Machine learning integration of multimodal data identifies key features of blood pressure regulation

Background Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP). Methods We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in Twin-sUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Bio-bank (QBB). Findings Our model explained 39.2% [4.5%, MAE:11.32 mmHg (95%CI, +/-0.65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45.2% (13.39%)] cohort and 30 of the top 50 features overlapped between cohorts. Interpretation We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies. Copyright (C) 2022 The Authors. Published by Elsevier B.V.

Automated summary

By integrating biochemical and dietary data, this study identifies the multifactorial contributors to blood pressure. Machine learning algorithms are used to identify important features and highlight the incremental value of each dimension. The findings are validated in an independent dataset, showing overlapping features between cohorts.