Metabolic Phenotyping in Epidemiology

g-mak-talisker-jan-2016-bristol-wwwMika Ala-Korpela and George Davey Smith

Metabolic phenotyping, nowadays most often termed metabolomics, is becoming increasingly applied in epidemiology. Recent technological developments, driven by mass spectrometry and nuclear magnetic resonance spectroscopy, have recently resulted in increasing numbers of quantitative molecular applications at an epidemiological scale. The results suggest that these kinds of new technologies are inevitably becoming common in research projects aiming for molecular understanding of metabolic health and diseases. It is also evident from the epidemiological applications that absolute quantification of identified molecular entities is the very key for biomedical applications, not to mention potential clinical translation of metabolomics methodologies and findings.

These recent developments triggered the idea for a themed issue of the IJE on Metabolic Phenotyping in Epidemiology. The issue was recently published and edited by George Davey Smith and myself. We entitled our editorial – Metabolic profiling – multitude of technologies with great research potential, but (when) will translation emerge? – to emphasise the great science undoubtedly already being done in this area but to put some critical attention to the seemingly widespread hype for clinical translation.

In the editorial we briefly discussed the history of metabolomics and the technologies that have led to the current lively and interesting phase of multiple and wide-ranging applications in epidemiology. We also pinpoint the importance of absolute quantification and molecular specificity together with independent biological replication and the challenges in aiming to understand causality. We also discuss some of the original research papers published in the themed issue and end our editorial with a paragraph entitled Is the future for metabolic phenotyping in epidemiology precarious? We noted that in the history of science and medicine remarkable leaps in progress have often been made due to novel physical or chemical technologies. The new omics methodologies have already taken a big step, particularly in combination with genomic data, but also generally in biomedical sciences. We ended by stating that valuable omics technologies are here to enrich epidemiology, and, eventually, help initiate a new era of systems epidemiology.

However, we also raised a list of issues that we think people should pay attention to, and be aware of, when applying metabolomics in epidemiology:

  • Particularly the spectroscopy-based chemometric approaches (supposed classification of individuals with or without some specific conditions) have for a long time (mis)guided metabolomics research – let’s get over it!
  • It is essential to embrace the basic requirements of good molecular epidemiology.
  • We should aim for molecular identification and absolute quantification – there is no translation without them.
  • Large-scale studies as well as independent biological replication are essential.
  • We should appreciate confounding and aim to understand causation.
  • We should use appropriate statistical tools and note the caveats in multiple testing, whilst appreciating that some of the methods that apply to the special constitution of germline genetics do not apply to phenotypes, including metabolic ones.
  • We should appreciate multiple metabolomics methodologies and multiomics combinations, aim to triangulate findings, explore multiple angles and try to put findings into a realistic biological perspective.
  • Self-critical interpretation of results, biological implications and potential clinical applicability are required, and hype should be avoided.

The themed issue was published in Volume 45, Issue 5 of the IJE. Here we list the contributions to the themed issue:

Editor’s Choice
Ebrahim S. Metabolomics, nutrition and why epidemiology matters. Int J Epidemiol (2016) 45 (5): 1307-1310.

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) 45 (5): 1311-1318.

Liam G. Fearnley and Michael Inouye, Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networksInt J Epidemiol (2016) 45 (5): 1319-1328.

Piyushkumar A. Mundra, Jonathan E. Shaw, and Peter J. Meikle, Lipidomic analyses in epidemiology. Int J Epidemiol 2016:45 (5): 1329-1338

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: 45 (5): 1351-1371.

Jessica McKay and Ivan Tkáč, Quantitative in vivo neurochemical profiling in humans: where are we now? Int J Epidemiol 2016: 45 (5): 1339-1350.

Reprints & Reflectiion
Moreton JR. Chylomicronemia, fat tolerance, and atherosclerosisInt J Epidemiol (2016) 45 (5): 1372-1379.

Varbo A, Langsted A, Nordestgaard BG. Commentary: Nonfasting remnant cholesterol simplifies triglyceride-rich lipoproteins for clinical use, and metabolomic phenotyping ignites scientific curiosityInt J Epidemiol (2016) 45 (5): 1379-1385.

Karpe F. Commentary: Chylomicronaemia, fat tolerance and atherosclerosis—a commentary on a landmark paperInt J Epidemiol (2016) 45 (5): 1385-1387.

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: 45 (5): 1406-1420.

Fangyi Gu, Andriy Derkach, Neal D. Freedman, et alCigarette smoking behaviour and blood metabolomics Int J Epidemiol 2016: 45 (5): 1421-1432.

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 2016:45 (5): 1433-1444.

Qin Wang, Peter Würtz, Kirsi Auro, et alEffects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence Int J Epidemiol 2016:45 (5): 1445-1457.

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 2016:45 (5): 1458-1468.

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 2016:45 (5): 1469-1481.

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:45 (5): 1482-1492.

Peter Würtz, Sarah Cook, Qin Wang, et alMetabolic profiling of alcohol consumption in 9778 young adults Int J Epidemiol 2016:45 (5): 1493-1506.

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 2016:45 (5): 1507-1516.

Douglas I. Walker, Karan Uppal, Luoping Zhang, et alHigh-resolution metabolomics of occupational exposure to trichloroethylene Int J Epidemiol 2016:45 (5): 1517-1527.

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 2016:45 (5): 1528-1538.

Würtz P, Wang Q, Niironen M, et alMetabolic signatures of birthweight in 18 288 adolescents and adults. Int J Epidemiol (2016) 45 (5): 1539-1550.

Millwood IY, Bennett DA, Walters RG, et al. A phenome-wide association study of a lipoprotein-associated phospholipase A2 loss-of-function variant in 90 000 Chinese adultsInt J Epidemiol (2016) 45 (5): 1588-1599.

Swerdlow DI, Kuchenbaecker KB, Shah S, et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studiesInt J Epidemiol (2016) 45 (5): 1600-1616.

Timpson NJ. Commentary: One size fits all: are there standard rules for the use of genetic instruments in Mendelian randomization? Int J Epidemiol (2016) 45 (5): 1617-1618.

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