The Leipzig Health Atlas (LHA) is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields in which our team has scientific and clinical expertise. Two basic characteristics are:

  1. an interoperable ontology-based semantic platform to share highly annotated data, novel ontologies, usable models and working software tools; 
  2. an advanced, application-oriented analytic pipeline for a clinical and scientific user community to provide disease-related phenotype classifications, omics based disease sub-classifications, risk predictions and simulation models for diseases and organ functions

How to use the Leipzig Health Atlas

Currently, we provide the following content and services:

Scientific projects

» List of scientific projects contained in the LHA.

Data sets

» Clinical data sets, OMICS data sets and SOM data sets for download.


» Models such as algorithm-based prediction or simulation models.


» Paper resulting from our work.

Tools and services

» Cohort Section Tool (i2b2)
» Basic Analysis Tool (tranSMART)
» Metadata Browser (MDR)

Scientific projects within the LHA

» Project Area 1: Semantic Data Integration, Ontologies and mining services
» Project Area 2: Application Development and Validation
» Project Area 3: Application Integration and Community Construction
» Project Area 4: Management

Latest Publications

Diabetes and hypertension contribute to normal-appearing white matter microstructural variability in the brain

Background and objectives: Obesity has been associated with increased risk of dementia. Grey and white matter (WM) of the brain are commonly used as biomarkers for early detection of dementia. However, considering WM, available neuroimaging studies had mainly small sample size and yielded less conclusive results (Kullmann et al., 2015). Recently, a positive correlation between obesity and fractional anisotropy (FA) in a middle age group was reported (Birdsill et al. 2017).

Light-Weighted Automatic Import of Standardized Ontologies into the Content Management System Drupal.

The amount of ontologies, which are utilizable for widespread domains, is growing steadily. BioPortal alone, embraces over 500 published ontologies with nearly 8 million classes. In contrast, the vast informative content of these ontologies is only directly intelligible by experts. To overcome this deficiency it could be possible to represent ontologies as web portals, which does not require knowledge about ontologies and their semantics, but still carries as much information as possible to the end-user. Furthermore, the conception of a complex web portal is a sophisticated process.

Structural connectivity of the reward network in obesity and its association with eating behavior

Background: Obesity is considered a multi-facetted neurobehavioral disorder associated with an increased risk for cardiovascular disease and stroke (Suk et al., 2003) as well as faster cognitive decline in aging (Debette et al., 2011). Higher BMI has been associated with altered food reward processing (Stice et al., 2008) and tendencies for disinhibited eating (Bellisle et al., 2004).

Association of Hippocampal Volumes with Cognitive Tasks in a Large Population-Based Cohort

Background: Hippocampal volume, assessed via high-resolution MRI, is associated with memory and visuospatial performance in humans (Squire, 2004) and specifically prone to develop atrophy with age (Apostolova,2015). This process has been linked to neurodegenerative diseases, such as Alzheimer’s disease (Apostolova,2015) and a decline of cognitive functions (Bruno,2016). However, due to differences in study-design and characteristics certain heterogeneity in results remains, in particular considering subfieldspecific effects (deFlores,2015).

Metadata Management for Data Integration in Medical Sciences - Experiences from the LIFE Study -

Clinical and epidemiological studies are commonly used in medical sciences. They typically collect data by using different input forms and information systems. Metadata describing input forms, database schemas and input systems are used for data integration but are typically distributed over different software tools; each uses portions of metadata, such as for loading (ETL), data presentation and analysis. In this paper, we describe an approach managing metadata centrally and consistently in a dedicated Metadata Repository (MDR). Metadata can be provided to different tools.