Channing Metabolomics Research

A Multidisciplinary Approach to Apply Metabolomic Data to Understand Etiology, Heterogeneity, and Pathobiology of Complex Diseases to Improve Diagnosis, Prognosis and Treatment

Metabolomics is the study of all small molecules within a biological specimen. Metabolomic data provide the most integrated profile of biological status, reflecting the “net results” of genetic, transcriptomic, proteomic, and environmental interactions. While the metabolome defines what has happened, it also provides biological clues into what may happen next.

The metabolome is therefore amenable to studies of disease predisposition, diagnosis, progression, and prediction. In most complex diseases, the perturbations involve dysregulation of multiple pathways. By using clinical, environmental, and genetic data in conjunction with descriptive metabolomic profiles, it is possible to identify the changes and biomarkers discriminating those with and without a disease, as well as perturbations occurring long before symptom debut.

Investigators at the Channing Division of Network Medicine (CDNM) are actively involved in using metabolomic data to understand complex diseases by interrogating and improving current approaches to analyze these data.

Current Research

Using a multidisciplinary approach, we apply metabolomic data to understand the etiology, heterogeneity, and pathobiology of complex diseases to improve diagnosis and prognosis, and to develop therapeutics for treatment.

Metabolomic data in large population-based cohorts and clinical trials

Nurses’ Health Study, Nurses’ Health Study II, Health Professional Follow-up Study, Childhood Asthma Management Program, Vitamin D Antenatal Asthma Reduction Trial, Genetic Epidemiology of Costa Rica Cohort, Partners Biobank, Normative Aging Study, and others.

Multiple metabolomes

We have a diverse array of metabolomic data across many disease phenotypes using various metabolomic platforms and biospecimens in large population-based cohorts:

  • Biospecimens: Plasma, serum, stool, and urine
  • Targeted, quantitative metabolomics focusing on specific metabolic pathways (e.g., lipid mediators, bile acids)
  • Comprehensive global metabolomic profiling generated from premier laboratories around the world using LC-MS and GC-MS methodologies

Metabolomics and other “omics” data

Our cohorts have a large number of other “omics” data types, including genomic, transcriptomic, microbiomic, and epigenomic data. We focus on integrating metabolomics data with other data types to study disease etiology:

  • Taking advantage of existing genetic data in our cohorts to interrogate the genetic informativeness of metabolites using metabolite quantitative trait loci (mQTL)
  • Using microbiome data in conjunction with stool and plasma metabolomics
  • Bridging genetic and genomic data with disease phenotypes with metabolomics data
  • Examining the exposome using exogenous metabolites of environmental contaminants

Systems biology and statistical approaches

  • Understanding the etiology of complex diseases using Conditional Gaussian Bayesian Networks (CBGN) and weighted gene correlated gene network analysis (WGCNA)
  • Causal inference and mediation approaches
  • Capitalizing the metabolic networks to understand the function of metabolites and disease

National and international collaborations

CDNM is an active member of the ongoing NIH consortium, Consortium of METabolomic Studies (COMETS), with most of our longitudinal cohorts enrolled in this consortium.