The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

多组学技术在生物医学队列中的应用有可能揭示患者水平的疾病特征和个体化的治疗反应。然而，多模态数据的规模和异构性使得集成和推理成为一项不平凡的任务。我们开发了一个基于深度学习的框架，即多组学变分自动编码器 (MOVE)，以整合这些数据，并将其应用于 789 名新诊断的 2 型糖尿病患者队列，这些患者具有来自 DIRECT 联盟的深度多组学表型分析。使用计算机扰动，我们确定了多模态数据集中的药物组学关联，用于 2 型糖尿病患者服用的 20 种最普遍的药物，其敏感性远高于单变量统计检验。从这些，我们和其他人，确定了二甲双胍和肠道微生物群之间的新关联，以及两种他汀类药物辛伐他汀和阿托伐他汀的相反分子反应。我们使用这些关联来量化药物间的相似性，评估多重用药的程度，并得出药物效应分布在多组学模式中的结论。