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.

识别导致疾病进展的因素，尤其是神经退行性疾病，引起了人们的极大兴趣。疾病进展可以描述为随时​​间变化的结果轨迹，例如，具有截距（时间为零时的严重程度）和斜率（变化率）的线性轨迹。两样本孟德尔随机化 (2SMR) 是一种用于识别观察数据中的一次暴露和一个结果之间的因果关系，同时避免混杂引起的偏差的技术。我们考虑采用多变量 2SMR 方法，使用疾病进展的多级模型来估计暴露对截距和斜率的因果影响。我们进行了一项模拟研究，将简单的单变量 2SMR 方法与多变量 2SMR 方法进行比较，其中一次暴露会影响自诊断后随时间线性变化的结果的截距和斜率。两种方法在六种不同情景下的模拟研究结果相似，没有证据表明存在非零偏差，并且适当覆盖了 95% 置信区间（截距为 93.4-96.2%，斜率为 94.5-96.0%）。多变量方法可以更好地联合覆盖截距和斜率效应。我们还将我们的方法应用于两个帕金森氏症队列，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 会影响疾病进展，但截距和斜率的置信区间都很宽。