Gabi Kastenmüller

Head of Research Group Systems Metabolomics

+49 89 3187-3578 g.kastenmueller@helmholtz-munich.de

Gebäude / Raum: 56, 151

“We are all different – and so is our metabolism. Analysis and integration of big metabolomics (omics) data sets will help us to understand the crucial role of metabolic disruptions and individuality in age-related diseases. They will give us new options for personalized prevention and treatment.”

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The main objective of my research is to identify the molecular mechanisms that translate genetic risk factors and their interplay with lifestyle and environmental factors into the development and progression of specific complex diseases such as diabetes, kidney and, Alzheimer’s disease, and depression. Thereby, I have a major focus on investigating how the individual metabolic make-up, as accessible through metabolomics, its changes over time, and its link to genetic variation affect human health. To make full use of the big omics data sets that are produced to address these research questions, we develop new strategies for analyzing, integrating, and visualizing the data and the derived results in bioinformatics frameworks and resources. In particular, we aim to

Unravel determinants of metabolic individuality in data (genetic, microbial, dietary) from large cohorts
Integrate data/results (metabolomic-centric, network-based) to identify processes disturbed in disease, e.g., Alzheimer’s disease
Map out metabolomic responses to physiological challenges such as exercise and diet
Build web tools for intuitive data/result exploration

Having a background in chemistry and computer science, I moved into bioinformatics for my PhD after having paused my career due to family obligations. I received my PhD from Technische Universität München, Germany, in 2009 and joined Karsten Suhre’s lab at Helmholtz Munich as a postdoc in the same year. During my postdoctoral training and a four-month stay at Metabolon Inc, USA, a commercial provider of metabolomics measurements, I gained wide experience in metabolomics and was involved in the analysis of one of the first mass spectrometry-based metabolomics studies at large scale in two population-based cohorts, before I started the adventure of setting up my own lab in 2011.

MetabolomicsBioinformaticsMetabolismData IntegrationBiostatisticsBig DataWebtool development

2019 - present

at the Computational Health Center / ICB, Helmholtz ZMunich

2017 - 2019

of the Institute of Bioinformatics and Systems Biology (IBIS), Helmholtz Munich

2014 - 2017

in the Department of Twin Research, King's College London, UK

2011 - 2019

at IBIS, Helmholtz Munich

2010

(May-Aug), Metabolon Inc., Durham, NC, USA

2009 - 2011

at Karsten Suhre's lab, IBIS, Helmholtz Munich

2006 - 2009

at Genome-oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität MMünchen (TUM)
Thesis: "In silico prediction and comparison of metabolic capabilities in sequenced genomes"

2001 - 2009

at IBIS, Helmholtz Munich

1999 - 2001

at Munich Information Center for Protein Sequences (MIPS), Max Planck Institute of Biochemistry, Munich

1993 - 1998

at Ludwig-Maximilians-Universität München (LMU)
Thesis: "Similarity search in 3D protein databases" (in German)
(Dipl.-Inform., German equiv. to Master’s degree)

1989 - 1995

at Ludwig-Maximilians-Universität München (LMU)
Thesis: "Light-induced reorientation of azo dye containing, cholesteric oligosiloxanes" (in German)
(Dipl.-Chem., German equiv. to Master’s degree)

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.

The metabolomist

Biocrates Podcast

Data sharing & sources of bias in metabolomics

Alice and Gabi Kastenmüller discuss data stratification, teamwork, and keeping tools open for the community.

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Dr. Gabi Kastenmüller

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Research Interests and Academic Career

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Professional Background

Head of Research Group “Systems Metabolomics”

Director (acting)

Honorary Lecturer

Head of Research Group “Metabolomics”

Visiting Research Fellow (J1)

Postdoctoral Fellow

Ph.D. Student

Research Associate

Research Associate

Studies in Computer Science

Studies in Chemistry

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The metabolomist

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