The use of metabolomics in population-based research.

Adv Nutr

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, and.

Published: November 2014

The NIH has made a significant commitment through the NIH Common Fund's Metabolomics Program to build infrastructure and capacity for metabolomics research, which should accelerate the field. Given this investment, it is the ideal time to start planning strategies to capitalize on the infrastructure being established. An obvious gap in the literature relates to the effective use of metabolomics in large-population studies. Although published reports from population-based studies are beginning to emerge, the number to date remains relatively small. Yet, there is great potential for using metabolomics in population-based studies to evaluate the effects of nutritional, pharmaceutical, and environmental exposures (the "exposome"); conduct risk assessments; predict disease development; and diagnose diseases. Currently, the majority of the metabolomics studies in human populations are in nutrition or nutrition-related fields. This symposium provided a timely venue to highlight the current state-of-science on the use of metabolomics in population-based research. This session provided a forum at which investigators with extensive experience in performing research within large initiatives, multi-investigator grants, and epidemiology consortia could stimulate discussion and ideas for population-based metabolomics research and, in turn, improve knowledge to help devise effective methods of health research.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4224215PMC
http://dx.doi.org/10.3945/an.114.006494DOI Listing

Publication Analysis

Top Keywords

metabolomics population-based
12
metabolomics
8
population-based studies
8
population-based nih
4
nih commitment
4
commitment nih
4
nih common
4
common fund's
4
fund's metabolomics
4
metabolomics program
4

Similar Publications

Increasing evidence suggests the involvement of metabolic alterations in neurological disorders, including Alzheimer's disease (AD), and highlights the significance of the peripheral metabolome, influenced by genetic factors and modifiable environmental exposures, for brain health. In this study, we examined 1,387 metabolites in plasma samples from 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study. We assessed the relation of metabolites with general cognition (G-factor) and magnetic resonance imaging (MRI) markers using linear regression and estimated the variance of these metabolites explained by genes, gut microbiome, lifestyle factors, common clinical comorbidities, and medication using gradient boosting decision tree analysis.

View Article and Find Full Text PDF

Missing teeth have been linked to incident cardiovascular disease, diabetes, and all-cause mortality. Our previous study revealed that signs of oral infections and inflammatory conditions (i.e.

View Article and Find Full Text PDF

Background: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples.

View Article and Find Full Text PDF

The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance.

View Article and Find Full Text PDF
Article Synopsis
  • Distal sensorimotor polyneuropathy (DSPN) is a prevalent neurological condition affecting older adults and those with obesity or diabetes, leading to significant health issues.
  • The Interpretable Multimodal Machine Learning (IMML) framework was used to predict the prevalence and incidence of DSPN by analyzing a diverse set of data from over 1,000 participants, including clinical, genomic, and metabolomic information.
  • Results showed that while clinical data alone could differentiate DSPN cases, combining it with additional molecular data improved prediction accuracy and identified potential biomarkers related to inflammation and fatty acid metabolism, offering new insights for treatment and prevention strategies.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!