Enzymatic methods may underestimate the total serum bile acid concentration

Towards routine high-throughput analysis of fecal bile acids: validation of an enzymatic cycling method for the quantification of total bile acids in human stool samples on fully automated clinical chemistry analyzers

OBJECTIVES

Bile acid diarrhea is a common but underdiagnosed condition. Because the gold standard test (75SeHCAT) is time-consuming and not widely available, fecal bile acid excretion is typically assessed by chromatography and mass spectrometry. Although enzymatic cycling assays are well established for the rapid and cost-effective analysis of total bile acids (TBA) in serum or plasma, their full potential has yet not been extended to stool samples in clinical routine.

METHODS

The performance of the 'Total bile acids 21 FS' reagent (DiaSys) was evaluated in fecal matrix according to CLSI guidelines and EU-IVD Regulations (2017/745), and compared to an established microplate-based kit (IDK®) by measuring patient stool samples (n=122). Method agreement was assessed by Passing-Bablok and Bland-Altman analysis. The quantification of eight individual BAs was assessed using HPLC-MS/MS as reference method.

RESULTS

The DiaSys assay showed linearity between 3.5 and 130 μmol/L, good repeatability, total precision, and reproducibility with CVs of 1.7 %, 3.5 %, and 3.0 %. Limit of blank (LoB), detection (LoD), and quantitation (LoQ) were ≤0.17, ≤0.3, and 3.5 μmol/L, respectively. No significant interference from endogenous substances was observed. The methods showed good correlation up to 140 μmol/L (r=0.988), despite differences in the quantification of individual BAs, with mean deviations of 7 % (DiaSys) and 31 % (IDK®), respectively.

CONCLUSIONS

The advantages of enzymatic TBA analysis on fully automated clinical chemistry platforms can be exploited for the routine analysis of stool samples. However, cycling assays may benefit from reference standards that take into account the composition of the fecal BA pool.

Performance evaluation of enzymatic total bile acid (TBA) routine assays: systematic comparison of five fifth-generation TBA cycling methods and their individual bile acid recovery from HPLC-MS/MS reference

OBJECTIVES

Serum total bile acid (TBA) levels are frequently assessed in clinical routine for the early detection of hepatobiliary dysfunction. However, the comparability of current 5th-generation TBA cycle assays based on 3α-hydroxysteroid dehydrogenase (3α-HSD) and their ability to quantify individual bile acids has not been systematically addressed.

METHODS

Patient serum samples (n=60) across the diagnostically relevant TBA range (1-200 μmol/L) were analyzed using five TBA routine assays from Abbott, DiaSys, Diazyme, Beijing Strong (BSBE) and Randox on the same analyzer (BioMajesty® JCA-BM6010/C). The assays were compared using Passing-Bablok regression and the recovery of 11 individual BAs was evaluated against RP-HPLC-MS/MS as non-enzymatic reference method.

RESULTS

Despite excellent correlation (Spearman r ≥0.99), the assays showed proportional differences (slope) ranging from 0.99 (BSBE/Randox) to 1.24 (Abbott/DiaSys). The assays showed considerable deviation in the recovery of competitor's calibrators and controls, and large heterogeneity in the recovery of individual BAs, with mean deviations from reference value between 13 % (DiaSys) and 42 % (Abbott). CA and TCA were measured most accurately and consistently, whereas GCA, CDCA, DCA, UDCA, and conjugates were over- or undermeasured to varying degrees.

CONCLUSIONS

The linear relationship and constant proportional bias between all five routine assays enable the harmonization of TBA measurements up to 60 μmol/L. However, for patient samples with high TBA levels and disease-specific overrepresentation of individual BAs, harmonization will require: i) optimized reaction conditions to equalize substrate specificity, and ii) calibration to a common, commutable reference material with well-defined BA composition instead of internal standards spiked with different BAs.

Comparison of colorimetric, fluorometric, and liquid chromatography-mass spectrometry assays for acetyl-coenzyme A

Acetyl-Coenzyme A is a central metabolite in catabolic and anabolic pathways as well as the acyl donor for acetylation reactions. Multiple quantitative measurement techniques for acetyl-CoA have been reported, including commercially available kits. Comparisons between techniques for acetyl-CoA measurement have not been reported. This lack of comparability between assays makes context-specific assay selection and interpretation of results reporting changes in acetyl-CoA metabolism difficult. We compared commercially available colorimetric ELISA and fluorometric enzymatic-based kits to liquid chromatography-mass spectrometry-based assays using tandem mass spectrometry (LC-MS/MS) and high-resolution mass spectrometry (LC-HRMS). The colorimetric ELISA kit did not produce interpretable results even with commercially available pure standards. The fluorometric enzymatic kit produced comparable results to the LC-MS-based assays depending on matrix and extraction. LC-MS/MS and LC-HRMS assays produced well-aligned results, especially when incorporating stable isotope-labeled internal standards. In addition, we demonstrated the multiplexing capability of the LC-HRMS assay by measuring a suite of short-chain acyl-CoAs in a variety of acute myeloid leukemia cell lines and patient cells.

A simple and reliable bile acid assay in human serum by LC-MS/MS

Background

Bile acids, as important signaling molecules and regulatory factors acting on glucose, lipid, and energy metabolism, are always involved in liver, biliary, and intestinal diseases. Development and validation of a simple liquid chromatography–tandem mass spectrometry (LC‐MS/MS) method for determination of bile acids is significant for the routine clinical testing.

Methods

Fifty microlitre of serum was mixed with 10 μl of the internal standard working solution and then 140 μl of methanol for protein precipitation. After centrifuged, the supernatant was directly used for LC‐MS/MS analysis.

Results

Good separation of all bile acid species was achieved. The method was validated with consistent linearity for individual bile acids, good recovery, low carryover, satisfactory sample stability, and analytical specificity against hemolysis, lipemia, and bilirubinemia. The intra‐day and the inter‐day imprecision values were in the range of 1.53%–10.63% and 3.01%–13.98%, respectively. No obvious matrix effect was observed. The reference intervals of bile acids in adults have been established for the clinical testing.

Conclusions

The low sample volume, simple sample preparation, good separation of all species, and satisfying validation results make this LC‐MS/MS approach suitable for usage as a high‐throughput assay in routine clinical laboratories.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.