Clinical lipidomics in the era of the big data.

Lipidomics as a branch of metabolomics provides unique information on the complex lipid profile in biological materials. In clinically focused studies, hundreds of lipids together with available clinical information proved to be an effective tool in the discovery of biomarkers and understanding of pathobiochemistry. However, despite the introduction of lipidomics nearly twenty years ago, only dozens of big data studies using clinical lipidomics have been published to date. In this review, we discuss the lipidomics workflow, statistical tools, and the challenges of standartisation. The consequent summary divided into major clinical areas of cardiovascular disease, cancer, diabetes mellitus, neurodegenerative and liver diseases is demonstrating the importance of clinical lipidomics. In these publications, the potential of lipidomics for prediction, diagnosis or finding new targets for the treatment of selected diseases can be seen. The first of these results have already been implemented in clinical practice in the field of cardiovascular diseases, while in other areas we can expect the application of the results summarized in this review in the near future.

Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine.

OBJECTIVES: The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used. METHODS: Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data. RESULTS: The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs. CONCLUSIONS: The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

Wide metabolite coverage LC-MS/MS assay for the diagnosis of inherited metabolic disorders in urine.

OBJECTIVE: The laboratory diagnosis of inherited metabolic disorders (IMD) has undergone significant development in recent decades, mainly due to the use of mass spectrometry, which allows rapid multicomponent analysis of a wide range of metabolites. Combined with advanced software tools, the diagnosis becomes more efficient as a benefit for both physicians and patients. METHODS: A hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry assay for determination of urinary purines, pyrimidines, N-acylglycines, N-acetylated amino acids, sugars, sugar alcohols and other diagnostically important biomarkers was developed and validated. Evaluation of the results consisting of utilisation of robust scaling and advanced visualization tools is simple and even suitable for urgent requirements. RESULTS: The developed method, covering 65 biomarkers, provides a comprehensive diagnostic platform for 51 IMD. For most analytes, linearity with R2 > 0.99, intra and inter-day accuracy between 80 and 120 % and precision lower than 20 % were achieved. Diagnostic workflow was evaluated on 47 patients and External Quality Assurance samples involving a total of 24 different IMD. Over seven years, more than 2300 urine samples from patients suspected for IMD have been routinely analysed. CONCLUSIONS: This method offers the advantage of a broad coverage of intermediate metabolites of interest and therefore may be a potential alternative and simplification for clinical laboratories that use multiple methods for screening these markers.

Specific gut bacterial and fungal microbiota pattern in the first half of pregnancy is linked to the development of gestational diabetes mellitus in the cohort including obese women.

Aims: Gestation is linked to changes in gut microbiota composition and function. Since gestational diabetes mellitus (GDM) can develop at any time of the pregnancy, we stratified the women into four groups according to the time and test used for the diagnosis. We focused on the gut microbiota pattern in early pregnancy to detect changes which could be linked to later GDM development. Methods: We collected stool samples from 104 pregnant women including obese individuals (first trimester body mass index median was 26.73). We divided the women into four groups according to routine screening of fasting plasma glucose (FPG) levels and oral glucose tolerance test (oGTT) in the first and third trimesters, respectively. We processed the stool samples for bacterial 16S rRNA and fungal ITS1 genes sequencing by Illumina MiSeq approach and correlated the gut microbiota composition with plasma short-chain fatty acid levels (SCFA). Results: We found that gut bacterial microbiota in the first trimester significantly differs among groups with different GDM onset based on unweighted UniFrac distances (p=0.003). Normoglycemic women had gut microbiota associated with higher abundance of family Prevotellaceae, and order Fusobacteriales, and genus Sutterella. Women diagnosed later during pregnancy either by FGP levels or by oGTT had higher abundances of genera Enterococcus, or Erysipelotrichaceae UCG-003, respectively. We observed significant enrichment of fungal genus Mucor in healthy pregnant women whereas Candida was more abundant in the group of pregnant women with impaired oGTT. Using correlation analysis, we found that Holdemanella negatively correlated with Blautia and Candida abundances and that Escherichia/Shigella abundance positively correlated and Subdoligranulum negatively correlated with plasma lipid levels. Coprococcus, Akkermansia, Methanobrevibacter, Phascolarctobacterium and Alistipes positively correlated with acetate, valerate, 2-hydroxybutyrate and 2-methylbutyrate levels, respectively, in women with GDM. Conclusions: We conclude that there are significant differences in the gut microbiota composition between pregnant women with and without GDM already at the early stage of pregnancy in our cohort that included also overweight and obese individuals. Specific microbial pattern associated with GDM development during early pregnancy and its correlation to plasma lipid or SCFA levels could help to identify women in higher risk of GDM development.

Evaluation of the Determination of Dabigatran, Rivaroxaban, and Apixaban in Lupus Anticoagulant-Positive Patients.

BACKGROUND: The effect of direct oral anticoagulants (DOAC) on laboratory tests dependent on the production of their targets, factor IIa and factor Xa, is a well-known problem and can cause both false positive and negative results. In particular, the situation in patients who develop lupus anticoagulant (LA) antibodies is highly complex. To evaluate the effectiveness of DOAC therapy in lupus-positive patients, 31 samples were enrolled in this retrospective study. All patient samples were spiked with three types of DOAC (dabigatran, DABI; rivaroxaban, RIVA; and apixaban, API) in a concentration that significantly influenced the screening test for LA and thus can mask the presence of LA. Subsequently, the DOAC was always unbound by the DOAC-Stop procedure. DOAC levels before and after binding were determined by functional assays, followed by liquid chromatography coupled with mass spectrometry (LC-MS) analysis. METHODS: The determination of DOAC levels was performed by direct thrombin assay and determination of anti-Xa activity with specific calibration as functional tests for DABI and xabans (API and RIVA). To determine concentration levels of API, DABI, and RIVA, our in-house LC-MS method was used. RESULTS: The results of LA-positive samples show significant differences between functional tests and the LC-MS method both before and after DOAC binding. CONCLUSIONS: The acute findings of the presence of LA-type antibodies fundamentally affects the determination of DOAC by functional tests, and in this case, it is necessary to use LC-MS analysis to determine the true value. If patients treated with DOAC develop LA of medium and higher titers, we do not recommend checking DOAC levels with functional tests.