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Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine.

Early and accurate pre-clinical and clinical biomarkers of hepatotoxicity facilitate the drug development process and the safety monitoring in clinical studies. We selected eight known model compounds to be administered to male Wistar rats to identify biomarkers of drug induced liver injury (DILI) using transcriptomics, metabolite profiling (metabolomics) and conventional endpoints. We specifically explored early biomarkers in serum and liver tissue associated with histopathologically evident acute hepatotoxicity. A tailored data analysis strategy was implemented to better differentiate animals with no treatment-related findings in the liver from animals showing evident hepatotoxicity as assessed by histopathological analysis. From the large number of assessed parameters, our data analysis strategy allowed us to identify five metabolites in serum and five in liver tissue, 58 transcripts in liver tissue and seven clinical chemistry markers in serum that were significantly associated with acute hepatotoxicity. The identified markers comprised metabolites such as taurocholic acid and putrescine (measured as sum parameter together with agmatine), classical clinical chemistry markers like AST (aspartate aminotransferase), ALT (alanine aminotransferase), and bilirubin, as well as gene transcripts like Igfbp1 (insulin-like growth factor-binding protein 1) and Egr1 (early growth response protein 1). The response pattern of the identified biomarkers was concordant across all types of parameters and sample matrices. Our results suggest that a combination of several of these biomarkers could significantly improve the robustness and accuracy of an early diagnosis of hepatotoxicity.