Development of biospecimen quality control tools and disease diagnostic markers by metabolic profiling

In metabolomics-based biomarker studies, the monitoring of pre-analytical variations is
crucial and requires quality control tools to enable proper sample quality evaluation. In
this dissertation work, biospecimen research and machine learning algorithms are applied
(1) to develop sample quality assessment tools and (2) to develop disease-specific diagnostic
models.
In this regard, a novel plasma sample quality assessment tool, the LacaScore, is presented.
The LacaScore plasma quality assessment is based on the plasma levels of ascorbic acid
and lactic acid. The biggest challenge in metabolomics analyses is that the sample quality
is often not known. The presented tool enhances the knowledge and importance of the
monitoring of pre-analytical variations, such as pre-centrifugation time and temperature,
prior to sample analysis in the emerging field of metabolomics. Based on the LacaScore,
decisions on the suitability/fit-for-purpose of a given sample or sample cohort can be
made.
In this dissertation work, the knowledge on sample quality was applied in a biomarker
discovery study based on cerebrospinal fluid (CSF) from early-stage Parkinson’s disease
(PD) patients. To date, no markers for the diagnosis of Parkinson’s disease are available.
In this work, a non-targeted GC-MS approach is presented and shows significant changes
in the metabolic profile in CSF from early-stage PD patients compared to matched healthy
control subjects. Based on these findings, a biomarker signature for the prediction of earlystage
PD has been developed by the application of sophisticated machine learning algorithms.
This disease-specific signature is composed of metabolites involved in inflammation,
glycosylation/glycation and oxidative stress response.
In summary, this dissertation illustrates the importance of sample quality monitoring
in biomarker studies that are often limited by small amounts of human body fluids. The
monitoring of sample quality enhances the robustness and reproducibility of biomarker
discovery studies. In addition, proper data analysis and powerful machine learning algorithms
enable the generation of potential disease diagnosis biomarker signatures.