Chinese General Practice ›› 2025, Vol. 28 ›› Issue (33): 4159-4165.DOI: 10.12114/j.issn.1007-9572.2024.0395
• Original Research • Previous Articles Next Articles
Received:
2024-09-05
Revised:
2025-01-15
Published:
2025-11-20
Online:
2025-09-17
Contact:
XU Jixiong
通讯作者:
徐积兄
作者简介:
作者贡献:
张书岑提出主要研究目标,负责研究的构思与设计,研究的实施,进行数据的收集与整理,统计学处理,图、表的绘制与展示,撰写论文;徐积兄负责文章的质量控制与审查,对文章整体负责,监督管理。
基金资助:
CLC Number:
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URL: https://www.chinagp.net/EN/10.12114/j.issn.1007-9572.2024.0395
特征 | 数据库 | 种族 | 病例组(例) | 对照组(例) | 样本量(例) | 数据库网址 |
---|---|---|---|---|---|---|
多发性硬化症 | 国际多发性硬化症遗传学联盟 | 欧洲 | 47 429 | 68 374 | 115 803 | http://gwas.mrcieu.ac.uk/ |
自身免疫性甲状腺功能亢进症 | 芬兰数据库 | 欧洲 | 1 991 | 305 175 | 307 166 | www.finngen.fi/en |
Graves病 | 芬兰数据库 | 欧洲 | 3 176 | 409 005 | 412 181 | www.finngen.fi/en |
自身免疫性甲状腺炎 | 芬兰数据库 | 欧洲 | 539 | 349 717 | 350 256 | www.finngen.fi/en |
Table 1 Data sources for exposure factors and outcome variables
特征 | 数据库 | 种族 | 病例组(例) | 对照组(例) | 样本量(例) | 数据库网址 |
---|---|---|---|---|---|---|
多发性硬化症 | 国际多发性硬化症遗传学联盟 | 欧洲 | 47 429 | 68 374 | 115 803 | http://gwas.mrcieu.ac.uk/ |
自身免疫性甲状腺功能亢进症 | 芬兰数据库 | 欧洲 | 1 991 | 305 175 | 307 166 | www.finngen.fi/en |
Graves病 | 芬兰数据库 | 欧洲 | 3 176 | 409 005 | 412 181 | www.finngen.fi/en |
自身免疫性甲状腺炎 | 芬兰数据库 | 欧洲 | 539 | 349 717 | 350 256 | www.finngen.fi/en |
暴露 | 结局 | Nsnp(个) | 方法 | β | SE | OR(95%CI) | P值 | PFDR值 | Q-pval值 | 水平多效性检验 | |
---|---|---|---|---|---|---|---|---|---|---|---|
PMR-Egger值 | PMR-Presso值 | ||||||||||
MS | AIH | 60 | 逆方差加权法 | 0.072 | 0.043 | 1.074(0.988~1.168) | 0.094 | 0.140 | 5.536×10-8 | 0.089 | — |
MR-Egger回归法 | -0.024 | 0.070 | 0.976(0.852~1.119) | 0.073 | 0.790 | ||||||
加权中位数法 | 0.076 | 0.054 | 1.079(0.972~1.199) | 0.155 | 0.232 | ||||||
加权模型 | 0.130 | 0.111 | 1.138(0.917~1.414) | 0.246 | 0.369 | ||||||
稳健校正轮廓评分 | 0.112 | 0.036 | 1.119(1.043~1.200) | 0.002 | 0.005 | ||||||
去偏反方差加权法 | 0.073 | 0.040 | 1.076(0.995~1.163) | 0.067 | 0.100 | ||||||
校正最大似然法-多效性分析 | 0.151 | 0.049 | 1.163(1.056~1.282) | 0.002 | 0.007 | ||||||
MS | GD | 57 | 逆方差加权法 | 0.038 | 0.037 | 1.039(0.966~1.116) | 0.303 | 0.303 | 1.739×10-10 | 0.262 | — |
MR-Egger回归法 | -0.016 | 0.060 | 0.984(0.875~1.107) | 0.790 | 0.790 | ||||||
加权中位数法 | 0.068 | 0.044 | 1.070(0.981~1.168) | 0.126 | 0.232 | ||||||
加权模型 | 0.108 | 0.071 | 1.114(0.968~1.281) | 0.137 | 0.369 | ||||||
稳健校正轮廓评分 | 0.080 | 0.029 | 1.083(1.023~1.146) | 0.006 | 0.009 | ||||||
去偏反方差加权法 | 0.039 | 0.034 | 1.039(0.973~1.110) | 0.254 | 0.254 | ||||||
校正最大似然法-多效性分析 | 0.104 | 0.046 | 1.110(1.015~1.214) | 0.023 | 0.023 | ||||||
MS | AIT | 62 | 逆方差加权法 | 0.134 | 0.054 | 1.144(1.029~1.272) | 0.013 | 0.039 | 0.356 | 0.359 | 0.349 |
MR-Egger回归法 | 0.068 | 0.090 | 1.070(0.897~1.277) | 0.454 | 0.790 | ||||||
加权中位数法 | 0.086 | 0.088 | 1.090(0.916~1.296) | 0.332 | 0.332 | ||||||
加权模型 | 0.031 | 0.083 | 1.031(0.876~1.213) | 0.714 | 0.714 | ||||||
稳健校正轮廓评分 | 0.131 | 0.055 | 1.139(1.023~1.275) | 0.018 | 0.018 | ||||||
去偏反方差加权法 | 0.137 | 0.054 | 1.146(1.031~1.275) | 0.012 | 0.036 | ||||||
校正最大似然法-多效性分析 | 0.131 | 0.056 | 1.140(1.022~1.273) | 0.019 | 0.023 |
Table 2 Positive MR analysis of multiple sclerosis on autoimmune thyroid diseases
暴露 | 结局 | Nsnp(个) | 方法 | β | SE | OR(95%CI) | P值 | PFDR值 | Q-pval值 | 水平多效性检验 | |
---|---|---|---|---|---|---|---|---|---|---|---|
PMR-Egger值 | PMR-Presso值 | ||||||||||
MS | AIH | 60 | 逆方差加权法 | 0.072 | 0.043 | 1.074(0.988~1.168) | 0.094 | 0.140 | 5.536×10-8 | 0.089 | — |
MR-Egger回归法 | -0.024 | 0.070 | 0.976(0.852~1.119) | 0.073 | 0.790 | ||||||
加权中位数法 | 0.076 | 0.054 | 1.079(0.972~1.199) | 0.155 | 0.232 | ||||||
加权模型 | 0.130 | 0.111 | 1.138(0.917~1.414) | 0.246 | 0.369 | ||||||
稳健校正轮廓评分 | 0.112 | 0.036 | 1.119(1.043~1.200) | 0.002 | 0.005 | ||||||
去偏反方差加权法 | 0.073 | 0.040 | 1.076(0.995~1.163) | 0.067 | 0.100 | ||||||
校正最大似然法-多效性分析 | 0.151 | 0.049 | 1.163(1.056~1.282) | 0.002 | 0.007 | ||||||
MS | GD | 57 | 逆方差加权法 | 0.038 | 0.037 | 1.039(0.966~1.116) | 0.303 | 0.303 | 1.739×10-10 | 0.262 | — |
MR-Egger回归法 | -0.016 | 0.060 | 0.984(0.875~1.107) | 0.790 | 0.790 | ||||||
加权中位数法 | 0.068 | 0.044 | 1.070(0.981~1.168) | 0.126 | 0.232 | ||||||
加权模型 | 0.108 | 0.071 | 1.114(0.968~1.281) | 0.137 | 0.369 | ||||||
稳健校正轮廓评分 | 0.080 | 0.029 | 1.083(1.023~1.146) | 0.006 | 0.009 | ||||||
去偏反方差加权法 | 0.039 | 0.034 | 1.039(0.973~1.110) | 0.254 | 0.254 | ||||||
校正最大似然法-多效性分析 | 0.104 | 0.046 | 1.110(1.015~1.214) | 0.023 | 0.023 | ||||||
MS | AIT | 62 | 逆方差加权法 | 0.134 | 0.054 | 1.144(1.029~1.272) | 0.013 | 0.039 | 0.356 | 0.359 | 0.349 |
MR-Egger回归法 | 0.068 | 0.090 | 1.070(0.897~1.277) | 0.454 | 0.790 | ||||||
加权中位数法 | 0.086 | 0.088 | 1.090(0.916~1.296) | 0.332 | 0.332 | ||||||
加权模型 | 0.031 | 0.083 | 1.031(0.876~1.213) | 0.714 | 0.714 | ||||||
稳健校正轮廓评分 | 0.131 | 0.055 | 1.139(1.023~1.275) | 0.018 | 0.018 | ||||||
去偏反方差加权法 | 0.137 | 0.054 | 1.146(1.031~1.275) | 0.012 | 0.036 | ||||||
校正最大似然法-多效性分析 | 0.131 | 0.056 | 1.140(1.022~1.273) | 0.019 | 0.023 |
暴露 | 结局 | Nsnp(个) | 方法 | β | SE | OR(95%CI) | P值 | PFDR值 | Q-pval值 | 水平多效性检验 | |
---|---|---|---|---|---|---|---|---|---|---|---|
PMR-Egger值 | PMR-Presso值 | ||||||||||
AIH | MS | 51 | 逆方差加权法 | -0.006 | 0.017 | 0.994(0.961~1.029) | 0.731 | 0.741 | 0.088 | 0.095 | — |
MR-Egger回归法 | 0.056 | 0.040 | 1.058(0.977~1.145) | 0.171 | 0.171 | ||||||
加权中位数法 | 0.010 | 0.026 | 1.010(0.960~1.063) | 0.687 | 0.811 | ||||||
加权模型 | 0.069 | 0.036 | 1.071(0.999~1.148) | 0.059 | 0.176 | ||||||
稳健校正轮廓评分 | -0.007 | 0.018 | 0.993(0.959~1.028) | 0.697 | 0.846 | ||||||
去偏反方差加权法 | -0.006 | 0.018 | 0.994(0.959~1.030) | 0.733 | 0.741 | ||||||
校正最大似然法-多效性分析 | -0.010 | 0.025 | 0.990(0.943~1.038) | 0.669 | 0.694 | ||||||
GD | MS | 57 | 逆方差加权法 | -0.036 | 0.020 | 0.964(0.927~1.003) | 0.068 | 0.205 | 0.330 | 0.103 | — |
MR-Egger回归法 | -0.123 | 0.056 | 0.884(0.792~0.987) | 0.032 | 0.096 | ||||||
加权中位数法 | -0.039 | 0.029 | 0.962(0.908~1.018) | 0.181 | 0.543 | ||||||
加权模型 | 0.021 | 0.056 | 1.021(0.916~1.139) | 0.705 | 0.849 | ||||||
稳健校正轮廓评分 | -0.041 | 0.023 | 0.960(0.917~1.004) | 0.077 | 0.230 | ||||||
去偏反方差加权法 | -0.038 | 0.021 | 0.963(0.924~1.003) | 0.068 | 0.204 | ||||||
校正最大似然法-多效性分析 | -0.040 | 0.022 | 0.960(0.919~1.003) | 0.070 | 0.211 | ||||||
AIT | MS | 19 | 逆方差加权法 | 0.006 | 0.018 | 1.006(0.971~1.043) | 0.741 | 0.741 | 0.628 | 0.073 | 0.615 |
MR-Egger回归法 | 0.118 | 0.061 | 1.125(0.998~1.268) | 0.072 | 0.107 | ||||||
加权中位数法 | 0.006 | 0.027 | 1.006(0.955~1.061) | 0.811 | 0.811 | ||||||
加权模型 | 0.007 | 0.034 | 1.007(0.942~1.076) | 0.849 | 0.849 | ||||||
稳健校正轮廓评分 | 0.004 | 0.020 | 1.004(0.965~1.045) | 0.846 | 0.846 | ||||||
去偏反方差加权法 | 0.006 | 0.019 | 1.006(0.969~1.045) | 0.741 | 0.741 | ||||||
校正最大似然法-多效性分析 | 0.008 | 0.019 | 1.008(0.971~1.046) | 0.694 | 0.694 |
Table 3 Reverse MR analysis of multiple sclerosis on autoimmune thyroid diseases
暴露 | 结局 | Nsnp(个) | 方法 | β | SE | OR(95%CI) | P值 | PFDR值 | Q-pval值 | 水平多效性检验 | |
---|---|---|---|---|---|---|---|---|---|---|---|
PMR-Egger值 | PMR-Presso值 | ||||||||||
AIH | MS | 51 | 逆方差加权法 | -0.006 | 0.017 | 0.994(0.961~1.029) | 0.731 | 0.741 | 0.088 | 0.095 | — |
MR-Egger回归法 | 0.056 | 0.040 | 1.058(0.977~1.145) | 0.171 | 0.171 | ||||||
加权中位数法 | 0.010 | 0.026 | 1.010(0.960~1.063) | 0.687 | 0.811 | ||||||
加权模型 | 0.069 | 0.036 | 1.071(0.999~1.148) | 0.059 | 0.176 | ||||||
稳健校正轮廓评分 | -0.007 | 0.018 | 0.993(0.959~1.028) | 0.697 | 0.846 | ||||||
去偏反方差加权法 | -0.006 | 0.018 | 0.994(0.959~1.030) | 0.733 | 0.741 | ||||||
校正最大似然法-多效性分析 | -0.010 | 0.025 | 0.990(0.943~1.038) | 0.669 | 0.694 | ||||||
GD | MS | 57 | 逆方差加权法 | -0.036 | 0.020 | 0.964(0.927~1.003) | 0.068 | 0.205 | 0.330 | 0.103 | — |
MR-Egger回归法 | -0.123 | 0.056 | 0.884(0.792~0.987) | 0.032 | 0.096 | ||||||
加权中位数法 | -0.039 | 0.029 | 0.962(0.908~1.018) | 0.181 | 0.543 | ||||||
加权模型 | 0.021 | 0.056 | 1.021(0.916~1.139) | 0.705 | 0.849 | ||||||
稳健校正轮廓评分 | -0.041 | 0.023 | 0.960(0.917~1.004) | 0.077 | 0.230 | ||||||
去偏反方差加权法 | -0.038 | 0.021 | 0.963(0.924~1.003) | 0.068 | 0.204 | ||||||
校正最大似然法-多效性分析 | -0.040 | 0.022 | 0.960(0.919~1.003) | 0.070 | 0.211 | ||||||
AIT | MS | 19 | 逆方差加权法 | 0.006 | 0.018 | 1.006(0.971~1.043) | 0.741 | 0.741 | 0.628 | 0.073 | 0.615 |
MR-Egger回归法 | 0.118 | 0.061 | 1.125(0.998~1.268) | 0.072 | 0.107 | ||||||
加权中位数法 | 0.006 | 0.027 | 1.006(0.955~1.061) | 0.811 | 0.811 | ||||||
加权模型 | 0.007 | 0.034 | 1.007(0.942~1.076) | 0.849 | 0.849 | ||||||
稳健校正轮廓评分 | 0.004 | 0.020 | 1.004(0.965~1.045) | 0.846 | 0.846 | ||||||
去偏反方差加权法 | 0.006 | 0.019 | 1.006(0.969~1.045) | 0.741 | 0.741 | ||||||
校正最大似然法-多效性分析 | 0.008 | 0.019 | 1.008(0.971~1.046) | 0.694 | 0.694 |
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