This work demonstrates the high potential of combining high-resolution mass spectrometry with chemometric tools, using metabolomics as a guided tool for anti-doping analysis. The administration of 7-keto-DHEA was studied as a proofof-concept of the effectiveness of the combination of knowledge-based and machine-learning approaches to differentiate the changes due to the athletic activities from those due to the recourse to doping substances and methods.Methods: Urine samples were collected from five healthy volunteers before and after an oral administration by identifying three time intervals. Raw data were acquired by injecting less than 1 μL of derivatized samples into a model 8890 gas chromatograph coupled to a model 7250 accurate-mass quadrupole time-of-flight analyzer (both from Agilent Technologies), by using a low-energy electron ionization source; the samples were then preprocessed to align peak retention times with the same accurate mass. The resulting data table was subjected to multivariate analysis.Results: Multivariate analysis showed a high similarity between the samples belonging to the same collection interval and a clear separation between the different excretion intervals. The discrimination between blank and long excretion groups may suggest the presence of long excretion markers, which are particularly significant in anti-doping analysis. Furthermore, matching the most significant features with some of the metabolites reported in the literature data demonstrated the rationality of the proposed metabolomics-based approach.Conclusions: The application of metabolomics tools as an investigation strategy could reduce the time and resources required to identify and characterize intake markers maximizing the information that can be extracted from the data and extending the research field by avoiding a priori bias. Therefore, metabolic fingerprinting of prohibited substance intakes could be an appropriate analytical approach to reduce the risk of false-positive/negative results, aiding in the interpretation of "abnormal" profiles and discrimination of pseudo-endogenous steroid intake in the anti-doping field.
This paper aimed to assess a method to measure eight thyroid-related compounds in serum by liquid chromatography-mass spectrometry (LC-MS/MS), to verify the correlation with radioimmunoassay (RIA), to evaluate the possible cross-reactivity, and to observe differences between athletes declaring the consumption of sodium levothyroxine and nonathletes serum samples. Validation was carried out to assess carryover, working range and linearity, limit of detection and limit of quantification, precision, matrix influence, recovery, accuracy, and uncertainty. Comparison between RIA and LC-MS/MS results was done. The assay was applied to serum samples, and comparison with RIA was done for T3 and T4 levels supported by RIA Thyroidstimulating hormone (TSH) measurements. Validation parameters showed satisfactory results. Correlation between RIA and LC-MS/MS for T3 and T4 showed good results, but a cross-reactivity between T3 and T3AA was observed. Although no significant differences were proved, preliminary comparison between athletes and nonathletes serum samples showed a shift towards high values of TSH and lower for T4 values in the athletes' group. Differences between thyronine and T4AA concentrations and ratios were observed. The trend of T4 values supported by TSH measures might indicate subclinical hypothyroidism in athletes. This represents one of the most controversial thyroid statuses as different criteria about its treatment are described, especially since one of the exogenous causes is inadequate levothyroxine therapy.Because the variation of thyroid hormones and TSH has been extensively studied in high-performance sports, it is worth considering the need to set an adequate reference interval to accurately assess the thyroid status in athletes.
Rationale: The instability of androst-5-ene-3,7-dione structures under acidic conditions is known. The formation of arimistane from 7-oxo-DHEA, influenced by the conditions of sample extraction, and mainly derivatization reaction and gas chromatography (GC) injector temperature, was described earlier, potentially leading to misinterpretation of results. By using a liquid chromatography (LC)-mass spectrometry (MS) (LC-MS) we investigated the stability of the 7-oxo-DHEA in two different solvents (methanol and dimethyl sulfoxide [DMSO]), and the arimistane formation after the application common analytical procedures. Additionally, in vitro and in vivo studies of 7-oxo-DHEA were performed.Methods: The stability of 7-oxo-DHEA was studied in solutions after 60 days storage at −20 C. In vitro studies were performed by incubating 7-oxo-DHEA with human liver microsomes (HLMs). Healthy volunteers collected urine samples before and after the administration of a single dose of 7-oxo-DHEA. Analyses were performed using high-performance LC (HPLC) coupled to a triple quadrupole mass spectrometer (MS/MS) and GC combustion isotope ratio mass spectrometry (GC-C-IRMS) following HPLC purification.Results: 7-oxo-DHEA was stable after 60 days in DMSO while a protic solvent as methanol promotes the degradation of 7-oxo-DHEA to arimistane. HLM incubations showed no formation of arimistane and the sample preparation only influenced the degradation of 7-oxo-DHEA when solvolysis was applied. After the administration study the presence of arimistane also after the hydrolysis with β-glucuronidase (Escherichia coli) was observed while using β-glucuronidase/arylsulfatase (Helix pomatia) showed the presence of arimistane already in blank samples collected before administration.Conclusions: Our results confirm arimistane as a valuable diagnostic marker of 7-oxo-DHEA administration, but also indicate that its formation is due to degradation processes rather than to metabolic biotransformation reactions.
RationaleSystematic electron ionization fragmentation studies of steroids have been performed to elucidate and trace their characteristic fragmentation patterns. However, the electron ionization source setting at 70 eV electron energy is much higher than the ionization potential (7–15 eV) of most organic compounds, leading to extensive fragmentation. We present a multifactorial study on optimizing a low‐energy electron ionization source to maximize molecular ion formation while minimizing the extent of fragmentation to improve the analytical sensitivity of steroids, especially the more thermolabile ones, while preserving the information that can be extracted from the data.MethodsTwenty‐seven steroid reference materials, chosen to cover four main classes of urinary steroids, were considered; gas chromatography/quadrupole time‐of‐flight (GC/qTOF) analyses were carried out using an Agilent Technologies model 8890 gas chromatograph coupled to an Agilent Technologies model 7250 accurate‐mass quadrupole time‐of‐flight (GC/qTOF) instrument. The effects of electron energy, emission current, and source temperature, as well as their potential interactions on steroid fragmentation pathways, have been assessed in full factorial experimental designs.ResultsThree parameters were specifically evaluated to improve the chromatographic/spectrometric response of the selected steroids: (i) degree of fragmentation; (ii) relative abundance of the molecular ion; and (iii) peak width. The first two were evaluated by screening designs that highlighted collision energy and source temperature as the most influential factors on the analytical responses of the considered steroids, while emission current always showed a non‐significant influence. Then, an optimization design was performed to select the final source setting by searching for the combination of factors that minimize peak tailing.ConclusionsThe proposed analytical approach permits a faster selection of optimal experimental conditions for steroidomics analysis using low‐energy electron ionization and high‐resolution mass spectrometry. The development of these designs of experiments (DoE) in full factorial design (FFD) allowed multiple inputs to be monitored at the same time, highlighting the possible interactions and estimating the effects of a factor in the different levels of the other factors considered.
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