Targeted inference to identify drug repositioning candidates in the Danish health registries

A. W. Jung, I. Louloudis, S. Brunak, L. Mortensen
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Abstract

Electronic health records can be used to track diagnoses and drug prescriptions in large heterogeneous populations over time. Coupled with recent advances in causal inference from observational data, these records offer new opportunities to emulate clinical trials and identify potential targets for drug repositioning. Here, we run a hypothesis generating cohort study of Danes aged 50 to 80 years from 2001 to 2015 (n = 2,512,380), covering a total of 23,371,354 years of observations. We examine prescription drugs at ATC level-4 and their effect on 9 major disease outcomes. Using Bayesian time-varying Cox regression and longitudinal minimum loss estimation, our analysis successfully reproduces known drug-disease associations from clinical trials, such as the reduction in the 3-year absolute risk of death associated with Statins (ATC:C10AA) -0.8% (95% CI =[-1.2%, -0.5%]) and -0.8% (95% CI =[-1.3%, -0.2%]) for females and males, respectively. Additionally, we discovered novel associations that suggest potential repositioning opportunities. For instance, Statins were associated with a reduction in the 3-year absolute risk of dementia by -0.3% (95% CI =[-0.5%, -0.1%]) for females and -0.2% (95% CI =[-0.4%, 0.1%]) for males. Furthermore, Biguanides (ATC:P01BB) stands out as a particularly interesting candidate with absolute risk reductions across various outcomes. In total, we identified 76 potential drug-disease pairs for further investigation. However, it should be stressed that the emulation of clinical trials here is solely of hypothesis generating nature and identified effects need to be corroborated with additional evidence, preferably from RTCs, as the risk of confounding by indication in this study is substantial. In summary, this study provides a large-scale screen of prescribed drugs and their effect on major debilitating disease in the Danish health registries. This provides an additional source of information that can be used in the search for possible repositioning candidates.
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有针对性地推断丹麦健康登记中的候选药物重新定位
电子健康记录可用于长期跟踪大量异质人群的诊断和药物处方。这些记录与观察数据因果推断的最新进展相结合,为模拟临床试验和确定药物重新定位的潜在目标提供了新的机会。在此,我们对 2001 年至 2015 年期间年龄在 50 岁至 80 岁之间的丹麦人(n = 2,512,380 人)进行了假设生成队列研究,共覆盖 23,371,354 年的观察数据。我们研究了 ATC 4 级处方药及其对 9 种主要疾病结果的影响。利用贝叶斯时变 Cox 回归和纵向最小损失估计,我们的分析成功地再现了临床试验中已知的药物-疾病关联,如他汀类药物(ATC:C10AA)对女性和男性的 3 年绝对死亡风险分别降低了 -0.8%(95% CI =[-1.2%,-0.5%])和 -0.8%(95% CI =[-1.3%,-0.2%])。此外,我们还发现了一些新的关联,表明可能存在重新定位的机会。例如,他汀类药物可使女性和男性3年痴呆绝对风险分别降低-0.3% (95% CI =[-0.5%, -0.1%])和-0.2% (95% CI =[-0.4%, 0.1%])。此外,双胍类药物(ATC:P01BB)是一个特别有趣的候选药物,它能降低各种结果的绝对风险。我们总共确定了 76 种潜在的药物-疾病配对,以供进一步研究。不过,需要强调的是,本研究中对临床试验的模仿仅是假设性的,确定的效果还需要更多的证据来证实,最好是从临床试验中获得,因为本研究中适应症混淆的风险很大。总之,本研究对丹麦健康登记中的处方药及其对主要衰弱性疾病的影响进行了大规模筛查。这为寻找可能的重新定位候选药物提供了额外的信息来源。
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