Specific Metabolic Pathways Underlie Noncommunicable Disease Multimorbidity

Metabolomics helps understand shared etiologies of comorbidities, which could result in better treatments for patients.

Most prospective metabolomics studies are focused on single diseases. One in four patients, however, presents with two or more chronic conditions at the same time, referred to as multimorbidity.

A new study led by Claudia Langenberg at the MRC Epidemiology Unit, University of Cambridge  has been published in Nature Medicine aimed to understand shared etiologies among co-morbidities. The scientists integrated Metabolon’s global metabolomics technology with deep phenotypic patient data to elucidate the biochemical changes that mark and may cause multiple incident diseases at the same time. The research team analyzed the levels of over 1,000 metabolites in the plasma of more than 11,000 participants from the European Prospective Investigation into Cancer (EPIC)-Norfolk study. Comprehensive profiling of blood metabolites is a powerful approach to identify biomarkers and signatures of disease, many of which share common biochemical perturbations across diverse pathways.

A detailed analysis of the human metabolome in so many individuals offered a comprehensive readout of human physiology that enabled to characterize pathways associated with and across 27 incident noncommunicable diseases. The findings showed that there are metabolic pathways that underpin multiple chronic conditions that we know associate and move together in the clinic like obesity and type II diabetes and cardiometabolic disorders. But, through this work, other less well-described correlative conditions like stomach cancer and Deep Vein Thrombosis (DVT) or colon cancer and Chronic obstructive pulmonary disease (COPD) were found to occur simultaneously as well.

Such metabolic insights suggest that there could be potential common mechanisms or processes that could inform on how to treat simultaneously occurring chronic conditions in patients.

This approach of understanding pathways of disease will be central to develop new models to improve prediction of individuals at risk and enable their appropriate clinical management.

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