Carl Beuchel

About Carl Beuchel:
No description specified

LHA ID: 7XKQDC73HX-7

Location:  Germany

ORCID: Not specified

Joined: 12th Aug 2019

Expertise: Not specified

Tools: Not specified

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LIFE - Leipzig Research Center for Civilization Diseases: inactive since

Goal of the Leipzig Research Center for Civilization Diseases (LIFE) is the investigation of civilization diseases like depression, diabetes, allergies or cardiovascular diseases. For this purpose we collect as much data as possible regarding health and living conditions of the population in Leipzig and provide these data for scientists of the University of Leipzig and other research institutions.

Programme: LIFE Management Cluster

Public web page: http://life.uni-leipzig.de/

Start date: 1st Jan 2009

LIFE Adult

This population-based study examined 10,000 participants randomly selected from the Leipzig population (2011 to 2014). A follow-up is to be carried out from 2017 - 2020. The study mainly included people aged between 40 and 79. All participants underwent a 6-hour study program and people over 60 years of age were invited two more times to in-depth study of cognition and depression and the brain (MRI, EEG). Extensive measurements of genome, metabolome and transcriptome are available. The LIFE-ADULT ...

Programme: LIFE Management Cluster

Public web page: http://life.uni-leipzig.de/en/adults/life_adult.html

Start date: 1st Jan 2009

Genetical Statistics and Systems Biology

The group is interested in omic-level studies including the genome, methylome, transcriptome, and metabolome of the general population as well as of various diseases including cardiovascular diseases, pneumonia, sepsis, obesity, and brain cancers. Additionally, we aim at transfering results of biomathematical model simulations into clinical practice e.g. by haematopoietic growth-factor opimization during cytotoxic chemotherapy and optimization of EPO applications in chronic kidney disease.

The ...

Programme: This Project is not associated with a Programme

Public web page: http://www.imise.uni-leipzig.de/en/Groups/GenStat/

Start date: 1st Jan 2013

Metabolite-Investigator: an integrated user-friendly workflow for metabolomics multi-study analysis
Genetical Statistics and Systems Biology

Abstract (Expand)

Motivation Many diseases have a metabolic background, which is increasingly investigated due to improved measurement techniques allowing high-throughput assessment of metabolic features in several body … fluids. Integrating data from multiple cohorts is of high importance to obtain robust and reproducible results. However, considerable variability across studies due to differences in sampling, measurement techniques and study populations needs to be accounted for. Results We present Metabolite-Investigator, a scalable analysis workflow for quantitative metabolomics data from multiple studies. Our tool supports all aspects of data pre-processing including data integration, cleaning, transformation, batch analysis as well as multiple analysis methods including uni- and multivariable factor-metabolite associations, network analysis and factor prioritization in one or more cohorts. Moreover, it allows identifying critical interactions between cohorts and factors affecting metabolite levels and inferring a common covariate model, all via a graphical user interface. Availability and implementation We constructed Metabolite-Investigator as a free and open web-tool and stand-alone Shiny-app. It is hosted at https://apps.health-atlas.de/metabolite-investigator/, the source code is freely available at https://github.com/cfbeuchel/Metabolite-Investigator. Supplementary information Supplementary data are available at Bioinformatics online.

Authors: Carl Beuchel, Holger Kirsten, Uta Ceglarek, Markus Scholz

Date Published: 16th Nov 2020

Publication Type: Journal article

Relation of Whole Blood Amino Acid and Acylcarnitine Metabolome to Age, Sex, BMI, Puberty, and Metabolic Markers in Children and Adolescents
Genetical Statistics and Systems Biology

Abstract (Expand)

BACKGROUND Changes in the metabolic fingerprint of blood during child growth and development are a largely under-investigated area of research. The examination of such aspects requires a cohort of …f healthy children and adolescents who have been subjected to deep phenotyping, including collection of biospecimens for metabolomic analysis. The present study considered whether amino acid (AA) and acylcarnitine (AC) concentrations are associated with age, sex, body mass index (BMI), and puberty during childhood and adolescence. It also investigated whether there are associations between amino acids (AAs) and acylcarnitines (ACs) and laboratory parameters of glucose and lipid metabolism, as well as liver, kidney, and thyroid parameters. METHODS A total of 3989 dried whole blood samples collected from 2191 healthy participants, aged 3 months to 18 years, from the LIFE Child cohort (Leipzig, Germany) were analyzed using liquid chromatography tandem mass spectrometry to detect levels of 23 AAs, 6 ACs, and free carnitine (C0). Age- and sex-related percentiles were estimated for each metabolite. In addition, correlations between laboratory parameters and levels of the selected AAs and ACs were calculated using hierarchical models. RESULTS Four different age-dependent profile types were identified for AAs and ACs. Investigating the association with puberty, we mainly identified peak metabolite levels at Tanner stages 2 to 3 in girls and stages 3 to 5 in boys. Significant correlations were observed between BMI standard deviation score (BMI-SDS) and certain metabolites, among them, branched-chain (leucine/isoleucine, valine) and aromatic (phenylalanine, tyrosine) amino acids. Most of the metabolites correlated significantly with absolute concentrations of glucose, glycated hemoglobin (HbA1c), triglycerides, cystatin C (CysC), and creatinine. After age adjustment, significant correlations were observed between most metabolites and CysC, as well as HbA1c. CONCLUSIONS During childhood, several AA and AC levels are related to age, sex, BMI, and puberty. Moreover, our data verified known associations but also revealed new correlations between AAs/ACs and specific key markers of metabolic function.

Authors: Josephin Hirschel, Mandy Vogel, Ronny Baber, Antje Garten, Carl Beuchel, Yvonne Dietz, Julia Dittrich, Antje Körner, Wieland Kiess, Uta Ceglarek

Date Published: 1st Apr 2020

Publication Type: Journal article

Clinical and lifestyle related factors influencing whole blood metabolite levels -- A comparative analysis of three large cohorts
Genetical Statistics and Systems Biology

Abstract (Expand)

Objective Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights …s into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels. Methods 63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N~=~16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses. Results Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained {\textgreater}1{\%} of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies. Conclusions We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

Authors: Carl Beuchel, Susen Becker, Julia Dittrich, Holger Kirsten, Anke Toenjes, Michael Stumvoll, Markus Loeffler, Holger Thiele, Frank Beutner, Joachim Thiery, Uta Ceglarek, Markus Scholz

Date Published: 17th Aug 2019

Publication Type: Not specified

Sequential organ failure assessment score is an excellent operationalization of disease severity of adult patients with hospitalized community acquired pneumonia - results from the prospective observational PROGRESS study.
Genetical Statistics and Systems Biology

Abstract (Expand)

BACKGROUND: CAP (Community acquired pneumonia) is frequent, with a high mortality rate and a high burden on health care systems. Development of predictive biomarkers, new therapeutic concepts, and … epidemiologic research require a valid, reproducible, and quantitative measure describing CAP severity. METHODS: Using time series data of 1532 patients enrolled in the PROGRESS study, we compared putative measures of CAP severity for their utility as an operationalization. Comparison was based on ability to correctly identify patients with an objectively severe state of disease (death or need for intensive care with at least one of the following: substantial respiratory support, treatment with catecholamines, or dialysis). We considered IDSA/ATS minor criteria, CRB-65, CURB-65, Halm criteria, qSOFA, PSI, SCAP, SIRS-Score, SMART-COP, and SOFA. RESULTS: SOFA significantly outperformed other scores in correctly identifying a severe state of disease at the day of enrollment (AUC = 0.948), mainly caused by higher discriminative power at higher score values. Runners-up were the sum of IDSA/ATS minor criteria (AUC = 0.916) and SCAP (AUC = 0.868). SOFA performed similarly well on subsequent study days (all AUC > 0.9) and across age groups. In univariate and multivariate analysis, age, sex, and pack-years significantly contributed to higher SOFA values whereas antibiosis before hospitalization predicted lower SOFA. CONCLUSIONS: SOFA score can serve as an excellent operationalization of CAP severity and is proposed as endpoint for biomarker and therapeutic studies. TRIAL REGISTRATION: clinicaltrials.gov NCT02782013 , May 25, 2016, retrospectively registered.

Authors: P. Ahnert, P. Creutz, K. Horn, F. Schwarzenberger, M. Kiehntopf, H. Hossain, M. Bauer, F. M. Brunkhorst, K. Reinhart, U. Volker, T. Chakraborty, M. Witzenrath, M. Loffler, N. Suttorp, M. Scholz

Date Published: 4th Apr 2019

Publication Type: Journal article

Human Diseases: pneumonia

Metabolite Investigator v0.1.2 (Docker Image)
Genetical Statistics and Systems Biology

#UPDATE

This archive has been replaced by the GitLab docker registry. Follow the Link under Data Portal (https://gitlab.com/imise-genstat/metabolite-investigator-archive/container_registry/1612096) to find all previous versions of the app (starting from 0.1.6). The images can be downloaded and run using a working docker environment using the following commands:

Using the initial release version 0.1.6 as an example

docker pull registry.gitlab.com/imise-genstat/metabolite-investigator-archive:0.1.6 ...

Creators: Markus Scholz, Jonas Wagner, Carl Beuchel, Holger Kirsten

Submitter: Carl Beuchel

Data file type: Other OMICs Data

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eQTL-Search
Genetical Statistics and Systems Biology

This shiny app facilitates the download and searching of the summary statistics from "Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci" (https://doi.org/10.1093/hmg/ddv194).

Creators: Markus Scholz, Carl Beuchel, Holger Kirsten

Submitter: Carl Beuchel

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CAP Severity Scores
Genetical Statistics and Systems Biology

This Shiny-App implements the calculation of several CAP (Community-Aquired-Pneumonia) severity scores for one or multiple patients based on user-updated data.

Creators: Markus Scholz, Maciej Rosolowski, Carl Beuchel

Submitter: Carl Beuchel

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The Platform for modeling of hematopoiesis and personalized control of hematopoietical side effects during chemotherapy
Genetical Statistics and Systems Biology
No description specified

Creators: Markus Scholz, Carl Beuchel, Yuri Kheifetz, Sibylle Schirm

Submitter: Carl Beuchel

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Metabolite-Investigator: Shiny-Tool for investigation of metabolite-covariate relationships
Genetical Statistics and Systems Biology

The main goal is to provide a principled analysis workflow addressing specific issues of mass-spectrometry metabolite measurements in the context of testing in multiple studies with a high number of hypotheses

Shiny-Application of an analysis pipeline for preprocessing, association and covariate selection of metabolite data with clinical and lifestyle factors in one or more seperate studies. Preprocessing steps include transformation, outlier filtering and batch-adjustment. Analyses include uni- ...

Creator: Carl Beuchel

Submitter: Carl Beuchel

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Description of the modelling, simulating, predictive and treatment managing tool for hematological disorders
Genetical Statistics and Systems Biology

Introduction The platform can be found at the following address: https://apps.health-atlas.de/hemato-models/ This platform enables data fitting, results presentation and next cycle prediction/management using hematopoietic models, developed by our modeling group in IMISE: a) Erythropoiesis model b) Individualised thrombopoiesis model [1] integrated with a model of bone-remodelling [2] as well as with PK models of few cytotoxic drugs [3,4] c) Integral hemathopoiesis model (above thrombopoiesis ...

Creators: Markus Scholz, Carl Beuchel, Sibylle Schirm, Yuri Kheifetz

Submitter: Carl Beuchel

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