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

Secular trends in the incidence of dementia in high-income countries: a protocol of a systematic review and a planned meta-analysis.

Publication Date
INTRODUCTION: A global dementia epidemic is projected for the year 2050 with an ever-rising number of individuals living with the syndrome worldwide. However, increasingly, studies are emerging from high-income countries (HIC) that show a positive trend towards a possible decrease in dementia occurrence. Therefore, we aim to systematically summarise evidence regarding secular trends in the incidence of dementia in HIC.

Which types of mental work demands may be associated with reduced risk of dementia?

Publication Date
INTRODUCTION: Previous studies have demonstrated that an overall high level of mental work demands decreased dementia risk. In our study, we investigated whether this effect is driven by specific mental work demands and whether it is exposure dependent. METHODS: Patients aged 75+ years were recruited from general practitioners and participated in up to seven assessment waves (every 1.5 years) of the longitudinal AgeCoDe study. Analyses of the impact of specific mental work demands on dementia risk were carried out via multivariate regression modeling (n = 2315).

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

Publication Date
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.

Psychometric evaluation of the Generalized Anxiety Disorder Screener GAD-7, based on a large German general population sample.

Publication Date
BACKGROUND: The Generalized Anxiety Disorder Scales GAD-7 and GAD-2 are instruments for the assessment of anxiety. The aims of this study are to test psychometric properties of these questionnaires, to provide normative values, and to investigate associations with sociodemographic factors, quality of life, psychological variables, and behavioral factors. METHODS: A German community sample (n=9721) with an age range of 18-80 years was surveyed using the GAD-7 and several other questionnaires.

Predicting brain-age from multimodal imaging data captures cognitive impairment.

Publication Date
The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction.