Using genetic information to define idiopathic pulmonary fibrosis in UK Biobank
Using genetic information to define idiopathic pulmonary fibrosis in UK Biobank
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英国生物银行利用遗传信息定义特发性肺纤维化
DOI：
10.1101/2022.04.01.22273306
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Leavy O
中科院分区：
文献类型：
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作者：
Leavy O
特发性肺纤维化（IPF）是一种复杂的异质性肺纤维化疾病，中位生存期为3年。IPF可以在人群研究中定义，例如英国生物银行，使用电子医疗记录（EHR）。然而，最近使用EHR进行的IPF遗传学研究显示，与临床数据集相比，已知遗传风险因素的效应量减弱，使用初级和二级保健EHR和问卷调查数据的各种组合，我们在英国生物库中定义了IPF病例，并使用IPF最大遗传风险变异的相关结果评估了定义（rs35705950-T，MUC5B）。我们进一步评估了根据诊断实践的变化，基于非IPF肺纤维化的共现代码和限制代码排除的影响。结果rs35705950-T与使用EHR和英国生物银行问卷数据定义的IPF相关性的比值比估计值显著，范围为2.06至3.09，低于使用临床来源的IPF数据集报告的比值比估计值（或：4.99至5.06）。然后，我们评估了排除可能表明错误分类的共现代码的病例以及排除在最新IPF诊断临床指南之前发生的EHR代码的效果。基于代码的情况下和代码发生的排除稍微接近的效果估计，以前报道的，但样本量大幅reduced.ConclusionWe表明，当使用英国生物银行识别IPF，rs35705950-T和IPF风险之间的关联的效果大小是小于临床衍生的IPF数据集。进一步基于代码的排除并没有导致效果估计更接近预期。虽然一般人群队列有助于增加研究样本量，但未来使用一般人群数据集进行的IPF研究应考虑EHR对IPF定义的这些限制。
IntroductionIdiopathic pulmonary fibrosis (IPF) is a complex, heterogeneous fibrotic lung disease with median survival of 3 years. IPF can be defined in population studies, such as UK Biobank, using electronic healthcare records (EHR). However, recent genetic studies of IPF using EHR have shown an attenuation of effect size for known genetic risk factors when compared with clinically-derived datasets, suggesting misclassification of cases.MethodUsing various combinations of primary and secondary care EHRs and questionnaire data we defined IPF cases in UK Biobank and evaluated the definitions using association results for the largest genetic risk variant for IPF (rs35705950-T,MUC5B). We further evaluated the impact of exclusions based on co-occurring codes for non-IPF pulmonary fibrosis and restricting codes according to changes in diagnostic practice.ResultsOdds ratio estimates for rs35705950-T associations with IPF defined using EHR and questionnaire data in UK Biobank were significant and ranged from 2.06 to 3.09, which was lower than those reported using clinically-derived IPF datasets (OR: 4.99 to 5.06). We then evaluated the effect of excluding cases with co-occurring codes that might indicate misclassification, and excluding EHR codes that occurred before the most recent clinical guidelines for diagnosis of IPF. Code-based exclusions of cases and code occurrences gave slightly closer effect estimates to those previously reported, but sample sizes were substantially reduced.ConclusionWe show that when using UK Biobank to identify IPF, the effect size for the association between rs35705950-T and IPF risk is smaller than for clinically-derived IPF datasets. Further code-based exclusions did not lead to effect estimates closer to those expected. Though general population cohorts help to increase study sample size, future IPF research using general population datasets should take these limitations of EHR definitions of IPF into consideration.