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Wang C, Geng L, Hou L. Analysis of carotid ultrasound in a high-stroke-risk population. Medicine (Baltimore) 2024; 103:e40383. [PMID: 39496038 PMCID: PMC11537612 DOI: 10.1097/md.0000000000040383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 10/16/2024] [Indexed: 11/06/2024] Open
Abstract
This study aims to explore the risk factors for carotid plaque (CP) and carotid common artery intima-media thickening (CCAIMT) and clarify the relationship between the risk factors with the number of CPs and the side of CCAIMT in a high-stroke-risk population in Qujing, Yunnan, China. Carotid ultrasonography was performed in 430 participants with high stroke risk, who were divided into different groups according to their ultrasound results. The risk factors and blood biochemical indices were recorded for assessment. The prevalence rates of CP and CCAIMT were 88.1% and 70.5%, respectively. Multivariate logistic regression analysis identified age and lack of physical exercise as risk factors of CP. Compared to participants without CP, participants who performed little physical exercise were prone to have one CP, while participants with risk factors for smoking, older age, and physical inactivity were more likely to have several CPs. Risk factors for CCAIMT were older age, male, and the levels of low density lipoprotein cholesterol. Risk factors for left CCAIMT included a history of hyperlipidemia and low density lipoprotein cholesterol, while male sex was the sole risk factor for right CCAIMT. Finally, male sex and advanced age were identified as risk factors for dual CCAIMT. The research reveals the risk factors for CP and CCAIMT, also clarifies the relationship between the risk factors, CP numbers, and the side of CCAIMT.
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Affiliation(s)
- ChunFang Wang
- Department of Neurology, Second People’s Hospital of Qujing, Qujing, China
| | - Lirong Geng
- Department of Neurology, Second People’s Hospital of Qujing, Qujing, China
| | - Lijun Hou
- Department of Neurology, Second People’s Hospital of Qujing, Qujing, China
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Fu J, Deng Y, Ma Y, Man S, Yang X, Yu C, Lv J, Wang B, Li L. National and Provincial-Level Prevalence and Risk Factors of Carotid Atherosclerosis in Chinese Adults. JAMA Netw Open 2024; 7:e2351225. [PMID: 38206625 PMCID: PMC10784858 DOI: 10.1001/jamanetworkopen.2023.51225] [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] [Received: 08/17/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024] Open
Abstract
Importance Epidemiologic studies on carotid atherosclerosis (CAS) based on nationwide ultrasonography measurements can contribute to understanding the future risk of cardiovascular diseases and identifying high-risk populations, thereby proposing more targeted prevention and treatment measures. Objectives To estimate the prevalence of CAS within the general population of China and to investigate its distribution among populations with potential risk factors and variation across diverse geographic regions. Design, Setting, and Participants This multicenter, population-based cross-sectional study used China's largest health check-up chain database to study 10 733 975 individuals aged 20 years or older from all 31 provinces in China who underwent check-ups from January 1, 2017, to June 30, 2022. Main Outcomes and Measures Carotid atherosclerosis was assessed and graded using ultrasonography as increased carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid stenosis (CS). The overall and stratified prevalences were estimated among the general population and various subpopulations based on demographic characteristics, geographic regions, and cardiovascular disease risk factors. Mixed-effects regression models were used to analyze the risk factors for CAS. Results Among 10 733 975 Chinese participants (mean [SD] age, 47.7 [13.4] years; 5 861 566 [54.6%] male), the estimated prevalences were 26.2% (95% CI, 25.0%-27.4%) for increased cIMT, 21.0% (95% CI, 19.8%-22.2%) for CP, and 0.56% (95% CI, 0.36%-0.76%) for CS. The prevalence of all CAS grades was higher among older adults (eg, increased cIMT: aged ≥80 years, 92.7%; 95% CI, 92.2%-93.3%), male participants (29.6%; 95% CI, 28.4%-30.7%), those residing in northern China (31.0%; 95% CI, 29.1%-32.9%), and those who had comorbid conditions, such as hypertension (50.8%; 95% CI, 49.7%-51.9%), diabetes (59.0%; 95% CI, 57.8%-60.1%), dyslipidemia (32.1%; 95% CI, 30.8%-33.3%), and metabolic syndrome (31.0%; 95% CI, 29.1%-32.9%). Most cardiovascular disease risk factors were independent risk factors for all CAS stages (eg, hypertension: 1.60 [95% CI, 1.60-1.61] for increased cIMT, 1.62 [95% CI, 1.62-1.63] for CP, and 1.48 [95% CI, 1.45-1.51] for CS). Moreover, the magnitude of the association between several cardiovascular disease risk factors and increased cIMT and CP differed between the sexes and geographic regions. Conclusions and Relevance These findings suggest that nearly one-quarter of Chinese adults have increased cIMT or CP. The burden of this disease is unevenly distributed across geographic regions and subpopulations and may require different levels of local planning, support, and management. Addressing these disparities is crucial for effectively preventing and managing cardiovascular and cerebrovascular diseases in China.
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Affiliation(s)
- Jingzhu Fu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuhan Deng
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
| | - Yuan Ma
- Meinian Institute of Health, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Sailimai Man
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Meinian Institute of Health, Beijing, China
| | - Xiaochen Yang
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Bo Wang
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Meinian Institute of Health, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
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Wu Z, Li X, Wen Q, Tao B, Qiu B, Zhang Q, Wang J. Serum LDL-C/HDL-C ratio and the risk of carotid plaques: a longitudinal study. BMC Cardiovasc Disord 2022; 22:501. [PMID: 36434516 PMCID: PMC9700971 DOI: 10.1186/s12872-022-02942-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 11/08/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Dyslipidemia contributes to an increased risk of carotid atherosclerosis. However, the association between the ratio of low-density lipoprotein cholesterol (LDL-C) to high-density lipoprotein cholesterol (HDL-C) and carotid plaque formation has not been well documented. This study aims to assess the role of LDL-C/HDL-C in the risk of carotid plaque formation in a Chinese population. METHODS We followed 2,191 participants who attended the annual routine health examination. Cox proportional hazards regression, restricted cubic spline (RCS), and subgroup analysis were applied to evaluate the association between the LDL-C/HDL-C ratio and carotid plaques. The hazard ratio (HR) and 95% confidence interval (CI) were used to estimate the strength of the association. RESULTS Among 2,191 participants, 388 had incident carotid plaques detected, with a median follow-up time of 1.05 years. Compared with subjects younger than 45 years, those aged 45 to 59 years (HR: 2.00, 95% CI: 1.55-2.58) and over 60 years (HR: 3.36, 95% CI: 2.47-4.58) had an increased risk of carotid plaque formation. Males (HR: 1.26, 95% CI: 1.01-1.56), diabetes (HR: 1.46, 95% CI: 1.06-2.01) and a high LDL-C/HDL-C ratio (HR: 1.22, 95% CI: 1.07-1.38) were significantly linked with the occurrence of carotid plaques. After adjusting for potential confounding factors, we observed that a high LDL-C/HDL-C ratio promoted carotid plaque events (HR: 1.30, 95% CI: 1.12-1.50). The RCS analysis revealed a significant nonlinear association. The association was stronger among females (P-interaction < 0.05). CONCLUSION A high LDL-C/HDL-C ratio could accelerate the occurrence of carotid plaques. Older men with diabetes and dyslipidemia are the critical target population. Women may be more likely to benefit from lipid-lowering interventions and thus avoid carotid plaque formation.
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Affiliation(s)
- Zhuchao Wu
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
| | - Xiaona Li
- grid.412676.00000 0004 1799 0784Department of Health Management Center, the First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, China ,grid.89957.3a0000 0000 9255 8984Department of Health Management, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
| | - Qin Wen
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
| | - Bilin Tao
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
| | - Beibei Qiu
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
| | - Qun Zhang
- grid.412676.00000 0004 1799 0784Department of Health Management Center, the First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, China ,grid.89957.3a0000 0000 9255 8984Department of Health Management, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
| | - Jianming Wang
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 211166 Nanjing, China
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