{"title":"[高雄激素血症检测指标与子痫前期的遗传因果关系分析]。","authors":"Chanyu Lin, Jingbo Chen, Xiaomiao Zhao","doi":"10.12182/20240560106","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Some epidemiological studies have shown that pregnant women who develop preeclampsia (PE) have elevated levels of testosterone in their maternal plasma compared to women with normal blood pressure during pregnancy, revealing a potential association between hyperandrogenism in women and PE. To explore the causal relationship between hyperandrogenism and PE, this study selected total testosterone (TT), bioavailable testosterone (BIOT), and sex hormone binding globulin (SHBG) as exposure factors and PE and chronic hypertension with superimposed PE as disease outcomes. Two-sample Mendelian randomization (MR) analyses were used to genetically dissect the causal relationships between the three exposure factors (TT, BIOT, and SHBG) and the outcomes of PE and chronic hypertension with superimposed PE.</p><p><strong>Methods: </strong>Two independent genome-wide association study (GWAS) databases were used for the two-sample MR analysis. In the GWAS data of female participants from the UK Biobank cohort, single nucleotide polymorphisms (SNPs) associated with TT, BIOT, and SHBG were analyzed, involving 230454, 188507, and 188908 samples, respectively. GWAS data on PE and chronic hypertension with superimposed PE from the Finnish database were used to calculate SNP, involving 3556 PE cases and 114735 controls, as well as 38 cases of chronic hypertension with superimposed PE and 114735 controls. To meet the assumptions of instrumental relevance and independence in MR analysis, SNPs associated with exposure were identified at the genome-wide level (<i>P</i><5.0×10<sup>-8</sup>), and those in linkage disequilibrium interference were excluded based on clustering thresholds of <i>R</i> <sup>2</sup><0.001 and an allele distance greater than 10000 kb. Known confounding factors, including previous PE, chronic kidney disease, chronic hypertension, diabetes, systemic lupus erythematosus, or antiphospholipid syndrome, were also identified and the relevant SNPs were removed. Finally, we extracted the outcome data based on the exposure-related SNPs in the outcome GWAS, integrating exposure and outcome data, and removing palindromic sequences. Five genetic causal analysis methods, including inverse variance-weighted method (IVW), MR-Egger regression, weighted median method, simple mode method, and weighted mode method, were used to infer causal relationships. In the IVW, it was assumed that the selected SNPs satisfied the three assumptions and provided the most ideal estimate of the effect. IVW was consequently used as the primary analysis method in this study. Considering the potential heterogeneity among the instrumental variables, random-effects IVW was used for MR analysis. The results were interpreted using odds ratios (OR) and the corresponding 95% confidence interval (CI) to explain the impact of exposure factors on PE and chronic hypertension with superimposed PE. If the CI did not include 1 and had a <i>P</i> value less than 0.05, the difference was considered statistically significant. Sensitivity analysis was conducted to assess heterogeneity and pleiotropy. Heterogeneity was examined using Cochran's <i>Q</i> test, and pleiotropy was assessed using MR-Egger intercept analysis. Additionally, leave-one-out analysis was conducted to examine whether individual SNPs were driving the causal associations. To further validate the findings, MR analyses were performed using the same methods and outcome variables, but with different exposure factors, including waist-to-hip ratio adjusted for BMI (WHRadjBMI) and 25-hydroxyvitamin D levels, with MR results for WHRadjBMI and PE serving as the positive controls and MR results for 25-hydroxyvitamin D levels and PE as the negative controls.</p><p><strong>Results: </strong>According to the criteria for selecting genetic instrumental variables, 186, 127, and 262 SNPs were identified as genetic instrumental variables significantly associated with testosterone indicators TT, BIOT, and SHBG. MR analysis did not find a causal relationship between the TT, BIOT, and SHBG levels and the risk of developing PE and chronic hypertension with superimposed PE. The IVW method predicted that genetically predicted TT (OR [95% CI]=1.018 [0.897-1.156], <i>P</i>=0.78), BIOT (OR [95% CI]=1.11 [0.874-1.408], <i>P</i>=0.392), and SHBG (OR [95% CI]=0.855 [0.659-1.109], <i>P</i>=0.239) were not associated with PE. Similarly, genetically predicted TT (OR [95% CI]=1.222 [0.548-2.722], <i>P</i>=0.624), BIOT (OR [95% CI]=1.066 [0.242-4.695], <i>P</i>=0.933), and SHBG (OR [95% CI]=0.529 [0.119-2.343], <i>P</i>=0.402) were not significantly associated with chronic hypertension with superimposed PE. Additionally, MR analysis using the MR-Egger method, weighted median method, simple mode method, and weighted mode method yielded consistent results, indicating no significant causal relationship between elevated testosterone levels and PE or chronic hypertension with superimposed PE. Heterogeneity was observed for SHBG in the analysis with PE (Cochran's <i>Q</i> test, <i>P</i>=0.01), and pleiotropy was detected for BIOT in the analysis with PE (MR-Egger intercept analysis, <i>P</i>=0.014), suggesting that the instrumental variables did not affect PE through BIOT. Other instrumental variables did not show significant heterogeneity or pleiotropy. Leave-one-out analysis confirmed that the results of the MR analysis were not driven by individual instrumental variables. Consistent with previous MR studies, the results of the control MR analyses using WHRadjBMI and 25-hydroxyvitamin D levels supported the accuracy of the MR analysis approach and the methods used in this study.</p><p><strong>Conclusion: </strong>The MR analysis results suggest that current genetic evidence does not support a causal relationship between TT, BIOT, and SHBG levels and the development of PE and chronic hypertension with superimposed PE. This study suggests that elevated testosterone may be a risk factor for PE but not a direct cause.</p>","PeriodicalId":39321,"journal":{"name":"四川大学学报(医学版)","volume":"55 3","pages":"566-573"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211795/pdf/","citationCount":"0","resultStr":"{\"title\":\"[Genetic Causation Analysis of Hyperandrogenemia Testing Indicators and Preeclampsia].\",\"authors\":\"Chanyu Lin, Jingbo Chen, Xiaomiao Zhao\",\"doi\":\"10.12182/20240560106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Some epidemiological studies have shown that pregnant women who develop preeclampsia (PE) have elevated levels of testosterone in their maternal plasma compared to women with normal blood pressure during pregnancy, revealing a potential association between hyperandrogenism in women and PE. To explore the causal relationship between hyperandrogenism and PE, this study selected total testosterone (TT), bioavailable testosterone (BIOT), and sex hormone binding globulin (SHBG) as exposure factors and PE and chronic hypertension with superimposed PE as disease outcomes. Two-sample Mendelian randomization (MR) analyses were used to genetically dissect the causal relationships between the three exposure factors (TT, BIOT, and SHBG) and the outcomes of PE and chronic hypertension with superimposed PE.</p><p><strong>Methods: </strong>Two independent genome-wide association study (GWAS) databases were used for the two-sample MR analysis. In the GWAS data of female participants from the UK Biobank cohort, single nucleotide polymorphisms (SNPs) associated with TT, BIOT, and SHBG were analyzed, involving 230454, 188507, and 188908 samples, respectively. GWAS data on PE and chronic hypertension with superimposed PE from the Finnish database were used to calculate SNP, involving 3556 PE cases and 114735 controls, as well as 38 cases of chronic hypertension with superimposed PE and 114735 controls. To meet the assumptions of instrumental relevance and independence in MR analysis, SNPs associated with exposure were identified at the genome-wide level (<i>P</i><5.0×10<sup>-8</sup>), and those in linkage disequilibrium interference were excluded based on clustering thresholds of <i>R</i> <sup>2</sup><0.001 and an allele distance greater than 10000 kb. Known confounding factors, including previous PE, chronic kidney disease, chronic hypertension, diabetes, systemic lupus erythematosus, or antiphospholipid syndrome, were also identified and the relevant SNPs were removed. Finally, we extracted the outcome data based on the exposure-related SNPs in the outcome GWAS, integrating exposure and outcome data, and removing palindromic sequences. Five genetic causal analysis methods, including inverse variance-weighted method (IVW), MR-Egger regression, weighted median method, simple mode method, and weighted mode method, were used to infer causal relationships. In the IVW, it was assumed that the selected SNPs satisfied the three assumptions and provided the most ideal estimate of the effect. IVW was consequently used as the primary analysis method in this study. Considering the potential heterogeneity among the instrumental variables, random-effects IVW was used for MR analysis. The results were interpreted using odds ratios (OR) and the corresponding 95% confidence interval (CI) to explain the impact of exposure factors on PE and chronic hypertension with superimposed PE. If the CI did not include 1 and had a <i>P</i> value less than 0.05, the difference was considered statistically significant. Sensitivity analysis was conducted to assess heterogeneity and pleiotropy. Heterogeneity was examined using Cochran's <i>Q</i> test, and pleiotropy was assessed using MR-Egger intercept analysis. Additionally, leave-one-out analysis was conducted to examine whether individual SNPs were driving the causal associations. To further validate the findings, MR analyses were performed using the same methods and outcome variables, but with different exposure factors, including waist-to-hip ratio adjusted for BMI (WHRadjBMI) and 25-hydroxyvitamin D levels, with MR results for WHRadjBMI and PE serving as the positive controls and MR results for 25-hydroxyvitamin D levels and PE as the negative controls.</p><p><strong>Results: </strong>According to the criteria for selecting genetic instrumental variables, 186, 127, and 262 SNPs were identified as genetic instrumental variables significantly associated with testosterone indicators TT, BIOT, and SHBG. MR analysis did not find a causal relationship between the TT, BIOT, and SHBG levels and the risk of developing PE and chronic hypertension with superimposed PE. The IVW method predicted that genetically predicted TT (OR [95% CI]=1.018 [0.897-1.156], <i>P</i>=0.78), BIOT (OR [95% CI]=1.11 [0.874-1.408], <i>P</i>=0.392), and SHBG (OR [95% CI]=0.855 [0.659-1.109], <i>P</i>=0.239) were not associated with PE. Similarly, genetically predicted TT (OR [95% CI]=1.222 [0.548-2.722], <i>P</i>=0.624), BIOT (OR [95% CI]=1.066 [0.242-4.695], <i>P</i>=0.933), and SHBG (OR [95% CI]=0.529 [0.119-2.343], <i>P</i>=0.402) were not significantly associated with chronic hypertension with superimposed PE. Additionally, MR analysis using the MR-Egger method, weighted median method, simple mode method, and weighted mode method yielded consistent results, indicating no significant causal relationship between elevated testosterone levels and PE or chronic hypertension with superimposed PE. Heterogeneity was observed for SHBG in the analysis with PE (Cochran's <i>Q</i> test, <i>P</i>=0.01), and pleiotropy was detected for BIOT in the analysis with PE (MR-Egger intercept analysis, <i>P</i>=0.014), suggesting that the instrumental variables did not affect PE through BIOT. Other instrumental variables did not show significant heterogeneity or pleiotropy. Leave-one-out analysis confirmed that the results of the MR analysis were not driven by individual instrumental variables. Consistent with previous MR studies, the results of the control MR analyses using WHRadjBMI and 25-hydroxyvitamin D levels supported the accuracy of the MR analysis approach and the methods used in this study.</p><p><strong>Conclusion: </strong>The MR analysis results suggest that current genetic evidence does not support a causal relationship between TT, BIOT, and SHBG levels and the development of PE and chronic hypertension with superimposed PE. This study suggests that elevated testosterone may be a risk factor for PE but not a direct cause.</p>\",\"PeriodicalId\":39321,\"journal\":{\"name\":\"四川大学学报(医学版)\",\"volume\":\"55 3\",\"pages\":\"566-573\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211795/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"四川大学学报(医学版)\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.12182/20240560106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"四川大学学报(医学版)","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.12182/20240560106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
摘要
目的:一些流行病学研究表明,与孕期血压正常的妇女相比,发生子痫前期(PE)的孕妇母体血浆中的睾酮水平升高,这揭示了妇女体内雄激素过多与子痫前期之间的潜在联系。为探讨高雄激素症与 PE 之间的因果关系,本研究选择总睾酮(TT)、生物可用睾酮(BIOT)和性激素结合球蛋白(SHBG)作为暴露因子,PE 和慢性高血压合并 PE 作为疾病结局。通过双样本孟德尔随机化(MR)分析,从遗传学角度剖析了三个暴露因素(TT、BIOT 和 SHBG)与 PE 和慢性高血压(叠加 PE)结果之间的因果关系:方法:使用两个独立的全基因组关联研究(GWAS)数据库进行双样本 MR 分析。在英国生物库队列女性参与者的 GWAS 数据中,分析了与 TT、BIOT 和 SHBG 相关的单核苷酸多态性(SNPs),分别涉及 230454、188507 和 188908 个样本。计算SNP时使用了芬兰数据库中关于PE和慢性高血压叠加PE的GWAS数据,涉及3556个PE病例和114735个对照组,以及38个慢性高血压叠加PE病例和114735个对照组。为了满足MR分析中工具相关性和独立性的假设,在全基因组水平(P-8)上确定了与暴露相关的SNP,并根据R 2P值小于0.05的聚类阈值排除了那些存在连锁不平衡干扰的SNP,差异被认为具有统计学意义。为评估异质性和多义性,进行了敏感性分析。异质性采用 Cochran's Q 检验,多向性采用 MR-Egger 截距分析。此外,还进行了剔除分析,以检查单个 SNP 是否驱动了因果关联。为了进一步验证研究结果,研究人员使用相同的方法和结果变量进行了MR分析,但暴露因素不同,包括根据体重指数调整后的腰臀比(WHRadjBMI)和25-羟维生素D水平,以WHRadjBMI和PE的MR结果作为阳性对照,以25-羟维生素D水平和PE的MR结果作为阴性对照:根据遗传工具变量的选择标准,186、127 和 262 个 SNPs 被确定为与睾酮指标 TT、BIOT 和 SHBG 显著相关的遗传工具变量。MR分析未发现TT、BIOT和SHBG水平与罹患PE和叠加PE的慢性高血压风险之间存在因果关系。根据 IVW 方法预测,遗传预测 TT(OR [95% CI]=1.018 [0.897-1.156],P=0.78)、BIOT(OR [95% CI]=1.11 [0.874-1.408],P=0.392)和 SHBG(OR [95% CI]=0.855 [0.659-1.109],P=0.239)与 PE 无关。同样,基因预测的 TT(OR [95% CI]=1.222 [0.548-2.722],P=0.624)、BIOT(OR [95% CI]=1.066 [0.242-4.695],P=0.933)和 SHBG(OR [95% CI]=0.529 [0.119-2.343],P=0.402)与慢性高血压并发 PE 无显著相关性。此外,使用 MR-Egger 法、加权中位数法、简单模式法和加权模式法进行的 MR 分析也得出了一致的结果,表明睾酮水平升高与 PE 或慢性高血压伴叠加 PE 之间没有明显的因果关系。在与 PE 的分析中,SHBG 出现了异质性(Cochran's Q 检验,P=0.01),在与 PE 的分析中,BIOT 出现了多义性(MR-Egger 截距分析,P=0.014),表明工具变量没有通过 BIOT 影响 PE。其他工具变量没有显示出明显的异质性或多义性。剔除分析证实,MR 分析结果并非由单个工具变量驱动。与以往的 MR 研究一致,使用 WHRadjBMI 和 25-hydroxyvitamin D 水平进行对照 MR 分析的结果支持了 MR 分析方法和本研究所用方法的准确性:MR分析结果表明,目前的遗传学证据并不支持TT、BIOT和SHBG水平与PE和叠加PE的慢性高血压之间存在因果关系。本研究表明,睾酮升高可能是 PE 的一个风险因素,但不是直接原因。
[Genetic Causation Analysis of Hyperandrogenemia Testing Indicators and Preeclampsia].
Objective: Some epidemiological studies have shown that pregnant women who develop preeclampsia (PE) have elevated levels of testosterone in their maternal plasma compared to women with normal blood pressure during pregnancy, revealing a potential association between hyperandrogenism in women and PE. To explore the causal relationship between hyperandrogenism and PE, this study selected total testosterone (TT), bioavailable testosterone (BIOT), and sex hormone binding globulin (SHBG) as exposure factors and PE and chronic hypertension with superimposed PE as disease outcomes. Two-sample Mendelian randomization (MR) analyses were used to genetically dissect the causal relationships between the three exposure factors (TT, BIOT, and SHBG) and the outcomes of PE and chronic hypertension with superimposed PE.
Methods: Two independent genome-wide association study (GWAS) databases were used for the two-sample MR analysis. In the GWAS data of female participants from the UK Biobank cohort, single nucleotide polymorphisms (SNPs) associated with TT, BIOT, and SHBG were analyzed, involving 230454, 188507, and 188908 samples, respectively. GWAS data on PE and chronic hypertension with superimposed PE from the Finnish database were used to calculate SNP, involving 3556 PE cases and 114735 controls, as well as 38 cases of chronic hypertension with superimposed PE and 114735 controls. To meet the assumptions of instrumental relevance and independence in MR analysis, SNPs associated with exposure were identified at the genome-wide level (P<5.0×10-8), and those in linkage disequilibrium interference were excluded based on clustering thresholds of R2<0.001 and an allele distance greater than 10000 kb. Known confounding factors, including previous PE, chronic kidney disease, chronic hypertension, diabetes, systemic lupus erythematosus, or antiphospholipid syndrome, were also identified and the relevant SNPs were removed. Finally, we extracted the outcome data based on the exposure-related SNPs in the outcome GWAS, integrating exposure and outcome data, and removing palindromic sequences. Five genetic causal analysis methods, including inverse variance-weighted method (IVW), MR-Egger regression, weighted median method, simple mode method, and weighted mode method, were used to infer causal relationships. In the IVW, it was assumed that the selected SNPs satisfied the three assumptions and provided the most ideal estimate of the effect. IVW was consequently used as the primary analysis method in this study. Considering the potential heterogeneity among the instrumental variables, random-effects IVW was used for MR analysis. The results were interpreted using odds ratios (OR) and the corresponding 95% confidence interval (CI) to explain the impact of exposure factors on PE and chronic hypertension with superimposed PE. If the CI did not include 1 and had a P value less than 0.05, the difference was considered statistically significant. Sensitivity analysis was conducted to assess heterogeneity and pleiotropy. Heterogeneity was examined using Cochran's Q test, and pleiotropy was assessed using MR-Egger intercept analysis. Additionally, leave-one-out analysis was conducted to examine whether individual SNPs were driving the causal associations. To further validate the findings, MR analyses were performed using the same methods and outcome variables, but with different exposure factors, including waist-to-hip ratio adjusted for BMI (WHRadjBMI) and 25-hydroxyvitamin D levels, with MR results for WHRadjBMI and PE serving as the positive controls and MR results for 25-hydroxyvitamin D levels and PE as the negative controls.
Results: According to the criteria for selecting genetic instrumental variables, 186, 127, and 262 SNPs were identified as genetic instrumental variables significantly associated with testosterone indicators TT, BIOT, and SHBG. MR analysis did not find a causal relationship between the TT, BIOT, and SHBG levels and the risk of developing PE and chronic hypertension with superimposed PE. The IVW method predicted that genetically predicted TT (OR [95% CI]=1.018 [0.897-1.156], P=0.78), BIOT (OR [95% CI]=1.11 [0.874-1.408], P=0.392), and SHBG (OR [95% CI]=0.855 [0.659-1.109], P=0.239) were not associated with PE. Similarly, genetically predicted TT (OR [95% CI]=1.222 [0.548-2.722], P=0.624), BIOT (OR [95% CI]=1.066 [0.242-4.695], P=0.933), and SHBG (OR [95% CI]=0.529 [0.119-2.343], P=0.402) were not significantly associated with chronic hypertension with superimposed PE. Additionally, MR analysis using the MR-Egger method, weighted median method, simple mode method, and weighted mode method yielded consistent results, indicating no significant causal relationship between elevated testosterone levels and PE or chronic hypertension with superimposed PE. Heterogeneity was observed for SHBG in the analysis with PE (Cochran's Q test, P=0.01), and pleiotropy was detected for BIOT in the analysis with PE (MR-Egger intercept analysis, P=0.014), suggesting that the instrumental variables did not affect PE through BIOT. Other instrumental variables did not show significant heterogeneity or pleiotropy. Leave-one-out analysis confirmed that the results of the MR analysis were not driven by individual instrumental variables. Consistent with previous MR studies, the results of the control MR analyses using WHRadjBMI and 25-hydroxyvitamin D levels supported the accuracy of the MR analysis approach and the methods used in this study.
Conclusion: The MR analysis results suggest that current genetic evidence does not support a causal relationship between TT, BIOT, and SHBG levels and the development of PE and chronic hypertension with superimposed PE. This study suggests that elevated testosterone may be a risk factor for PE but not a direct cause.
四川大学学报(医学版)Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
0.70
自引率
0.00%
发文量
8695
期刊介绍:
"Journal of Sichuan University (Medical Edition)" is a comprehensive medical academic journal sponsored by Sichuan University, a higher education institution directly under the Ministry of Education of the People's Republic of China. It was founded in 1959 and was originally named "Journal of Sichuan Medical College". In 1986, it was renamed "Journal of West China University of Medical Sciences". In 2003, it was renamed "Journal of Sichuan University (Medical Edition)" (bimonthly).
"Journal of Sichuan University (Medical Edition)" is a Chinese core journal and a Chinese authoritative academic journal (RCCSE). It is included in the retrieval systems such as China Science and Technology Papers and Citation Database (CSTPCD), China Science Citation Database (CSCD) (core version), Peking University Library's "Overview of Chinese Core Journals", the U.S. "Index Medica" (IM/Medline), the U.S. "PubMed Central" (PMC), the U.S. "Biological Abstracts" (BA), the U.S. "Chemical Abstracts" (CA), the U.S. EBSCO, the Netherlands "Abstracts and Citation Database" (Scopus), the Japan Science and Technology Agency Database (JST), the Russian "Abstract Magazine", the Chinese Biomedical Literature CD-ROM Database (CBMdisc), the Chinese Biomedical Periodical Literature Database (CMCC), the China Academic Journal Network Full-text Database (CNKI), the Chinese Academic Journal (CD-ROM Edition), and the Wanfang Data-Digital Journal Group.