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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
R
2
<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.
本研究采用Cochran's
Q
统计量进行异质性检验，
P
<0.05为差异有统计学意义。采用MR-Egger截距分析来进行多效性检验，
P
<0.05为差异有统计学意义，表明工具变量存在多效性，即工具变量不通过暴露因素影响结局，这表明结果的可靠性受到了多效性的影响。此外，本研究采用留一法检验是否存在单个SNP驱动因果关联。
2. 结果
2.1. 工具变量筛选结果
过滤掉不符合在全基因组水平（
P
<5.0×10
−8
）与暴露相关的SNP，排除存在连锁不平衡的SNP位点，TT、BIOT、SHBG所有SNP中分别有235、156、351个SNP满足相关性假设。经过剔除高血压、糖尿病、类风湿性关节炎等混杂因素相关SNP后，TT、BIOT、SHBG所有SNP中分别有213、138、302个SNP被认定满足独立性假设。
TT的SNP的
F
值范围是23.65～1194.13，BIOT的SNP的
F
值范围是25.44～1060.35，SHBG的SNP的
F
值范围是20.76～1318.22，均大于10，因此不存在弱工具变量。
TT、BIOT、SHBG与PE的随机效应IVW法分析结果显示：遗传预测的TT与PE〔OR（95%CI）=1.018（0.897～1.156），
P
=0.78〕的发病无关；遗传预测的BIOT与PE〔OR（95%CI）=1.11（0.874～1.408），
P
=0.392〕的发病无关；遗传预测的SHBG与子痫前期〔OR（95%CI）=0.855（0.659～1.109），
P
=0.239〕的发病无关。随机效应IVW法分析结果表明高雄激素血症检测指标中的TT、BIOT、SHBG与PE之间没有显著的因果关联。
TT、BIOT、SHBG与CH-PE的随机效应IVW法分析结果显示：遗传预测的TT与CH-PE〔OR（95%CI）=1.222（0.548～2.722），
P
=0.624〕的发病无关；遗传预测的BIOT与CH-PE〔OR（95%CI）=1.066（0.242～4.695），
P
=0.933〕的发病无关；遗传预测的SHBG与CH-PE〔OR（95%CI）=0.529（0.119～2.343），
P
=0.402〕的发病无关。随机效应IVW法分析结果表明高雄激素血症检测指标中的TT、BIOT、SHBG与CH-PE之间没有显著的因果关联。
Scatter plots of MR analysis of TT, BIOT, SHBG, and CH-PE
TT、BIOT、SHBG与CH-PE的MR分析散点图
TT: total testosterone; BIOT: bioavailable testosterone; SHBG: sex hormone-binding globulin; SNP: single nucleotide polymorphism; CH-PE: chronic hypertension with superimposed preeclampsia.
TT: total testosterone; BIOT: bioavailable testosterone; SHBG: sex hormone-binding globulin; IVW: inverse variance weighted; WM: weighted median.
正如预期，阳性对照中，MR结果显示遗传预测的WHRadjBMI与PE风险相关〔OR（95%CI）=1.291（1.047～1.593），
P
=0.017〕；对于阴性对照，MR结果显示遗传预测的25-羟基维生素D水平与PE风险无关〔OR（95%CI）=1.017（0.819～1.263），
P
=0.879〕；见资源附件中附图S1、S2。MR结果显示遗传预测的WHRadjBMI与CH-PE风险无关〔OR（95%CI）=1.145（0.35～3.746），
P
=0.823〕，遗传预测的25-羟基维生素D水平与CH-PE风险无关〔OR（95%CI）=2.165（0.588～7.976），
P
=0.246〕，WHRadjBMI和25-羟基维生素D水平与CH-PE的MR结果参见资源附件中附图S3、附图S4。
2.3. 敏感性分析结果
SHBG与PE的Cochran's
Q
检验结果表明SHBG与PE的MR分析中所使用的SNP存在异质性（
P
<0.05），由于随机效应IVW法选取了随机效应模型，其结果依然具有可靠性。TT、BIOT与PE和TT、BIOT、SHBG与CH-PE的Cochran's
Q
检验结果均提示SNP之间不存在异质性（
P
>0.05），见
表2
。
表 2. Sensitivity analysis.
Sensitivity analysis
敏感性分析
Outcome
Exposure
P
for heterogeneity test
P
for pleiotropy test
TT: total testosterone; BIOT: bioavailable testosterone; SHBG: sex hormone-binding globulin; PE: preeclampsia; CH-PE: chronic hypertension with superimposed preeclampsia.
0.769
0.615
0.078
0.014
0.169
CH-PE
0.906
0.231
0.120
0.576
0.679
0.361
BIOT与PE的MR-Egger截距分析结果表明BIOT与PE的MR分析中所使用的SNP存在多效性（
P
<0.05），即工具变量不通过BIOT而影响PE的发生。TT、SHBG与PE和TT、BIOT、SHBG与CH-PE的MR-Egger截距分析结果均提示不存在多效性（
P
>0.05），见
表2
。通过留一法验证，MR分析的结果不受单个工具变量的干扰，见资源附件中附图S5～附图S10。
Author Contribution
LIN Chanyu is responsible for data curation, formal analysis, investigation, methodology, software, validation, visualization, and writing--review and editing. CHEN Jingbo is responsible for supervision, writing--original draft, and writing--review and editing. ZHAO Xiaomiao is responsible for conceptualization, funding acquisition, project administration, resources, and supervision. All authors consented to the submission of the article to the Journal. All authors approved the final version to be published and agreed to take responsibility for all aspects of the work.
利益冲突
所有作者均声明不存在利益冲突
Declaration of Conflicting Interests
All authors declare no competing interests.
Funding Statement
国家自然科学基金（No. 82271661）资助
Contributor Information
婵余 林 (Chanyu LIN), Email: 1179709605@qq.com.
晓苗 赵 (Xiaomiao ZHAO), Email: zhaoxmiao@163.com.
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