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Chen L, Chen S, Bai X, Su M, He L, Li G, He G, Yang Y, Zhang X, Cui J, Xu W, Song L, Yang H, He W, Zhang Y, Li X, Hu S. Low-Density Lipoprotein Cholesterol, Cardiovascular Disease Risk, and Mortality in China. JAMA Netw Open 2024; 7:e2422558. [PMID: 39023892 PMCID: PMC11258592 DOI: 10.1001/jamanetworkopen.2024.22558] [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: 02/14/2024] [Accepted: 05/17/2024] [Indexed: 07/20/2024] Open
Abstract
Importance Limited evidence supports the association between low-density lipoprotein cholesterol (LDL-C) and mortality across different atherosclerotic cardiovascular disease (ASCVD) risk stratifications. Objective To explore the associations between LDL-C levels and mortality and to identify the optimal ranges of LDL-C with the lowest risk of mortality in populations with diverse ASCVD risk profiles. Design, Setting, and Participants The ChinaHEART project is a prospective cohort study that recruited residents aged 35 to 75 years from 31 provinces in mainland China between November 2014 and December 2022. Participants were categorized into low-risk, primary prevention, and secondary prevention cohorts on the basis of their medical history and ASCVD risk. Data analysis was performed from December 2022 to October 2023. Main Outcomes and Measures The primary end point was all-cause mortality, and secondary end points included cause-specific mortality. Mortality data were collected from the National Mortality Surveillance System and Vital Registration. The association between LDL-C levels and mortality was assessed by using Cox proportional hazard regression models with various adjusted variables. Results A total of 4 379 252 individuals were recruited, and 3 789 025 (2 271 699 women [60.0%]; mean [SD] age, 56.1 [10.0] years) were included in the current study. The median (IQR) LDL-C concentration was 93.1 (70.9-117.3) mg/dL overall at baseline. During a median (IQR) follow-up of 4.6 (3.1-5.8) years, 92 888 deaths were recorded, including 38 627 cardiovascular deaths. The association between LDL-C concentration and all-cause or cardiovascular disease (CVD) mortality was U-shaped in both the low-risk cohort (2 838 354 participants) and the primary prevention cohort (829 567 participants), whereas it was J-shaped in the secondary prevention cohort (121 104 participants). The LDL-C levels corresponding to the lowest CVD mortality were 117.8 mg/dL in the low-risk group, 106.0 mg/dL in the primary prevention cohort, and 55.8 mg/dL in the secondary prevention cohort. The LDL-C concentration associated with the lowest all-cause mortality (90.9 mg/dL vs 117.0 mg/dL) and CVD mortality (87 mg/dL vs 114.6 mg/dL) were both lower in individuals with diabetes than in individuals without diabetes in the overall cohort. Conclusions and Relevance This study found that the association between LDL-C and mortality varied among different ASCVD risk cohorts, suggesting that stricter lipid control targets may be needed for individuals with higher ASCVD risk and those with diabetes.
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Affiliation(s)
- Liang Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shi Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueke Bai
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingming Su
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Linkang He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guangyu Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guangda He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Yang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianlan Cui
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Xu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijuan Song
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hao Yang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyan He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xi Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Shenzhen, China
- Central China Sub-center of the National Center for Cardiovascular Diseases, Zhengzhou, China
| | - Shengshou Hu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Yang S, Wang X, Li Y, Zhou L, Guo G, Wu M. The association between telomere length and blood lipids: a bidirectional two-sample Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1338698. [PMID: 38863926 PMCID: PMC11165217 DOI: 10.3389/fendo.2024.1338698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/10/2024] [Indexed: 06/13/2024] Open
Abstract
Background Observational studies suggest an association between telomere length (TL) and blood lipid (BL) levels. Nevertheless, the causal connections between these two traits remain unclear. We aimed to elucidate whether genetically predicted TL is associated with BL levels via Mendelian randomization (MR) and vice versa. Methods We obtained genetic instruments associated with TL, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-1 (ApoA-1) and apolipoprotein B (ApoB) from large-scale genome-wide association studies (GWASs). The causal relationships between TL and BL were investigated via bidirectional MR, multivariable MR and mediation analysis methods. The inverse variance weighted (IVW) method was employed as the principal methodology, complemented by several other estimators to enhance the robustness of the analysis. Results In the forward MR analyses, we identified significant positive correlation between genetically predicted TL and the levels of TG (β=0.04, 95% confidence interval [CI]: 0.01 to 0.06, p = 0.003). In the reverse MR analysis, TG (β=0.02, 95% CI: 0.01 to 0.03, p = 0.004), LDL-C (β=0.03, 95% CI: 0.01 to 0.04, p = 0.001) and ApoB (β=0.03, 95% CI: 0.01 to 0.04, p = 9.71×10-5) were significantly positively associated with TL, although this relationship was not observed in the multivariate MR analysis. The mediation analysis via two-step MR showed no significant mediation effects acting through obesity-related phenotypes in analysis of TL with TG, while the effect of LDL-C on TL was partially mediated by body mass index (BMI) in the reverse direction, with mediated proportion of 12.83% (95% CI: 0.62% to 25.04%). Conclusions Our study indicated that longer TL were associated with higher TG levels, while conversely, higher TG, LDL-C, and ApoB levels predicted longer TL, with BMI partially mediating these effects. Our findings present valuable insights into the development of preventive strategies and interventions that specifically target TL-related aging and age-related diseases.
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Affiliation(s)
- Shengjie Yang
- Guang’an men Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xinyue Wang
- Guang’an men Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yujuan Li
- Guang’an men Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lijun Zhou
- Guang’an men Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Gang Guo
- Qilu Hospital of Shandong University, Jinan, China
| | - Min Wu
- Guang’an men Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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Hamilton FW, Hughes DA, Spiller W, Tilling K, Davey Smith G. Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol. Eur J Epidemiol 2024; 39:451-465. [PMID: 38789826 PMCID: PMC11219394 DOI: 10.1007/s10654-024-01113-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 03/07/2024] [Indexed: 05/26/2024]
Abstract
Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.
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Affiliation(s)
- Fergus W Hamilton
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Road, BS8 2PS, Bristol, UK.
- Infection Science, North Bristol NHS Trust, Bristol, UK.
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Road, BS8 2PS, Bristol, UK
| | - Wes Spiller
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Road, BS8 2PS, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Road, BS8 2PS, Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Road, BS8 2PS, Bristol, UK
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Sun Y, Cao D, Zhang Y, Wu Y, Jia Z, Cui Y, Li D, Cao X, Jiang J. Appraising associations between signature lipidomic biomarkers and digestive system cancer risk: novel evidences from a prospective cohort study of UK Biobank and Mendelian randomization analyses. Lipids Health Dis 2024; 23:61. [PMID: 38419059 PMCID: PMC10900802 DOI: 10.1186/s12944-024-02053-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND The roles of serum lipids on digestive system cancer (DSC) risk were still inconclusive. In this study, we systematically assessed indicative effects of signature lipidomic biomarkers (high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)) on DSC (oesophagus, stomach, colorectal, liver, gallbladder, and pancreas cancers) risk. METHODS HDL-C, LDL-C, and TG concentration measurements were respectively analyzed with enzyme immunoinhibition, enzymatic selective protection, and GPO-POD methods in AU5800 supplied from Beckman Coulter. The diagnoses of DSCs were coded using International Classification of Diseases, Tenth Revision (ICD-10) codes updated until October 2022 in the UK Biobank (UKB). In this study, we assessed phenotypic association patterns between signature lipidomic biomarkers and DSC risk using restricted cubic splines (RCSs) in multivariable-adjusted Cox proportional hazards regression models. Moreover, linear and nonlinear causal association patterns of signature lipidomic biomarkers with DSC risk were determined by linear and nonlinear Mendelian randomization (MR) analyses. RESULTS A median follow-up time of 11.8 years was recorded for 319,568 participants including 6916 DSC cases. A suggestive independent nonlinear phenotypic association was observed between LDL-C concentration and stomach cancer risk (Pnonlinearity < 0.05, Poverall < 0.05). Meanwhile, a remarkable independent linear negative phenotypic association was demonstrated between HDL-C concentration and stomach cancer risk (Pnonlinearity > 0.05, Poverall < 0.008 (0.05/6 outcomes, Bonferroni-adjusted P)), and suggestive independent linear positive associations were observed between HDL-C concentration and colorectal cancer risk, and between TG concentration and gallbladder cancer risk (Pnonlinearity > 0.05, Poverall < 0.05). Furthermore, based on nonlinear and linear MR-based evidences, we observed an suggestive independent negative causal association (hazard ratio (HR) per 1 mmol/L increase: 0.340 (0.137-0.843), P = 0.020) between LDL-C and stomach cancer risk without a nonlinear pattern (Quadratic P = 0.901, Cochran Q P = 0.434). Meanwhile, subgroup and stratified MR analyses both supported the category of LDL-C ≥ 4.1 mmol/L was suggestively protective against stomach cancer risk, especially among female participants (HR: 0.789 (0.637-0.977), P = 0.030) and participants aged 60 years or older (HR: 0.786 (0.638-0.969), P = 0.024), and the category of TG ≥ 2.2 mmol/L concluded to be a suggestive risk factor for gallbladder cancer risk in male participants (HR: 1.447 (1.020-2.052), P = 0.038) and participants aged 60 years or older (HR: 1.264 (1.003-1.593), P = 0.047). CONCLUSIONS Our findings confirmed indicative roles of signature lipidomic biomarkers on DSC risk, notably detecting suggestive evidences for a protective effect of high LDL-C concentration on stomach cancer risk, and a detrimental effect of high TG concentration on gallbladder cancer risk among given participants.
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Affiliation(s)
- Yuanlin Sun
- Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Donghui Cao
- Department of Clinical Epidemiology, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Yang Zhang
- Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Yanhua Wu
- Department of Clinical Epidemiology, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Zhifang Jia
- Department of Clinical Epidemiology, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Yingnan Cui
- Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Dongming Li
- Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China
| | - Xueyuan Cao
- Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China.
| | - Jing Jiang
- Department of Clinical Epidemiology, The First Hospital of Jilin University, Changchun, 130021, Jilin Province, China.
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Yang G, Mason AM, Wood AM, Schooling CM, Burgess S. Dose-Response Associations of Lipid Traits With Coronary Artery Disease and Mortality. JAMA Netw Open 2024; 7:e2352572. [PMID: 38241044 PMCID: PMC10799266 DOI: 10.1001/jamanetworkopen.2023.52572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 12/01/2023] [Indexed: 01/22/2024] Open
Abstract
Importance Apolipoprotein B (apoB), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) are associated with coronary artery disease (CAD). However, trial evidence for the association of intensive LDL-C lowering and TG lowering with mortality is less definitive. Objectives To investigate the associations of apoB, LDL-C, and TG with CAD and mortality, both overall and by sex and age, and to characterize the shapes of these associations. Design, Setting, and Participants This genetic association study used linear and nonlinear mendelian randomization (MR) to analyze a population-based cohort of individuals of European ancestry from the UK Biobank, which recruited participants from 2006 to 2010 with follow-up information updated until September 2021. Data analysis occurred from December 2022 to November 2023. Exposures Genetically predicted apoB, LDL-C, and TG. Main Outcomes and Measures The primary outcomes were CAD, all-cause mortality, and cause-specific mortality. Genetic associations with CAD were calculated using logistic regression, associations with all-cause mortality using Cox proportional hazards regression, and associations with cause-specific mortality using cause-specific Cox proportional hazards regression with censoring for other causes of mortality. Results This study included 347 797 participants (mean [SD] age, 57.2 [8.0] years; 188 330 female [54.1%]). There were 23 818 people who developed CAD and 23 848 people who died. Genetically predicted apoB was positively associated with risk of CAD (odds ratio [OR], 1.65 per SD increase; 95% CI 1.57-1.73), all-cause mortality (hazard ratio [HR], 1.11; 95% CI, 1.06-1.16), and cardiovascular mortality (HR, 1.36; 95% CI, 1.24-1.50), with some evidence for larger associations in male participants than female participants. Findings were similar for LDL-C. Genetically predicted TG was positively associated with CAD (OR, 1.60; 95% CI 1.52-1.69), all-cause mortality (HR, 1.08; 95% CI, 1.03-1.13), and cardiovascular mortality (HR, 1.21; 95% CI, 1.09-1.34); however, sensitivity analyses suggested evidence of pleiotropy. The association of genetically predicted TG with CAD persisted but it was no longer associated with mortality outcomes after controlling for apoB. Nonlinear MR suggested that all these associations were monotonically increasing across the whole observed distribution of each lipid trait, with no diminution at low lipid levels. Such patterns were observed irrespective of sex or age. Conclusions and relevance In this genetic association study, apoB (or, equivalently, LDL-C) was associated with increased CAD risk, all-cause mortality, and cardiovascular mortality, all in a dose-dependent way. TG may increase CAD risk independent of apoB, although the possible presence of pleiotropy is a limitation. These insights highlight the importance of apoB (or, equivalently, LDL-C) lowering for reducing cardiovascular morbidity and mortality across its whole distribution.
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Affiliation(s)
- Guoyi Yang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Amy M. Mason
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
| | - Angela M. Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Graduate School of Public Health and Health Policy, City University of New York, New York
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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Liu Y, Li L, Li J, Liu H, Geru A, Wang Y, Li Y, Sia CH, Lip GYH, Yang Q, Zhou X. Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants. Clin Appl Thromb Hemost 2024; 30:10760296241271338. [PMID: 39140863 PMCID: PMC11325470 DOI: 10.1177/10760296241271338] [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: 08/15/2024] Open
Abstract
BACKGROUND Intracranial haemorrhage (ICH) poses a significant threat to patients on Direct Oral Anticoagulants (DOACs), with existing risk scores inadequately predicting ICH risk in these patients. We aim to develop and validate a predictive model for ICH risk in DOAC-treated patients. METHODS 24,794 patients treated with a DOAC were identified in a province-wide electronic medical and health data platform in Tianjin, China. The cohort was randomly split into a 4:1 ratio for model development and validation. We utilized forward stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost) to select predictors. Model performance was compared using the area under the curve (AUC) and net reclassification index (NRI). The optimal model was stratified and compared with the DOAC model. RESULTS The median age is 68.0 years, and 50.4% of participants are male. The XGBoost model, incorporating six independent factors (history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, low-density lipoprotein cholesterol levels), demonstrated superior performance in the development dateset. It showed moderate discrimination (AUC: 0.68, 95% CI: 0.64-0.73), outperforming existing DOAC scores (ΔAUC = 0.063, P = 0.003; NRI = 0.374, P < 0.001). Risk categories significantly stratified ICH risk (low risk: 0.26%, moderate risk: 0.74%, high risk: 5.51%). Finally, the model demonstrated consistent predictive performance in the internal validation. CONCLUSION In a real-world Chinese population using DOAC therapy, this study presents a reliable predictive model for ICH risk. The XGBoost model, integrating six key risk factors, offers a valuable tool for individualized risk assessment in the context of oral anticoagulation therapy.
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Affiliation(s)
- Yuanyuan Liu
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
- Department of Cardiology, Qingzhou People's Hospital, Weifang, Shandong 262500, China
| | - Linjie Li
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Jingge Li
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Hangkuan Liu
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - A Geru
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Yulong Wang
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Yongle Li
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Ching-Hui Sia
- Yong Loo-Lin School of Medicine, National University of Singapore, 1E, Kent, Ridge Road, Singapore 119228, Singapore
- Department of Cardiology, National University Heart Centre, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Qing Yang
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Xin Zhou
- Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China
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Attanasio A, Halasz G, Piepoli MF. Editorial comments: focus on cardiovascular risk in type 2 diabetes mellitus and metabolic disorders. Eur J Prev Cardiol 2023; 30:1167-1169. [PMID: 37669783 DOI: 10.1093/eurjpc/zwad253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Affiliation(s)
- Andrea Attanasio
- Clinical Cardiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
| | - Geza Halasz
- Azienda Ospedaliera San Camillo-Forlanini, Circonvallazione Gianicolense, 87, 00152 Rome, Italy
| | - Massimo F Piepoli
- Clinical Cardiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
- Department of Preventive Cardiology, Wroclaw Medical University, Wroclaw, Poland
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Lütjohann D, Klör HU, Stellaard F. Measurement of Serum Low Density Lipoprotein Cholesterol and Triglyceride-Rich Remnant Cholesterol as Independent Predictors of Atherosclerotic Cardiovascular Disease: Possibilities and Limitations. Nutrients 2023; 15:2202. [PMID: 37432317 DOI: 10.3390/nu15092202] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 07/12/2023] Open
Abstract
The serum low density lipoprotein cholesterol (LDL-C) concentration is the dominant clinical parameter to judge a patient's risk of developing cardiovascular disease (CVD). Recent evidence supports the theory that cholesterol in serum triglyceride-rich lipoproteins (TRLs) contributes significantly to the atherogenic risk, independent of LDL-C. Therefore, combined analysis of both targets and adequate treatment may improve prevention of CVD. The validity of TRL-C calculation is solely dependent on the accuracy of the LDL-C measurement. Direct measurement of serum LDL- C is more accurate than established estimation procedures based upon Friedewald, Martin-Hopkins, or Sampson equations. TRL-C can be easily calculated as total C minus high density lipoprotein C (HDL-C) minus LDL-C. Enhanced serum LDL-C or TRL-C concentrations require different therapeutic approaches to lower the atherogenic lipoprotein C. This review describes the different atherogenic lipoproteins and their possible analytical properties and limitations.
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Affiliation(s)
- Dieter Lütjohann
- Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, 53127 Bonn, Germany
| | - Hans-Ulrich Klör
- Department of Internal Medicine III, University of Gießen, 35392 Gießen, Germany
| | - Frans Stellaard
- Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, 53127 Bonn, Germany
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