New diagnostics for TB in children
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According to the World Health Organization (WHO), tuberculosis is one of the ten most common causes of death in children under the age of five. One of the main reasons for this is that the disease is often not diagnosed correctly and in good time. This particularly affects regions with few resources. Available tests are too expensive, complex or technically complicated. There is an urgent need for new, child-friendly test procedures that can be carried out using simple samples (e.g. saliva). To change this, the "New TB Diagnostics" research group, led by Dr. med. Laura Olbrich, DPhil (LMU Clinical Scientist of the Year 2024), is evaluating new diagnostics. To this end, studies on new TB diagnostics are prepared, conducted and evaluated.
One focus is RaPaed-AIDA-TB (Rapid and Accurate Diagnosis of Paediatric TB - An AIDA (Assessment of Innovative Diagnostic and Algorithms for Early and Sensitive Detection of Acute TB)), one of the largest TB diagnostic studies in children. Results of the project were published in October 2023 in the journal The Lancet Infectious Diseases (see LMU news release, 31.10.2023).
The research group has also been contributing to the development of TB diagnostic recommendations by the World Health Organization (WHO). -
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Decide TB
Rapid and accurate diagnosis of paediatric tuberculosis (RaPaed TB)
ADOPT RaPaed-TB
ADOPT RaPaed-TB: Three priorities for better diagnoses
The ADOPT RaPaed-TB project builds on the large RaPaed-TB study, completed in 2023, which investigated better ways to diagnose TB in children. The international study was coordinated in Munich and conducted in South Africa, Mozambique, Tanzania, Malawi, and India. A total of 975 children were included in the study. The study tested a blood test with a three-gene signature, urine and stool tests, and a novel immune cell marker.
The ADOPT RaPaed-TB now aims to use these valuable samples and data to develop improved diagnostics.
There are three focus areas:
New blood markers: The study is looking for patterns of gene activity and proteins that can indicate tuberculosis (transcriptomic and proteomic signatures).
Sputum-free tests: They are developing urine, stool, and blood tests that can directly detect TB bacteria.
Data combination: Combining all available data—clinical, laboratory, and biomarker results. Then, evaluate these data using modern methods, such as artificial intelligence, machine learning, and Bayesian models, to identify highly accurate diagnostic signatures.
By building on proven methods and using cutting-edge analysis, the goal is to create child-friendly TB tests that are fast, reliable, and suitable for use in different healthcare settings—helping more children receive the diagnosis and treatment they need.
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