Chenyang Hou1, Yun Wang2, Xinxia Sui2, Wei Xin3, Qingzhi Hou4, Jihu Yi2, Huichen Yao5, Weihua Liu6, Zhiyuan Yu7, Lichuan Xia8, Qing Guo9. 1. Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China. 2. Department of Public Health and Health Management, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, Shandong, China. 3. Provincial Hospital affiliated to Shandong First Medical University, Shandong First Medical University Medical Science and Technology Innovation Center, Jinan, Shandong, China. weixin@mail.sdu.edu.cn. 4. Department of Public Health and Health Management, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, Shandong, China. qingzhihou@163.com. 5. Department of Cardiovascular Medicine, The third affiliated hospital of Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, Shandong, China. 6. School of Nursing, Shandong First Medical University (Shandong Academy of Medical Sciences), Taian, Shandong, China. 7. Centers for Disease Control and Prevention of Rizhao, Rizhao, Shandong, China. 8. Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo, Shandong, China. 9. Department of Nursing, The second affiliated hospital of Shandong first medical university, Taian, Shandong, China.
Abstract
BACKGROUND: Previous observational studies focused on the association of serum magnesium (SMg) and chronic kidney disease (CKD), but the conclusion was inconsistent. To investigate the causal relationship of SMg and CKD, we performed a two-sample mendelian randomization (TSMR) analysis using publicly datasets. METHOD: In mendelian randomization (MR) analysis, we used single nucleotide polymorphisms (SNPs) which had genetic statistical significance with SMg but not associated with kidney function and confounding factors as instrumental variable (IV). To select SNPs, we used publicly database of Genome Wide Association Study (GWAS) and Chronic Kidney Disease Genetics (CKDGen) Confirms. We used inverse-variance weighted (IVW), weighted median, MR-Egger regression, weighted mode, and simple mode approaches in TSMR analysis. RESULTS: We selected 4 SNPs (rs4072037, rs7965584, rs11144134 and rs448378) as IV. In IVW approach, the result of MR analysis for CKD was OR = 0.55, 95% CI: 0.06, 4.75, P = 0.58; for estimated glomerular filtration rate from creatinine (eGFR)crea was β = -0.06, 95% CI: -1.08, 0.07, P = 0.39; for estimated glomerular filtration rate from cystatin C (eGFR)cys was β = -0.03, 95% CI: -0.43, 0.36, P = 0.86, respectively per SD increase in SMg. When subgroup by diabetes mellitus (DM), the results for DM-eGFRcrea was β = -0.33, 95% CI: -0.85, 0.19, P = 0.21; and for non-DM-eGFRcrea was β = -0.03, 95% CI: -0.16, 0.11, P = 0.71. The results of other four MR approaches were consistent with IVW approach (all P > 0.05). CONCLUSION: Our TSMR analysis showed that SMg had no causal effect on kidney function and progress CKD in European descent. As for the results about overall population, the verified study is needed in future study.
BACKGROUND: Previous observational studies focused on the association of serum magnesium (SMg) and chronic kidney disease (CKD), but the conclusion was inconsistent. To investigate the causal relationship of SMg and CKD, we performed a two-sample mendelian randomization (TSMR) analysis using publicly datasets. METHOD: In mendelian randomization (MR) analysis, we used single nucleotide polymorphisms (SNPs) which had genetic statistical significance with SMg but not associated with kidney function and confounding factors as instrumental variable (IV). To select SNPs, we used publicly database of Genome Wide Association Study (GWAS) and Chronic Kidney Disease Genetics (CKDGen) Confirms. We used inverse-variance weighted (IVW), weighted median, MR-Egger regression, weighted mode, and simple mode approaches in TSMR analysis. RESULTS: We selected 4 SNPs (rs4072037, rs7965584, rs11144134 and rs448378) as IV. In IVW approach, the result of MR analysis for CKD was OR = 0.55, 95% CI: 0.06, 4.75, P = 0.58; for estimated glomerular filtration rate from creatinine (eGFR)crea was β = -0.06, 95% CI: -1.08, 0.07, P = 0.39; for estimated glomerular filtration rate from cystatin C (eGFR)cys was β = -0.03, 95% CI: -0.43, 0.36, P = 0.86, respectively per SD increase in SMg. When subgroup by diabetes mellitus (DM), the results for DM-eGFRcrea was β = -0.33, 95% CI: -0.85, 0.19, P = 0.21; and for non-DM-eGFRcrea was β = -0.03, 95% CI: -0.16, 0.11, P = 0.71. The results of other four MR approaches were consistent with IVW approach (all P > 0.05). CONCLUSION: Our TSMR analysis showed that SMg had no causal effect on kidney function and progress CKD in European descent. As for the results about overall population, the verified study is needed in future study.
Authors: Yan Xie; Benjamin Bowe; Ali H Mokdad; Hong Xian; Yan Yan; Tingting Li; Geetha Maddukuri; Cheng-You Tsai; Tasheia Floyd; Ziyad Al-Aly Journal: Kidney Int Date: 2018-08-03 Impact factor: 10.612