Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression
Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression
复制标题
DOI：
10.1007/s10654-023-01093-2
复制
发表时间：
2023-05
影响因子：
13.6
通讯作者：
M. Lawton;Y. Ben-Shlomo;A. Gkatzionis;Michele T M Hu;D. Grosset;K. Tilling
中科院分区：
文献类型：
--
作者：
M. Lawton;Y. Ben-Shlomo;A. Gkatzionis;Michele T M Hu;D. Grosset;K. Tilling
确定导致疾病进展的因素，特别是神经退行性疾病，是相当有趣的。疾病进展可以描述为结果随时间变化的轨迹，例如，线性轨迹具有截距（时间为零时的严重程度）和斜率（变化率）。两样本孟德尔随机化（2SMR）是一种确定观察数据中一次暴露与一种结果之间因果关系，同时避免因混杂而产生偏差的技术。我们考虑使用疾病进展的多水平模型来估计暴露对截距和斜率的因果效应的多变量方法来研究2SMR。我们进行了一项模拟研究，比较naïve单变量2SMR方法和多变量2SMR方法，其中一次暴露会影响结果的截距和斜率，这些截距和斜率自诊断以来随时间线性变化。在六种不同的情况下，两种方法的模拟研究结果相似，没有证据表明非零偏差和95%置信区间的适当覆盖（截距93.4-96.2%，斜率94.5-96.0%）。多变量方法提供了更好的截距和斜率效应的联合覆盖。我们还将我们的方法应用于两个帕金森病队列，以检查体重指数对疾病进展的影响。没有强有力的证据表明BMI影响疾病进展，但是截距和斜率的置信区间都很宽。
Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time—for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4–96.2% and the slope 94.5–96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson’s cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.