Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

Abstract

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.

Fig. 1. Integrating multi-omics data with a VAE.

a, Principle of integration and analysis approach using MOVE. Individual-level non-omics and multi-omics data were used as input to a VAE. The optimal network hyperparameters were estimated from the summed test set error across all individuals in the test (test likelihood), training reconstruction accuracy, and model stability. Significant drug–omics associations were identified by perturbing drug status from no (0) to yes (1) for all individuals that were not already administered the drug. b, UMAP representation of the latent representation from the 789 people with newly diagnosed T2D. Individuals were colored according to their z-scaled Matsuda index from low (blue), average (yellow), and high (red). c, Overlap in significant drug–omics associations between standard t-test (two-sided, Benjamini–Hochberg FDR < 0.01) on the input data, MOVE t-test (multi-stage Bonferroni-corrected, P adjust < 0.05) and MOVE Bayes approaches (FDR Bayes < 0.05). The different methods of multiple testing correction corresponded to FDR of 0.05 on the ground-truth dataset. The overlap between MOVE t-test and MOVE Bayes was used for further analysis (n = 573). d, The number of significant associations found between drugs and features in the multi-omics datasets using MOVE t-test and MOVE Bayes (purple), t-test (green) or ANOVA (orange). See c for information on the tests. e, Fraction of features in the multi-omics datasets that was found by MOVE to be significantly associated with at least one drug (n = 20). The lower and upper hinges correspond to the first and third quartiles. The upper and lower whiskers extend from the hinge to the highest and lowest values, respectively, but no further than 1.5× interquartile range from the hinge. Data beyond the ends of whiskers are outliers and are plotted individually. Source data

Fig. 2. Significant associations between drugs, clinical, and multi-omics features.

a, Significant associations between drugs and clinical features. Effects are given as effect size (z-scaled units) from negative (blue) to positive (red). Significant associations identified by both MOVE t-test and MOVE Bayes are indicated using a star. Features (y-axis) and drugs (x-axis) are clustered using hierarchical clustering on the basis of Euclidean distances. b, As in a but showing per individual-level associations of metformin to multi-omics features demonstrating that associations are highly stable across individuals. Features (y-axis) and newly diagnosed T2D individuals (x-axis). Source data

Fig. 3. Drug associations with metagenomics species and drug–drug similarities.

a, Display of effect sizes (z-scaled units) for (outer to inner) metformin, simvastatin, atorvastatin, omeprazole, lansoprazole, paracetamol, and codeine. Only significant associations to any of the drugs are shown and effect size is visualized as brown (negative), gray (none), and green (positive). Selected omics features are indicated. The Gene Ontologies element represents significantly over-represented Gene Ontology terms using transcriptomics (hypergeometric test, FDR < 0.05) (green). The innermost ring indicates SHAP importance for the individual features in the encoding from input data to the latent representation. b, Effect size (z-scaled units) (x-axis) of the human gut metagenomics species that were significantly associated with metformin (orange) or omeprazole (teal). c, Drug–drug similarities by comparing drug-response profiles across the multi-omics datasets. Cosine similarity indicated from no similarity (blue) to identical profiles (red). d, Average effect (z-score) of drugs for the omics datasets. All 20 drugs are shown, however, only metformin (red), omeprazole (purple), atorvastatin (green), and simvastatin (blue) are indicated. All other drugs are colored gray without a text label. e, Distribution of multi-omics ranks for the different drugs. The ranks are determined as a number between 1–20 (drugs) on the basis of the average effect size from d. The boxes are colored according to number of individuals taking a particular drug from 0 (white) to 323 (purple). There was no correlation between rank scores and number of individuals taking a drug (PCC = 0.14). The lower and upper hinges correspond to the first and third quartiles. The upper and lower whiskers extend from the hinge to the highest and lowest values, respectively, but no further than 1.5× interquartile range from the hinge. Data beyond the ends of whiskers are outliers and are plotted individually. Source data