Profiling and comparison of toxicant metabolites in hair and urine using a mass spectrometry-based metabolomic data processing method

Shih, Chia Lung; Wu, Hung Tsung; Liao, Pao Mei; Hsu, Jen Yi; Tsao, Chia Yun; Zgoda, Victor G.; Liao, Pao Chi

doi:10.1016/j.aca.2018.11.009

Cited by 17 publications

(18 citation statements)

References 17 publications

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“…However, its use as a normalization factor is currently under debate since factors such as diet, muscle mass, age, physical activity and menstrual cycle can affect creatinine levels (Cross et al 2011;Davison and Noble 1981;Décombaz et al 1979;James et al 1988;Skinner et al 1996). The urine volume excreted during 24 h is also used as a normalization factor for metabolomics studies (Godzien et al 2011) (Shih et al 2019), but this parameter is not always accessible, especially in the case of human samples. Osmolality, which is considered one of the most reliable methods for evaluating overall urine metabolite concentration (Chadha et al 2001), can also be used as a normalization factor in metabolomics (Yamamoto et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

et al. 2021

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Introduction Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods. Objectives To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study. Methods We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models. Results Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination. Conclusions This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples. Trial registration number: NCT03335644

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Section: Introductionmentioning

confidence: 99%

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

et al. 2021

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“…Six tentative exposure marker signals were inferred as the three known DPHP metabolites (oxo-MPHP (M13), OH-MPHP (M16, M17, and M18), and cx-MPHxP (M24)) based on their speculated structures. The chemical structures (M10, M25, and M26) have been identified by our group [ 10 , 11 ]. Two chemical structures (oxo-MPHxP and OH-MPHxP) that have not been reported in the literature were identified in this study.…”

Section: Resultsmentioning

confidence: 99%

Exposure marker discovery of di-2(propylheptyl) phthalate using ultra-performance liquid chromatography-mass spectrometry and a rat model

Shih

Hsu

Tien

et al. 2019

Journal of Food and Drug Analysis

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Di-(2-propylheptyl) phthalate (DPHP) is a plasticizer and has been suggested to be a subchronic toxicant in rats. DPHP has been approved to be used in food containers and handling by the U.S. Food and Drug Administration. The use of DPHP is still increasing, and the risk of human exposure to DPHP via food may be high. Exposure markers measured in human samples are commonly used to monitor human exposure levels. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and a rat model were used to discover tentative DPHP exposure markers. DPHP and mono-(2-propylheptyl) phthalate (MPHP) were used as the precursors for calculating metabolite candidates using biotransformation mass changes of known enzymatic reactions. A rat model was designed to validate these metabolite candidates as tentative exposure markers. A total of 28 signals show dose–response relationships and these signals contain a few isomers. The chemical structures of 15 tentative exposure marker signals were speculated based on the product ion mass spectra from MS/MS analysis. These 15 signals included 7 chemical structures and some of them may be isomers. The different arrangement of the atoms in space of these isomers should be validated by standard compounds in the future studies. Among the 7 speculated chemical structures, 2 structures were novel tentative DPHP metabolites, and 5 structures have been previously reported in the literature. The results indicate that using UPLC-MS and a rat model can be used to identify tentative toxicant exposure markers.

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“…18 Moreover, the biotransformation products in human hair could reflect long-term chemical exposure, and 5 exposure markers were discovered in the hair samples from the rat after exposure to di(2-propylheptyl) phthalate (DPHP) for 21 days. 21 Moreover, there were only 2 exposure markers identified in both urine and hair samples, indicating that the properties of chemical substances might be different between urine and hair. 21 Therefore, by using the hair as an alternative biospecimen, novel metabolic biomarkers of AD could be discovered that help identify the risk factors associated with the prevention of the progression of AD onset.…”

Section: ■ Introductionmentioning

confidence: 99%

“…21 Moreover, there were only 2 exposure markers identified in both urine and hair samples, indicating that the properties of chemical substances might be different between urine and hair. 21 Therefore, by using the hair as an alternative biospecimen, novel metabolic biomarkers of AD could be discovered that help identify the risk factors associated with the prevention of the progression of AD onset.…”

Section: ■ Introductionmentioning

confidence: 99%

Identifying Hair Biomarker Candidates for Alzheimer’s Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

Chang

Hsu

Hsiao

et al. 2023

J. Am. Soc. Mass Spectrom.

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High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

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Profiling and comparison of toxicant metabolites in hair and urine using a mass spectrometry-based metabolomic data processing method

Cited by 17 publications

References 17 publications

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

Exposure marker discovery of di-2(propylheptyl) phthalate using ultra-performance liquid chromatography-mass spectrometry and a rat model

Identifying Hair Biomarker Candidates for Alzheimer’s Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

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