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