Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Draisma, Harmen H.M.; Pool, René; Kobl, Michael; Jansen, Ritsert C.; Petersen, Ann-Kristin; Vaarhorst, Anika A. M.; Yet, İdil; Haller, Toomas; Demirkan, Ayşe; Esko, Tõnu; Zhu, Gu; Böhringer, Stefan; Beekman, Marian; Klinken, Jan B. van; Römisch-Margl, Werner; Prehn, Cornelia; Adamski, Jerzy; Craen, Anton J. M. de; Leeuwen, Elisabeth M. van; Amin, Najaf; Dharuri, Harish; Westra, Harm-Jan; Franke, Lude; Geus, Eco J. C. de; Hottenga, Jouke Jan; Willemsen, Gonneke; Henders, Anjali K.; Montgomery, Grant W.; Nyholt, Dale R.; Whitfield, John B.; Penninx, Brenda W. J. H.; Spector, Tim D.; Metspalu, Andres; Slagboom, P. Eline; Dijk, Ko Willems van; Hoen, Peter A.C. ‘t; Strauch, Konstantin; Martin, Nicholas G.; Ommen, Gert‐Jan B. van; Illig, Thomas; Bell, Jordana T.; Mangino, Massimo; Suhre, Karsten; McCarthy, Mark I.; Gieger, Christian; Isaacs, Aaron; Duijn, Cornelia M. van; Boomsma, Dorret I.

doi:10.1038/ncomms8208

Cited by 181 publications

(187 citation statements)

References 49 publications

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“…These were electrospray-Ionization MS, measured in 878 participants, using the method described before (Demirkan et al 2012) and AbsoluteIDQTM p150 Kit of Biocrates Life Sciences AG measured in 989 participants as details mentioned before (Draisma et al 2015). Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

et al. 2017

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BackgroundThe growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.ObjectivesWe aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).MethodsWe analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.ResultsThe 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age < 50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI < 25 kg/m2) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC = 0.89).ConclusionsOur novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-017-1239-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

et al. 2017

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“…Participants from the Erasmus Rucphen Family (ERF) study ( N = 905) were metabolically profiled from fasting blood samples using the Biocrates AbsoluteIDQ-p150 kit platform (Supplementary Table 2) [32], which measures a subset of metabolites from the P180 and excludes many of the amines. A previously described quality control (QC) protocol was applied [32].…”

Section: Methodsmentioning

confidence: 99%

Metabolic network failures in Alzheimer's disease: A biochemical road map

Toledo

Arnold

Kastenmüller

et al. 2017

Alzheimer's & Dementia

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Introduction The Alzheimer’s Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer’s disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods Fasting serum samples from the Alzheimer’s Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

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“…28 These exposures are important because combined with genetic factors they contribute to interindividual metabolic variation and account for most human disease. 29–31 Multiple studies show that consumption of different diets contributes to interindividual metabolic variations. 32–34 …”

Section: Human Metabolomementioning

confidence: 99%

Computational Metabolomics: A Framework for the Million Metabolome

Uppal

Walker

Liu

et al. 2016

Chem. Res. Toxicol.

200

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“Sola dosis facit venenum.” These words of Paracelsus, “the dose makes the poison”, can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80–85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

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Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Cited by 181 publications

References 49 publications

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Metabolic network failures in Alzheimer's disease: A biochemical road map

Computational Metabolomics: A Framework for the Million Metabolome

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