DOI: 10.1016/j.lanwpc.2024.101012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/18/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024]
Abstract
Background While polygenic risk scores (PRS) could enable the streamlining of organised cancer screening programmes, its current discriminative ability is limited. We conducted a cost-effectiveness analysis to trade-off the benefits and harms of PRS-stratified cancer screening in China. Methods The validated National Cancer Center (NCC) modelling framework for six cancers (lung, liver, breast, gastric, colorectum, and oesophagus) was used to simulate cancer incidence, progression, stage-specific cancer detection, and risk of death. We estimated the number of cancer deaths averted, quality-adjusted life-years (QALY) gained, number needed to screen (NNS), overdiagnosis, and incremental cost-effectiveness ratio (ICER) of one-time PRS-stratified screening strategy (screening 25% of PRS-defined high-risk population) for a birth cohort at age 60 in 2025, compared with unstratified screening strategy (screening 25% of general population) and no screening strategy. We applied lifetime horizon, societal perspective, and 3% discount rate. An ICER less than $18,364 per QALY gained is considered cost-effective. Findings One-time cancer screening for population aged 60 was the most cost-effective strategy compared to screening at other ages. Compared with an unstratified screening strategy, the PRS-stratified screening strategy averted more cancer deaths (61,237 vs. 40,329), had a lower NNS to prevent one death (307 vs. 451), had a slightly higher overdiagnosis (14.1% vs. 13.8%), and associated with an additional 130,045 QALYs at an additional cost of $1942 million, over a lifetime horizon. The ICER for all six cancers combined was $14,930 per QALY gained, with the ICER varying from $7928 in colorectal cancer to $39,068 in liver cancer. ICER estimates were sensitive to changes in risk threshold and cost of PRS tools. Interpretation PRS-stratified screening strategy modestly improves clinical benefit and cost-effectiveness of organised cancer screening programmes. Reducing the costs of polygenic risk stratification is needed before PRS implementation. Funding The Chinese Academy of Medical Sciences, the Jing-jin-ji Special Projects for Basic Research Cooperation, and the Sanming Project of the Medicine in Shenzhen.
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Affiliation(s)
- Changfa Xia
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - He Li
- Office of National Cancer Regional Medical Centre in Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Siyi He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Chappell E, Arbour L, Laksman Z. The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology. J Cardiovasc Dev Dis 2024; 11:56. [PMID: 38392270 PMCID: PMC10888590 DOI: 10.3390/jcdd11020056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
Novel genetic risk markers have helped us to advance the field of cardiovascular epidemiology and refine our current understanding and risk stratification paradigms. The discovery and analysis of variants can help us to tailor prognostication and management. However, populations underrepresented in cardiovascular epidemiology and cardiogenetics research may experience inequities in care if prediction tools are not applicable to them clinically. Therefore, the purpose of this article is to outline the barriers that underrepresented populations can face in participating in genetics research, to describe the current efforts to diversify cardiogenetics research, and to outline strategies that researchers in cardiovascular epidemiology can implement to include underrepresented populations. Mistrust, a lack of diverse research teams, the improper use of sensitive biodata, and the constraints of genetic analyses are all barriers for including diverse populations in genetics studies. The current work is beginning to address the paucity of ethnically diverse genetics research and has already begun to shed light on the potential benefits of including underrepresented and diverse populations. Reducing barriers for individuals, utilizing community-driven research processes, adopting novel recruitment strategies, and pushing for organizational support for diverse genetics research are key steps that clinicians and researchers can take to develop equitable risk stratification tools and improve patient care.
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Affiliation(s)
- Elias Chappell
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Laura Arbour
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada
| | - Zachary Laksman
- Department of Medicine and the School of Biomedical Engineering, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Huang K, Jia J, Liang F, Li J, Niu X, Yang X, Chen S, Cao J, Shen C, Liu X, Yu L, Lu F, Wu X, Zhao L, Li Y, Hu D, Huang J, Liu Y, Gu D, Liu F, Lu X. Fine Particulate Matter Exposure, Genetic Susceptibility, and the Risk of Incident Stroke: A Prospective Cohort Study. Stroke 2024; 55:92-100. [PMID: 38018834 DOI: 10.1161/strokeaha.123.043812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/12/2023] [Indexed: 11/30/2023]
Abstract
BACKGROUND Both genetic factors and environmental air pollution contribute to the risk of stroke. However, it is unknown whether the association between air pollution and stroke risk is influenced by the genetic susceptibilities of stroke and its risk factors. METHODS This prospective cohort study included 40 827 Chinese adults without stroke history. Satellite-based monthly fine particulate matter (PM2.5) estimation at 1-km resolution was used for exposure assessment. Based on 534 identified genetic variants from genome-wide association studies in East Asians, we constructed 6 polygenic risk scores for stroke and its risk factors, including atrial fibrillation, blood pressure, type 2 diabetes, body mass index, and triglyceride. The Cox proportional hazards model was applied to evaluate the hazard ratios and 95% CIs for the associations of PM2.5 and polygenic risk score with incident stroke and the potential effect modifications. RESULTS Over a median follow-up of 12.06 years, 3147 incident stroke cases were documented. Compared with the lowest quartile of PM2.5 exposure, the hazard ratio (95% CI) for stroke in the highest quartile group was 2.72 (2.42-3.06). Among individuals at high genetic risk, the relative risk of stroke was 57% (1.57; 1.40-1.76) higher than those at low genetic risk. Although no statistically significant interaction was found, participants with both the highest PM2.5 and high genetic risk showed the highest risk of stroke, with ≈4× that of the lowest PM2.5 and low genetic risk group (hazard ratio, 3.55 [95% CI, 2.84-4.44]). Similar upward gradients were observed in the risk of stroke when assessing the joint effects of PM2.5 and genetic risks of blood pressure, type 2 diabetes, body mass index, atrial fibrillation, and triglyceride. CONCLUSIONS Long-term exposure to PM2.5 was associated with a higher risk of incident stroke across different genetic susceptibilities. Our findings highlighted the great importance of comprehensive assessment of air pollution and genetic risk in the prevention of stroke.
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Affiliation(s)
- Keyong Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Jiajing Jia
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Fengchao Liang
- School of Public Health and Emergency Management (F. Liang), Southern University of Science and Technology, Shenzhen, China
| | - Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoge Niu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
- Department of Nephrology, Henan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial Clinical Research Center for Kidney Disease, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, China (X.N.)
| | - Xueli Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, China (X.Y.)
| | - Shufeng Chen
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Jie Cao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Chong Shen
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers (C.S.), Chinese Academy of Medical Sciences, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, China (C.S.)
| | - Xiaoqing Liu
- Division of Epidemiology, Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou, China (X. Liu)
| | - Ling Yu
- Department of Cardiology, Fujian Provincial People's Hospital, Fuzhou, China (L.Y.)
| | - Fanghong Lu
- Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, China (F. Lu)
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China (X.W.)
| | - Liancheng Zhao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Ying Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Dongsheng Hu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, China (D.H.)
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, China (D.H.)
| | - Jianfeng Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA (Y. Liu)
| | - Dongfeng Gu
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
- School of Medicine (D.G), Southern University of Science and Technology, Shenzhen, China
| | - Fangchao Liu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (K.H., J.J., J.L., X.N., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu)
- Key Laboratory of Cardiovascular Epidemiology (K.H., J.J., J.L., S.C., J.C., L.Z., Y. Li, J.H., D.G., F. Liu, X. Lu), Chinese Academy of Medical Sciences, Beijing, China
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9
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Li J, Lu X. Cardiovascular risk assessment: The key path toward precision prevention. Chronic Dis Transl Med 2023; 9:273-276. [PMID: 37915392 PMCID: PMC10617312 DOI: 10.1002/cdt3.90] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 11/03/2023] Open
Affiliation(s)
- Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
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10
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Liu H, Li J, Liu F, Huang K, Cao J, Chen S, Li H, Shen C, Hu D, Huang J, Lu X, Gu D. Efficacy and safety of low levels of low-density lipoprotein cholesterol: trans-ancestry linear and non-linear Mendelian randomization analyses. Eur J Prev Cardiol 2023; 30:1207-1215. [PMID: 37040432 DOI: 10.1093/eurjpc/zwad111] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 04/13/2023]
Abstract
AIMS LDL cholesterol (LDL-C) is a well-established risk factor for coronary artery disease (CAD). However, the optimal LDL-C level with regard to efficacy and safety remains unclear. We aimed to investigate the causal relationships between LDL-C and efficacy and safety outcomes. METHODS AND RESULTS We analyzed 353 232 British from the UK Biobank and 41 271 Chinese from the China-PAR project. Linear and non-linear Mendelian randomization (MR) analyses were performed to evaluate the causal relation between genetically proxied LDL-C and CAD, all-cause mortality, and safety outcomes (including haemorrhagic stroke, diabetes mellitus, overall cancer, non-cardiovascular death, and dementia). No significant non-linear associations were observed for CAD, all-cause mortality, and safety outcomes (Cochran Q P > 0.25 in British and Chinese) with LDL-C levels above the minimum values of 50 and 20 mg/dL in British and Chinese, respectively. Linear MR analyses demonstrated a positive association of LDL-C with CAD [British: odds ratio (OR) per unit mmol/L increase, 1.75, P = 7.57 × 10-52; Chinese: OR, 2.06, P = 9.10 × 10-3]. Furthermore, stratified analyses restricted to individuals with LDL-C levels less than the guideline-recommended 70 mg/dL demonstrated lower LDL-C levels were associated with a higher risk of adverse events, including haemorrhagic stroke (British: OR, 0.72, P = 0.03) and dementia (British: OR, 0.75, P = 0.03). CONCLUSION In British and Chinese populations, we confirmed a linear dose-response relationship of LDL-C with CAD and found potential safety concerns at low LDL-C levels, providing recommendations for monitoring adverse events in people with low LDL-C in the prevention of cardiovascular disease.
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Affiliation(s)
- Hongwei Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Jianxin Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Fangchao Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Keyong Huang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Jie Cao
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Shufeng Chen
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Hongfan Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Chong Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Jianfeng Huang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Xiangfeng Lu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Dongfeng Gu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
- School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Avenue, Nanshan District, Shenzhen, 518055, China
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11
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Yang S, Sun Z, Sun D, Yu C, Guo Y, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Lu Y, Burgess S, Avery D, Clarke R, Chen J, Chen Z, Li L, Lv J. Associations of polygenic risk scores with risks of stroke and its subtypes in Chinese. Stroke Vasc Neurol 2023:svn-2023-002428. [PMID: 37640499 DOI: 10.1136/svn-2023-002428] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/11/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND AND PURPOSE Previous studies, mostly focusing on the European population, have reported polygenic risk scores (PRSs) might achieve risk stratification of stroke. We aimed to examine the association strengths of PRSs with risks of stroke and its subtypes in the Chinese population. METHODS Participants with genome-wide genotypic data in China Kadoorie Biobank were split into a potential training set (n=22 191) and a population-based testing set (n=72 150). Four previously developed PRSs were included, and new PRSs for stroke and its subtypes were developed. The PRSs showing the strongest association with risks of stroke or its subtypes in the training set were further evaluated in the testing set. Cox proportional hazards regression models were used to estimate the association strengths of different PRSs with risks of stroke and its subtypes (ischaemic stroke (IS), intracerebral haemorrhage (ICH) and subarachnoid haemorrhage (SAH)). RESULTS In the testing set, during 872 919 person-years of follow-up, 8514 incident stroke events were documented. The PRSs of any stroke (AS) and IS were both positively associated with risks of AS, IS and ICH (p<0.05). The HR for per SD increment (HRSD) of PRSAS was 1.10 (95% CI 1.07 to 1.12), 1.10 (95% CI 1.07 to 1.12) and 1.13 (95% CI 1.07 to 1.20) for AS, IS and ICH, respectively. The corresponding HRSD of PRSIS was 1.08 (95% CI 1.06 to 1.11), 1.08 (95% CI 1.06 to 1.11) and 1.09 (95% CI 1.03 to 1.15). PRSICH was positively associated with the risk of ICH (HRSD=1.07, 95% CI 1.01 to 1.14). PRSSAH was not associated with risks of stroke and its subtypes. The addition of current PRSs offered little to no improvement in stroke risk prediction and risk stratification. CONCLUSIONS In this Chinese population, the association strengths of current PRSs with risks of stroke and its subtypes were moderate, suggesting a limited value for improving risk prediction over traditional risk factors in the context of current genome-wide association study under-representing the East Asian population.
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Affiliation(s)
- Songchun Yang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhijia Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Dong Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yan Lu
- NCDs Prevention and Control Department, Suzhou CDC, Suzhou, Jiangsu, China
| | - Sushila Burgess
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
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12
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Huang Q, Lan X, Chen H, Li H, Sun Y, Ren C, Xing C, Bo X, Wang J, Jin X, Song L. Association between genetic predisposition and disease burden of stroke in China: a genetic epidemiological study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 36:100779. [PMID: 37547044 PMCID: PMC10398595 DOI: 10.1016/j.lanwpc.2023.100779] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/31/2023] [Accepted: 04/17/2023] [Indexed: 08/08/2023]
Abstract
Background Stroke ranks second worldwide and first in China as a leading cause of death and disability. It has a polygenic architecture and is influenced by environmental and lifestyle factors. However, it remains unknown as to whether and how much the genetic predisposition of stroke is associated with disease burden. Methods Allele frequency from the whole genome sequencing data in the Chinese Millionome Database of 141,418 individuals and trait-specific polygenic risk score models were applied to estimate the provincial genetic predisposition to stroke, stroke-related risk factors and stroke-related drug response. Disease burden including mortality, disability-adjusted life years (DALYs), years of life lost(YLLs), years lived with disability (YLDs) and prevalence in China was collected from the Global Burden Disease study. The association between stroke genetic predisposition and the epidemiological burden was assessed and then quantified in both regression-based models and machine learning-based models at a provincial resolution. Findings Among the 30 administrative divisions in China, the genetic predisposition of stroke was characterized by a north-higher-than-south gradient (p < 0.0001). Genetic predisposition to stroke, blood pressure, body mass index, and alcohol use were strongly intercorrelated (rho >0.6; p < 0.05 after Bonferroni correction for each comparison). Genetic risk imposed an independent effect of approximately 1-6% on mortality, DALYs and YLLs. Interpretation The distribution pattern of stroke genetic predisposition is different at a macroscopic level, and it subtly but significantly impacts the epidemiological burden. Further research is warranted to identify the detailed aetiology and potential translation into public health measures. Funding Beijing Municipal Science and Technology Commission (Z191100006619106), CAMS Innovation Fund for Medical Sciences (CAMS-I2M, 2023-I2M-1-001), the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17), National Natural Science Foundation of China (32000398, 32171441 to X.J.), Natural Science Foundation of Guangdong Province, China (2017A030306026 to X.J.), and National Key R&D Program of China (2022YFC2502402).
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Affiliation(s)
- Qiya Huang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xianmei Lan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen, China
| | - Hebing Chen
- Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Hao Li
- Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Yu Sun
- Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Chao Ren
- Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Chao Xing
- Eugene McDermott Center for Human Growth and Development, Department of Bioinformatics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Jizheng Wang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Jin
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- BGI-Shenzhen, Shenzhen, China
| | - Lei Song
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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13
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Li C, Pan Y, Zhang R, Huang Z, Li D, Han Y, Larkin C, Rao V, Sun X, Kelly TN. Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment. Circ Res 2023; 132:1628-1647. [PMID: 37289909 PMCID: PMC10328558 DOI: 10.1161/circresaha.123.321999] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
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Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Davey Li
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Yunan Han
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Claire Larkin
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Varun Rao
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
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14
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Liu J, Wang L, Cui X, Shen Q, Wu D, Yang M, Dong Y, Liu Y, Chen H, Yang Z, Liu Y, Zhu M, Ma H, Jin G, Qian Y. Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study. Nutrients 2023; 15:2144. [PMID: 37432247 DOI: 10.3390/nu15092144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 07/12/2023] Open
Abstract
The aim of this study was to generate a polygenic risk score (PRS) for type 2 diabetes (T2D) and test whether it could be used in identifying high-risk individuals for lifestyle intervention in a Chinese cohort. We genotyped 80 genetic variants among 5024 participants without non-communicable diseases at baseline in the Wuxi Non-Communicable Diseases cohort (Wuxi NCDs cohort). During the follow-up period of 14 years, 440 cases of T2D were newly diagnosed. Using Cox regression, we found that the PRS of 46 SNPs identified by the East Asians was relevant to the future T2D. Participants with a high PRS (top quintile) had a two-fold higher risk of T2D than the bottom quintile (hazard ratio: 2.06, 95% confidence interval: 1.42-2.97). Lifestyle factors were considered, including cigarette smoking, alcohol consumption, physical exercise, diet, body mass index (BMI), and waist circumference (WC). Among high-PRS individuals, the 10-year incidence of T2D slumped from 6.77% to 3.28% for participants having ideal lifestyles (4-6 healthy lifestyle factors) compared with poor lifestyles (0-2 healthy lifestyle factors). When integrating the high PRS, the 10-year T2D risk of low-clinical-risk individuals exceeded that of high-clinical-risk individuals with a low PRS (3.34% vs. 2.91%). These findings suggest that the PRS of 46 SNPs could be used in identifying high-risk individuals and improve the risk stratification defined by traditional clinical risk factors for T2D. Healthy lifestyles can reduce the risk of a high PRS, which indicates the potential utility in early screening and precise prevention.
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Affiliation(s)
- Jia Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Xuan Cui
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qian Shen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Dun Wu
- College of Arts and Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Man Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yunqiu Dong
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yongchao Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Hai Chen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Zhijie Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yaqi Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yun Qian
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
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Integrating polygenic and clinical risks to improve stroke risk stratification in prospective Chinese cohorts. SCIENCE CHINA. LIFE SCIENCES 2023:10.1007/s11427-022-2280-3. [PMID: 36881318 DOI: 10.1007/s11427-022-2280-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 01/13/2023] [Indexed: 03/08/2023]
Abstract
The utility of the polygenic risk score (PRS) to identify individuals at higher risk of stroke beyond clinical risk remains unclear, and we clarified this using Chinese population-based prospective cohorts. Cox proportional hazards models were used to estimate the 10-year risk, and Fine and Gray's models were used for hazard ratios (HRs), their 95% confidence intervals (CIs), and the lifetime risk according to PRS and clinical risk categories. A total of 41,006 individuals aged 30-75 years with a mean follow-up of 9.0 years were included. Comparing the top versus bottom 5% of the PRS, the HR was 3.01 (95%CI 2.03-4.45) in the total population, and similar findings were observed within clinical risk strata. Marked gradients in the 10-year and lifetime risk across PRS categories were also found within clinical risk categories. Notably, among individuals with intermediate clinical risk, the 10-year risk for those in the top 5% of the PRS (7.3%, 95%CI 7.1%-7.5%) reached the threshold of high clinical risk (⩾7.0%) for initiating preventive treatment, and this effect of the PRS on refining risk stratification was evident for ischemic stroke. Even among those in the top 10% and 20% of the PRS, the 10-year risk would also exceed this level when aged ⩾50 and ⩾60 years, respectively. Overall, the combination of the PRS with the clinical risk score improved the risk stratification within clinical risk strata and distinguished actual high-risk individuals with intermediate clinical risk.
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Xia X, Zhang Y, Wei Y, Wang MH. Statistical Methods for Disease Risk Prediction with Genotype Data. Methods Mol Biol 2023; 2629:331-347. [PMID: 36929084 DOI: 10.1007/978-1-0716-2986-4_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.
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Affiliation(s)
- Xiaoxuan Xia
- JC School of Public Health and Primary Care, the Chinese University of Hong Kong (CUHK), Shatin, Hong Kong
- Department of Statistics, the Chinese University of Hong Kong (CUHK), Shatin, Hong Kong
| | | | - Yingying Wei
- Department of Statistics, the Chinese University of Hong Kong (CUHK), Shatin, Hong Kong
| | - Maggie Haitian Wang
- JC School of Public Health and Primary Care, the Chinese University of Hong Kong (CUHK), Shatin, Hong Kong.
- CUHK Shenzhen Institute, Shenzhen, China.
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Zhang Y, Qiu X, Jin Q, Ji C, Yuan P, Cui M, Zhang J, Chen L. Influencing factors of home exercise adherence in elderly patients with stroke: A multiperspective qualitative study. Front Psychiatry 2023; 14:1157106. [PMID: 37091695 PMCID: PMC10113471 DOI: 10.3389/fpsyt.2023.1157106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/06/2023] [Indexed: 04/25/2023] Open
Abstract
Introduction Evidence has shown that stroke exercise rehabilitation is the most effective way to improve disease prognosis, but home exercise adherence in elderly patients with stroke is low due to they are more likely to have movement disorders, cognitive disorders, mental disorders, etc. Currently, most studies on exercise adherence in elderly patients with stroke are quantitative, and there is a lack of qualitative studies from the perspective of patients, caregivers, and medical staff. Considering the importance of home exercise adherence in elderly patients with stroke, the present study aimed to explore the influencing factors of home exercise adherence in them and summarize the potential ways to improve it. Methods From October to December 2022, 9 medical staff, 12 elderly patients with stroke and 7 caregivers from a level A tertiary hospital and community health service center in Nanjing, Jiangsu Province were selected by the purposive sampling and were interviewed in a face-to-face semi-structured way. The data were analyzed and summarized by the phenomenological analysis of Colaizzi's method. Results The influencing factors of home exercise adherence in elderly patients with stroke can be summarized into 3 themes and 8 subthemes. These were individual factors (physical impairment, exercise self-efficacy, and depression), family factors (caregiving ability and emotional support); and stroke rehabilitation environment (exercise prescription, monitoring and feedback, and organizational policy). Conclusion Home exercise adherence in elderly patients with stroke was influenced by many factors. Medical staff should assess the patient's physical function and depression, establish a multi-support system, formulate personalized exercise prescription, pay attention to the monitoring and feedback of home-based exercise rehabilitation, and improve the home-based rehabilitation model for stroke, so as to improve the home exercise adherence in elderly patients with stroke and promote the best rehabilitation effect.
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Affiliation(s)
- Yuanxing Zhang
- Department of Neurosurgery, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xichenhui Qiu
- Health Science Center, Shenzhen University, Shenzhen, China
| | - Qiansheng Jin
- Department of Neurosurgery, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Cuiling Ji
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Ping Yuan
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Mengjiao Cui
- Department of Neurosurgery, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Juanjuan Zhang
- Department of Dermatology, Liuyang Hospital of Traditional Chinese Medicine, The Second Affiliated Hospital of Integrated Traditional Chinese and Western Medicine of Hunan University of Chinese Medicine, Changsha, Hunan, China
- *Correspondence: Juanjuan Zhang,
| | - Lu Chen
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
- Lu Chen,
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Wang Y, Qiu L, Wang Y, He Z, Lan X, Cui L, Wang Y. Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram. Front Med (Lausanne) 2022; 9:936249. [PMID: 36530894 PMCID: PMC9747750 DOI: 10.3389/fmed.2022.936249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/15/2022] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Stroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables. MATERIALS AND METHODS A total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed. RESULTS The genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability. CONCLUSION The pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.
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Affiliation(s)
- Yaxuan Wang
- Department of Anesthesiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Luying Qiu
- Department of Neurology, Key Laboratory for Neurological Big Data of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yuye Wang
- Department of Neurology, Key Laboratory for Neurological Big Data of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhiyi He
- Department of Neurology, Key Laboratory for Neurological Big Data of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xue Lan
- School of Health Management, China Medical University, Shenyang, China
| | - Lei Cui
- School of Health Management, China Medical University, Shenyang, China
| | - Yanzhe Wang
- Department of Neurology, Key Laboratory for Neurological Big Data of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
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Wang J, Li J, Liu F, Huang K, Yang X, Liu X, Cao J, Chen S, Shen C, Yu L, Lu F, Zhao L, Li Y, Hu D, Huang J, Gu D, Lu X. Genetic Predisposition, Fruit Intake and Incident Stroke: A Prospective Chinese Cohort Study. Nutrients 2022; 14:nu14235056. [PMID: 36501087 PMCID: PMC9740837 DOI: 10.3390/nu14235056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
The aim of this study was to evaluate the association between fruit intake and stroke risk considering the genetic predisposition. We used data from 34,871 participants from the project of Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR project) from 2007 to 2020. A polygenic risk score comprising 534 genetic variants associated with stroke and its related factors was constructed to categorize individuals into low, intermediate, and high genetic risk groups. The associations of genetic and fruit intake with incident stroke were assessed by the Cox proportional hazard regression. We documented 2586 incident strokes during a median follow-up of 11.2 years. Compared with fruit intake < 200 g/week, similar relative risk reductions in stroke with adherence to fruit intake > 100 g/day across the genetic risk categories were observed (28−32%), but the absolute risk reductions were relatively larger in the highest genetic risk group (p for trend = 0.03). In comparison to those with a fruit intake < 200 g/week, those with a fruit intake >100 g/day in the low, intermediate, and high genetic risk groups had an average of 1.45 (95% CI, 0.61−2.31), 2.12 (1.63−2.59), and 2.19 (1.13−3.22) additional stroke-free years at aged 35, respectively. Our findings suggest that individuals with a high genetic risk could gain more absolute risk reductions and stroke-free years than those with a low genetic risk from increasing fruit intake for the stroke primary prevention.
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Affiliation(s)
- Jun Wang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Fangchao Liu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Keyong Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Xueli Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
| | - Xiaoqing Liu
- Division of Epidemiology, Guangdong Provincial People’s Hospital and Cardiovascular Institute, Guangzhou 510080, China
| | - Jie Cao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Shufeng Chen
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Chong Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ling Yu
- Department of Cardiology, Fujian Provincial Hospital, Fuzhou 350014, China
| | - Fanghong Lu
- Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan 250062, China
| | - Liancheng Zhao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Ying Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen 518071, China
| | - Jianfeng Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Dongfeng Gu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- Correspondence: ; Tel./Fax: +86-10-60866599
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20
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Tian T, Li Y, Li J, Zhang G, Wang J, Wan C, Fang J, Wu D, Zhou Y, Qin Y, Zhu H, Liu D, Zhu W. Genetic influence on brain volume alterations related to self-reported childhood abuse. Front Neurosci 2022; 16:1019718. [PMID: 36203798 PMCID: PMC9530554 DOI: 10.3389/fnins.2022.1019718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
As an important predictor of adulthood psychopathology, self-reported childhood abuse appears heritable and is associated with brain abnormalities. However, the specific genetic mechanisms behind these brain alterations remain largely unknown. This study recruited young adults who reported different degrees of childhood abuse from the community. In order to fully understand the influence of genes on brain changes related to self-reported childhood abuse, various experiments were conducted in this study. Firstly, volume changes of gray matter and white matter related to childhood abuse were investigated by using advanced magnetic resonance imaging techniques. After sequencing the whole exons, we further investigated the relationship between polygenic risk score, brain volume alterations, and childhood abuse score. Furthermore, transcription-neuroimaging association analysis was used to identify risk genes whose expressions were associated with brain volume alterations. The gray matter volumes of left caudate and superior parietal lobule, and white matter volumes of left cerebellum and right temporal lobe-basal ganglia region were significantly correlated with the childhood abuse score. More importantly, brain volume changes mediated the influence of polygenic risk on self-reported childhood abuse. Additionally, transcription-neuroimaging association analysis reported 63 risk genes whose expression levels were significantly associated with childhood abuse-related brain volume changes. These genes are involved in multiple biological processes, such as nerve development, synaptic transmission, and cell construction. Combining data from multiple perspectives, our work provides evidence of brain abnormalities associated with childhood abuse, and further indicates that polygene genetic risk and risk gene expression may affect the occurrence of childhood abuse by brain regulation, which provides insights into the molecularpathology and neuromechanism of childhood adversity. Paying attention to the physical and mental health of high-risk children may be a fundamental way to prevent childhood abuse and promote lifelong mental health.
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21
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Hu C, Huang C, Li J, Liu F, Huang K, Liu Z, Yang X, Liu X, Cao J, Chen S, Li H, Shen C, Yu L, Wu X, Li Y, Hu D, Huang J, Lu X, Gu D. Causal associations of alcohol consumption with cardiovascular diseases and all-cause mortality among Chinese males. Am J Clin Nutr 2022; 116:771-779. [PMID: 35687413 DOI: 10.1093/ajcn/nqac159] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 06/06/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The causal effects of moderate alcohol consumption on cardiovascular diseases (CVDs) are continuously debated, especially on coronary artery disease (CAD). OBJECTIVES We aimed to explore the causal associations of alcohol consumption with CVDs and all-cause mortality among Chinese males. METHODS A prospective cohort study was conducted in 40,386 Chinese males, with 17,676 being genotyped for the rs671 variant in the aldehyde dehydrogenase 2 (ALDH2) gene. A Cox proportional hazards model was conducted to estimate the effects of self-reported alcohol consumption. Mendelian randomization (MR) analysis was performed to explore the causality using rs671 as an instrumental variable. RESULTS During the follow-up of 303,353 person-years, 2406 incident CVDs and 3195 all-cause mortalities were identified. J-shaped associations of self-reported alcohol consumption with incident CVD and all-cause mortality were observed, showing decreased risks for light (≤25 g/d) and moderate drinkers (25-≤60 g/d). However, MR analyses revealed a linear association of genetically predicted alcohol consumption with the incident CVD (P-trend = 0.02), including both CAD (P-trend = 0.03) and stroke (P-trend = 0.02). The HRs (95% CIs) for incident CVD across increasing tertiles of genetically predicted alcohol consumption were 1 (reference), 1.18 (1.01, 1.38), and 1.22 (1.03, 1.46). After excluding heavy drinkers, the risk of incident CVD and all-cause mortality was increased by 27% and 20% per standard drink increment of genetically predicted alcohol consumption, respectively. CONCLUSIONS Our analyses extend the evidence of the harmful effect of alcohol consumption to total CVD (including CAD) and all-cause mortality, highlighting the potential health benefits of lowering alcohol consumption, even among light-to-moderate male drinkers.
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Affiliation(s)
- Chunyu Hu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunyan Huang
- Department of Chronic Disease Prevention and Control, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Jianxin Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fangchao Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Keyong Huang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhongying Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueli Yang
- Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xiaoqing Liu
- Division of Epidemiology, Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou, China
| | - Jie Cao
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shufeng Chen
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongfan Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chong Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ling Yu
- Department of Cardiology, Fujian Provincial Hospital, Fuzhou, China
| | - Xigui Wu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China.,Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, China
| | - Jianfeng Huang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangfeng Lu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongfeng Gu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,School of Medicine, Southern University of Science and Technology, Shenzhen, China
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22
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Ding X, Fang W, Yuan X, Seery S, Wu Y, Chen S, Zhou H, Wang G, Li Y, Yuan X, Wu S. Associations Between Healthy Lifestyle Trajectories and the Incidence of Cardiovascular Disease With All-Cause Mortality: A Large, Prospective, Chinese Cohort Study. Front Cardiovasc Med 2021; 8:790497. [PMID: 34988131 PMCID: PMC8720765 DOI: 10.3389/fcvm.2021.790497] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Lifestyles generally change across the life course yet no prospective study has examined direct associations between healthy lifestyle trajectories and subsequent cardiovascular disease (CVD) or all-cause mortality risk. Methods: Healthy lifestyle score trajectories during 2006-2007, 2008-2009, and 2010-2011 were collated through latent mixture modeling. An age-scale based Cox proportional hazard regression model was implemented to calculate hazard ratios (HR) with corresponding 95% confidence intervals (CI) for developing CVD or all-cause mortality across healthy lifestyle trajectories. Results: 52,248 participants were included with four distinct trajectories identified according to healthy lifestyle scores over 6 years i.e., low-stable (n = 11,248), high-decreasing (n = 7,374), low-increasing (n = 7,828), and high-stable (n = 25,799). Compared with the low-stable trajectory, the high-stable trajectory negatively correlated with lower subsequent risk of developing CVD (HR, 0.73; 95% CI, 0.65-0.81), especially stroke (HR, 0.70; 95% CI, 0.62-0.79), and all-cause mortality (HR, 0.89; 95% CI, 0.80-0.99) under a multivariable-adjusted model. A protective effect for CVD events was observed only in men and in those without diabetes, while a reduced risk of all-cause mortality was observed only in those older than 60 years, though interactions were not statistically significant. Marginally significant interactions were observed between the changing body mass index (BMI) group, healthy lifestyle score trajectories and stratified analysis. This highlighted an inverse correlation between the high-stable trajectory and CVD in BMI decreased and stable participants as well as all-cause mortality in the stable BMI group. The low-increasing trajectory also had reduced risk of CVD only when BMI decreased and in all-cause mortality only when BMI was stable. Conclusions: Maintaining a healthy lifestyle over 6 years corresponds with a 27% lower risk of CVD and an 11% lower risk in all-cause mortality, compared with those engaging in a consistently unhealthy lifestyle. The benefit of improving lifestyle could be gained only after BMI change is considered further. This study provides further evidence from China around maintaining/improving healthy lifestyles to prevent CVD and early death.
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Affiliation(s)
- Xiong Ding
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Wei Fang
- Shantou University Medical College, Shantou, China
| | - Xiaojie Yuan
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, China
| | - Samuel Seery
- Division of Health Research, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom
| | - Ying Wu
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Shuohua Chen
- Department of Cardiology, Kailuan General Hospital, Tangshan, China
| | - Hui Zhou
- College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan, China
| | - Guodong Wang
- Department of Cardiology, Kailuan General Hospital, Tangshan, China
| | - Yun Li
- School of Public Health, North China University of Science and Technology, Tangshan, China
- Yun Li
| | - Xiaodong Yuan
- Department of Neurosurgery, Kailuan General Hospital, Tangshan, China
- Xiaodong Yuan
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Tangshan, China
- *Correspondence: Shouling Wu
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