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Sun JY, Zhang H, Zhang Y, Wang L, Sun BL, Gao F, Liu G. Impact of serum calcium levels on total body bone mineral density: A mendelian randomization study in five age strata. Clin Nutr 2021; 40:2726-2733. [PMID: 33933738 DOI: 10.1016/j.clnu.2021.03.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 03/01/2021] [Accepted: 03/09/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND & AIMS Mendelian randomization (MR) studies have reported the causal association between serum calcium levels and bone mineral density (BMD). The results showed that genetically increased serum calcium levels in individuals with normal calcium levels did not increase BMD and could even reduce BMD. However, whether there are differences in the association between serum calcium and BMD in different age strata remains unclear. METHODS We selected eight serum calcium genetic variants with genome-wide significance (P < 5.00E-08) as the potential instrumental variables. We conducted an MR analysis to evaluate the impact of serum calcium levels on total body BMD in five age strata, 0-15, 15-30, 30-45, 45-60, and ≥60 years, using large-scale serum calcium (61,079 individuals) and total body BMD genome-wide association study (66,628 individuals) datasets. For pleiotropy analysis, we used a manual method and four common statistical methods, namely the MR-Egger intercept, MR-PRESSO, heterogeneity, and Steiger filtering tests. For MR analysis, we selected four MR methods, namely inverse-variance weighted, weighted median, MR-Egger, and MR-PRESSO. In addition to the univariable MR analysis, we conducted a multivariate MR analysis taking into account the effect of serum parathyroid hormone levels. RESULTS Univariable MR analysis using the inverse-variance weighted method indicated that per 0.5-mg/dL increase (about 1 standard deviation) in serum calcium levels was statistically significantly associated with reduced total body BMD only in the ≥60 years stratum (effect estimate (beta) = -0.545, 95% confidence interval (CI): -0.892 to -0.198, P = 0.002). The weighted median regression (beta = -0.446, 95% CI: -0.821 to -0.094, P = 1.40E-02) and MR-PRESSO (beta = -0.545, 95% CI: -0.892 to -0.198, P = 0.022) MR methods further supported this suggestive association. The multivariable MR analysis also found a significant association between increased serum calcium levels and reduced total body BMD in the ≥60 years stratum (beta = -0.547, 95% CI: -0.934 to -0.16, P = 0.006). CONCLUSIONS Our results provide genetic evidence that increased serum calcium levels did not improve BMD in the general population and that the elevated serum calcium levels in generally healthy populations, especially in adults older than 60 years, may even reduce the BMD. Our results are comparable with those of recent MR findings.
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Affiliation(s)
- Jing-Yi Sun
- Shandong Provincial Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250021, China
| | - Haihua Zhang
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China
| | - Yan Zhang
- Department of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China
| | - Longcai Wang
- Department of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China
| | - Bao-Liang Sun
- Key Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Feng Gao
- Department of Trauma and Emergency Surgeon, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China; Key Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China; National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China; Beijing Key Laboratory of Hypoxia Translational Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
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Si S, Hou L, Chen X, Li W, Liu X, Liu C, Li Y, Yuan T, Li J, Wang B, Li H, Xue F. Exploring the causal roles of circulating remnant lipid profile on cardiovascular and cerebrovascular diseases: Mendelian randomization study. J Epidemiol 2021; 32:205-214. [PMID: 33441507 PMCID: PMC8979919 DOI: 10.2188/jea.je20200305] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Background Causal evidence of circulating lipids especially the remnant cholesterol with cardiovascular and cerebrovascular disease (CVD) is lacking. This research aimed to explore the causal roles of extensive lipid traits especially the remnant lipids in CVD. Methods Two-sample Mendelian randomization (TSMR) analysis was performed based on large-scale meta-analysis datasets in European ancestry. The causal effect of 15 circulating lipid profiles including 6 conventional lipids and 9 remnant lipids on coronary heart disease (CHD) and ischemic stroke (IS), as well as the subtypes, was assessed. Results Apolipoprotein B (Apo B), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) were still important risk factors for CHD and myocardial infarction (MI) but not for IS. Apo B is the strongest which increased the CHD and MI risk by 44% and 41%, respectively. The odds ratios (ORs) of total TG on CHD and MI were 1.25 (95% confidence interval [CI], 1.13–1.38) and 1.24 (95% CI, 1.11–1.38), respectively. A one standard deviation difference increased TG in medium very-low-density lipoproteins (M.VLDL.TG), TG in small VLDL (S.VLDL.TG), TG in very small VLDL (XS.VLDL.TG), TG in intermediate-density lipoproteins (IDL.TG), TG in very large HDL (XL.HDL.TG), and TG in small HDL (S.HDL.TG) particles also robustly increased the risk of CHD and MI by 9–28% and 9–27%, respectively. TG in very/extremely large VLDL (XXL.VLDL.TG and XL.VLDL.TG) were insignificant or even negatively associated with CHD (in multivariable TSMR), and negatively associated with IS as well. Conclusion The remnant lipids presented heterogeneity and two-sided effects for the risk of CHD and IS that may partially rely on the particle size. The findings suggested that the remnant lipids were required to be intervened according to specific components. This research confirms the importance of remnant lipids and provides causal evidence for potential targets for intervention.
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Affiliation(s)
- Shucheng Si
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Lei Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Xiaolu Chen
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Wenchao Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Xinhui Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Congcong Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Yunxia Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Tonghui Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Jiqing Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Bojie Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University
| | - Hongkai Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University.,Institute for Medical Dataology, Shandong University
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University.,Institute for Medical Dataology, Shandong University.,National Institute of Health Data Science of China
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Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits. Cell Syst 2021; 12:5-22. [PMID: 33476553 DOI: 10.1016/j.cels.2020.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/15/2020] [Accepted: 10/07/2020] [Indexed: 02/08/2023]
Abstract
Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.
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Hou L, Li H, Si S, Yu Y, Sun X, Liu X, Yan R, Yu Y, Wang C, Yang F, Wang Q, Xue F. Exploring the causal pathway from bilirubin to CVD and diabetes in the UK biobank cohort study: Observational findings and Mendelian randomization studies. Atherosclerosis 2020; 320:112-121. [PMID: 33485635 DOI: 10.1016/j.atherosclerosis.2020.12.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/15/2020] [Accepted: 12/02/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Some studies reported that mildly elevated serum bilirubin levels were associated with decreased risk of cardiovascular disease (CVD) and diabetes. Whether these are causal relationships remains unclear. This study aims to examine the causal effects of bilirubin on CVD, diabetes and their subtypes. METHODS The data we used in this study includes individual data from the UK Biobank cohort with 331,002 white British participants, and summary data from published genome wide associations studies (GWAS) findings. We used individual data to perform logistic regression for the observational study and two-stage least squares method for the Mendelian randomization (MR) study. We also performed several traditional MR methods and MR-TRYX by summary data. RESULTS The observational study supported the association relationships between bilirubin and CVD and diabetes and their subtypes. Results of MR showed strong evidence for negative causal associations of loge total bilirubin with CVD [OR 0.92, 95%CI 0.88-0.95, p-value 2.15 × 10-6], coronary heart disease [OR 0.90, 95%CI 0.85-0.96, p-value 1.54 × 10-3] and hypertensive diseases [OR 0.91, 95%CI 0.88-0.95, p-value 5.89 × 10-6], but no evidence for diabetes [OR 0.94, 95%CI 0.86-1.02, p-value 0.14] and its subtypes. We also obtained similar results for direct bilirubin. We found that blood pressure, cholesterol, C-reactive protein, alcohol and white blood cell count played important roles in the causal pathway from bilirubin to CVD. Two sample MR and sensitivity analyses showed consistent results with one sample MR. CONCLUSIONS Genetically determined bilirubin was negatively associated with the risk of CVD but had no evident causal association with diabetes in the UK Biobank cohort of white British.
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Affiliation(s)
- Lei Hou
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Hongkai Li
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Shucheng Si
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Yuanyuan Yu
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Xiaoru Sun
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Xinhui Liu
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Ran Yan
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Yifan Yu
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Chuan Wang
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Fan Yang
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Qing Wang
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China
| | - Fuzhong Xue
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China; Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China.
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McMartin A, Conley D. Commentary: Mendelian randomization and education–Challenges remain. Int J Epidemiol 2020; 49:1193-1206. [DOI: 10.1093/ije/dyaa160] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2020] [Indexed: 02/07/2023] Open
Affiliation(s)
- Andrew McMartin
- Department of Sociology and Office of Population Research, Wallace Hall, Princeton University, Princeton, NJ 08540, USA
| | - Dalton Conley
- Department of Sociology and Office of Population Research, Wallace Hall, Princeton University, Princeton, NJ 08540, USA
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2020; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.2] [Citation(s) in RCA: 357] [Impact Index Per Article: 89.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 01/01/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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57
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2019; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.1] [Citation(s) in RCA: 521] [Impact Index Per Article: 104.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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