Details about this human disease
Synonyms (1)metabolic disease
Definitions (1)
A disease that involving errors in metabolic processes of building or degradation of molecules.
Related items
LIFE Child wants to find out how environmental factors and lifestyles can affect the health of children and adolescents. The aim of the study is to examine civilization diseases such as allergies, obesity, diabetes, cardiovascular diseases in more detail. Furthermore, the study also focuses on healthy mental and physical development from the infant to the young adult. The team of the LIFE Child study around Prof. Dr. med. Wieland Kiess would like to examine and question about 5,000 subjects aged ...
Programme: LIFE Management Cluster
Public web page: http://www.life-child.de/
Start date: 1st Jan 2009
Organisms: Homo sapiens
Human Diseases: disease of anatomical entity, disease of mental health, disease of metabolism
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
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
Organisms: Homo sapiens
Human Diseases: mood disorder, dementia, coronary artery disease, obesity
Example study for people with hypertension or obesity as inclusion criteria. This ontology uses the Core Ontology of Phenotypes as a general phenotypic model.
Submitter: Christoph Beger
Biological problem addressed: Model Analysis Type
Investigation: Phenotype Knowledge Model
Human Diseases: hypertension, obesity
Models: No Models
Data files: OWL representation of the Phenotype Specificati...
Snapshots: No snapshots
This assay bundles all synthetic FHIR data of the PhenoMan evaluation. The data arised from a subset of a Synthea(TM) generated data set. We truncated some resource types like Encounter and Provider to reduce the size of the data set and to speed up the import in a FHIR health data store.
Submitter: Christoph Beger
Resource type: Result Dataset of Clinical Study
Technology type: Technology Type
Investigation: Ontology-based Phenotyping
Human Diseases: asthma, bronchial disease, hypertension, obesity
Data files: PhenoMan Evaluation - AllergyIntolerance FHIR R..., PhenoMan Evaluation - Condition FHIR Resources, PhenoMan Evaluation - Observation FHIR Resources, PhenoMan Evaluation - Patient FHIR Resources
Snapshots: No snapshots
We modelled the algorithm for determining Type 2 Diabetes Mellitus (T2DM) cases presented by PheKB.org using Phenotype Manager (PhenoMan).
Submitter: Alexandr Uciteli
Biological problem addressed: Model Analysis Type
Investigation: Ontology-based Phenotyping
Human Diseases: diabetes mellitus
Models: No Models
Data files: T2DM Case 1 Reasoner Report, T2DM Case 2 Reasoner Report, T2DM Case 3 Reasoner Report, T2DM Case 4 Reasoner Report, T2DM Case 5 Reasoner Report, T2DM Graphical Representation, T2DM Ontology, T2DM Tabular Representation
Snapshots: No snapshots
Abstract (Expand)
Authors: M. Zivkovic, A. Tonjes, R. Baber, K. Wirkner, M. Loeffler, C. Engel
Date Published: 1st Mar 2019
Publication Type: Not specified
Human Diseases: glucose metabolism disease
PubMed ID: 30815561
Citation: Endocrinol Diabetes Metab. 2018 Jul 11;1(4):e00030. doi: 10.1002/edm2.30. eCollection 2018 Oct.
Abstract (Expand)
Authors: H. Loeffler-Wirth, M. Vogel, T. Kirsten, F. Glock, T. Poulain, A. Korner, M. Loeffler, W. Kiess, H. Binder
PubMed ID: 30212520
Citation: PLoS One. 2018 Sep 13;13(9):e0203628. doi: 10.1371/journal.pone.0203628. eCollection 2018.
Abstract (Expand)
Authors: K. Hohloch, B. Altmann, M. Pfreundschuh, M. Loeffler, N. Schmitz, F. Zettl, M. Ziepert, L. Trumper
Date Published: 2nd Dec 2017
Publication Type: Not specified
Human Diseases: obesity, B-cell lymphoma
PubMed ID: 29193018
Citation: Br J Haematol. 2018 Jan;180(2):236-245. doi: 10.1111/bjh.15029. Epub 2017 Nov 28.
Abstract (Expand)
Authors: H. Loeffler-Wirth, M. Vogel, T. Kirsten, F. Glock, T. Poulain, A. Korner, M. Loeffler, W. Kiess, H. Binder
PubMed ID: 29053732
Citation: PLoS One. 2017 Oct 20;12(10):e0186881. doi: 10.1371/journal.pone.0186881. eCollection 2017.
Abstract (Expand)
Authors: F. Beyer, S. Kharabian Masouleh, J. M. Huntenburg, L. Lampe, T. Luck, S. G. Riedel-Heller, M. Loeffler, M. L. Schroeter, M. Stumvoll, A. Villringer, A. V. Witte
PubMed ID: 28397392
Citation: Hum Brain Mapp. 2017 Jul;38(7):3502-3515. doi: 10.1002/hbm.23605. Epub 2017 Apr 11.
Abstract (Expand)
Authors: M. Keller, L. Hopp, X. Liu, T. Wohland, K. Rohde, R. Cancello, M. Klos, K. Bacos, M. Kern, F. Eichelmann, A. Dietrich, M. R. Schon, D. Gartner, T. Lohmann, M. Dressler, M. Stumvoll, P. Kovacs, A. M. DiBlasio, C. Ling, H. Binder, M. Bluher, Y. Bottcher
PubMed ID: 28123940
Citation: Mol Metab. 2016 Nov 16;6(1):86-100. doi: 10.1016/j.molmet.2016.11.003. eCollection 2017 Jan.
Abstract (Expand)
Authors: Rui Zhang, Frauke Beyer, L. Lampe, T. Luck, S. G. Riedel-Heller, M. Stumvoll, Markus Löffler, M. L. Schroeter, A. Villringer, A. V. Witte
Date Published: 2017
Publication Type: Not specified
Human Diseases: diabetes mellitus, obesity, hypertension
DOI: 10.1159/000480486
Citation:
Abstract (Expand)
Authors: Rui Zhang, L. Lampe, Frauke Beyer, Sebastian Huhn, S. K. Masouleh, T. Luck, S. G. Riedel-Heller, Markus Löffler, M. L. Schroeter
Citation:
Abstract (Expand)
Authors: H. Loffler-Wirth, E. Willscher, P. Ahnert, K. Wirkner, C. Engel, M. Loeffler, H. Binder
PubMed ID: 27467550
Citation: PLoS One. 2016 Jul 28;11(7):e0159887. doi: 10.1371/journal.pone.0159887. eCollection 2016.
Abstract (Expand)
Authors: S. Huhn, S. Kharabian Masouleh, M. Stumvoll, A. Villringer, A. V. Witte
Date Published: 29th Jul 2015
Publication Type: Not specified
Human Diseases: cardiovascular system disease, diabetes mellitus
PubMed ID: 26217224
Citation: Front Aging Neurosci. 2015 Jul 8;7:132. doi: 10.3389/fnagi.2015.00132. eCollection 2015.
Creators: Alexandr Uciteli, Christoph Beger, Toralf Kirsten, Heinrich Herre, Franz Matthies, Ralph Schäfermeier
Submitter: Christoph Beger
Data file type: Not specified
Human Diseases: obesity, hypertension
Investigations: Phenotype Knowledge Model
Studies: Ontology-based Phenotype Knowledge Model
Resources: Example Phenotype Specification Ontology for In...
This data file contains FHIR bundles of observation resources, which were used for the evaluation of the PhenoMan. Originally the observation data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server. Please import the patient resources prior to the observations.
This data file contains 8,026,380 observations.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
This data file contains FHIR bundles of patient resources, which were used for the evaluation of the PhenoMan. Originally the patient data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server.
This data file contains 66,018 patients.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
This data file contains FHIR bundles of allergy intolerance resources, which were used for the evaluation of the PhenoMan. Originally the allergy intolerance data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server. Please import the patient resources prior to the allergy intolerances.
This data file contains 563 allergy intolerances.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
This data file contains FHIR bundles of condition resources, which were used for the evaluation of the PhenoMan. Originally the condition data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server. Please import the patient resources prior to the conditions.
This data file contains 139,763 conditions.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
Emerging health geography research analyzes and visualizes health behaviors and outcomes in relation to urban environmental data and related official statistics to produce “health reports” for specific areas. This can be extended to developments in urban planning, e.g., to identify districts or neighborhoods for social investments or restrictions.
We mapped anthropometric data the large LIFE Adult population (10,000 participants) onto the Leipzig map. In particular, we derived the body mass index ...
Creator: Ying-Chi Lin
Submitter: René Hänsel
Model type: Not specified
Model format: Not specified
Environment: Not specified
Organism: Not specified
Human Disease: obesity
Investigations: No Investigations
Studies: No Studies
Resources: No Resources
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
Model type: Metabolic network
Model format: R package
Environment: Shiny
Organism: Homo sapiens
Human Disease: disease of metabolism
Investigations: No Investigations
Studies: No Studies
Resources: No Resources