Kastenmüller Lab

Our mission is to understand the role of metabolism and metabolic individuality in complex human diseases using systems metabolomics.

Visit Gabi Kastenmüller's website

Our mission is to understand the role of metabolism and metabolic individuality in complex human diseases using systems metabolomics.

Visit Gabi Kastenmüller's website

Understand the role of metabolism and metabolic individuality in the development, prevention, and treatment of diseases using systems metabolomics.

Using metabolomic data, the main objective of our research is to identify metabolic mechanisms that translate genetic risk factors and their interplay with lifestyle and environmental factors into the development and progression of complex diseases, including Alzheimer's disease and chronic kidney disease. Thereby, we have a major focus on investigating how a person's individual metabolic make-up, its changes over time, and its link to genetic variation affect health and disease. We use metabolomics and other omics data from large epidemiological cohorts in combination with advanced computational approaches as tools to access and understand relevant metabolic individuality and its determinants in healthy populations. This forms the basis for elucidating the role of metabolic disruption in age-related diseases, their co-occurrence (multi-morbidity), and heterogeneity in a systems medicine context. Our ultimate goal is to translate our results into applied precision medicine by shifting metabolomics from a valuable research tool to a practical clinical instrument for monitoring metabolic health.

How does age-related metabolic decline affect brain health?

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CKDNapp - a versatile and free toolkit for physicians treating patients with chronic kidney disease

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Can metabolite profiles serve as intermediate readouts for the effects of genetic variation and life style on health and disease?

Biomarker Discovery for Diagnostic and Prognostic Purposes - New Advances in Precision and Personalized Medicine - Conceptual Illustration

How does diet and exercise affect our metabolism and how can we use them to optimize and personalize disease prevention?

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Have you ever struggled with extracting published results from supplements? We make the full wealth of omics results from our large-scale association studies available to easy-to-use web-based tools.

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Gabi Kastenmüller

Head of Research Group Systems Metabolomics

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Matthias Arnold

Head of Team CompNeuro

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Orhan Bellur

PhD candidate

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Josef Bless

Postdoc

Sai Krishna Bharadwaj Marella

PhD candidate

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Yacoub Abelard Njipouombe Nsangou

PhD candidate

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Pia Pfeiffer

Master student

Werner Römisch-Margl

Senior Scientist

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Daniela Schranner

Postdoc

Lara Vehovec

PhD candidate

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Multi-omics Data Integration

AD Atlas

The AD Atlas is a network-based data integration resource for investigating Alzheimer's Disease, its biomarkers, and associated endophenotypes in a multi-omics context.

AD Atlas

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Challenge Studies

Metabolomics & Exercise

Profiling the metabolic response to acute and long-term exercise.

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Online Association Databases

OMICSCIENCE

With omicscience.org we provide easy access to the results of several metabolome-wide association studies which otherwise would be hidden in the publication supplements.

OMICSCIENCE

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Challenge Studies

Metabolomics & Nutrition

How does our food shape the composition of our metabolome?

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Data Visualization & Exploration

The HuMet-Repository

Visualize and explore the time-resolved responses of the human metabolism to various challenges.

HuMet-Repository

Genomic Annotation Resource

SNIPA Webservice

A continuously updated interactive, genetic variant-centered annotation browser.

SNiPA

Understand the role of metabolic individuality in health and disease using systems metabolomics.

We are always looking for highly motivated students to join our group.

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Schranner D, Wackerhage H, Weinisch P, Schlegel J, Bremer S, Scherr J, Römisch-Margl W, Riermeier A, Zelger O, Stöcker F, Artati A, Witting M, Krumsiek J, Halle M, Schönfelder M, Kastenmüller G.

Characterizing Human Oxidative, Anabolic and Glycolytic Metabolism in Athletes with Extreme Physiologies Background: Regular physical activity is known to benefit health but the long-term effects of specific exercise training on human metabolism remain incompletely described. In this study, we comprehensively characterized the blood metabolomes of male athletes with distinct exercise-adapted metabolic profiles, comparing endurance athletes (n = 11), sprinters (n = 8), and natural body builders (n = 9) as models for highly oxidative, glycolytic, and anabolic metabolism, respectively.

Methods: Serum samples of these athletes and a control group of male untrained individuals (n = 7) were collected both at rest and after maximum exercise. Using untargeted metabolomics profiling and weighted correlation network analysis, we examined associations of metabolites and metabolite modules with athlete groups and their characteristic traits (e.g., cardiovascular fitness or muscularity).

Results: Our analyses revealed distinct metabolic signatures for the different groups: a highly anabolic metabolism was characterized by lower levels of sulfated steroids; a highly oxidative metabolism by higher levels of phospholipids; and a highly glycolytic metabolism by lower levels of sphingomyelins. In response to maximum exercise, 130 metabolites changed across all groups (e.g., N-lactoyl amino acids, acylcholines, energy metabolites), while 57 metabolites showed differences in magnitude or direction of change between groups (e.g., fatty acid oxidative products, cortisol).

Conclusion: Our findings demonstrate that exercise-induced adaptations in metabolism distinctly shape the human serum metabolome and influence the metabolic response to exercise. These insights are relevant for diseases driven by dysfunctional metabolism, such as impaired fat oxidation and dysregulated glycolysis (e.g., diabetes, dementia) and muscle wasting (e.g., sarcopenia), where our specialized populations may serve as useful models.

Njipouombe Nsangou YA, Kumar Halder R, Uddin A, Engel L, Kotsis F, Schultheiss UT, Raffler J, Kosch R, Altenbuchinger M, Zacharias HU, Kastenmüller G, Dönitz J.

Use of Client-Side Machine Learning Models for Privacy-Preserving Healthcare Predictions - A Deployment Case Study Introduction:
Machine learning (ML) and deep learning (DL) models in healthcare traditionally rely on server-centric architectures, where sensitive patient data is transmitted to external servers for processing via frameworks like Flask, raising significant privacy concerns. This work demonstrates a privacy-preserving approach by executing healthcare prediction models entirely within the web browser.

Methods:
Our approach leverages existing browser-based machine learning and deep learning technologies such as TensorFlow.js and ONNX Runtime Web, along with direct JavaScript implementations, to ensure all computations remain on the client side. We showcase three implementation strategies based on model complexity: direct JavaScript implementation for simple equation-based models, ONNX-based conversion and execution for medium-complexity models like Random Forest and finally TensorFlow.js deployment for complex deep learning models such as Optimized Convolutional Neural Networks.

Results:
Our results indicate that client-side deployment is both feasible and effective for healthcare prediction models, preserving original performance metrics while offering substantial privacy benefits.

Conclusion:
This approach guarantees patient data never leaves the user’s device, eliminating risks associated with data transmission and making it particularly advantageous in healthcare settings where data confidentiality is critical, while also supporting offline functionality.

Arnold M, Buyukozkan M, Doraiswamy PM, Nho K, Wu T, Gudnason V, Launer LJ, Wang-Sattler R, Adamski J; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Metabolomics Consortium; De Jager PL, Ertekin-Taner N, Bennett DA, Saykin AJ, Peters A, Suhre K, Kaddurah-Daouk R, Kastenmüller G, Krumsiek J.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease Impaired glucose uptake in the brain is an early presymptomatic manifestation of Alzheimer’s disease (AD), with symptom-free periods of varying duration that likely reflect individual differences in metabolic resilience. We propose a systemic “bioenergetic capacity”, the individual ability to maintain energy homeostasis under pathological conditions. Using fasting serum acylcarnitine profiles from the AD Neuroimaging Initiative as a blood-based readout for this capacity, we identified subgroups with distinct clinical and biomarker presentations of AD. Our data suggests that improving beta-oxidation efficiency can decelerate bioenergetic aging and disease progression. The estimated treatment effects of targeting the bioenergetic capacity were comparable to those of recently approved anti-amyloid therapies, particularly in individuals with specific mitochondrial genotypes linked to succinylcarnitine metabolism. Taken together, our findings provide evidence that therapeutically enhancing bioenergetic health may reduce the risk of symptomatic AD. Furthermore, monitoring the bioenergetic capacity via blood acylcarnitine measurements can be achieved using existing clinical assays.

Weinisch P, Raffler J, Römisch-Margl W, Arnold M, Mohney RP, Rist MJ, Prehn C, Skurk T, Hauner H, Daniel H, Suhre K, Kastenmüller G.

The HuMet Repository: Watching human metabolism at work Metabolism oscillates between catabolic and anabolic states depending on food intake, exercise, or stresses that change a multitude of metabolic pathways simultaneously. We present the HuMet Repository for exploring dynamic metabolic responses to oral glucose/lipid loads, mixed meals, 36-h fasting, exercise, and cold stress in healthy subjects. Metabolomics data from blood, urine, and breath of 15 young, healthy men at up to 56 time points are integrated and embedded within an interactive web application, enabling researchers with and without computational expertise to search, visualize, analyze, and contextualize the dynamic metabolite profiles of 2,656 metabolites acquired on multiple platforms. With examples, we demonstrate the utility of the resource for research into the dynamics of human metabolism, highlighting differences and similarities in systemic metabolic responses across challenges and the complementarity of metabolomics platforms. The repository, providing a reference for healthy metabolite changes to six standardized physiological challenges, is freely accessible through a web portal.

Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A; Alnylam Human Genetics; AstraZeneca Genomics Initiative; Biogen Biobank Team; Bristol Myers Squibb; Genentech Human Genetics; GlaxoSmithKline Genomic Sciences; Pfizer Integrative Biology; Population Analytics of Janssen Data Sciences; Regeneron Genetics Center; Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD.

Plasma proteomic associations with genetics and health in the UK Biobank The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics.

Singh P, Gollapalli K, Mangiola S, Schranner D, Yusuf MA, Chamoli M, Shi SL, Lopes Bastos B, Nair T, Riermeier A, Vayndorf EM, Wu JZ, Nilakhe A, Nguyen CQ, Muir M, Kiflezghi MG, Foulger A, Junker A, Devine J, Sharan K, Chinta SJ, Rajput S, Rane A, Baumert P, Schönfelder M, Iavarone F, di Lorenzo G, Kumari S, Gupta A, Sarkar R, Khyriem C, Chawla AS, Sharma A, Sarper N, Chattopadhyay N, Biswal BK, Settembre C, Nagarajan P, Targoff KL, Picard M, Gupta S, Velagapudi V, Papenfuss AT, Kaya A, Ferreira MG, Kennedy BK, Andersen JK, Lithgow GJ, Ali AM, Mukhopadhyay A, Palotie A, Kastenmüller G, Kaeberlein M, Wackerhage H, Pal B, Yadav VK.

Taurine deficiency as a driver of aging Aging is associated with changes in circulating levels of various molecules, some of which remain undefined. We find that concentrations of circulating taurine decline with aging in mice, monkeys, and humans. A reversal of this decline through taurine supplementation increased the health span (the period of healthy living) and life span in mice and health span in monkeys. Mechanistically, taurine reduced cellular senescence, protected against telomerase deficiency, suppressed mitochondrial dysfunction, decreased DNA damage, and attenuated inflammaging. In humans, lower taurine concentrations correlated with several age-related diseases and taurine concentrations increased after acute endurance exercise. Thus, taurine deficiency may be a driver of aging because its reversal increases health span in worms, rodents, and primates and life span in worms and rodents. Clinical trials in humans seem warranted to test whether taurine deficiency might drive aging in humans.

Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C.

Rare and common genetic determinants of metabolic individuality and their effects on human health Garrod’s concept of ‘chemical individuality’ has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant–metabolite associations (P < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant–metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.

Richa Batra, Matthias Arnold, Maria A. Wörheide, Mariet Allen, Xue Wang, Colette Blach, Allan I. Levey, Nicholas T. Seyfried, Nilüfer Ertekin-Taner, David A. Bennett, Gabi Kastenmüller, Rima F. Kaddurah-Daouk, Jan Krumsiek, and Alzheimer’s Disease Metabolomics Consortium (ADMC)

The Landscape of Metabolic Brain Alterations in Alzheimer’s Disease. Introduction
Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in the brain has been missing.

Methods
We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures.

Results
We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology.

Discussion
This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow-up studies.

Sebastian Gehlert, Patrick Weinisch, Werner Römisch-Margl, Richard T. Jaspers, Anna Artati, Jerzy Adamski, Kenneth A. Dyar, Thorben Aussieker, Daniel Jacko, Wilhelm Bloch, Henning Wackerhage, and Gabi Kastenmüller

Effects of Acute and Chronic Resistance Exercise on the Skeletal Muscle Metabolome. Resistance training promotes metabolic health and stimulates muscle hypertrophy, but the precise routes by which resistance exercise (RE) conveys these health benefits are largely unknown. Aim: To investigate how acute RE affects human skeletal muscle metabolism. Methods: We collected vastus lateralis biopsies from six healthy male untrained volunteers at rest, before the first of 13 RE training sessions, and 45 min after the first and last bouts of RE. Biopsies were analysed using untargeted mass spectrometry-based metabolomics. Results: We measured 617 metabolites covering a broad range of metabolic pathways. In the untrained state RE altered 33 metabolites, including increased 3-methylhistidine and N-lactoylvaline, suggesting increased protein breakdown, as well as metabolites linked to ATP (xanthosine) and NAD (N1-methyl-2-pyridone-5-carboxamide) metabolism; the bile acid chenodeoxycholate also increased in response to RE in muscle opposing previous findings in blood. Resistance training led to muscle hypertrophy, with slow type I and fast/intermediate type II muscle fibre diameter increasing by 10.7% and 10.4%, respectively. Comparison of post-exercise metabolite levels between trained and untrained state revealed alterations of 46 metabolites, including decreased N-acetylated ketogenic amino acids and increased beta-citrylglutamate which might support growth. Only five of the metabolites that changed after acute exercise in the untrained state were altered after chronic training, indicating that training induces multiple metabolic changes not directly related to the acute exercise response. Conclusion: The human skeletal muscle metabolome is sensitive towards acute RE in the trained and untrained states and reflects a broad range of adaptive processes in response to repeated stimulation.

Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M.

Multi-omics integration in biomedical research - A metabolomics-centric review Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Pietzner M, Stewart ID, Raffler J, Khaw KT, Michelotti GA, Kastenmüller G, Wareham NJ, Langenberg C.

Plasma metabolites to profile pathways in noncommunicable disease multimorbidity Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite–disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver (https://omicscience.org/apps/mwasdisease/) to facilitate future research and meta-analyses.

Arnold M, Nho K, Kueider-Paisley A, Massaro T, Huynh K, Brauner B, MahmoudianDehkordi S, Louie G, Moseley MA, Thompson JW, John-Williams LS, Tenenbaum JD, Blach C, Chang R, Brinton RD, Baillie R, Han X, Trojanowski JQ, Shaw LM, Martins R, Weiner MW, Trushina E, Toledo JB, Meikle PJ, Bennett DA, Krumsiek J, Doraiswamy PM, Saykin AJ, Kaddurah-Daouk R, Kastenmüller G.

Sex and APOE ε4 genotype modify the Alzheimer's disease serum metabolome Late-onset Alzheimer’s disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE ε4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE ε4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE ε4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE ε4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

Maria A. Ulmer, Jan Krumsiek, Serge Nataf, Kwangsik Nho, Anna K. Greenwood, Jesse C. Wiley, Lina-Liv Willruth, Tong Wu, Orhan Bellur, Bharadwaj Marella, Kevin Huynh, Patrick Weinisch, Werner Römisch-Margl, Nick Lehner, Yacoub A. Njipouombe Nsangou, The AMP-AD Consortium, The Alzheimer’s Disease Neuroimaging Initiative, The Alzheimer’s Disease Metabolomics Consortium, Jan Baumbach, Peter J. Meikle, Andrew J. Saykin, P. Murali Doraiswamy, Cornelia van Duijn, Karsten Suhre, Rima Kaddurah-Daouk, Gabi Kastenmüller, Matthias Arnold

An Integrated Molecular Atlas of Alzheimer’s Disease. Alzheimer’s disease (AD) is a complex neurodegenerative disorder with multifactorial etiology and widespread molecular manifestations. Investigating molecular disease associations in a broader multi-level context across omics modalities remains one central challenge in AD research, despite the increasing availability of large-scale omics data. The AD Atlas, an online multi-omics resource, provides access to harmonized, disease-relevant data from over 25 large studies on 20,363 protein-coding genes, 8,396 proteins, 1,328 metabolites and 43 AD-related phenotypes interconnected by 979,190 significant associations. Results from AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives are complemented with molecular associations from population-based studies in a comprehensive network resource to provide a genome-scale molecular view on AD. In a deep learning-based evaluation of the AD Atlas content, we demonstrate the utility of the network for data-driven identification of modules strongly enriched for AD-related functional domains. We provide full access to the AD Atlas at www.adatlas.org.

Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, Walter K, Menni C, Chen L, Vasquez L, Valdes AM, Hyde CL, Wang V, Ziemek D, Roberts P, Xi L, Grundberg E; Multiple Tissue Human Expression Resource (MuTHER) Consortium; Waldenberger M, Richards JB, Mohney RP, Milburn MV, John SL, Trimmer J, Theis FJ, Overington JP, Suhre K, Brosnan MJ, Gieger C, Kastenmüller G, Spector TD, Soranzo N.

An atlas of genetic influences on human blood metabolites Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

Gispert-Llaurado, M. ; Gheorghita, I. ; Vehovec, L. ; Ferré, N. ; Ramírez, N. ; Horak, J. ; Demmelmair, H. ; Patro-Golab, B. ; Grote, V. ; Koletzko, B. ; Kastenmüller, G. ; Kratochwill, K. ; Verduci, E. ; Gruszfeld, D. ; Escribano, J. ; Luque, V.

Association between diet and metabolome in childhood and adolescence: A systematic review.

Hu, E.Y. ; Oleshko, S. ; Firmani, S. ; Cheng, H. ; Zhu, Z. ; Ulmer, M.A. ; Arnold, M. ; Colomé-Tatché, M. ; Tang, J. ; Xhonneux, S. ; Marsico, A.

Enhancing link prediction in biomedical knowledge graphs with BioPathNet.

Amin, N. ; Liu, J. ; Sproviero, W. ; Arnold, M. ; Batra, R. ; Bonnechere, B. ; Chiou, Y.J. ; Fernandes, M. ; Krumsiek, J. ; Newby, D. ; Nho, K. ; Kim, J.P. ; Saykin, A.J. ; Shi, L. ; Winchester, L.M. ; Yang, Y. ; Nevado-Holgado, A.J. ; Kastenmüller, G. ; Kaddurah-Daouk, R. ; van Duijn, C.M.

Interplay between age, APOE Ɛ4 and the metabolome in plasma and brain in Alzheimer's disease.

Arnold, M. ; Buyukozkan, M. ; Doraiswamy, P.M. ; Nho, K. ; Wu, T. ; Gudnason, V. ; Launer, L.J. ; Wang-Sattler, R. ; Adamski, J. ; de Jager, P.L. ; Ertekin-Taner, N. ; Bennett, D.A. ; Saykin, A.J. ; Peters, A. ; Suhre, K. ; Kaddurah-Daouk, R. ; Kastenmüller, G. ; Krumsiek, J.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease.

Bork, J. ; Markus, M.R.P. ; Ewert, R. ; Nauck, M. ; Templin, C. ; Völzke, H. ; Kastenmüller, G. ; Artati, A. ; Adamski, J. ; Dörr, M. ; Friedrich, N. ; Bahls, M.

The metabolic signature of cardiorespiratory fitness.

Borkowski, K. ; Liang, N. ; Zhao, N. ; Arnold, M. ; Huynh, K. ; Karu, N. ; MahmoudianDehkordi, S. ; Kueider-Paisley, A. ; Kanekiyo, T. ; Bu, G. ; Kaddurah-Daouk, R.

APOE genotype influences on the brain metabolome of aging mice - role for mitochondrial energetics in mechanisms of resilience in APOE2 genotype.

Ge, J. ; Han, S. ; Shi, M. ; Harada, M. ; Yu, S. ; Zheng, J. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Schlesinger, S. ; Koenig, W. ; Linkohr, B. ; Thorand, B. ; Suhre, K. ; Gieger, C. ; Peters, A. ; Wang-Sattler, R.

Integrative metabolomics of targeted and non-targeted analyses in T2D progression.

Montanari, S. ; Jansen, R. ; Schranner, D. ; Kastenmüller, G. ; Arnold, M. ; Janiri, D. ; Sani, G. ; Bhattacharyya, S. ; Mahmoudian Dehkordi, S. ; Dunlop, B.W. ; Rush, A.J. ; Penninx, B.W.H.J. ; Kaddurah-Daouk, R. ; Milaneschi, Y.

Acylcarnitines metabolism in depression: Association with diagnostic status, depression severity and symptom profile in the NESDA cohort.

Neth, B.J. ; Huynh, K. ; Giles, C. ; Wang, T. ; Mellett, N.A. ; Duong, T. ; Blach, C. ; Schimmel, L. ; Register, T.C. ; Blennow, K. ; Zetterberg, H. ; Batra, R. ; Schweickart, A. ; Dilmore, A.H. ; Martino, C. ; Arnold, M. ; Krumsiek, J. ; Han, X. ; Dorrestein, P.C. ; Knight, R. ; Meikle, P.J. ; Craft, S. ; Kaddurah-Daouk, R.

Consuming a modified Mediterranean ketogenic diet reverses the peripheral lipid signature of Alzheimer's disease in humans.

Njipouombe Nsangou, Y.A. ; Kumar Halder, R. ; Uddin, A. ; Engel, L. ; Kotsis, F. ; Schultheiss, U.T. ; Raffler, J. ; Kosch, R. ; Altenbuchinger, M. ; Zacharias, H.U. ; Kastenmüller, G. ; Dönitz, J.

Use of client-side machine learning models for privacy-preserving healthcare predictions - a deployment case study.

Pietzner, M. ; Beuchel, C. ; Demircan, K. ; Hoffmann Anton, J. ; Zeng, W. ; Römisch-Margl, W. ; Yasmeen, S. ; Uluvar, B. ; Zoodsma, M. ; Koprulu, M. ; Kastenmüller, G. ; Carrasco-Zanini, J. ; Langenberg, C.

Machine learning-guided deconvolution of plasma protein levels.

Rouskas, K. ; Bocher, O. ; Simistiras, A. ; Emmanouil, C. ; Mantas, P. ; Skoulakis, A. ; Park, Y.-C. ; Dimopoulos, A. ; Glentis, S. ; Kastenmüller, G. ; Zeggini, E. ; Dimas, A.S.

Periodic dietary restriction of animal products induces metabolic reprogramming in humans with effects on cardiometabolic health.

Saad, R. ; Costeira, R. ; Matias-Garcia, P.R. ; Villicaña, S. ; Gieger, C. ; Suhre, K. ; Peters, A. ; Kastenmüller, G. ; Rodriguez-Mateos, A. ; Dias, C. ; Menni, C. ; Waldenberger, M. ; Bell, J.T.

Theobromine is associated with slower epigenetic ageing.

Tucholski, T. ; Maennel, A. ; Njipouombe Nsangou, Y.A. ; Schuchardt, S. ; Gruber, M. ; Kellermeier, F. ; Dettmer, K. ; Oefner, P.J. ; Gronwald, W. ; Altenbuchinger, M. ; Dönitz, J. ; Zacharias, H.U.

MetaboSERV-a platform for selecting, exchanging, and visualizing metabolomics data with controlled data access.

Wang, T. ; Arnold, M. ; Huynh, K. ; Weinisch, P. ; Giles, C. ; Mellett, N.A. ; Duong, T. ; Marella, B. ; Nho, K. ; De Livera, A. ; Han, X. ; Blach, C. ; Yu, C. ; McNeil, J.J. ; Lacaze, P. ; Saykin, A.J. ; Kastenmüller, G. ; Meikle, P.J. ; Kaddurah-Daouk, R.

Trajectory of plasma lipidome associated with the risk of late-onset Alzheimer's disease: A longitudinal cohort study.

Batra, R. ; Krumsiek, J. ; Wang, X. ; Allen, M. ; Blach, C. ; Kastenmüller, G. ; Arnold, M. ; Ertekin-Taner, N. ; Kaddurah-Daouk, R.

Comparative brain metabolomics reveals shared and distinct metabolic alterations in Alzheimer's disease and progressive supranuclear palsy.

Carper, D. ; Lac, M. ; Coue, M. ; Labour, A. ; Märtens, A. ; Banda, J.A.A. ; Mazeyrie, L. ; Mechta, M. ; Ingerslev, L.R. ; Elhadad, M.A. ; Petit, J.V. ; Maslo, C. ; Monbrun, L. ; Del Carmine, P. ; Sainte-Marie, Y. ; Bourlier, V. ; Laurens, C. ; Mithieux, G. ; Joanisse, D.R. ; Coudray, C. ; Feillet-Coudray, C. ; Montastier, E. ; Viguerie, N. ; Tavernier, G. ; Waldenberger, M. ; Peters, A. ; Wang-Sattler, R. ; Adamski, J. ; Suhre, K. ; Gieger, C. ; Kastenmüller, G. ; Illig, T. ; Lichtinghagen, R. ; Seissler, J. ; Mounier, R. ; Hiller, K. ; Jordan, J. ; Barrès, R. ; Kuhn, M. ; Pesta, D. ; Moro, C.

Loss of atrial natriuretic peptide signaling causes insulin resistance, mitochondrial dysfunction, and low endurance capacity.

Harada, M. ; Adam, J. ; Covic, M. ; Ge, J. ; Brandmaier, S. ; Muschet, C. ; Huang, J. ; Han, S. ; Rommel, M. ; Rotter, M. ; Heier, M. ; Mohney, R.P. ; Krumsiek, J. ; Kastenmüller, G. ; Rathmann, W. ; Zou, Z. ; Zukunft, S. ; Scheerer, M.F. ; Neschen, S. ; Adamski, J. ; Gieger, C. ; Peters, A. ; Ankerst, D.P. ; Meitinger, T. ; Alderete, T.L. ; Hrabě de Angelis, M. ; Suhre, K. ; Wang-Sattler, R.

Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

Jansen, R. ; Milaneschi, Y. ; Schranner, D. ; Kastenmüller, G. ; Arnold, M. ; Han, X. ; Dunlop, B.W. ; Rush, A.J. ; Kaddurah-Daouk, R. ; Penninx, B.W.J.H.

The metabolome-wide signature of major depressive disorder.

Liang, N. ; Nho, K. ; Newman, J.W. ; Arnold, M. ; Huynh, K. ; Meikle, P.J. ; Borkowski, K. ; Kaddurah-Daouk, R.

Peripheral inflammation is associated with brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer's disease.

Martinelli, F. ; Heinken, A. ; Henning, A.K. ; Ulmer, M.A. ; Hensen, T. ; González, A. ; Arnold, M. ; Asthana, S. ; Budde, K. ; Engelman, C.D. ; Estaki, M. ; Grabe, H.J. ; Heston, M.B. ; Johnson, S. ; Kastenmüller, G. ; Martino, C. ; McDonald, D. ; Rey, F.E. ; Kilimann, I. ; Peters, O. ; Wang, X. ; Spruth, E.J. ; Schneider, A. ; Fliessbach, K. ; Wiltfang, J. ; Hansen, N. ; Glanz, W. ; Buerger, K. ; Janowitz, D. ; Laske, C. ; Munk, M.H. ; Spottke, A. ; Roy, N. ; Nauck, M. ; Teipel, S. ; Knight, R. ; Kaddurah-Daouk, R.F. ; Bendlin, B.B. ; Hertel, J. ; Thiele, I.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease.

Nguyen, B.H.P. ; Garger, D. ; Lu, D. ; Maalmi, H. ; Prokisch, H. ; Thorand, B. ; Adamski, J. ; Kastenmüller, G. ; Waldenberger, M. ; Gieger, C. ; Peters, A. ; Suhre, K. ; Bönhof, G.J. ; Rathmann, W. ; Roden, M. ; Grallert, H. ; Ziegler, D. ; Herder, C. ; Menden, M.P.

Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy.

Pandey, R.S. ; Arnold, M. ; Batra, R. ; Krumsiek, J. ; Kotredes, K.P. ; Garceau, D. ; Williams, H. ; Sasner, M. ; Howell, G.R. ; Kaddurah-Daouk, R. ; Carter, G.W.

Metabolomics profiling reveals distinct, sex-specific signatures in serum and brain metabolomes in mouse models of Alzheimer's disease.

Pietzner, M. ; Denaxas, S. ; Yasmeen, S. ; Ulmer, M.A. ; Nakanishi, T. ; Arnold, M. ; Kastenmüller, G. ; Hemingway, H. ; Langenberg, C.

Complex patterns of multimorbidity associated with severe COVID-19 and long COVID.

Sharma, S. ; Dong, Q. ; Haid, M. ; Adam, J. ; Bizzotto, R. ; Fernandez-Tajes, J.J. ; Jones, A.G. ; Tura, A. ; Artati, A. ; Prehn, C. ; Kastenmüller, G. ; Koivula, R.W. ; Franks, P.W. ; Walker, M. ; Forgie, I.M. ; Giordano, G.N. ; Pavo, I. ; Ruetten, H. ; Dermitzakis, M. ; McCarthy, M.I. ; Pedersen, O. ; Schwenk, J.M. ; Tsirigos, K.D. ; De Masi, F. ; Brunak, S. ; Viñuela, A. ; Mari, A. ; McDonald, T.J. ; Kokkola, T. ; Adamski, J. ; Pearson, E.R. ; Grallert, H.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study.

Telpoukhovskaia, M.A. ; Murdy, T.J. ; Marola, O.J. ; Charland, K. ; MacLean, M. ; Luquez, T. ; Lish, A.M. ; Neuner, S. ; Dunn, A. ; Onos, K.D. ; Wiley, J. ; Archer, D. ; Huentelman, M.J. ; Arnold, M. ; Menon, V. ; Goate, A. ; Van Eldik, L.J. ; Territo, P.R. ; Howell, G.R. ; Carter, G.W. ; O'Connell, K.M.S. ; Kaczorowski, C.C.

New directions for Alzheimer's disease research from the Jackson Laboratory Center for Alzheimer's and Dementia Research 2022 workshop.

Weinisch, P. ; Raffler, J. ; Römisch-Margl, W. ; Arnold, M. ; Mohney, R.P. ; Rist, M.J. ; Prehn, C. ; Skurk, T. ; Hauner, H. ; Daniel, H. ; Suhre, K. ; Kastenmüller, G.

The HuMet Repository: Watching human metabolism at work.

Yu, S. ; Han, S. ; Shi, M. ; Harada, M. ; Ge, J. ; Li, X. ; Cai, X. ; Heier, M. ; Kastenmüller, G. ; Suhre, K. ; Gieger, C. ; Koenig, W. ; Rathmann, W. ; Peters, A. ; Wang-Sattler, R.

Prediction of myocardial infarction using a combined generative adversarial network model and feature-enhanced loss function.

Amin, N. ; Liu, J. ; Bonnechere, B. ; MahmoudianDehkordi, S. ; Arnold, M. ; Batra, R. ; Chiou, Y.J. ; Fernandes, M. ; Ikram, M.A. ; Kraaij, R. ; Krumsiek, J. ; Newby, D. ; Nho, K. ; Radjabzadeh, D. ; Saykin, A.J. ; Shi, L. ; Sproviero, W. ; Winchester, L. ; Yang, Y. ; Nevado-Holgado, A.J. ; Kastenmüller, G. ; Kaddurah-Daouk, R. ; van Duijn, C.M.

Interplay of metabolome and gut microbiome in individuals with major depressive disorder vs control individuals.

Borkowski, K. ; Seyfried, N.T. ; Arnold, M. ; Lah, J.J. ; Levey, A.I. ; Hales, C.M. ; Dammer, E.B. ; Blach, C. ; Louie, G. ; Kaddurah-Daouk, R. ; Newman, J.W.

Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism.

Chen, T. ; Wang, L. ; Xie, G. ; Kristal, B.S. ; Zheng, X. ; Sun, T. ; Arnold, M. ; Louie, G. ; Li, M. ; Wu, L. ; MahmoudianDehkordi, S. ; Sniatynski, M.J. ; Borkowski, K. ; Guo, Q. ; Kuang, J. ; Wang, J. ; Nho, K. ; Ren, Z. ; Kueider-Paisley, A. ; Blach, C. ; Kaddurah-Daouk, R. ; Jia, W.

Serum bile acids improve prediction of Alzheimer's progression in a sex-dependent manner.

Costeira, R. ; Evangelista, L. ; Wilson, R. ; Yan, X. ; Hellbach, F. ; Sinke, L. ; Christiansen, C. ; Villicaña, S. ; Masachs, O.M. ; Tsai, P.C. ; Mangino, M. ; Menni, C. ; Berry, S.E. ; Beekman, M. ; van Heemst, D. ; Slagboom, P.E. ; Heijmans, B.T. ; Suhre, K. ; Kastenmüller, G. ; Gieger, C. ; Peters, A. ; Small, K.S. ; Linseisen, J. ; Waldenberger, M. ; Bell, J.T.

Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome.

Jo, T. ; Kim, J. ; Bice, P. ; Huynh, K. ; Wang, T. ; Arnold, M. ; Meikle, P.J. ; Giles, C. ; Kaddurah-Daouk, R. ; Saykin, A.J. ; Nho, K. ; Kueider-Paisley, A. ; Doraiswamy, P.M. ; Blach, C. ; Moseley, A. ; Thompson, W. ; St John-Williams, L. ; Mahmoudiandehkhordi, S. ; Tenenbaum, J. ; Welsh-Balmer, K. ; Plassman, B. ; Risacher, S.L. ; Alzheimer's Disease Metabolomics Consortium (ADMC) (Kastenmüller, G.) ; Han, X. ; Baillie, R. ; Knight, R. ; Dorrestein, P. ; Brewer, J. ; Mayer, E. ; Labus, J. ; Baldi, P. ; Gupta, A. ; Fiehn, O. ; Barupal, D. ; Meikle, P. ; Mazmanian, S. ; Rader, D. ; Kling, M. ; Shaw, L. ; Trojanowski, J. ; van Duijin, C. ; Nevado-Holgado, A. ; Bennett, D. ; Krishnan, R. ; Keshavarzian, A. ; Vogt, R. ; Ikram, A. ; Hankemeier, T. ; Price, N. ; Funk, C. ; Baloni, P. ; Jia, W. ; Wishart, D. ; Brinton, R. ; Chang, R. ; Farrer, L. ; Au, R. ; Qiu, W. ; Würtz, P. ; Koal, T. ; Mangravite, L. ; Suhre, K. ; Newman, J. ; Moreno, H. ; Foroud, T. ; Sacks, F. ; Jansson, J. ; Weiner, M.W. ; Aisen, P. ; Petersen, R. ; Jack, C.R. ; Jagust, W. ; Trojanowki, J.Q. ; Toga, A.W. ; Beckett, L. ; Green, R.C. ; Morris, J.C. ; Perrin, R.J. ; Shaw, L.M. ; Khachaturian, Z. ; Carrillo, M. ; Potter, W. ; Barnes, L. ; Bernard, M. ; Gonzalez, H. ; Ho, C. ; Hsiao, J.K. ; Jackson, J. ; Masliah, E. ; Masterman, D. ; Okonkwo, O. ; Perrin, R. ; Ryan, L. ; Silverberg, N. ; Fleisher, A. ; Sacrey, D.T. ; Fockler, J. ; Conti, C.

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: Application to metabolome data.

Kotsis, F. ; Bächle, H. ; Altenbuchinger, M. ; Dönitz, J. ; Njipouombe Nsangou, Y.A. ; Meiselbach, H. ; Kosch, R. ; Salloch, S. ; Bratan, T. ; Zacharias, H.U. ; Schultheiss, U.T.

Expectation of clinical decision support systems: A survey study among nephrologist end-users.

Lee, H. ; Aylward, A.J. ; Pearse, R.V. ; Lish, A.M. ; Hsieh, Y.C. ; Augur, Z.M. ; Benoit, C.R. ; Chou, V. ; Knupp, A. ; Pan, C. ; Goberdhan, S. ; Duong, D.M. ; Seyfried, N.T. ; Bennett, D.A. ; Taga, M.F. ; Huynh, K. ; Arnold, M. ; Meikle, P.J. ; de Jager, P.L. ; Menon, V. ; Young, J.E. ; Young-Pearse, T.L.

Cell-type-specific regulation of APOE and CLU levels in human neurons by the Alzheimer's disease risk gene SORL1.

Nogal, A. ; Alkis, T. ; Lee, Y. ; Kifer, D. ; Hu, J. ; Murphy, R.A. ; Huang, Z. ; Wang-Sattler, R. ; Kastenmüller, G. ; Linkohr, B. ; Barrios, C. ; Crespo, M. ; Gieger, C. ; Peters, A. ; Price, J. ; Rexrode, K.M. ; Yu, B. ; Menni, C.

Predictive metabolites for incident myocardial infarction: A two-step meta-analysis of individual patient data from six cohorts comprising 7,897 individuals from the the COnsortium of METabolomic Studies.

Singh, P. ; Gollapalli, K. ; Mangiola, S. ; Schranner, D. ; Yusuf, M.A. ; Chamoli, M. ; Shi, S.L. ; Lopes Bastos, B. ; Nair, T. ; Riermeier, A. ; Vayndorf, E.M. ; Wu, J.Z. ; Nilakhe, A. ; Nguyen, C.Q. ; Muir, M. ; Kiflezghi, M.G. ; Foulger, A. ; Junker, A. ; Devine, J. ; Sharan, K. ; Chinta, S.J. ; Rajput, S. ; Rane, A. ; Baumert, P. ; Schönfelder, M. ; Iavarone, F. ; di Lorenzo, G. ; Kumari, S. ; Gupta, A. ; Sakar, R. ; Khyriem, C. ; Chawla, A.S. ; Sharma, A. ; Sarper, N. ; Chattopadhyay, N. ; Biswal, B.K. ; Settembre, C. ; Nagarajan, P. ; Targoff, K.L. ; Picard, M. ; Gupta, S. ; Velagapudi, V. ; Papenfuss, A.T. ; Kaya, A. ; Ferreira, M.G. ; Kennedy, B.K. ; Andersen, J.K. ; Lithgow, G.J. ; Ali, A.M. ; Mukhopadhyay, A. ; Palotie, A. ; Kastenmüller, G. ; Kaeberlein, M. ; Wackerhage, H. ; Pal, B. ; Yadav, V.K.

Taurine deficiency as a driver of aging.

Sun, B.B. ; Chiou, J. ; Traylor, M. ; Benner, C. ; Hsu, Y.H. ; Richardson, T.G. ; Surendran, P. ; Mahajan, A. ; Robins, C. ; Vasquez-Grinnell, S.G. ; Hou, L. ; Kvikstad, E.M. ; Burren, O.S. ; Davitte, J. ; Ferber, K.L. ; Gillies, C.E. ; Hedman, A.K. ; Hu, S. ; Lin, T. ; Mikkilineni, R. ; Pendergrass, R.K. ; Pickering, C. ; Prins, B. ; Baird, D. ; Chen, C.Y. ; Ward, L.D. ; Deaton, A.M. ; Welsh, S. ; Willis, C.M. ; Lehner, N. ; Arnold, M. ; Wörheide, M. ; Suhre, K. ; Kastenmüller, G. ; Sethi, A. ; Cule, M. ; Raj, A. ; Kang, H.M. ; Burkitt-Gray, L. ; Melamud, E. ; Black, M.H. ; Fauman, E.B. ; Howson, J.M.M. ; McCarthy, M.I. ; Nioi, P. ; Petrovski, S. ; Scott, R.A. ; Smith, E.N. ; Szalma, S. ; Waterworth, D.M. ; Mitnaul, L.J. ; Szustakowski, J.D. ; Gibson, B.W. ; Miller, M.R. ; Whelan, C.D.

Plasma proteomic associations with genetics and health in the UK Biobank.

Thareja, G. ; Belkadi, A. ; Arnold, M. ; Albagha, O.M.E. ; Graumann, J. ; Schmidt, F. ; Grallert, H. ; Peters, A. ; Gieger, C. ; Suhre, K.

Differences and commonalities in the genetic architecture of protein quantitative trait loci in European and Arab populations.

van der Spek, A. ; Stewart, I.D. ; Kühnel, B. ; Pietzner, M. ; Alshehri, T. ; Gauß, F. ; Hysi, P.G. ; MahmoudianDehkordi, S. ; Heinken, A. ; Luik, A.I. ; Ladwig, K.-H. ; Kastenmüller, G. ; Menni, C. ; Hertel, J. ; Ikram, M.A. ; de Mutsert, R. ; Suhre, K. ; Gieger, C. ; Strauch, K. ; Völzke, H. ; Meitinger, T. ; Mangino, M. ; Flaquer, A. ; Waldenberger, M. ; Peters, A. ; Thiele, I. ; Kaddurah-Daouk, R. ; Dunlop, B.W. ; Rosendaal, F.R. ; Wareham, N.J. ; Spector, T.D. ; Kunze, S. ; Grabe, H.J. ; Mook-Kanamori, D.O. ; Langenberg, C. ; van Duijn, C.M. ; Amin, N.

Circulating metabolites modulated by diet are associated with depression.

Aboulmaouahib, B. ; Kastenmüller, G. ; Suhre, K. ; Zöllner, S. ; Weissensteiner, H. ; Prehn, C. ; Adamski, J. ; Gieger, C. ; Wang-Sattler, R. ; Lichtner, P. ; Strauch, K. ; Flaquer, A.

First mitochondrial genome wide association study with metabolomics.

Baloni, P. ; Arnold, M. ; Buitrago, L.E. ; Nho, K. ; Moreno, H.D. ; Huynh, K. ; Brauner, B. ; Louie, G. ; Kueider-Paisley, A. ; Suhre, K. ; Saykin, A.J. ; Ekroos, K. ; Meikle, P.J. ; Hood, L. ; Price, N.D. ; Doraiswamy, P.M. ; Funk, C.C. ; Hernández, A.I. ; Kastenmüller, G. ; Baillie, R. ; Han, X. ; Kaddurah-Daouk, R.

Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer's disease.

Batra, R. ; Arnold, M. ; Wörheide, M. ; Allen, M. ; Wang, X. ; Blach, C. ; Levey, A.I. ; Seyfried, N.T. ; Ertekin-Taner, N. ; Bennett, D.A. ; Kastenmüller, G. ; Kaddurah-Daouk, R.F. ; Krumsiek, J.

The landscape of metabolic brain alterations in Alzheimer's disease.

Brydges, C.R. ; Bhattacharyya, S. ; Dehkordi, S.M. ; Milaneschi, Y. ; Penninx, B. ; Jansen, R. ; Kristal, B.S. ; Han, X. ; Arnold, M. ; Kastenmüller, G. ; Bekhbat, M. ; Mayberg, H.S. ; Craighead, W.E. ; Rush, A.J. ; Fiehn, O. ; Dunlop, B.W. ; Kaddurah-Daouk, R.

Metabolomic and inflammatory signatures of symptom dimensions in major depression.

Cadby, G. ; Giles, C. ; Melton, P.E. ; Huynh, K. ; Mellett, N.A. ; Duong, T. ; Nguyen, A. ; Cinel, M. ; Smith, A. ; Olshansky, G. ; Wang, T. ; Brozynska, M. ; Inouye, M. ; McCarthy, N.S. ; Ariff, A. ; Hung, J. ; Hui, J. ; Beilby, J. ; Dubé, M.P. ; Watts, G.F. ; Shah, S. ; Wray, N.R. ; Lim, W.L.F. ; Chatterjee, P. ; Martins, I. ; Laws, S.M. ; Porter, T. ; Vacher, M. ; Bush, A.I. ; Rowe, C.C. ; Villemagne, V.L. ; Ames, D. ; Masters, C.L. ; Taddei, K. ; Arnold, M. ; Kastenmüller, G. ; Nho, K. ; Saykin, A.J. ; Han, X. ; Kaddurah-Daouk, R. ; Martins, R.N. ; Blangero, J. ; Meikle, P.J. ; Moses, E.K.

Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease.

Chetnik, K. ; Benedetti, E. ; Gomari, D.P. ; Schweickart, A. ; Batra, R. ; Buyukozkan, M. ; Wang, Z. ; Arnold, M. ; Zierer, J. ; Suhre, K. ; Krumsiek, J.

maplet: An extensible R toolbox for modular and reproducible metabolomics pipelines.

Fiamoncini, J. ; Rist, M.J. ; Frommherz, L. ; Giesbertz, P. ; Pfrang, B. ; Kremer, W. ; Huber, F. ; Kastenmüller, G. ; Skurk, T. ; Hauner, H. ; Suhre, K. ; Daniel, H. ; Kulling, S.E.

Dynamics and determinants of human plasma bile acid profiles during dietary challenges.

Gehlert, S. ; Weinisch, P. ; Römisch-Margl, W. ; Jaspers, R.T. ; Artati, A. ; Adamski, J. ; Dyar, K.A. ; Aussieker, T. ; Jacko, D. ; Bloch, W. ; Wackerhage, H. ; Kastenmüller, G.

Effects of acute and chronic resistance exercise on the skeletal muscle metabolome.

Milaneschi, Y. ; Arnold, M. ; Kastenmüller, G. ; Dehkordi, S.M. ; Krishnan, R.R. ; Dunlop, B.W. ; Rush, A.J. ; Penninx, B.W.J.H. ; Kaddurah-Daouk, R.

Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression.

Suls, J. ; Salive, M.E. ; Koroukian, S.M. ; Alemi, F. ; Silber, J.H. ; Kastenmüller, G. ; Klabunde, C.N.

Emerging approaches to multiple chronic condition assessment.

Surendran, P. ; Stewart, I.D. ; Au Yeung, V.P.W. ; Pietzner, M. ; Raffler, J. ; Wörheide, M. ; Li, C. ; Smith, R.F. ; Wittemans, L.B.L. ; Bomba, L. ; Menni, C. ; Zierer, J. ; Rossi, N. ; Sheridan, P.A. ; Watkins, N.A. ; Mangino, M. ; Hysi, P.G. ; Falchi, M. ; Spector, T.D. ; Michelotti, G.A. ; Arlt, W. ; Lotta, L.A. ; Denaxas, S. ; Hemingway, H. ; Gamazon, E.R. ; Howson, J.M.M. ; Wareham, N.J. ; Kastenmüller, G. ; Fauman, E.B. ; Suhre, K. ; Butterworth, A.S. ; Langenberg, C.

Rare and common genetic determinants of metabolic individuality and their effects on human health.

Thareja, G. ; Evans, A.M. ; Wood, S.D. ; Stephan, N. ; Zaghlool, S. ; Halama, A. ; Kastenmüller, G. ; Belkadi, A. ; Albagha, O.M.E. ; Suhre, K.

Ratios of acetaminophen metabolites identify new loci of pharmacogenetic relevance in a genome-wide association study.

Wang, T. ; Huynh, K. ; Giles, C. ; Mellett, N.A. ; Duong, T. ; Nguyen, A. ; Lim, W.L.F. ; Smith, A.A.T. ; Olshansky, G. ; Cadby, G. ; Hung, J. ; Hui, J. ; Beilby, J. ; Watts, G.F. ; Chatterjee, P. ; Martins, I. ; Laws, S.M. ; Bush, A.I. ; Rowe, C.C. ; Villemagne, V.L. ; Ames, D. ; Masters, C.L. ; Taddei, K. ; Doré, V. ; Fripp, J. ; Arnold, M. ; Kastenmüller, G. ; Nho, K. ; Saykin, A.J. ; Baillie, R. ; Han, X. ; Martins, R.N. ; Moses, E.K. ; Kaddurah-Daouk, R. ; Meikle, P.J.

APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies.

Weinisch, P. ; Fiamoncini, J. ; Schranner, D. ; Raffler, J. ; Skurk, T. ; Rist, M.J. ; Römisch-Margl, W. ; Prehn, C. ; Adamski, J. ; Hauner, H. ; Daniel, H. ; Suhre, K. ; Kastenmüller, G.

Dynamic patterns of postprandial metabolic responses to three dietary challenges.

Zacharias, H.U. ; Altenbuchinger, M. ; Schultheiss, U.T. ; Raffler, J. ; Kotsis, F. ; Ghasemi, S. ; Ali, I. ; Kollerits, B. ; Metzger, M. ; Steinbrenner, I. ; Sekula, P. ; Massy, Z.A. ; Combe, C. ; Kalra, P.A. ; Kronenberg, F. ; Stengel, B. ; Eckardt, K.U. ; Köttgen, A. ; Schmid, M. ; Gronwald, W. ; Oefner, P.J.

A predictive model for progression of CKD to kidney failure based on routine laboratory tests.

Andörfer, L. ; Holtfreter, B. ; Weiss, S. ; Matthes, R. ; Pitchika, V. ; Schmidt, C.O. ; Samietz, S. ; Kastenmüller, G. ; Nauck, M. ; Völker, U. ; Völzke, H. ; Csonka, L.N. ; Suhre, K. ; Pietzner, M. ; Kocher, T.

Salivary metabolites associated with a 5-year tooth loss identified in a population-based setting.

Borkowski, K. ; Taha, A.Y. ; Pedersen, T.L. ; de Jager, P.L. ; Bennett, D.A. ; Arnold, M. ; Kaddurah-Daouk, R. ; Newman, J.W.

Serum metabolomic biomarkers of perceptual speed in cognitively normal and mildly impaired subjects with fasting state stratification.

Brydges, C.R. ; Fiehn, O. ; Mayberg, H.S. ; Schreiber, H. ; Dehkordi, S.M. ; Bhattacharyya, S. ; Cha, J. ; Choi, K.S. ; Craighead, W.E. ; Krishnan, R.R. ; Rush, A.J. ; Dunlop, B.W. ; Kaddurah-Daouk, R. ; Mood Disorders Precision Medicine Consortium (Kastenmüller, G.) ; Mood Disorders Precision Medicine Consortium (Arnold, M.)

Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature.

Deutelmoser, H. ; Scherer, D. ; Brenner, H. ; Waldenberger, M. ; Suhre, K. ; Kastenmüller, G. ; Lorenzo Bermejo, J.

Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data.

Horgusluoglu, E. ; Neff, R. ; Song, W.M. ; Wang, M. ; Wang, Q. ; Arnold, M. ; Krumsiek, J. ; Galindo-Prieto, B. ; Ming, C. ; Nho, K. ; Kastenmüller, G. ; Han, X. ; Baillie, R. ; Zeng, Q. ; Andrews, S. ; Cheng, H. ; Hao, K. ; Goate, A. ; Bennett, D.A. ; Saykin, A.J. ; Kaddurah-Daouk, R. ; Zhang, B.

Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.

Huang, J. ; Covic, M. ; Huth, C. ; Rommel, M. ; Adam, J. ; Zukunft, S. ; Prehn, C. ; Wang, L. ; Nano, J. ; Scheerer, M.F. ; Neschen, S. ; Kastenmüller, G. ; Gieger, C. ; Laxy, M. ; Schliess, F. ; Adamski, J. ; Suhre, K. ; Hrabě de Angelis, M. ; Peters, A. ; Wang-Sattler, R.

Validation of candidate phospholipid biomarkers of chronic kidney disease in hyperglycemic individuals and their organ-specific exploration in leptin receptor-deficient db/db mouse.

Klaus, V. ; Schriever, S.C. ; Monroy Kuhn, J.M. ; Peter, A. ; Irmler, M. ; Tokarz, J. ; Prehn, C. ; Kastenmüller, G. ; Beckers, J. ; Adamski, J. ; Königsrainer, A. ; Müller, T.D. ; Heni, M. ; Tschöp, M.H. ; Pfluger, P.T. ; Lutter, D.

Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism.

Lotta, L.A. ; Pietzner, M. ; Stewart, I.D. ; Wittemans, L.B.L. ; Li, C. ; Bonelli, R. ; Raffler, J. ; Biggs, E.K. ; Oliver-Williams, C. ; Auyeung, V.P.W. ; Luan, J. ; Wheeler, E. ; Paige, E. ; Surendran, P. ; Michelotti, G.A. ; Scott, R.A. ; Burgess, S. ; Zuber, V. ; Sanderson, E. ; Koulman, A. ; Imamura, F. ; Forouhi, N.G. ; Khaw, K.T. ; Bahlo, M. ; Griffin, J.L. ; Kastenmüller, G. ; Gribble, F.M. ; Reimann, F. ; Fauman, E. ; Wareham, N.J. ; Langenberg, C.

A cross-platform approach identifies genetic regulators of human metabolism and health.

MahmoudianDehkordi, S. ; Ahmed, A.T. ; Bhattacharyya, S. ; Han, X. ; Baillie, R.A. ; Arnold, M. ; Skime, M.K. ; John-Williams, L.S. ; Moseley, M.A. ; Thompson, K. ; Louie, G. ; Riva-Posse, P. ; Craighead, W.E. ; McDonald, W. ; Krishnan, R. ; Rush, A.J. ; Frye, M.A. ; Dunlop, B.W. ; Weinshilboum, R.M. ; Kaddurah-Daouk, R.

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression.

Nho, K. ; Kueider-Paisley, A. ; Arnold, M. ; MahmoudianDehkordi, S. ; Risacher, S.L. ; Louie, G. ; Blach, C. ; Baillie, R. ; Han, X. ; Kastenmüller, G. ; Doraiswamy, P.M. ; Kaddurah-Daouk, R. ; Saykin, A.J.

Serum metabolites associated with brain amyloid beta deposition, cognition and dementia progression.

Pietzner, M. ; Stewart, I.D. ; Raffler, J. ; Khaw, K.T. ; Michelotti, G.A. ; Kastenmüller, G. ; Wareham, N.J. ; Langenberg, C.

Plasma metabolites to profile pathways in noncommunicable disease multimorbidity.

Pietzner, M. ; Wheeler, E. ; Carrasco-Zanini, J. ; Cortes, A. ; Koprulu, M. ; Wörheide, M. ; Oerton, E. ; Cook, J. ; Stewart, I.D. ; Kerrison, N.D. ; Luan, J. ; Raffler, J. ; Arnold, M. ; Arlt, W. ; O'Rahilly, S. ; Kastenmüller, G. ; Gamazon, E.R. ; Hingorani, A.D. ; Scott, R.A. ; Wareham, N.J. ; Langenberg, C.

Mapping the proteo-genomic convergence of human diseases.

Portero, V. ; Nicol, T. ; Podliesna, S. ; Marchal, G.A. ; Baartscheer, A. ; Casini, S. ; Tadros, R. ; Treur, J.L. ; Tanck, M.W.T. ; Cox, I.J. ; Probert, F. ; Hough, T.A. ; Falcone, S. ; Beekman, L. ; Müller-Nurasyid, M. ; Kastenmüller, G. ; Gieger, C. ; Peters, A. ; Kääb, S. ; Sinner, M.F. ; Blease, A. ; Verkerk, A.O. ; Bezzina, C.R. ; Potter, P.K. ; Remme, C.A.

Chronically elevated branched chain amino acid levels are pro-arrhythmic.

Schranner, D. ; Schönfelder, M. ; Römisch-Margl, W. ; Scherr, J. ; Schlegel, J. ; Zelger, O. ; Riermeier, A. ; Kaps, S. ; Prehn, C. ; Adamski, J. ; Söhnlein, Q. ; Stocker, F. ; Kreuzpointner, F. ; Halle, M. ; Kastenmüller, G. ; Wackerhage, H.

Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes.

Wörheide, M. ; Krumsiek, J. ; Kastenmüller, G. ; Arnold, M.

Multi-omics integration in biomedical research – A metabolomics-centric review.

Zacharias, H.U. ; Hertel, J. ; Johar, H. ; Pietzner, M. ; Lukaschek, K. ; Atasoy, S. ; Kunze, S. ; Völzke, H. ; Nauck, M. ; Friedrich, N. ; Kastenmüller, G. ; Grabe, H.J. ; Gieger, C. ; Krumsiek, J. ; Ladwig, K.-H.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression.

Ahmed, A.T. ; MahmoudianDehkordi, S. ; Bhattacharyya, S. ; Arnold, M. ; Liu, D. ; Neavin, D. ; Moseley, M.A. ; Thompson, K. ; Williams, L.S.J. ; Louie, G. ; Skime, M.K. ; Wang, L. ; Riva-Posse, P. ; McDonald, W.M. ; Bobo, W.V. ; Craighead, W.E. ; Krishnan, R. ; Weinshilboum, R.M. ; Dunlop, B.W. ; Millington, D.S. ; Rush, A.J. ; Frye, M.A. ; Kaddurah-Daouk, R.

Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes.

Arnold, M. ; Nho, K. ; Kueider-Paisley, A. ; Massaro, T. ; Huynh, K. ; Brauner, B. ; MahmoudianDehkordi, S. ; Louie, G. ; Moseley, M.A. ; Thompson, K. ; St John-Williams, L. ; Tenenbaum, J.D. ; Blach, C. ; Chang, R. ; Brinton, R.D. ; Baillie, R. ; Han, X. ; Trojanowski, J.Q. ; Shaw, L.M. ; Martins, R. ; Weiner, M.W. ; Trushina, E. ; Toledo, J.B. ; Meikle, P.J. ; Bennett, D.A. ; Krumsiek, J. ; Doraiswamy, P.M. ; Saykin, A.J. ; Kaddurah-Daouk, R. ; Kastenmüller, G.

Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome.

Baloni, P. ; Funk, C.C. ; Yan, J. ; Yurkovich, J.T. ; Kueider-Paisley, A. ; Nho, K. ; Heinken, A. ; Jia, W. ; MahmoudianDehkordi, S. ; Louie, G. ; Saykin, A.J. ; Arnold, M. ; Kastenmüller, G. ; Griffiths, W.J. ; Thiele, I. ; Kaddurah-Daouk, R. ; Price, N.D.

Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer's Disease.

Benedetti, E. ; Pučić-Baković, M. ; Keser, T. ; Gerstner, N. ; Büyüközkan, M. ; Štambuk, T. ; Selman, M.H.J. ; Rudan, I. ; Polašek, O. ; Hayward, C. ; Al-Amin, H. ; Suhre, K. ; Kastenmüller, G. ; Lauc, G. ; Krumsiek, J.

A strategy to incorporate prior knowledge into correlation network cutoff selection.

Bernath, M.M. ; Bhattacharyya, S. ; Nho, K. ; Barupal, D.K. ; Fiehn, O. ; Baillie, R. ; Risacher, S.L. ; Arnold, M. ; Jacobson, T. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; Kaddurah-Daouk, R. ; Saykin, A.J.

Serum triglycerides in Alzheimer disease: Relation to neuroimaging and CSF biomarkers.

Chouvarine, P. ; Giera, M. ; Kastenmüller, G. ; Artati, A. ; Adamski, J. ; Bertram, H. ; Hansmann, G.

Trans-right ventricle and transpulmonary metabolite gradients in human pulmonary arterial hypertension.

Faquih, T. ; van Smeden, M. ; Luo, J. ; le Cessie, S. ; Kastenmüller, G. ; Krumsiek, J. ; Noordam, R. ; van Heemst, D. ; Rosendaal, F.R. ; van Hylckama Vlieg, A. ; Willems van Dijk, K. ; Mook-Kanamori, D.O.

A workflow for missing values imputation of untargeted metabolomics data.

Huang, J. ; Huth, C. ; Covic, M. ; Troll, M. ; Adam, J. ; Zukunft, S. ; Prehn, C. ; Wang, L. ; Nano, J. ; Scheerer, M.F. ; Neschen, S. ; Kastenmüller, G. ; Suhre, K. ; Laxy, M. ; Schliess, F. ; Gieger, C. ; Adamski, J. ; Hrabě de Angelis, M. ; Peters, A. ; Wang-Sattler, R.

Machine learning approaches reveal metabolic signatures of incident chronic kidney disease in individuals with prediabetes and type 2 diabetes.

Huynh, K. ; Lim, W.L.F. ; Giles, C. ; Jayawardana, K.S. ; Salim, A. ; Mellett, N.A. ; Smith, A.A.T. ; Olshansky, G. ; Drew, B.G. ; Chatterjee, P. ; Martins, I. ; Laws, S.M. ; Bush, A.I. ; Rowe, C.C. ; Villemagne, V.L. ; Ames, D. ; Masters, C.L. ; Arnold, M. ; Nho, K. ; Saykin, A.J. ; Baillie, R. ; Han, X. ; Kaddurah-Daouk, R. ; Meikle, P.J.

Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer’s disease.

Kling, M.A. ; Goodenowe, D.B. ; Senanayake, V. ; MahmoudianDehkordi, S. ; Arnold, M. ; Massaro, T.J. ; Baillie, R. ; Han, X. ; Leung, Y.Y. ; Saykin, A.J. ; Nho, K. ; Kueider-Paisley, A. ; Tenenbaum, J.D. ; Wang, L.S. ; Shaw, L.M. ; Trojanowski, J.Q. ; Kaddurah-Daouk, R.F.

Circulating ethanolamine plasmalogen indices in Alzheimer's disease: Relation to diagnosis, cognition, and CSF tau.

Langenau, J. ; Oluwagbemigun, K. ; Brachem, C. ; Lieb, W. ; di Giuseppe, R. ; Artati, A. ; Kastenmüller, G. ; Weinhold, L. ; Schmid, M. ; Nöthlings, U.

Blood metabolomic profiling confirms and identifies biomarkers of food intake.

Nag, A. ; Kurushima, Y. ; Bowyer, R.C.E. ; Wells, P.M. ; Weiss, S. ; Pietzner, M. ; Kocher, T. ; Raffler, J. ; Völker, U. ; Mangino, M. ; Spector, T.D. ; Milburn, M.V. ; Kastenmüller, G. ; Mohney, R.P. ; Suhre, K. ; Menni, C. ; Steves, C.J.

Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels.

Ott, R. ; Pawlow, X. ; Weiss, A. ; Hofelich, A. ; Herbst, M. ; Hummel, N. ; Prehn, C. ; Adamski, J. ; Römisch-Margl, W. ; Kastenmüller, G. ; Ziegler, A.-G. ; Hummel, S.

Intergenerational metabolomic analysis of mothers with a history of gestational diabetes mellitus and their offspring.

Otto, L. ; Budde, K. ; Kastenmüller, G. ; Kaul, A. ; Völker, U. ; Völzke, H. ; Adamski, J. ; Kühn, J.P. ; Krumsiek, J. ; Artati, A. ; Nauck, M. ; Friedrich, N. ; Pietzner, M.

Associations between adipose tissue volume and small molecules in plasma and urine among asymptomatic subjects from the general population.

Pietzner, M. ; Wheeler, E. ; Carrasco-Zanini, J. ; Raffler, J. ; Kerrison, N.D. ; Oerton, E. ; Auyeung, V.P.W. ; Luan, J. ; Finan, C. ; Casas, J.P. ; Ostroff, R. ; Williams, S.A. ; Kastenmüller, G. ; Ralser, M. ; Gamazon, E.R. ; Wareham, N.J. ; Hingorani, A.D. ; Langenberg, C.

Genetic architecture of host proteins involved in SARS-CoV-2 infection.

Schlosser, P. ; Li, Y. ; Sekula, P. ; Raffler, J. ; Grundner-Culemann, F. ; Pietzner, M. ; Cheng, Y. ; Wuttke, M. ; Steinbrenner, I. ; Schultheiss, U.T. ; Kotsis, F. ; Kacprowski, T. ; Forer, L. ; Hausknecht, B. ; Ekici, A.B. ; Nauck, M. ; Völker, U. ; Walz, G. ; Oefner, P.J. ; Kronenberg, F. ; Mohney, R.P. ; Köttgen, M. ; Suhre, K. ; Eckardt, K.U. ; Kastenmüller, G. ; Köttgen, A.

Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans.

Schranner, D. ; Kastenmüller, G. ; Schönfelder, M. ; Römisch-Margl, W. ; Wackerhage, H.

Metabolite concentration changes in humans after a bout of exercise: A systematic review of exercise metabolomics studies.

Sieberts, S.K. ; Perumal, T.M. ; Carrasquillo, M.M. ; Allen, M. ; Reddy, J.S. ; Hoffman, G.E. ; Dang, K.K. ; Calley, J. ; Ebert, P.J. ; Eddy, J. ; Wang, X. ; Greenwood, A.K. ; Mostafavi, S. ; Omberg, L. ; Peters, M.A. ; Logsdon, B.A. ; de Jager, P.L. ; Ertekin-Taner, N. ; Mangravite, L.M. ; The AMP-AD Consortium (Arnold, M.)

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions.

Wang, J. ; Wei, R. ; Xie, G. ; Arnold, M. ; Kueider-Paisley, A. ; Louie, G. ; Mahmoudian Dehkordi, S. ; Blach, C. ; Baillie, R. ; Han, X. ; de Jager, P.L. ; Bennett, D.A. ; Kaddurah-Daouk, R. ; Jia, W.

Peripheral serum metabolomic profiles inform central cognitive impairment.

Zhao, N. ; Ren, Y. ; Yamazaki, Y. ; Qiao, W. ; Li, F. ; Felton, L.M. ; MahmoudianDehkordi, S. ; Kueider-Paisley, A. ; Sonoustoun, B. ; Arnold, M. ; Shue, F. ; Zheng, J. ; Attrebi, O.N. ; Martens, Y.A. ; Li, Z. ; Bastea, L. ; Meneses, A.D. ; Chen, K. ; Thompson, K. ; St John-Williams, L. ; Tachibana, M. ; Aikawa, T. ; Oue, H. ; Job, L. ; Yamazaki, A. ; Liu, C.C. ; Storz, P. ; Asmann, Y.W. ; Ertekin-Taner, N. ; Kanekiyo, T. ; Kaddurah-Daouk, R. ; Bu, G.

Alzheimer's risk factors age, APOE genotype, and sex drive distinct. 3 molecular pathways.

Barupal, D.K. ; Baillie, R. ; Fan, S. ; Saykin, A.J. ; Meikle, P.J. ; Arnold, M. ; Nho, K. ; Fiehn, O. ; Kaddurah-Daouk, R. ; Alzheimer's Disease Neuroimaging Initiative ; Alzheimer Disease Metabolomics Consortium

Sets of coregulated serum lipids are associated with Alzheimer's disease pathophysiology.

Bhattacharyya, S. ; Ahmed, A.T. ; Arnold, M. ; Liu, D. ; Luo, C. ; Zhu, H. ; MahmoudianDehkordi, S. ; Neavin, D. ; Louie, G. ; Dunlop, B.W. ; Frye, M.A. ; Wang, L. ; Weinshilboum, R.M. ; Krishnan, R.R. ; Rush, A.J. ; Kaddurah-Daouk, R.

Metabolomic signature of exposure and response to citalopram/escitalopram in depressed outpatients.

Deelen, J. ; Kettunen, J. ; Fischer, K. ; van der Spek, A. ; Trompet, S. ; Kastenmüller, G. ; Boyd, A.W. ; Zierer, J. ; van den Akker, E.B. ; Amin, N. ; Demirkan, A. ; Ghanbari, M. ; van Heemst, D. ; Ikram, M.A. ; van Klinken, J.B. ; Mooijaart, S.P. ; Peters, A. ; Salomaa, V. ; Sattar, N. ; Spector, T.D. ; Tiemeier, H. ; Verhoeven, A. ; Waldenberger, M. ; Würtz, P. ; Davey Smith, G. ; Metspalu, A. ; Perola, M. ; Menni, C. ; Geleijnse, J.M. ; Drenos, F. ; Beekman, M. ; Jukema, J.W. ; van Duijn, C.M. ; Slagboom, P.E.

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals.

di Giuseppe, R. ; Koch, M. ; Nöthlings, U. ; Kastenmüller, G. ; Artati, A. ; Adamski, J. ; Jacobs, G. ; Lieb, W.

Metabolomics signature associated with circulating serum selenoprotein P levels.

Do, K.T. ; Rasp, D.J.N.P. ; Kastenmüller, G. ; Suhre, K. ; Krumsiek, J.

MoDentify: Phenotype-driven module identification in metabolomics networks at different resolutions.

Goudey, B. ; Fung, B.J. ; Schieber, C. ; Faux, N.G. ; Alzheimer's Disease Neuroimaging Initiative (Kastenmüller, G. ; Arnold, M.)

A blood-based signature of cerebrospinal fluid Aβ_1-42 status.

Hamad, S. ; Adornetto, G. ; Naveja, J.J. ; Ravindranath, A.C. ; Raffler, J. ; Campillos, M.

HitPickV2: A web server to predict targets of chemical compounds.

Liebsch, C. ; Pitchika, V. ; Pink, C. ; Samietz, S. ; Kastenmüller, G. ; Artati, A. ; Suhre, K. ; Adamski, J. ; Nauck, M. ; Völzke, H. ; Friedrich, N. ; Kocher, T. ; Holtfreter, B. ; Pietzner, M.

The saliva metabolome in association to oral health status.

Masuch, A. ; Budde, K. ; Kastenmüller, G. ; Artati, A. ; Adamski, J. ; Völzke, H. ; Nauck, M. ; Pietzner, M.

Metabolic signature associated with parameters of the complete blood count in apparently healthy individuals.

Matejka, K. ; Stückler, F. ; Salomon, M. ; Ensenauer, R. ; Reischl, E. ; Hoerburger, L. ; Grallert, H. ; Kastenmüller, G. ; Peters, A. ; Daniel, H. ; Krumsiek, J. ; Theis, F.J. ; Hauner, H. ; Laumen, H.

Dynamic modelling of an ACADS genotype in fatty acid oxidation - Application of cellular models for the analysis of common genetic variants.

Nho, K. ; Kueider-Paisley, A. ; MahmoudianDehkordi, S. ; Arnold, M. ; Risacher, S.L. ; Louie, G. ; Blach, C. ; Baillie, R.A. ; Han, X. ; Kastenmüller, G. ; Jia, W. ; Xie, G. ; Ahmad, S. ; Hankemeier, T. ; van Duijn, C.M. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; Saykin, A.J. ; Kaddurah-Daouk, R.

Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers.

Nho, K. ; Kueider-Paisley, A. ; Ahmad, S. ; MahmoudianDehkordi, S. ; Arnold, M. ; Risacher, S.L. ; Louie, G. ; Blach, C. ; Baillie, R. ; Han, X. ; Kastenmüller, G. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; van Duijn, M. ; Saykin, A.J. ; Kaddurah-Daouk, R.

Association of altered liver enzymes with alzheimer disease diagnosis, cognition, neuroimaging measures, and cerebrospinal fluid biomarkers.

Pietzner, M. ; Köhrle, J. ; Lehmpuhl, I. ; Budde, K. ; Kastenmüller, G. ; Brabant, G. ; Völzke, H. ; Artati, A. ; Adamski, J. ; Völker, U. ; Nauck, M. ; Friedrich, N. ; Homuth, G.

A thyroid hormone-independent molecular fingerprint of 3,5-diiodothyronine suggests a strong relation with coffee metabolism in humans.

Quell, J. ; Römisch-Margl, W. ; Haid, M. ; Krumsiek, J. ; Skurk, T. ; Halama, A. ; Stephan, N. ; Adamski, J. ; Hauner, H. ; Mook-Kanamori, D.O. ; Mohney, R.P. ; Daniel, H. ; Suhre, K. ; Kastenmüller, G.

Characterization of bulk phosphatidylcholine compositions in human plasma using side-chain resolving lipidomics.

St John-Williams, L. ; MahmoudianDehkordi, S. ; Arnold, M. ; Massaro, T. ; Blach, C. ; Kastenmüller, G. ; Louie, G. ; Kueider-Paisley, A. ; Han, X. ; Baillie, R. ; Motsinger-Reif, A.A. ; Rotroff, D. ; Nho, K. ; Saykin, A.J. ; Risacher, S.L. ; Koal, T. ; Moseley, M.A. ; Tenenbaum, J.D. ; Thompson, K. ; Kaddurah-Daouk, R. ; Alzheimer Disease Metabolomics Consortium

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.

Wildberg, C. ; Masuch, A. ; Budde, K. ; Kastenmüller, G. ; Artati, A. ; Rathmann, W. ; Adamski, J. ; Kocher, T. ; Völzke, H. ; Nauck, M. ; Friedrich, N. ; Pietzner, M.

Plasma metabolomics to identify and stratify patients with impaired glucose tolerance.

Yu, B. ; Flexeder, C. ; McGarrah, R.W. ; Wyss, A. ; Morrison, A.C. ; North, K.E. ; Boerwinkle, E. ; Kastenmüller, G. ; Gieger, C. ; Suhre, K. ; Karrasch, S. ; Peters, A. ; Wagner, G.R. ; Michelotti, G.A. ; Mohney, R.P. ; Schulz, H. ; London, S.J.

Metabolomics identifies novel blood biomarkers of pulmonary function and COPD in the general population.

Adler, A. ; Kirchmeier, P. ; Reinhard, J. ; Brauner, B. ; Dunger, I. ; Fobo, G. ; Frishman, G. ; Montrone, C. ; Mewes, H.-W. ; Arnold, M. ; Ruepp, A.

PhenoDis: A comprehensive database for phenotypic characterization of rare cardiac diseases.

Arnold, M. ; Raffler, J. ; Suhre, K. ; Kastenmüller, G.

Datenbasierte Funktionsvorhersage krankheitsrelevanter genetischer Varianten.

Barrios, C. ; Zierer, J. ; Würtz, P. ; Haller, T. ; Metspalu, A. ; Gieger, C. ; Thorand, B. ; Meisinger, C. ; Waldenberger, M. ; Raitakari, O. ; Lehtimäki, T. ; Otero, S. ; Rodríguez, E. ; Pedro-Botet, J. ; Kähönen, M. ; Ala-Korpela, M. ; Kastenmüller, G. ; Spector, T.D. ; Pascual, J. ; Menni, C.

Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations.

Do, K.T. ; Wahl, S. ; Raffler, J. ; Molnos, S. ; Laimighofer, M. ; Adamski, J. ; Suhre, K. ; Strauch, K. ; Peters, A. ; Gieger, C. ; Langenberg, C. ; Stewart, I.D. ; Theis, F.J. ; Grallert, H. ; Kastenmüller, G. ; Krumsiek, J.

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.

Haid, M. ; Muschet, C. ; Wahl, S. ; Römisch-Margl, W. ; Prehn, C. ; Möller, G. ; Adamski, J.

Long-Term Stability of Human Plasma Metabolites during Storage at-80 degrees C.

Halama, A. ; Kulinski, M. ; Dib, S.S. ; Zaghlool, S.B. ; Siveen, K.S. ; Iskandarani, A. ; Zierer, J. ; Prabhu, K.S. ; Satheesh, N.J. ; Bhagwat, A.M. ; Uddin, S. ; Kastenmüller, G. ; Elemento, O. ; Gross, S.S. ; Suhre, K.

Accelerated lipid catabolism and autophagy are cancer survival mechanisms under inhibited glutaminolysis.

Kaul, A. ; Masuch, A. ; Budde, K. ; Kastenmüller, G. ; Artati, A. ; Adamski, J. ; Völzke, H. ; Nauck, M. ; Friedrich, N. ; Pietzner, M.

Molecular fingerprints of iron parameters among a population-based sample.

Köttgen, A. ; Raffler, J. ; Sekula, P. ; Kastenmüller, G.

Genome-wide association studies of metabolite concentrations (mGWAS): Relevance for nephrology.

Lacruz, M.E. ; Kluttig, A. ; Tiller, D. ; Medenwald, D. ; Giegling, I. ; Rujescu, D. ; Prehn, C. ; Adamski, J. ; Greiser, K.H. ; Kastenmüller, G.

Instability of personal human metabotype is linked to all-cause mortality.

Lange, T. ; Budde, K. ; Homuth, G. ; Kastenmüller, G. ; Artati, A. ; Krumsiek, J. ; Völzke, H. ; Adamski, J. ; Petersmann, A. ; Völker, U. ; Nauck, M. ; Friedrich, N. ; Pietzner, M.

Comprehensive metabolic profiling reveals a lipid-rich fingerprint of free thyroxine far beyond classic parameters.

MahmoudianDehkordi, S. ; Arnold, M. ; Nho, K. ; Ahmad, S. ; Jia, W. ; Xie, G. ; Louie, G. ; Kueider-Paisley, A. ; Moseley, M.A. ; Thompson, K. ; St John Williams, L. ; Tenenbaum, J.D. ; Blach, C. ; Baillie, R.A. ; Han, X. ; Bhattacharyya, S. ; Toledo, J.B. ; Schafferer, S. ; Klein, S. ; Koal, T. ; Risacher, S.L. ; Kling, M.A. ; Motsinger-Reif, A. ; Rotroff, D.M. ; Jack, J.R. ; Hankemeier, T. ; Bennett, D.A. ; de Jager, P.L. ; Trojanowski, J.Q. ; Shaw, L.M. ; Weiner, M.W. ; Doraiswamy, P.M. ; van Duijn, C.M. ; Saykin, A.J. ; Kastenmüller, G. ; Kaddurah-Daouk, R.

Altered bile acid profile associates with cognitive impairment in Alzheimer's disease-An emerging role for gut microbiome.

Masuch, A. ; Pietzner, M. ; Bahls, M. ; Budde, K. ; Kastenmüller, G. ; Zylla, S. ; Artati, A. ; Adamski, J. ; Völzke, H. ; Dörr, M. ; Felix, S.B. ; Nauck, M. ; Friedrich, N.

Metabolomic profiling implicates adiponectin as mediator of a favorable lipoprotein profile associated with NT-proBNP.

Pietzner, M. ; Budde, K. ; Homuth, G. ; Kastenmüller, G. ; Henning, A.-K. ; Artati, A. ; Krumsiek, J. ; Völzke, H. ; Adamski, J. ; Lerch, M.M. ; Kühn, J.P. ; Nauck, M. ; Friedrich, N.

Hepatic steatosis is associates with adverse molecular signatures in subjects without diabetes.

Pitchika, A. ; Jolink, M. ; Winkler, C. ; Hummel, S. ; Hummel, N. ; Krumsiek, J. ; Kastenmüller, G. ; Raab, J. ; Kordonouri, O. ; Ziegler, A.-G. ; Beyerlein, A.

Associations of maternal type 1 diabetes with childhood adiposity and metabolic health in the offspring: A prospective cohort study.

Saulnier-Blache, J.S. ; Wilson, R. ; Klavins, K. ; Graham, D. ; Alesutan, I. ; Kastenmüller, G. ; Wang-Sattler, R. ; Adamski, J. ; Roden, M. ; Rathmann, W. ; Seissler, J. ; Meisinger, C. ; Koenig, W. ; Thiery, J. ; Suhre, K. ; Peters, A. ; Kuro-O, M. ; Lang, F. ; Dallmann, G. ; Delles, C. ; Voelkl, J. ; Waldenberger, M. ; Bascands, J.-L. ; Klein, J. ; Schanstra, J.P.

Ldlr(-/-) and ApoE(-/-) mice better mimic the human metabolite signature of increased carotid intima media thickness compared to other animal models of cardiovascular disease.

Ldlr-/- and ApoE-/- mice better mimic the human metabolite signature of increased carotid intima media thickness compared to other animal models of cardiovascular disease.

Sedlmeier, A. ; Kluttig, A. ; Giegling, I. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Lacruz, M.E.

The human metabolic profile reflects macro- and micronutrient intake distinctly according to fasting time.

Tsepilov, Y.A. ; Sharapov, S.Z. ; Zaytseva, O.O. ; Krumsiek, J. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Wang-Sattler, R. ; Strauch, K. ; Gieger, C. ; Aulchenko, Y.S.

A network-based conditional genetic association analysis of the human metabolome.

Varma, V.R. ; Oommen, A.M. ; Varma, S. ; Casanova, R. ; An, Y. ; Andrews, R.M. ; O'Brien, R.M. ; Pletnikova, O. ; Troncoso, J.C. ; Toledo, J.B. ; Baillie, R.A. ; Arnold, M. ; Kastenmüller, G. ; Nho, K. ; Doraiswamy, P.M. ; Saykin, A.J. ; Kaddurah-Daouk, R. ; Legido-Quigley, C. ; Thambisetty, M.

Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study.

Ward-Caviness, C.K. ; Agha, G. ; Chen, B.H. ; Pfeifer, L. ; Wilson, R. ; Wolf, P. ; Gieger, C. ; Schwartz, J. ; Vokonas, P.S. ; Hou, L. ; Just, A.C. ; Bandinelli, S. ; Hernandez, D.G. ; Singleton, A.B. ; Prokisch, H. ; Meitinger, T. ; Kastenmüller, G. ; Ferrucci, L. ; Baccarelli, A.A. ; Waldenberger, M. ; Peters, A.

Analysis of repeated leukocyte DNA methylation assessments reveals persistent epigenetic alterations after an incident myocardial infarction.

Zaghlool, S.B. ; Mook-Kanamori, D.O. ; Kader, S. ; Stephan, N. ; Halama, A. ; Engelke, R. ; Sarwath, H. ; Al-Dous, E.K. ; Mohamoud, Y.A. ; Römisch-Margl, W. ; Adamski, J. ; Kastenmüller, G. ; Friedrich, N. ; Visconti, A. ; Tsai, P.C. ; Spector, T. ; Bell, J. ; Falchi, M. ; Wahl, A. ; Waldenberger, M. ; Peters, A. ; Gieger, C. ; Pezer, M. ; Lauc, G. ; Graumann, J. ; Malek, J.A. ; Suhre, K.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation.

Zierer, J. ; Jackson, M.A. ; Kastenmüller, G. ; Mangino, M. ; Long, T.C. ; Telenti, A. ; Mohney, R.P. ; Small, K.S. ; Bell, J.T. ; Steves, C.J. ; Valdes, A.M. ; Spector, T.D. ; Menni, C.

The fecal metabolome as a functional readout of the gut microbiome.

Entenmann, L. ; Pietzner, M. ; Artati, A. ; Hannemann, A. ; Henning, A.K. ; Kastenmüller, G. ; Völzke, H. ; Nauck, M. ; Adamski, J. ; Wallaschofski, H. ; Friedrich, N.

Comprehensive metabolic characterization of serum osteocalcin action in a large non-diabetic sample.

Hertel, J. ; König, J. ; Homuth, G. ; Van der Auwera, S. ; Wittfeld, K. ; Pietzner, M. ; Kacprowski, T. ; Pfeifer, L. ; Kretschmer, A. ; Waldenberger, M. ; Kastenmüller, G. ; Artati, A. ; Suhre, K. ; Adamski, J. ; Langner, S. ; Völker, U. ; Völzke, H. ; Nauck, M. ; Friedrich, N. ; Grabe, H.J.

Evidence for stress-like alterations in the HPA-Axis in women taking oral contraceptives.

Keuper, M. ; Sachs, S. ; Walheim, E. ; Berti, L. ; Raedle, B. ; Tews, D. ; Fischer-Posovszky, P. ; Wabitsch, M. ; Hrabě de Angelis, M. ; Kastenmüller, G. ; Tschöp, M.H. ; Jastroch, M. ; Staiger, H. ; Hofmann, S.M.

Activated macrophages control human adipocyte mitochondrial bioenergetics via secreted factors.

Kim, H.I. ; Raffler, J. ; Lu, W. ; Lee, J.J. ; Abbey, D. ; Saleheen, D. ; Rabinowitz, J.D. ; Bennett, M.J. ; Hand, N.J. ; Brown, C.T. ; Rader, D.J.

Fine mapping and functional analysis reveal a role of SLC22A1 in acylcarnitine transport.

Koch, M. ; Freitag-Wolf, S. ; Schlesinger, S. ; Borggrefe, J. ; Hov, J.R. ; Jensen, M.K. ; Pick, J. ; Markus, M.R. ; Höpfner, T. ; Jacobs, G. ; Siegert, S. ; Artati, A. ; Kastenmüller, G. ; Römisch-Margl, W. ; Adamski, J. ; Illig, T. ; Nothnagel, M. ; Karlsen, T.H. ; Schreiber, S. ; Franke, A. ; Krawczak, M. ; Nöthlings, U. ; Lieb, W.

Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample.

Kremer, L.S. ; Bader, D.M. ; Mertes, C. ; Kopajtich, R. ; Pichler, G. ; Iuso, A. ; Haack, T.B. ; Graf, E. ; Schwarzmayr, T. ; Terrile, C. ; Konarikova, E. ; Repp, B. ; Kastenmüller, G. ; Adamski, J. ; Lichtner, P. ; Leonhardt, C. ; Funalot, B. ; Donati, A. ; Tiranti, V. ; Lombes, A. ; Jardel, C. ; Gläser, D. ; Taylor, R.W. ; Ghezzi, D. ; Mayr, J.A. ; Rötig, A. ; Freisinger, P. ; Distelmaier, F. ; Strom, T.M. ; Meitinger, T. ; Gagneur, J. ; Prokisch, H.

Genetic diagnosis of Mendelian disorders via RNA sequencing.

Menni, C. ; Migaud, M. ; Kastenmüller, G. ; Pallister, T. ; Zierer, J. ; Peters, A. ; Mohney, R.P. ; Spector, T.D. ; Bagnardi, V. ; Gieger, C. ; Moore, S.C. ; Valdes, A.M.

Metabolomic profiling of long-term weight change: Role of oxidative stress and urate levels in weight gain.

Molnos, S. ; Baumbach, C. ; Wahl, S. ; Müller-Nurasyid, M. ; Strauch, K. ; Wang-Sattler, R. ; Waldenberger, M. ; Meitinger, T. ; Adamski, J. ; Kastenmüller, G. ; Suhre, K. ; Peters, A. ; Grallert, H. ; Theis, F.J. ; Gieger, C.

pulver: An R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

Pietzner, M. ; Kaul, A.K. ; Henning, A.-K. ; Kastenmüller, G. ; Artati, A. ; Lerch, M.M. ; Adamski, J. ; Nauck, M. ; Friedrich, N.

Comprehensive metabolic profiling of chronic low-grade inflammation among generally healthy individuals.

Piontek, U. ; Wallaschofski, H. ; Kastenmüller, G. ; Suhre, K. ; Völzke, H. ; Do, K.T. ; Artati, A. ; Nauck, M. ; Adamski, J. ; Friedrich, N. ; Pietzner, M.

Sex-specific metabolic profiles of androgens and its main binding protein SHBG in a middle aged population without diabetes.

Quell, J. ; Römisch-Margl, W. ; Colombo, M. ; Krumsiek, J. ; Evans, A.M. ; Mohney, R.P. ; Salomaa, V. ; de Faire, U. ; Groop, L.C. ; Agakov, F.V. ; Looker, H.C. ; McKeigue, P.M. ; Colhoun, H.M. ; Kastenmüller, G.

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies.

Rueedi, R. ; Mallol, R. ; Raffler, J. ; Lamparter, D. ; Friedrich, N. ; Vollenweider, P. ; Waeber, G. ; Kastenmüller, G. ; Kutalik, Z. ; Bergmann, S.

Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy.

Sekula, P. ; Dettmer, K. ; Vogl, F.C. ; Gronwald, W. ; Ellmann, L. ; Mohney, R.P. ; Eckardt, K.U. ; Suhre, K. ; Kastenmüller, G. ; Oefner, P.J. ; Köttgen, A.

From discovery to translation: Characterization of c-mannosyltryptophan and pseudouridine as markers of kidney function.

Siskos, A.P. ; Jain, P. ; Römisch-Margl, W. ; Bennett, M.J. ; Achaintre, D. ; Asad, Y. ; Marney, L. ; Richardson, L. ; Koulman, A. ; Griffin, J.L. ; Raynaud, F. ; Scalbert, A. ; Adamski, J. ; Prehn, C. ; Keun, H.C.

Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma.

St John-Williams, L. ; Blach, C. ; Toledo, J.B. ; Rotroff, D.M. ; Kim, S. ; Klavins, K. ; Baillie, R.A. ; Han, X. ; MahmoudianDehkordi, S. ; Jack, J.R. ; Massaro, T.J. ; Lucas, J.E. ; Louie, G. ; Motsinger-Reif, A.A. ; Risacher, S.L. ; Saykin, A.J. ; Kastenmüller, G. ; Arnold, M. ; Koal, T. ; Moseley, M.A. ; Mangravite, L.M. ; Peters, M.A. ; Tenenbaum, J.D. ; Thompson, K. ; Kaddurah-Daouk, R.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

Suhre, K. ; Arnold, M. ; Bhagwat, A.M. ; Cotton, R.J. ; Engelke, R. ; Raffler, J. ; Sarwath, H. ; Thareja, G. ; Wahl, A. ; DeLisle, R.K. ; Gold, L. ; Pezer, M. ; Lauc, G. ; El-Din Selim, M.A. ; Mook-Kanamori, D.O. ; Al-Dous, E.K. ; Mohamoud, Y.A. ; Malek, J.A. ; Strauch, K. ; Grallert, H. ; Peters, A. ; Kastenmüller, G. ; Gieger, C. ; Graumann, J.

Connecting genetic risk to disease end points through the human blood plasma proteome.

Toledo, J.B. ; Arnold, M. ; Kastenmüller, G. ; Chang, R. ; Baillie, R.A. ; Han, X. ; Thambisetty, M. ; Tenenbaum, J.D. ; Suhre, K. ; Thompson, K. ; John-Williams, L.S. ; MahmoudianDehkordi, S. ; Rotroff, D.M. ; Jack, J.R. ; Motsinger-Reif, A. ; Risacher, S.L. ; Blach, C. ; Lucas, J.E. ; Massaro, T. ; Louie, G. ; Zhu, H. ; Dallmann, G. ; Klavins, K. ; Koal, T. ; Kim, S. ; Nho, K. ; Shen, L. ; Casanova, R. ; Varma, S. ; Legido-Quigley, C. ; Moseley, M.A. ; Zhu, K. ; Henrion, M.Y. ; van der Lee, S.J. ; Harms, A.C. ; Demirkan, A. ; Hankemeier, T. ; van Duijn, C.M. ; Trojanowski, J.Q. ; Shaw, L.M. ; Saykin, A.J. ; Weiner, M.W. ; Doraiswamy, P.M. ; Kaddurah-Daouk, R.

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

Adam, J. ; Brandmaier, S. ; Leonhardt, J. ; Scheerer, M.F. ; Mohney, R.P. ; Xu, T. ; Bi, J. ; Rotter, M. ; Troll, M. ; Chi, S. ; Heier, M. ; Herder, C. ; Rathmann, W.G. ; Giani, G. ; Adamski, J. ; Illig, T. ; Strauch, K. ; Li, Y. ; Gieger, C. ; Peters, A. ; Suhre, K. ; Ankerst, D.P. ; Meitinger, T. ; Hrabě de Angelis, M. ; Roden, M. ; Neschen, S. ; Kastenmüller, G. ; Wang-Sattler, R.

Metformin effect on non-targeted metabolite profiles in patients with type 2 diabetes and multiple murine tissues.

Altmaier, E. ; Menni, C. ; Heier, M. ; Meisinger, C. ; Thorand, B. ; Quell, J. ; Kobl, M. ; Römisch-Margl, W. ; Valdes, A.M. ; Mangino, M. ; Waldenberger, M. ; Strauch, K. ; Illig, T. ; Adamski, J. ; Spector, T. ; Gieger, C. ; Suhre, K. ; Kastenmüller, G.

The pharmacogenetic footprint of ACE inhibition: A population-based metabolomics study.

Beger, R.D. ; Dunn, W.A, Jr. ; Schmidt, M.A. ; Gross, S.S. ; Kirwan, J.A. ; Cascante, M. ; Brennan, L. ; Wishart, D.S. ; Oresič, M. ; Hankemeier, T. ; Broadhurst, D.I. ; Lane, A.N. ; Suhre, K. ; Kastenmüller, G. ; Sumner, S.J. ; Thiele, I. ; Fiehn, O. ; Kaddurah-Daouk, R.

Metabolomics enables precision medicine: “A White Paper, Community Perspective”.

Fall, T. ; Salihovic, S. ; Brandmaier, S. ; Nowak, C. ; Ganna, A. ; Gustafsson, S. ; Broeckling, C.D. ; Prenni, J.E. ; Kastenmüller, G. ; Peters, A. ; Magnusson, P.K. ; Wang-Sattler, R. ; Giedraitis, V. ; Berne, C. ; Gieger, C. ; Pedersen, N.L. ; Ingelsson, E. ; Lind, L.

Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes.

Fard, D. ; Läer, K. ; Rothämel, T. ; Schürmann, P. ; Arnold, M. ; Cohen, M. ; Vennemann, M. ; Pfeiffer, H. ; Bajanowski, T. ; Pfeufer, A. ; Dörk, T. ; Klintschar, M.

Candidate gene variants of the immune system and sudden infant death syndrome.

Knacke, H. ; Pietzner, M. ; Do, K.T. ; Römisch-Margl, W. ; Kastenmüller, G. ; Völker, U. ; Völzke, H. ; Krumsiek, J. ; Artati, A. ; Wallaschofski, H. ; Nauck, M. ; Suhre, K. ; Adamski, J. ; Friedrich, N.

Metabolic fingerprints of circulating IGF-I and the IGF-I/IGFBP-3 ration: A multi-fluid metabolomics study.

Lacruz, M.E. ; Kluttig, A. ; Tiller, D. ; Medenwald, D. ; Giegling, I. ; Rujescu, D. ; Prehn, C. ; Adamski, J. ; Frantz, S. ; Greiser, K.H. ; Emeny, R.T. ; Kastenmüller, G. ; Haerting, J.

Cardiovascular risk factors associated with blood metabolite concentrations and their alterations over a 4-year period in a population-based cohort.

Much, D. ; Beyerlein, A. ; Kindt, A. ; Krumsiek, J. ; Stückler, F. ; Rossbauer, M. ; Hofelich, A. ; Wiesenäcker, D. ; Hivner, S. ; Herbst, M. ; Römisch-Margl, W. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Theis, F.J. ; Ziegler, A.-G. ; Hummel, S.

Lactation is associated with altered metabolomic signatures in women with gestational diabetes.

Muschaweckh, A. ; Buchholz, V.R. ; Fellenzer, A. ; Hessel, C. ; König, P.A ; Tao, S. ; Tao, R. ; Heikenwälder, M. ; Busch, D.H. ; Korn, T. ; Kastenmüller, G. ; Drexler, I. ; Gasteiger, G.

Antigen-dependent competition shapes the local repertoire of tissue-resident memory CD8⁺ T cells.

Pallister, T. ; Haller, T. ; Thorand, B. ; Altmaier, E. ; Cassidy, A. ; Martin, T. ; Jennings, A. ; Mohney, R.P. ; Gieger, C. ; MacGregor, A. ; Kastenmüller, G. ; Metspalu, A. ; Spector, T.D. ; Menni, C.

Metabolites of milk intake: A metabolomic approach in UK twins with findings replicated in two European cohorts.

Schulte, E.C. ; Altmaier, E. ; Berger, H.S. ; Do, K.T. ; Kastenmüller, G. ; Wahl, S. ; Adamski, J. ; Peters, A. ; Krumsiek, J. ; Suhre, K. ; Haslinger, B. ; Ceballos-Baumann, A. ; Gieger, C. ; Winkelmann, J.

Alterations in lipid and inositol metabolisms in two dopaminergic disorders.

Sekula, P. ; Goek, O.N. ; Quaye, L. ; Barrios, C. ; Levey, A.S. ; Römisch-Margl, W. ; Menni, C. ; Yet, I. ; Gieger, C. ; Inker, L.A. ; Adamski, J. ; Gronwald, W. ; Illig, T. ; Dettmer, K. ; Krumsiek, J. ; Oefner, P.J. ; Valdes, A.M. ; Meisinger, C. ; Coresh, J. ; Spector, T.D. ; Mohney, R.P. ; Suhre, K. ; Kastenmüller, G. ; Köttgen, A.

A metabolome-wide association study of kidney function and disease in the general population.

Suhre, K. ; Raffler, J. ; Kastenmüller, G.

Biochemical insights from population studies with genetics and metabolomics.

Vehmas, A.P. ; Adam, M. ; Laajala, T.D. ; Kastenmüller, G. ; Prehn, C. ; Rozman, J. ; Ohlsson, C. ; Fuchs, H. ; Hrabě de Angelis, M. ; Gailus-Durner, V. ; Elo, L.L. ; Aittokallio, T. ; Adamski, J. ; Corthals, G.L. ; Poutanen, M. ; Strauss, L.

Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse.

Vogt, S. ; Wahl, S. ; Kettunen, J. ; Breitner-Busch, S. ; Kastenmüller, G. ; Gieger, C. ; Suhre, K. ; Waldenberger, M. ; Kratzsch, J. ; Perola, M. ; Salomaa, V. ; Blankenberg, S. ; Zeller, T. ; Soininen, P. ; Kangas, A.J. ; Peters, A. ; Grallert, H. ; Ala-Korpela, M. ; Thorand, B.

Characterization of the metabolic profile associated with serum 25-hydroxyvitamin D: A cross-sectional analysis in population-based data.

Ward-Caviness, C.K. ; Breitner-Busch, S. ; Wolf, K. ; Cyrys, J. ; Kastenmüller, G. ; Wang-Sattler, R. ; Schneider, A.E. ; Peters, A.

Short-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.

Yet, I. ; Menni, C. ; Shin, S.Y. ; Mangino, M. ; Soranzo, N. ; Adamski, J. ; Suhre, K. ; Spector, T.D. ; Kastenmüller, G. ; Bell, J.T.

Genetic influences on metabolite levels: A comparison across metabolomic platforms.

Yousri, N.A. ; Kastenmüller, G. ; AlHaq, W.G. ; Holle, R. ; Kääb, S. ; Mohney, R.P. ; Gieger, C. ; Peters, A. ; Adamski, J. ; Suhre, K. ; Arayssi, T.

Diagnostic and prognostic metabolites identified for joint symptoms in the KORA population.

Zierer, J. ; Kastenmüller, G. ; Suhre, K. ; Gieger, C. ; Codd, V. ; Tsai, P.C. ; Bell, J. ; Peters, A. ; Strauch, K. ; Schulz, H. ; Weidinger, S. ; Mohney, R.P. ; Samani, N.J. ; Spector, T. ; Mangino, M. ; Menni, C.

Metabolomics profiling reveals novel markers for leukocyte telomere length.

Zierer, J. ; Pallister, T. ; Tsai, P.C. ; Krumsiek, J. ; Bell, J.T. ; Lauc, G. ; Spector, T.D. ; Menni, C. ; Kastenmüller, G.

Exploring the molecular basis of age-related disease comorbidities using a multi-omics graphical model.

Anton, G. ; Wilson, R. ; Yu, Z. ; Prehn, C. ; Zukunft, S. ; Adamski, J. ; Heier, M. ; Meisinger, C. ; Römisch-Margl, W. ; Wang-Sattler, R. ; Hveem, K. ; Wolfenbuttel, B.H.R. ; Peters, A. ; Kastenmüller, G. ; Waldenberger, M.

Pre-analytical sample quality: Metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples.

Arnold, M. ; Raffler, J. ; Pfeufer, A. ; Suhre, K. ; Kastenmüller, G.

SNiPA: An interactive, genetic variant-centered annotation browser.

Bartel, J. ; Krumsiek, J. ; Schramm, K. ; Adamski, J. ; Gieger, C. ; Herder, C. ; Carstensen, M. ; Peters, A. ; Rathmann, W. ; Roden, M. ; Strauch, K. ; Suhre, K. ; Kastenmüller, G. ; Prokisch, H. ; Theis, F.J.

The human blood metabolome-transcriptome interface.

Diakopoulos, K.N. ; Lesina, M. ; Wörmann, S. ; Song, L. ; Aichler, M. ; Schild, L. ; Artati, A. ; Römisch-Margl, W. ; Wartmann, T. ; Fischer, R. ; Kabiri, Y. ; Zischka, H. ; Halangk, W. ; Demir, I.E. ; Pilsak, C. ; Walch, A.K. ; Mantzoros, C.S. ; Steiner, J.M. ; Erkan, M. ; Schmid, R.M. ; Witt, H. ; Adamski, J. ; Algül, H.

Impaired autophagy induces chronic atrophic pancreatitis in mice via sex- and nutrition-dependent processes.

Do, K.T. ; Kastenmüller, G. ; Mook-Kanamori, D.O. ; Yousri, N.A. ; Theis, F.J. ; Suhre, K. ; Krumsiek, J.

Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva.

Draisma, H.H. ; Pool, R. ; Kobl, M. ; Jansen, R.C. ; Petersen, A.-K. ; Vaarhorst, A.A. ; Yet, I. ; Haller, T. ; Demirkan, A. ; Esko, T. ; Zhu, G. ; Böhringer, S. ; Beekman, M. ; van Klinken, J.B. ; Römisch-Margl, W. ; Prehn, C. ; Adamski, J. ; de Craen, A.J. ; van Leeuwen, E.M. ; Amin, N. ; Dharuri, H. ; Westra, H.J. ; Franke, L. ; de Geus, E.J. ; Hottenga, J.J. ; Willemsen, G. ; Henders, A.K. ; Montgomery, G.W. ; Nyholt, D.R. ; Whitfield, J.B. ; Penninx, B.W. ; Spector, T.D. ; Metspalu, A. ; Slagboom, P.E. ; van Dijk, K.W. ; 't Hoen, P.A. ; Strauch, K. ; Martin, N.G. ; van Ommen, G.J. ; Illig, T. ; Bell, J.T. ; Mangino, M. ; Suhre, K. ; McCarthy, M.I. ; Gieger, C. ; Isaacs, A. ; van Duijn, C.M. ; Boomsma, D.I.

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

Halama, A. ; Horsch, M. ; Kastenmüller, G. ; Möller, G. ; Kumar, P. ; Prehn, C. ; Laumen, H. ; Hauner, H. ; Hrabě de Angelis, M. ; Beckers, J. ; Suhre, K. ; Adamski, J.

Metabolic switch during adipogenesis: From branched chain amino acid catabolism to lipid synthesis.

Kahle-Stephan, M. ; Schäfer, A. ; Seelig, A. ; Schultheiß, J. ; Wu, M. ; Aichler, M. ; Leonhardt, J. ; Rathkolb, B. ; Rozman, J. ; Sarioglu, H. ; Hauck, S.M. ; Ueffing, M. ; Wolf, E. ; Kastenmüller, G. ; Adamski, J. ; Walch, A.K. ; Hrabě de Angelis, M. ; Neschen, S.

High fat diet-induced modifications in membrane lipid and mitochondrial-membrane protein signatures precede the development of hepatic insulin resistance in mice.

Kastenmüller, G. ; Raffler, J. ; Gieger, C. ; Suhre, K.

Genetics of human metabolism: An update.

Krumsiek, J. ; Mittelstraß, K. ; Do, K.T. ; Stückler, F. ; Ried, J.S. ; Adamski, J. ; Peters, A. ; Illig, T. ; Kronenberg, F. ; Friedrich, N. ; Nauck, M. ; Pietzner, M. ; Mook-Kanamori, D.O. ; Suhre, K. ; Gieger, C. ; Grallert, H. ; Theis, F.J. ; Kastenmüller, G.

Gender-specific pathway differences in the human serum metabolome.

Livshits, G. ; Macgregor, A.J. ; Gieger, C. ; Malkin, I. ; Moayyeri, A. ; Grallert, H. ; Emeny, R.T. ; Spector, T. ; Kastenmüller, G. ; Williams, F.M.

An omics investigation into chronic widespread musculoskeletal pain reveals epiandrosterone sulfate as a potential biomarker.

Menni, C. ; Graham, D. ; Kastenmüller, G. ; Alharbi, N.H. ; Alsanos, S.M. ; McBride, M. ; Mangino, M. ; Titcombe, P. ; Shin, S.Y. ; Psatha, M. ; Geisendorfer, T. ; Huber, A. ; Peters, A. ; Wang-Sattler, R. ; Xu, T. ; Brosnan, M.J. ; Trimmer, J. ; Reichel, C. ; Mohney, R.P. ; Soranzo, N. ; Edwards, M.H. ; Cooper, C. ; Church, A.C. ; Suhre, K. ; Gieger, C. ; Dominiczak, A.F. ; Spector, T.D. ; Padmanabhan, S. ; Valdes, A.M.

Metabolomic identification of a novel pathway of blood pressure regulation involving hexadecanedioate.

Raffler, J. ; Friedrich, N. ; Arnold, M. ; Kacprowski, T. ; Rueedi, R. ; Altmaier, E. ; Bergmann, S. ; Budde, K. ; Gieger, C. ; Homuth, G. ; Pietzner, M. ; Römisch-Margl, W. ; Strauch, K. ; Völzke, H. ; Waldenberger, M. ; Wallaschofski, H. ; Nauck, M. ; Völker, U. ; Kastenmüller, G. ; Suhre, K.

Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality.

Schwab, S. ; Zierer, A. ; Schneider, A.E. ; Heier, M. ; Koenig, W. ; Kastenmüller, G. ; Waldenberger, M. ; Peters, A. ; Thorand, B.

Vitamin E supplementation is associated with lower levels of C-reactive protein only in higher dosages and combined with other antioxidants: The Cooperative Health Research in the Region of Augsburg (KORA) F4 study.

Suhre, K. ; Schwartz, J.E. ; Sharma, V.K. ; Chen, Q. ; Lee, J.R. ; Muthukumar, T. ; Dadhania, D.M. ; Ding, R. ; Ikle, D.N. ; Bridges, N.D. ; Williams, N.M. ; Kastenmüller, G. ; Karoly, E.D. ; Mohney, R.P. ; Abecassis, M. ; Friedewald, J. ; Knechtle, S.J. ; Becker, Y.T. ; Samstein, B. ; Shaked, A. ; Gross, S.S. ; Suthanthiran, M.

Urine metabolite profiles predictive of human kidney allograft status.

Tsepilov, Y.A. ; Shin, S.Y. ; Soranzo, N. ; Spector, T.D. ; Prehn, C. ; Adamski, J. ; Kastenmüller, G. ; Wang-Sattler, R. ; Strauch, K. ; Gieger, C. ; Aulchenko, Y.S. ; Ried, J.S.

Non-additive effects of genes in human metabolomics.

Wahl, S. ; Vogt, S. ; Stückler, F. ; Krumsiek, J. ; Bartel, J. ; Kacprowski, T. ; Schramm, K. ; Carstensen, M. ; Rathmann, W. ; Roden, M. ; Jourdan, C. ; Kangas, A.J. ; Soininen, P. ; Ala-Korpela, M. ; Nöthlings, U. ; Boeing, H. ; Theis, F.J. ; Meisinger, C. ; Waldenberger, M. ; Suhre, K. ; Homuth, G. ; Gieger, C. ; Kastenmüller, G. ; Illig, T. ; Linseisen, J. ; Peters, A. ; Prokisch, H. ; Herder, C. ; Thorand, B. ; Grallert, H.

Multi-omic signature of body weight change: Results from a population-based cohort study.

Xu, T. ; Brandmaier, S. ; Messias, A.C. ; Herder, C. ; Draisma, H.H. ; Demirkan, A. ; Yu, Z. ; Ried, J.S. ; Haller, T. ; Heier, M. ; Campillos, M. ; Fobo, G. ; Stark, R.G. ; Holzapfel, C. ; Adam, J. ; Chi, S. ; Rotter, M. ; Panni, T. ; Quante, A.S. ; He, Y. ; Prehn, C. ; Römisch-Margl, W. ; Kastenmüller, G. ; Willemsen, G. ; Pool, R. ; Kasa, K. ; van Dijk, K.W. ; Hankemeier, T. ; Meisinger, C. ; Thorand, B. ; Ruepp, A. ; Hrabě de Angelis, M. ; Li, Y. ; Wichmann, H.-E. ; Stratmann, B. ; Strauch, K. ; Metspalu, A. ; Gieger, C. ; Suhre, K. ; Adamski, J. ; Illig, T. ; Rathmann, W. ; Roden, M. ; Peters, A. ; van Duijn, C.M. ; Boomsma, D.I. ; Meitinger, T. ; Wang-Sattler, R.

Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes.

Yousri, N.A. ; Mook-Kanamori, D.O. ; Selim, M.M.E.D. ; Takiddin, A.H. ; Al-Homsi, H. ; Al-Mahmoud, K.A.S. ; Karoly, E.D. ; Krumsiek, J. ; Do, K.T. ; Neumaier, U. ; Mook-Kanamori, M.J. ; Rowe, J. ; Chidiac, O.M. ; McKeon, C. ; Al Muftah, W.A. ; Kader, S.A. ; Kastenmüller, G. ; Suhre, K.

A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control.

Zierer, J. ; Menni, C. ; Kastenmüller, G. ; Spector, T.D.

Integration of 'omics' data in aging research: From biomarkers to systems biology.

Albrecht, E. ; Waldenberger, M. ; Krumsiek, J. ; Evans, A.M. ; Jeratsch, U. ; Breier, M. ; Adamski, J. ; Koenig, W. ; Zeilinger, S. ; Fuchs, C. ; Klopp, N. ; Theis, F.J. ; Wichmann, H.-E. ; Suhre, K. ; Illig, T. ; Strauch, K. ; Peters, A. ; Gieger, C. ; Kastenmüller, G. ; Döring, A. ; Meisinger, C.

Metabolite profiling reveals new insights into the regulation of serum urate in humans.

Altmaier, E. ; Fobo, G. ; Heier, M. ; Thorand, B. ; Meisinger, C. ; Römisch-Margl, W. ; Waldenberger, M. ; Gieger, C. ; Illig, T. ; Adamski, J. ; Suhre, K. ; Kastenmüller, G.

Metabolomics approach reveals effects of antihypertensives and lipid-lowering drugs on the human metabolism.

Bleves, S. ; Dunger, I. ; Walter, M.C. ; Frangoulidis, D. ; Kastenmüller, G. ; Voulhoux, R. ; Ruepp, A.

HoPaCI-DB: Host-Pseudomonas and Coxiella interaction database.

Jourdan, C. ; Linseisen, J. ; Meisinger, C. ; Petersen, A.-K. ; Gieger, C. ; Rawal, R. ; Illig, T. ; Heier, M. ; Peters, A. ; Wallaschofski, H. ; Nauck, M. ; Kastenmüller, G. ; Suhre, K. ; Prehn, C. ; Adamski, J. ; Koenig, W. ; Roden, M. ; Wichmann, H.-E. ; Völzke, H.

Associations between thyroid hormones and serum metabolite profiles in an euthyroid population.

Mathew, S. ; Krug, S. ; Skurk, T. ; Halama, A. ; Stank, A. ; Artati, A. ; Prehn, C. ; Malek, J.A. ; Kastenmüller, G. ; Römisch-Margl, W. ; Adamski, J. ; Hauner, H. ; Suhre, K.

Metabolomics of Ramadan fasting: An opportunity for the controlled study of physiological responses to food intake.

Mook-Kanamori, D.O. ; Römisch-Margl, W. ; Kastenmüller, G. ; Prehn, C. ; Petersen, A.K. ; Illig, T. ; Gieger, C. ; Wang-Sattler, R. ; Meisinger, C. ; Peters, A. ; Adamski, J. ; Suhre, K.

Increased amino acids levels and the risk of developing hypertriglyceridemia in a 7-year follow-up.

Petersen, A.-K. ; Zeilinger, S. ; Kastenmüller, G. ; Römisch-Margl, W. ; Brugger, M. ; Peters, A. ; Meisinger, C. ; Strauch, K. ; Hengstenberg, C. ; Pagel, P. ; Huber, F. ; Mohney, R.P. ; Grallert, H. ; Illig, T. ; Adamski, J. ; Waldenberger, M. ; Gieger, C. ; Suhre, K.

Epigenetics meets metabolomics: An epigenome-wide association study with blood serum metabolic traits.

Schramm, K. ; Marzi, C. ; Schurmann, C. ; Carstensen, M. ; Reinmaa, E. ; Biffar, R. ; Eckstein, G.N. ; Gieger, C. ; Grabe, H.J. ; Homuth, G. ; Kastenmüller, G. ; Mägi, R. ; Metspalu, A. ; Mihailov, E. ; Peters, A. ; Petersmann, A. ; Roden, M. ; Strauch, K. ; Suhre, K. ; Teumer, A. ; Völker, U. ; Völzke, H. ; Wang-Sattler, R. ; Waldenberger, M. ; Meitinger, T. ; Illig, T. ; Herder, C. ; Grallert, H. ; Prokisch, H.

Mapping the genetic architecture of gene regulation in whole blood.

Shin, S.-Y. ; Petersen, A.-K. ; Wahl, S. ; Zhai, G. ; Römisch-Margl, W. ; Small, K.S. ; Döring, A. ; Kato, B.S. ; Peters, A. ; Grundberg, E. ; Prehn, C. ; Wang-Sattler, R. ; Wichmann, H.-E. ; Hrabě de Angelis, M. ; Illig, T. ; Adamski, J. ; Deloukas, P. ; Spector, T.D. ; Suhre, K. ; Gieger, C. ; Soranzo, N.

Interrogating causal pathways linking genetic variants, small molecule metabolites and circulating lipids.

Shin, S.Y. ; Fauman, E.B. ; Petersen, A.-K. ; Krumsiek, J. ; Santos, R. ; Huang, J. ; Arnold, M. ; Erte, I. ; Forgetta, V. ; Yang, T.P. ; Walter, K. ; Menni, C. ; Chen, L. ; Vasquez, L. ; Valdes, A.M. ; Hyde, C.L. ; Wang, V. ; Ziemek, D. ; Roberts, P. ; Xi, L. ; Grundberg, E. ; Waldenberger, M. ; Richards, J.B. ; Mohney, R.P. ; Milburn, M.V. ; John, S.L. ; Trimmer, J. ; Theis, F.J. ; Overington, J.P. ; Suhre, K. ; Brosnan, M.J. ; Gieger, C. ; Kastenmüller, G. ; Spector, T.D. ; Soranzo, N.

An atlas of genetic influences on human blood metabolites.

Wahl, S. ; Krug, S. ; Then, C. ; Kirchhofer, A. ; Kastenmüller, G. ; Brand, T. ; Skurk, T. ; Claussnitzer, M. ; Huth, C. ; Heier, M. ; Meisinger, C. ; Peters, A. ; Thorand, B. ; Gieger, C. ; Prehn, C. ; Römisch-Margl, W. ; Adamski, J. ; Suhre, K. ; Illig, T. ; Grallert, H. ; Laumen, H. ; Seissler, J. ; Hauner, H.

Comparative analysis of plasma metabolomics response to metabolic challenge tests in healthy subjects and influence of the FTO obesity risk allele.

Yousri, N.A. ; Kastenmüller, G. ; Gieger, C. ; Shin, S.-Y. ; Erte, I. ; Menni, C. ; Peters, A. ; Meisinger, C. ; Mohney, R.P. ; Illig, T. ; Adamski, J. ; Soranzo, N. ; Spector, T.D. ; Suhre, K.

Long term conservation of human metabolic phenotypes and link to heritability.

Altmaier, E. ; Emeny, R.T. ; Krumsiek, J. ; Lacruz, M.E. ; Lukaschek, K. ; Häfner, S. ; Kastenmüller, G. ; Römisch-Margl, W. ; Prehn, C. ; Mohney, R.P. ; Evans, A.M. ; Milburn, M.V. ; Illig, T. ; Adamski, J. ; Theis, F.J. ; Suhre, K. ; Ladwig, K.-H.

Metabolomic profiles in individuals with negative affectivity and social inhibition: A population-based study of Type D personality.

Ferrannini, E. ; Natalai, A. ; Camastra, S. ; Nannipieri, M. ; Mari, A. ; Adam, K.-P. ; Milburn, M.V. ; Kastenmüller, G. ; Adamski, J. ; Tuomi, T. ; Lyssenko, V. ; Groop, L. ; Gall, W.E.

Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance.

Goek, O.-N. ; Prehn, C. ; Sekula, P. ; Römisch-Margl, W. ; Döring, A. ; Gieger, C. ; Heier, M. ; Koenig, W. ; Wang-Sattler, R. ; Illig, T. ; Suhre, K. ; Adamski, J. ; Köttgen, A. ; Meisinger, C.

Metabolites associate with kidney function decline and incident chronic kidney disease in the general population.

Menni, C. ; Zhai, G. ; MacGregor, A. ; Prehn, C. ; Römisch-Margl, W. ; Suhre, K. ; Adamski, J. ; Cassidy, A. ; Illig, T. ; Spector, T.D. ; Valdes, A.M.

Targeted metabolomics profiles are strongly correlated with nutritional patterns in women.

Menni, C. ; Kastenmüller, G. ; Petersen, A.-K. ; Bell, J.T. ; Psatha, M. ; Tsai, P.C. ; Gieger, C. ; Schulz, H. ; Erte, I. ; John, S. ; Brosnan, M.J. ; Wilson, S.G. ; Tsaprouni, L. ; Lim, E.M. ; Stuckey, B. ; Deloukas, P. ; Mohney, R.P. ; Suhre, K. ; Spector, T.D. ; Valdes, A.M.

Metabolomic markers reveal novel pathways of ageing and early development in human populations.

Menni, C. ; Fauman, E. ; Erte, I. ; Perry, J.R. ; Kastenmüller, G. ; Shin, S.Y. ; Petersen, A.-K. ; Hyde, C. ; Psatha, M. ; Ward, K.J. ; Yuan, W. ; Milburn, M.V. ; Palmer, C.N. ; Frayling, T.M. ; Trimmer, J. ; Bell, J.T. ; Gieger, C. ; Mohney, R.P. ; Brosnan, M.J. ; Suhre, K. ; Soranzo, N. ; Spector, T.D.

Biomarkers for type 2 diabetes and impaired fasting glucose using a non-targeted metabolomics approach.

Montrone, C. ; Kokkaliaris, K.D. ; Loeffler, D. ; Lechner, M. ; Kastenmüller, G. ; Schroeder, T. ; Ruepp, A.

HSC-Explorer: A curated database for hematopoietic stem cells.

Raffler, J. ; Römisch-Margl, W. ; Petersen, A.-K. ; Pagel, P. ; Blöchl, F. ; Hengstenberg, C. ; Illig, T. ; Meisinger, C. ; Stark, K. ; Wichmann, H.-E. ; Adamski, J. ; Gieger, C. ; Kastenmüller, G. ; Suhre, K.

Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma.

Then, C. ; Wahl, S. ; Kirchhofer, A. ; Grallert, H. ; Krug, S. ; Kastenmüller, G. ; Römisch-Margl, W. ; Claussnitzer, M. ; Illig, T. ; Heier, M. ; Meisinger, C. ; Adamski, J. ; Thorand, B. ; Huth, C. ; Peters, A. ; Prehn, C. ; Heukamp, I. ; Laumen, H. ; Lechner, A. ; Hauner, H. ; Seissler, J.

Plasma metabolomics reveal alterations of sphingo- and glycerophospholipid levels in non-diabetic carriers of the transcription factor 7-like 2 polymorphism rs7903146.

Wjst, M. ; Sargurupremraj, M. ; Arnold, M.

Genome-wide association studies in asthma: What they really told us about pathogenesis.

Xu, T. ; Holzapfel, C. ; Dong, X. ; Bader, E. ; Yu, Z. ; Prehn, C. ; Perstorfer, K. ; Jaremek, M. ; Römisch-Margl, W. ; Rathmann, W. ; Li, Y. ; Wichmann, H.-E. ; Wallaschofski, H. ; Ladwig, K.-H. ; Theis, F.J. ; Suhre, K. ; Adamski, J. ; Illig, T. ; Peters, A. ; Wang-Sattler, R.

Effects of smoking and smoking cessation on human serum metabolite profile: Results from the KORA cohort study.

Arnold, B. ; Hauser, W. ; Arnold, M. ; Bernateck, M. ; Bernardy, K. ; Bruckle, W. ; Friedel, E. ; Hesselschwerdt, H.J. ; Jackel, W. ; Köllner, V. ; Kühn, E. ; Petzke, F. ; Settan, M. ; Weigl, M. ; Winter, E. ; Offenbacher, M.

Multimodale Therapie des Fibromyalgiesyndroms. Systematische Übersicht, Metaanalyse und Leitlinie.

Arnold, M. ; Ellwanger, D.C. ; Hartsperger, M.L. ; Pfeufer, A. ; Stuempflen, V.

Cis-acting polymorphisms affect complex traits through modifications of microRNA regulation pathways.

Arnold, M. ; Hartsperger, M.L. ; Baurecht, H. ; Rodriguez, E. ; Wachinger, B. ; Franke, A. ; Kabesch, M. ; Winkelmann, J. ; Pfeufer, A. ; Romanos, M. ; Illig, T. ; Mewes, H.-W. ; Stuempflen, V. ; Weidinger, S.

Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases.

Goek, O.N. ; Döring, A. ; Gieger, C. ; Heier, M. ; Koenig, W. ; Prehn, C. ; Römisch-Margl, W. ; Wang-Sattler, R. ; Illig, T. ; Suhre, K. ; Sekula, P. ; Zhai, G.J. ; Adamski, J. ; Köttgen, A. ; Meisinger, C.

Serum metabolite concentrations and decreased GFR in the general population.

Jourdan, C. ; Petersen, A.-K. ; Gieger, C. ; Döring, A. ; Illig, T. ; Wang-Sattler, R. ; Meisinger, C. ; Peters, A. ; Adamski, J. ; Prehn, C. ; Suhre, K. ; Altmaier, E. ; Kastenmüller, G. ; Römisch-Margl, W. ; Theis, F.J. ; Krumsiek, J. ; Wichmann, H.-E. ; Linseisen, J.

Body fat free mass is associated with the serum metabolite profile in a population-based study.

Krug, S. ; Kastenmüller, G. ; Stückler, F. ; Rist, M.J. ; Skurk, T. ; Sailer, M. ; Raffler, J. ; Römisch-Margl, W. ; Adamski, J. ; Prehn, C. ; Frank, T. ; Engel, K.-H. ; Hofmann, T. ; Luy, B. ; Zimmermann, R. ; Moritz, F. ; Schmitt-Kopplin, P. ; Krumsiek, J. ; Kremer, W. ; Huber, F. ; Oeh, U. ; Theis, F.J. ; Szymczak, W. ; Hauner, H. ; Suhre, K. ; Daniel, H.

The dynamic range of the human metabolome revealed by challenges.

Krumsiek, J. ; Suhre, K. ; Evans, A.M. ; Mitchell, M.W. ; Mohney, R.P. ; Milburn, M.V. ; Wägele, B. ; Römisch-Margl, W. ; Illig, T. ; Adamski, J. ; Gieger, C. ; Theis, F.J. ; Kastenmüller, G.

Mining the unknown: A systems approach to metabolite identification combining genetic and metabolic information.

Lechner, M. ; Höhn, V. ; Brauner, B. ; Dunger, I. ; Fobo, G. ; Frishman, G. ; Montrone, C. ; Kastenmüller, G. ; Wägele, B. ; Ruepp, A.

CIDeR: Multifactorial interaction networks in human diseases.

Petersen, A.-K. ; Stark, K. ; Musameh, M.D. ; Nelson, C.P. ; Römisch-Margl, W. ; Kremer, W. ; Raffler, J. ; Krug, S. ; Skurk, T. ; Rist, M.J. ; Daniel, H. ; Hauner, H. ; Adamski, J. ; Tomaszewski, M. ; Döring, A. ; Peters, A. ; Wichmann, H.-E. ; Kaess, B.M. ; Kalbitzer, H.R. ; Huber, F. ; Pfahlert, V. ; Samani, N.J. ; Kronenberg, F. ; Dieplinger, H. ; Illig, T. ; Hengstenberg, C. ; Suhre, K. ; Gieger, C. ; Kastenmüller, G.

Genetic associations with lipoprotein subfractions provide information on their biological nature.

Renner, S. ; Römisch-Margl, W. ; Prehn, C. ; Krebs, S. ; Adamski, J. ; Göke, B. ; Blum, H. ; Suhre, K. ; Roscher, A.A. ; Wolf, E.

Changing metabolic signatures of amino acids and lipids during the prediabetic period in a pig model with impaired incretin function and reduced β-cell mass.

Römisch-Margl, W. ; Prehn, C. ; Bogumil, R. ; Röhring, C. ; Suhre, K. ; Adamski, J.

Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics.

Wahl, S. ; Yu, Z. ; Kleber, M. ; Singmann, P. ; Holzapfel, C. ; He, Y. ; Mittelstraß, K. ; Polonikov, A. ; Prehn, C. ; Römisch-Margl, W. ; Adamski, J. ; Suhre, K. ; Grallert, H. ; Illig, T. ; Wang-Sattler, R. ; Reinehr, T.

Childhood obesity is associated with changes in the serum metabolite profile.

Wang-Sattler, R. ; Yu, Z. ; Herder, C. ; Messias, A.C. ; Floegel, A. ; He, Y. ; Heim, K. ; Campillos, M.J. ; Holzapfel, C. ; Thorand, B. ; Grallert, H. ; Xu, T. ; Bader, E. ; Huth, C. ; Mittelstraß, K. ; Döring, A. ; Meisinger, C. ; Gieger, C. ; Prehn, C. ; Römisch-Margl, W. ; Carstensen, M. ; Xie, L. ; Yamanaka-Okumura, H. ; Xing, G. ; Ceglarek, U. ; Thiery, J. ; Giani, G. ; Lickert, H. ; Lin, X. ; Li, Y. ; Boeing, H. ; Joost, H.-G. ; Hrabě de Angelis, M. ; Rathmann, W. ; Suhre, K. ; Prokisch, H. ; Peters, A. ; Meitinger, T. ; Roden, M. ; Wichmann, H.-E. ; Pischon, T. ; Adamski, J. ; Illig, T.

Novel biomarkers for pre-diabetes identified by metabolomics.

Yu, Z. ; Zhai, G. ; Singmann, P. ; He, Y. ; Xu, T. ; Prehn, C. ; Römisch-Margl, W. ; Lattka, E. ; Gieger, C. ; Soranzo, N. ; Heinrich, J. ; Standl, M. ; Thiering, E. ; Mittelstraß, K. ; Wichmann, H.-E. ; Peters, A. ; Suhre, K. ; Li, Y. ; Adamski, J. ; Spector, T.D. ; Illig, T. ; Wang-Sattler, R.

Human serum metabolic profiles are age dependent.

Altmaier, E. ; Kastenmüller, G. ; Römisch-Margl, W. ; Thorand, B. ; Weinberger, K.M. ; Illig, T. ; Adamski, J. ; Döring, A. ; Suhre, K.

Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics.

Fuchs, H. ; Gailus-Durner, V. ; Adler, T. ; Aguilar-Pimentel, J.A. ; Becker, L. ; Calzada-Wack, J. ; Da Silva-Buttkus, P. ; Neff, F. ; Götz, A.A. ; Hans, W. ; Hölter, S.M. ; Horsch, M. ; Kastenmüller, G. ; Kemter, E. ; Lengger, C. ; Maier, H. ; Matloka, M. ; Möller, G. ; Naton, B. ; Prehn, C. ; Puk, O. ; Rácz, I. ; Rathkolb, B. ; Römisch-Margl, W. ; Rozman, J. ; Wang-Sattler, R. ; Schrewe, A. ; Stoeger, C. ; Tost, M. ; Adamski, J. ; Aigner, B. ; Beckers, J. ; Behrendt, H. ; Busch, D.H. ; Esposito, I. ; Graw, J. ; Illig, T. ; Ivandic, B. ; Klingenspor, M. ; Klopstock, T. ; Kremmer, E. ; Mempel, M. ; Neschen, S. ; Ollert, M. ; Schulz, S. ; Suhre, K. ; Wolf, E. ; Wurst, W. ; Zimmer, A. ; Hrabě de Angelis, M.

Mouse phenotyping.

Kastenmüller, G. ; Römisch-Margl, W. ; Wägele, B. ; Altmaier, E. ; Suhre, K.

metaP-Server: A web-based metabolomics data analysis tool.

Mittelstraß, K. ; Ried, J.S. ; Yu, Z. ; Krumsiek, J. ; Gieger, C. ; Prehn, C. ; Römisch-Margl, W. ; Polonikov, A. ; Peters, A. ; Theis, F.J. ; Meitinger, T. ; Kronenberg, F. ; Weidinger, S. ; Wichmann, H.-E. ; Suhre, K. ; Wang-Sattler, R. ; Adamski, J. ; Illig, T.

Discovery of sexual dimorphisms in metabolic and genetic biomarkers.

Suhre, K. ; Römisch-Margl, W. ; Hrabě de Angelis, M. ; Adamski, J. ; Luippold, G. ; Augustin, R.

Identification of a potential biomarker for FABP4 inhibition: The power of lipidomics in preclinical drug testing.

Suhre, K. ; Wallaschofski, H. ; Raffler, J. ; Friedrich, N. ; Haring, R. ; Michael, K. ; Wasner, C. ; Krebs, A. ; Kronenberg, F. ; Chang, D. ; Meisinger, C. ; Wichmann, H.-E. ; Hoffmann, W. ; Völzke, H. ; Völker, U. ; Teumer, A. ; Biffar, R. ; Kocher, T. ; Felix, S.B. ; Illig, T. ; Kroemer, H.K. ; Gieger, C. ; Römisch-Margl, W. ; Nauck, M.

A genome-wide association study of metabolic traits in human urine.

Suhre, K. ; Shin, S.Y. ; Petersen, A.-K. ; Mohney, R.P. ; Meredith, D. ; Wägele, B. ; Altmaier, E. ; CARDIoGRAM Consortium (Lichtner, P. ; Eckstein, G.N. ; Fischer, G. ; Strom, T.M. ; Peters, A. ; Holle, R. ; John, J.) ; Deloukas, P. ; Erdmann, J. ; Grundberg, E. ; Hammond, C.J. ; Hrabě de Angelis, M. ; Kastenmüller, G. ; Köttgen, A. ; Kronenberg, F. ; Mangino, M. ; Meisinger, C. ; Meitinger, T. ; Mewes, H.-W. ; Milburn, M.V. ; Prehn, C. ; Raffler, J. ; Ried, J.S. ; Römisch-Margl, W. ; Samani, N.J. ; Small, K.S. ; Wichmann, H.-E. ; Zhai, G. ; Illig, T. ; Spector, T.D. ; Adamski, J. ; Soranzo, N. ; Gieger, C.

Human metabolic individuality in biomedical and pharmaceutical research.

Yu, Z. ; Kastenmüller, G. ; He, Y. ; Belcredi, P. ; Möller, G. ; Prehn, C. ; Mendes, J. ; Wahl, S. ; Römisch-Margl, W. ; Ceglarek, U. ; Polonikov, A. ; Dahmen, N. ; Prokisch, H. ; Xie, L. ; Li, Y. ; Wichmann, H.-E. ; Peters, A. ; Kronenberg, F. ; Suhre, K. ; Adamski, J. ; Illig, T. ; Wang-Sattler, R.

Differences between human plasma and serum metabolite profiles.

Illig, T. ; Gieger, C. ; Zhai, G.J. ; Römisch-Margl, W. ; Wang-Sattler, R. ; Prehn, C. ; Altmaier, E. ; Kastenmüller, G. ; Kato, B.S. ; Mewes, H.-W. ; Meitinger, T. ; Hrabě de Angelis, M. ; Kronenberg, F. ; Soranzo, N. ; Wichmann, H.-E. ; Spector, T.D. ; Adamski, J. ; Suhre, K.

A genome-wide perspective of genetic variation in human metabolism.

Altmaier, E. ; Kastenmüller, G. ; Römisch-Margl, W. ; Thorand, B. ; Weinberger, K.M. ; Adamski, J. ; Illig, T. ; Döring, A. ; Suhre, K.

Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics.

Kastenmüller, G. ; Schenk, M.E. ; Gasteiger, J. ; Mewes, H.-W.

Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes.

Kastenmüller, G. ; Gasteiger, J. ; Mewes, H.-W.

An environmental perspective on large-scale genome clustering based on metabolic capabilities.

Römisch-Margl, W. ; Eisenreich, W. ; Haase, I. ; Bacher, A. ; Fischer, M.

2,5-diamino-6-ribitylamino-4(3H)-pyrimidinone 5'-phosphate synthases of fungi and archaea.

Walter, M.C. ; Rattei, T. ; Arnold, R. ; Güldener, U. ; Münsterkötter, M. ; Nenova, K. ; Kastenmüller, G. ; Tischler, P. ; Wölling, A. ; Volz, A. ; Pongratz, N. ; Jost, R. ; Mewes, H.-W. ; Frishman, D.

Pedant covers all complete RefSeq genomes.

Güldener, U. ; Münsterkötter, M. ; Kastenmüller, G. ; Strack, N. ; van Helden, J. ; Lemer, C. ; Richelles, J. ; Wodak, S.J. ; García-Martínez, J. ; Pérez-Ortín, J.E. ; Michael, H. ; Kaps, A. ; Talla, E. ; Dujon, B. ; André, B. ; Souciet, J.L. ; de Montigny, J. ; Bon, E. ; Gaillardin, C. ; Mewes, H.-W.

CYGD: The Comprehensive Yeast Genome Database.