Metabolic Phenotyping in Epidemiology

ala-korpela_squareMika Ala-Korpela

Metabolic phenotyping, nowadays most often termed metabolomics, is becoming increasingly applied in molecular epidemiology. Recent technological developments resulting in increased numbers of quantitative molecular applications of metabolomics triggered the idea for a themed issue of the IJE on Metabolic Phenotyping in Epidemiology edited by George Davey Smith and myself. 

These developments have been driven by mass spectrometry and nuclear magnetic resonance spectroscopy as the two key experimental methodologies that currently allow for studies at an epidemiological scale. The pivotal role of absolute quantification of identified molecular entities in epidemiology and genetics is evident from a multitude of recent applications. The scientific evidence suggests that these kinds of new technologies will aid molecular understanding of metabolic health and diseases. The focus of the themed issue is on the applications of quantitative metabolic phenotyping.

The themed issue will be published in Volume 45, Issue 5 of the IJE. Here we share some of the contributions to the issue, already published on advance access online:

Mika Ala-Korpela and George Davey Smith, Metabolic profiling–multitude of technologies with great research potential, but (when) will translation emerge? Int J Epidemiol 2016: doi: 10.1093/ije/dyw305.

Liam G. Fearnley and Michael Inouye, Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networksInt J Epidemiol 2016: doi: 10.1093/ije/dyw046.

Piyushkumar A. Mundra, Jonathan E. Shaw, and Peter J. Meikle, Lipidomic analyses in epidemiologyInt J Epidemiol 2016: doi: 10.1093/ije/dyw112.

Naomi J Rankin, David Preiss, Paul Welsh, et al. Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future. Int J Epidemiol 2016: doi: 10.1093/ije/dyw271.

Jessica McKay and Ivan Tkáč, Quantitative in vivo neurochemical profiling in humans: where are we now? Int J Epidemiol 2016: doi: 10.1093/ije/dyw235.

Stefan Dietrich, Anna Floegel, Martina Troll, et alRandom Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis Int J Epidemiol 2016: doi: 10.1093/ije/dyw145.

Fangyi Gu, Andriy Derkach, Neal D. Freedman, et alCigarette smoking behaviour and blood metabolomics Int J Epidemiol 2016: doi: 10.1093/ije/dyv330.

Qian Xiao, Steven C. Moore, Sarah K. Keadle, et alObjectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study Int J Epidemiol 2016: doi: 10.1093/ije/dyw033.

Qin Wang, Peter Würtz, Kirsi Auro, et alEffects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence Int J Epidemiol 2016: doi: 10.1093/ije/dyw147.

Shakira M Nelson, Orestis A Panagiotou, Gabriella M Anic, et alMetabolomics analysis of serum 25-hydroxy-vitamin D in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study Int J Epidemiol 2016: doi: 10.1093/ije/dyw148.

Susanne Vogt, Simone Wahl, Johannes Kettunen, et alCharacterization of the metabolic profile associated with serum 25-hydroxyvitamin D: a cross-sectional analysis in population-based data Int J Epidemiol 2016: doi: 10.1093/ije/dyw222.

Yan Zheng, Yanping Li, Qibin Qi, et alCumulative consumption of branched-chain amino acids and incidence of type 2 diabetes Int J Epidemiol 2016: doi: 10.1093/ije/dyw143.

Peter Würtz, Sarah Cook, Qin Wang, et alMetabolic profiling of alcohol consumption in 9778 young adults Int J Epidemiol 2016: doi: 10.1093/ije/dyw175.

Gaokun Qiu, Yan Zheng, Hao Wang, et alPlasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults Int J Epidemiol 2016: doi: 10.1093/ije/dyw221.

Douglas I. Walker, Karan Uppal, Luoping Zhang, et alHigh-resolution metabolomics of occupational exposure to trichloroethylene Int J Epidemiol 2016: doi: 10.1093/ije/dyw218.

Cavin K. Ward-Caviness, Susanne Breitner, Kathrin Wolf, et alShort-term NO2 exposure is associated with long-chain fatty acids in prospective cohorts from Augsburg, Germany: results from an analysis of 138 metabolites and three exposures Int J Epidemiol 2016: doi: 10.1093/ije/dyw247.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s