Urinary Metabolites Research Articles

Urinary excretion profiles of the orexin receptor antagonist suvorexant and its metabolites.

Suvorexant is an orexin receptor antagonist used in the treatment of insomnia. In this study, we investigated the urinary excretion profiles of suvorexant and its major metabolites, including conjugates, to obtain fundamental information for proving exposure to suvorexant in criminal cases. Urine specimens were collected from three subjects for maximum 168h after a single oral ingestion of suvorexant (10mg), and suvorexant and its metabolites in urine were determined using liquid chromatography-tandem mass spectrometry with a C18 semi-micro column. The carboxylic and hydroxy metabolites (M4 and M9) were identified with authentic standards synthesized in our laboratory, and their glucuronides and other hydroxy metabolites (M8 and M10) were tentatively detected based on measured exact masses and product ion spectra of them. Suvorexant, M4 and M9 would be detectable for 20-34h, 6-7days and 42-61h after intake, respectively. The quantitative results demonstrated that the molar ratios of accumulated amounts of M4 and M9 including their glucuronides excreted in urine to dose ranged about 2.6-6.2% and 0.37-0.51%, respectively, while that of the unchanged parent was much lower (0.011-0.013%). The ratios of the amount of glucuronide to the total amount of M4 and M9 excreted in urine was less than 10% and approximately 90%, respectively. The urinary excretion profiles indicated that M4 and M9 would be effective indicators for proving suvorexant intake, and M4 could be detected until one week after intake even without enzymatic hydrolysis (limit of detection: 0.05ng/mL).

Association between urinary arsenic and hearing threshold shifts in adults in the United States, National Health and Nutrition Examination Survey, 2015–2016

BackgroundHearing loss (HL) is a common sensory disorder in humans. Studies on the relationship between arsenic, which is a highly toxic and widely distributed heavy metal with a health risk to humans, and hearing status in humans are contradictory and mostly focused on people living in arsenic-contaminated areas. This study investigated the association between urinary arsenic levels and hearing threshold shifts in the general population in the United States.MethodsOverall, 1,017 adults (aged 20–69 years) from the National Health and Nutrition Examination Survey (2015–2016) were included in this study. HL was defined as pure-tone average (PTA) ≥ 20 dB at frequencies 500, 1,000, 2000, and 4,000 Hz in the better-hearing ear. Total urinary arsenic (uAs) and dimethylarsinic acid (uDMA) levels were analyzed. Multivariate linear regression analyses and smooth curve fitting were performed to evaluate the correlations between uDMA, uAs, and low-, speech-, and high-frequency hearing levels.ResultsThe mean age of the participants was 42.13 ± 13.66 years, including weighted 48.67% men and 12.88% participants with sensorineural HL. After adjusting for potential confounders in the multivariate linear regression model, higher uDMA levels were significantly associated with poor low-, and speech-frequency PTAs, with no differences among participants by age or sex. Smooth curve fitting indicated a nonlinear relationship between uAs and high-frequency PTA hearing threshold shifts. The uAs levels were positively associated with high-frequency PTA until the turning point of 1.54 (adjusted β 4.53, 95% CI 1.16, 7.90; p = 0.0085), beyond which this association was not observed (adjusted β −0.43, 95% CI −1.57, 0.71; p = 0.4600).ConclusionWe found positive associations between urinary arsenic metabolites uDMA, uAs levels and poor hearing threshold shifts in US adults. This study provides new evidence for the association between arsenic exposure and auditory function.

Urinary Metabolite Profiles of Participants with Overweight and Obesity Prescribed a Weight Loss High Fruit and Vegetable Diet: A Single Arm Intervention Study

Background/Objectives: Thus far, no studies have examined the relationship between fruit and vegetable (F and V) intake, urinary metabolite quantities, and weight change. Therefore, the aim of the current study was to explore changes in urinary metabolomic profiles during and after a 10-week weight loss intervention where participants were prescribed a high F and V diet (7 servings daily). Methods: Adults with overweight and obesity (n = 34) received medical nutrition therapy counselling to increase their F and V intakes to national targets (7 servings a day). Data collection included weight, dietary intake, and urine samples at baseline at week 2 and week 10. Urinary metabolite profiles were quantified using 1H NMR spectroscopy. Machine learning statistical approaches were employed to identify novel urine-based metabolite biomarkers associated with high F and V diet patterns at weeks 2 and 10. Metabolic changes appearing in urine in response to diet were quantified using Metabolite Set Enrichment Analysis (MSEA). Results: Energy intake was significantly lower (p = 0.02) at week 10 compared with baseline. Total F and V intake was significantly higher at week 2 and week 10 (p < 0.05). In total, 123 urinary metabolites were quantified. At week 10, 21 metabolites showed significant changes relative to baseline. Of these, 11 metabolites also significantly changed at week 2. These overlapping metabolites were acetic acid, dimethylamine, choline, fumaric acid, glutamic acid, L-tyrosine, histidine, succinic acid, uracil, histamine, and 2-hydroxyglutarate. Ridge Classifier and Linear Discriminant Analysis provided best prediction accuracy values of 0.96 when metabolite level of baseline was compared to week 10. Conclusions: Urinary metabolites quantified represent potential candidate biomarkers of high F and V intake, associated with a reduction in energy intake. Further studies are needed to validate these findings in larger population studies.

Organophosphate flame retardants and their metabolites in paired dog food and urine: Pet exposure through food consumption.

Companion dogs are exposed to various chemicals. However, our understanding of the sources and pathways of chemical exposure in pets remains limited. In this study, we collected urine samples from 47 dogs and corresponding samples of the food they consumed to analyze the concentrations and dietary exposure to organophosphate flame retardants (OPFRs) and their metabolites (mOPFRs). Triphenyl phosphate (TPHP) and its metabolite, diphenyl phosphate (DHPH), were the predominant compounds detected in dog food and urine samples. The concentration of mOPFRs in urine decreased as body weight increased; however, neither sex nor age significantly influenced mOPFR levels in dog urine. The estimated daily intake of OPFRs (343 ng/kg bw/day) through food consumption (EDIfood) was comparable to the previously reported levels of polycyclic aromatic hydrocarbons (324 ng/kg bw/day) and higher than those of pesticides (214 ng/kg bw/day), parabens (120 ng/kg bw/day), and polychlorinated biphenyls (103 ng/kg bw/day). By calculating the ratio of EDIfood to the cumulative daily intake based on urinary mOPFR concentrations, it was found that dietary sources contributed to 66% of the total TPHP exposure in dogs. This finding was further supported by Spearman's correlation analysis between parent OPFR concentrations in dog food and mOPFR levels in urine (p < 0.01), indicating that dietary intake may be one of the most significant exposure pathways for OPFRs in dogs. To the best of our knowledge, this is the first study to investigate the levels of OPFR exposure in paired dog food and urine samples.

Biomarkers of exposure to nicotine and selected toxicants in individuals who use alternative tobacco products sold in Japan and Canada in 2018-2019.

Comparisons of nicotine and toxicant exposure between people who use different alternative tobacco products remains underexplored. This cross-sectional, multi-country study analyzed urinary metabolites of nicotine, NNK, and volatile organic compounds (acrolein, acrylamide, acrylonitrile) among established users (n=550) in Japan and Canada. Participants exclusively or concurrently used nicotine vaping products (NVPs; Canada only), heated tobacco products (HTPs; Japan only), and combustible cigarettes (CCs; Japan and Canada), or abstained (Japan and Canada). All product groups showed substantial nicotine exposure. Both HTPs and NVPs exposed exclusive users to lower toxicant levels than exclusive CC use. Canadian participants who exclusively used NVPs exhibited lower NNK and acrolein exposure but higher acrylamide exposure than Japanese participants who exclusively used HTPs. Concurrent use of CCs alongside alternative products exposed users to higher toxicant levels compared to exclusive use of either alternative product. Exclusive use of alternative tobacco products results in significant nicotine exposure but substantially lower toxicant exposure compared to exclusive CC use. People who use HTPs in Japan may experience higher exposure to nicotine and certain toxicants (NNK, acrolein) than people who use NVPs in Canada. Concurrent use results suggest that partially substituting CCs with alternative products may reduce toxicant exposure, but to a lesser extent than completely transitioning to alternative products. Exposure patterns between two popular alternative tobacco products differ. The overall toxicant exposure from these products is lower than CCs, providing critical data for regulatory decisions and public health considerations.

Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles

Background: Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. Methods: We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles. In total, 148 (34.02%) patients were with kidney stones, 34 (7.82%) with ureter stones, and 163 (34.83%) with multiple location stones, all of whom had detailed urinary metabolite data. Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. The Super Learner Ensemble Method also demonstrated high predictive performance with slightly lower AUC values compared to Random Forest. Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. Results: We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. Conclusion: Machine learning techniques show promise in revealing the links between urological stone disease and 24-h urinary metabolic data. Enhancing the prediction accuracy of these models leads to improved dietary or pharmacological prevention strategies.

Detection and Identification of New Chlorphenesin Carbamate Metabolites in Human Urine and Metabolomic Characterization Using Liquid Chromatography-Q Exactive-HF-Orbitrap-Mass Spectrometry.

Chlorphenesin carbamate is a skeletal muscle relaxant, and one of its metabolites is 4-chlorophenoxyacetic acid (4-CPA), which is included on the Prohibited List of the World Anti-Doping Agency (WADA). The non-prohibited substance, chlorphenesin carbamate, as a potential source of 4-CPA, needs to be identified more strongly in doping control protocols. In this paper, the metabolism of chlorphenesin carbamate in human urine was studied. Ten volunteers were recruited to take chlorphenesin carbamate tablets orally, and urine samples were collected before and after being tested. The urine samples were detected by liquid chromatography-Q Exactive HF orbitrap mass spectrometry (LC-Q Exactive HF-MS) in full MS and full MS-ddMS2 scanning modes. Based on accurate molecular formulae determination and fragmentation pattern analysis by mass spectrometry, a total of 29 chlorphenesin carbamate metabolites were found, comprising 18 newly identified metabolites and 11 previously reported metabolites. There were five potential metabolic processes listed: hydroxylation, amide hydrolysis, C-oxidation, O-glucuronidation, sulfation, and their combinations. Chlorphenesin carbamate and the 29 metabolites were compared for their detection windows, and the findings indicated that the two recently reported metabolites, M9 and M10, had longer detection windows than the metabolites documented by the WADA. These metabolites are anticipated to be long-lasting biomarkers for the detection of chlorphenesin carbamate intake. By analyzing the results of metabolomic profiling, it was found that the metabolites with significant changes were mainly related to histidine metabolism and β-alanine metabolism pathways. This paper provides a comprehensive report on the metabolic profile of urinary chlorphenesin carbamate and reveals a point of view the changes in the metabolic in the human body, which is conducive to supporting the detection of chlorphenesin carbamate and meclofenoxate for doping control, as well as a better understanding of the mechanism of action of chlorphenesin carbamate as a drug.

Identification of 4-Chloromethylcathinone Metabolites in Urine with GC-MS and LC-QTOF

Identification of 4-Chloromethylcathinone Metabolites in Urine with GC-MS and LC-QTOF

Rapid non-separative determination of prevailing organophosphate flame retardants metabolites in urine by means of a restricted access material coupled to tandem mass spectrometry

Rapid non-separative determination of prevailing organophosphate flame retardants metabolites in urine by means of a restricted access material coupled to tandem mass spectrometry

Associations between volatile organic compounds exposure and multiple oxidative damage biomarkers: Method development, human exposure, and application for e-waste pollution prediction

Atmospheric monitoring studies reveal substantial health risks from exposure to volatile organic compounds (VOCs) for workers and nearby children in e-waste recycling areas (ERA), yet internal exposure risks are seldom examined. To address this, we developed a method to simultaneously analyze 12 urinary VOC metabolites (mVOCs) and oxidative damage biomarkers (ODBs) in workers and children from ERA and general adults from control areas using ultrahigh performance liquid chromatography coupled with quadrupole/orbitrap high-resolution mass spectrometry (UPLC-Orbitrap-HRMS). The results showed that e-waste workers exhibited significantly higher levels of VOC exposure and ODBs than e-waste children and control adults. Exceeding 91.1 %, 69.1 %, 20.8 %, 19.7 %, and 3.26 % of e-waste workers faced non-carcinogenic risk from exposure to acrolein, acrylonitrile, acrylamide, 1,3-butadiene, and 1,2-dichloroethane, respectively. The weighted quantile sum, quantile g-computation, and Bayesian kernel machine regression models consistently indicated significant positive associations between these VOC mixtures and cholesterol ODB levels (i.e., glycocholic acid, cholic acid, and glycochenodeoxycholic acid), highlighting the necessity for improved protective measures for occupational workers. Interestingly, cholesterol ODBs significantly mediated the association between VOCs exposure and nucleic acid ODBs, accounting for 12.0–26.0 % of the association with 8-hydroxy-2′-deoxyguanosine (an oxidative DNA damage biomarker) and 25.4–53.4 % with 8-hydroxyguanosine (an oxidative RNA damage biomarker). This suggests that cholesterol ODBs potentially serve as better indicators of health risks from VOC exposure than nucleic acid ODBs. Additionally, the combination of mVOCs and ODBs (Mean AUC: 0.906, ACC: 0.821) as exposure fingerprints outperformed either mVOCs (Mean AUC: 0.878, ACC: 0.802) or ODBs (Mean AUC: 0.843, ACC: 0.768) alone in predicting the presence of e-waste pollution, underscoring the importance of integrating exposure and effect biomarker fingerprints to accurately capture e-waste pollution characteristic. Our findings offer a novel approach for screening e-waste pollution in unknow e-waste recycling sites and provide a foundation for developing high-precision prediction models for other polluting industries.

Impact of Short-Term Diesel Exhaust Exposure on Prothrombotic Markers in Chronic Obstructive Pulmonary Disease: A Randomized, Double-Blind, Crossover Study.

Rationale: Growing evidence suggests that air pollution exposure is a major risk factor in chronic obstructive pulmonary disease (COPD) that is associated with an increased prothrombotic state and adverse cardiovascular outcomes. However, much of this work is based on observational data or human exposure studies involving younger participants. The biological causality and mechanism of air pollution-induced prothrombotic response in patients with COPD remain to be explored. Objectives: The main aim of this work was to investigate the impact of short-term diesel exhaust (DE) exposure on circulating prothrombotic markers-fibrinogen and plasminogen activator inhibitor-1 (PAI-1)-and urinary eicosanoids in patients with COPD. Methods: Twenty-nine research participants were recruited in this randomized, double-blind, crossover, controlled human exposure study to DE. Participants included former smokers with and without mild or moderate COPD (ex-smokers [ES] and COPD group) and healthy never-smokers without COPD (nonsmoker [NS] group). Each participant was exposed to DE (300 μg/m3 of particulate matter with an aerodynamic diameter ≤2.5 μm) and filtered air for 2 hours on different occasions, in randomized order, separated by a 4-week washout. Blood and urine samples were collected before and 24 hours after each exposure. Plasma fibrinogen and serum PAI-1 concentrations were quantified using enzyme-linked immunosorbent assays. Urinary eicosanoid concentrations were quantified using ultraperformance liquid chromatography coupled to tandem mass spectrometry. Linear mixed-effects models were used for statistical comparisons. Results: Participants with COPD showed an increase in plasma fibrinogen (effect estimate, 1.27 [1.06-1.53]; P = 0.01) after DE relative to filtered air, but no significant DE-associated change in serum PAI-1 (0.95 [0.87-1.04]; P = 0.26). In never-smokers and ex-smokers without COPD, fibrinogen (NS group, 1.10 [0.99-1.23]; P = 0.08; ES group, 0.86 [0.68-1.09]; P = 0.08] and PAI-1 (NS group, 1.12 [0.96-1.32]; P = 0.15; ES group, 0.90 [0.79-1.03]; P = 0.13) were not changed after DE exposure. Participants with COPD showed a DE-attributable increase in urinary thromboxane B2 (TXB2) metabolite concentrations as follows: 11-dehydro-TXB2 (1.45 [1.02-2.08]; P = 0.04) and 2,3-dinor-TXB2 (1.45 [1.05-2.00]; P = 0.03). Conclusions: Participants with COPD had increased plasma fibrinogen and urinary TXB2 metabolites after short-term DE exposure, suggesting they may be more susceptible to a pollution-attributable prothrombotic response than healthy control subjects or ex-smokers without COPD. Clinical trial registered with www.clinicaltrials.gov (NCT02236039).

Volatile organic compounds (VOC) metabolites in urine are associated with increased systemic inflammation levels, and smokers are identified as a vulnerable population

BackgroundPrevious studies indicated that exposure to VOCs was linked to increased systemic inflammation levels. However, the dose-response relationships between urine VOCs metabolites and systemic inflammation have not been established, and the key metabolite of the toxic compounds has not been identified. MethodsWe used data in 7007 US adults in the NHANES cycles (2011–2018) across 8 years. Urinary VOC metabolites were measured using ultra-performance liquid chromatography and electrospray tandem mass spectrometry (UPLC-ESI/MSMS). VOC metabolites were adjusted by urinary creatinine level before analysis. Systemic inflammation was assessed by systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) indices. Generalized linear models, restricted cubic splines (RCS), and weighted quantile sum (WQS) regression were applied to evaluate the associations, exposure-response (E-R) curve and identify the key contributor compound, adjusting for gender, age, race, BMI, marital condition, education level, smoking level, alcohol consumption and physical activity. Smoking status was assessed as an effect modifier. ResultsSignificant and robust positive correlations were found between 8 VOC metabolites and both SII and SIRI. They were N-Acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA), N-Acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA), N-Acetyl-S-(3,4-dihydroxybutyl)-L-cysteine (DHBMA), N-Acetyl-S-(3-hydroxypropyl)-L-cysteine (3HPMA), mandelic acid (MA), N-Acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine (MHBMA3), phenylglyoxylic acid (PGA), and N-Acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine (HPMMA). The RCS curves showed J-shaped or exponential shaped E-R relationships for most VOC metabolites. WQS regression found that exposure to the mixture of VOC metabolites was related to increased systemic inflammation, and MA was the key VOC metabolite contributing most to systemic inflammation levels. Smokers exhibited higher levels of urinary VOCs and larger susceptibility to VOC-related increases in SII and SIRI compared to non-smokers. ConclusionThis study demonstrated a strong link between urinary VOC metabolites and increased systemic inflammation, and smokers were more susceptible. Our findings highlighted the significance of reducing VOC exposure to mitigate the inflammation levels, particularly for smokers.

Parameter estimation for stable distributions and their mixture

In this paper, we consider estimating the parameters of univariate α-stable distributions and their mixtures. First, using a Gaussian kernel density distribution estimator, we propose an estimation method based on the characteristic function. The optimal bandwidth parameter was selected using a plug-in method. We highlight another estimation procedure for the Maximum Likelihood framework based on the False position algorithm to find a numerical root of the log-likelihood through the score functions. For mixtures of α-stable distributions, the EM algorithm and the Bayesian estimation method have been modified to propose an efficient and valuable tool for parameter estimation. The proposed methods can be generalised to multiple mixtures, although we have limited the mixture study to two components. A simulation study is carried out to evaluate the performance of our methods, which are then applied to real data. Our results appear to accurately estimate mixtures of α-stable distributions. Applications concern the estimation of the number of replicates in the Mayotte COVID-19 dataset and the distribution of the N-acetyltransferase activity of the Bechtel et al. data for a urinary caffeine metabolite implicated in carcinogens. We compare the proposed methods, together with a detailed discussion. We conclude with the limitations of this study, together with other forthcoming work and a future implementation of an R package or Python library for the proposed methods in data modelling.

Adrenal oncocytoma: a rare presentation of a benign 18F-fluorodeoxyglucose PET avid virilising adrenal tumour.

A woman in her 70s presented with features of hyperandrogenism including clitoral enlargement and deepening of her voice. Biochemical investigations revealed raised plasma androgens and urinary androgen metabolites and imaging findings showed a highly F-18 fluorodeoxyglucose (FDG)-PET avid left adrenal tumour initially suspected to be a malignant adrenocortical carcinoma (ACC). She subsequently underwent an uncomplicated laparoscopic adrenalectomy where complete resection of her tumour was achieved. Histopathological analysis demonstrated a benign adrenal oncocytoma with no evidence of malignancy. This case illustrates a rare presentation of a functioning virilising adrenal oncocytoma as a benign mimic of ACC.

Urinary metabolomics provide insights into coronary artery disease in individuals with type 1 diabetes.

Type 1 diabetes increases the risk of coronary artery disease (CAD). High-throughput metabolomics may be utilized to identify metabolites associated with disease, thus, providing insight into disease pathophysiology, and serving as predictive markers in clinical practice. Urine is less tightly regulated than blood, and therefore, may enable earlier discovery of disease-associated markers. We studied urine metabolomics in relation to incident CAD in individuals with type 1 diabetes. We prospectively studied CAD in 2501 adults with type 1 diabetes from the Finnish Diabetic Nephropathy Study. 209 participants experienced incident CAD within the 10-year follow-up. We analyzed the baseline urine samples with a high-throughput targeted urine metabolomics platform, which yielded 54 metabolites. With the data, we performed metabolome-wide survival analyses, correlation network analyses, and metabolomic state profiling for prediction of incident CAD. Urinary 3-hydroxyisobutyrate was associated with decreased 10-year incident CAD, which according to the network analysis, likely reflects younger age and improved kidney function. Urinary xanthosine was associated with 10-year incident CAD. In the network analysis, xanthosine correlated with baseline urinary allantoin, which is a marker of oxidative stress. In addition, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. Metabolomic state profiling supported the usage of CAD-associated urinary metabolites to improve prediction accuracy, especially during shorter follow-up. Furthermore, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. The network analysis further suggested glomerular filtration rate to influence the urinary metabolome differently between individuals with and without future CAD. We have performed the first high-throughput urinary metabolomics analysis on CAD in individuals with type 1 diabetes and found xanthosine, 3-hydroxyisobutyrate, trans-aconitate, and 4-deoxythreonate to be associated with incident CAD. In addition, metabolomic state profiling improved prediction of incident CAD.

Non-invasive diagnosis of papillary thyroid microcarcinoma using a novel metabolomics analysis of urine.

The severity of thyroid cancer is judged on the basis of histologic and clinical features. A limited number of studies have considered urinary metabolite signatures for its diagnosis, and no reliable urinary metabolite biomarkers have been proposed. This diagnostic method would be particularly valuable because of its non-invasive nature. A nuclear magnetic resonance (NMR)-based metabolomics approach was used as the analytical platform to study the urine samples of patients with PTMC. Urine samples collected from 41 PTMC patients, 52 healthy subjects, and 13 patients with benign tumors were analyzed using 1H-NMR spectroscopy to identify metabolic changes. PLS-DA, or partial least squares discriminant analysis, was used to analyze the NMR spectra. A double cross-validation method and randomization tests were used to validate PLS-DA models. Clear discriminations between PTMC patients and healthy controls, as well as between PTMC patients and patients with benign tumors were obtained. Collectively, pi-methyhistidine, trimethylamine, myo-inositol, acetate, suberate, azelate, mannitol, tau-methylhistine, ascorbate, 3-aminoisobutyric acid, 2-oxoglutarate, and methanol contributed to the discrimination. Apart from myo-inositol and methanol, all of these metabolites exhibited increased levels in the urine samples of PTMC patients as compared to that of patients with benign tumors. The application of this NMR-based metabolomics approach allowed the detection of anomalous metabolic traits directly connected PTMC, potentially yielding a more sensitive and comprehensive diagnostic results for PTMC.

Exploring the impact of 3,3’-diindolylmethane on the urinary estrogen profile of premenopausal women

Background3,3’-diindolylmethane (DIM) is a phytonutrient derived from cruciferous vegetables that is an often-used supplement in the complementary and alternative medicine space. The most common goal for providers when recommending DIM to their patients is to alter estrogen metabolism, yet research into DIM’s effect on the estrogen profile is lacking in the published literature. The objective of this study was to comprehensively evaluate DIM’s effect on the urinary estrogen profile.MethodsIn this retrospective cohort study, we analyzed data from a clinical laboratory, including urinary estrogen and estrogen metabolite concentrations. Analyte concentrations were determined from dried urine samples using a gas chromatography-tandem mass spectrometry assay. Individuals were separated into two groups, either reporting taking DIM (N = 909) or reporting not taking DIM (N = 18,385). Comparisons between individuals in these two groups were made using the Wilcoxon rank sum test. Additionally, we were also able to explore a subset of women who had laboratory results in the database before and after initiating DIM treatment (N = 53). In this subset, differences were assessed with Wilcoxon signed rank tests.ResultsIn the larger group that was separated into women reporting either DIM use or no use, significant differences were observed in the concentrations of almost every urinary estrogen and estrogen metabolite (with the only exception being 2-methoxyestrone) in the urinary estrogen profiles of those taking DIM compared to those not taking DIM (all P values < 0.001). In the smaller subset of individuals with results before and after initiating DIM use, differences were only seen in 4 of the urinary estrogens and estrogen metabolites (P < 0.001 for estrone, estradiol, estriol, and 16-hydroxyestrone). Differences in total estrogens were significant in both the larger group and the smaller subset (both with P < 0.001). Additionally, observed differences in the ratios of metabolites followed a similar trend with more significant differences observed in the larger group. Notably, the 2-hydroxyestrone:16-hydroxyestrone ratio increased significantly in both the larger group and the smaller subset with results before and after DIM use.ConclusionsThe results of this study provide the most comprehensive evaluation to date of DIM’s effect on the urinary estrogen profile. Additionally, the results demonstrate that the dried urine collection and accompanying assay used capture changes that are similar in direction, but not necessarily magnitude, to previous reports in the literature. Considered together, these two things highlight the clinical validity and utility of this approach to the evaluation of DIM supplementation and suggest the need for additional studies using this approach to fully understand the potential clinical utility of DIM.

Advances in Surface-Enhanced Raman Spectroscopy for Urinary Metabolite Analysis: Exploiting Noble Metal Nanohybrids

This review examines recent advances in surface-enhanced Raman spectroscopy (SERS) for urinary metabolite analysis, focusing on the development and application of noble metal nanohybrids. We explore the diverse range of hybrid materials, including carbon-based, metal–organic-framework (MOF), silicon-based, semiconductor, and polymer-based systems, which have significantly improved SERS performance for detecting key urinary biomarkers. The principles underlying SERS enhancement in these nanohybrids are discussed, elucidating both electromagnetic and chemical enhancement mechanisms. We analyze various fabrication methods that enable precise control over nanostructure morphology, composition, and surface chemistry. The review critically evaluates the analytical performance of different hybrid systems for detecting specific urinary metabolites, considering factors such as sensitivity, selectivity, and stability. We address the analytical challenges associated with SERS-based urinary metabolite analysis, including sample preparation, matrix effects, and data interpretation. Innovative solutions, such as the integration of SERS with microfluidic devices and the application of machine learning algorithms for spectral analysis, are highlighted. The potential of these advanced SERS platforms for point-of-care diagnostics and personalized medicine is discussed, along with future perspectives on wearable SERS sensors and multi-modal analysis techniques. This comprehensive overview provides insights into the current state and future directions of SERS technology for urinary metabolite detection, emphasizing its potential to revolutionize non-invasive health monitoring and disease diagnosis.

Phosphonate-Based Aza-Macrocycle Ligands for Low-Temperature, Stable Chelation of Medicinally Relevant Rare Earth Radiometals and Radiofluorination.

Radioisotopes of fluorine (18F), scandium (43/44Sc, 47Sc), lutetium (177Lu), and yttrium (86Y, 90Y) have decay properties ideally suited for targeted nuclear imaging and therapy with small biologics, such as peptides and antibody fragments. However, a single-molecule strategy to introduce these radionuclides into radiopharmaceuticals under mild conditions to afford inert in vivo complexes is critically lacking. Here, we introduce H4L2 and H4L3, two small-cavity macrocyclic chelator structural isomers bearing a single phosphonate functional group. Potentiometry and spectrophotometry were employed to determine H4L2 and H4L3's ability to form a single [M(L)]- species with metals of different sizes (Sc3+, Lu3+, and Y3+) under physiologically relevant conditions. NMR spectroscopy and density functional theory (DFT) calculations suggest modulation of H4L2 and H4L3's inner-sphere hydration across the Sc3+/Lu3+/Y3+ series. Radiochemical labeling experiments with 18F, 44Sc, 177Lu, and 86Y reveal that H4L2 selectively chelates radioscandium at room temperature with high apparent molar activity (AMA, 462 mCi/μmol), while radiofluorination remains inaccessible. In contrast, H4L3 enables room temperature radiochelation 44Sc, 177Lu, and 86Y (AMA: 96-275 mCi/μmol) and incorporates 18F via the Sc-18F methodology to form [18F][ScF(L3)]2-. In vivo biodistribution analysis at 1 h postinjection confirms the broad utility of H4L3: all four radiochemical complexes clear off-target organs and remain >98% intact in urine metabolite analyses. The scope of room temperature radiochemical labeling, paired with facile 18F incorporation, to afford in vivo compatible complexes exceeds the clinical gold standard chelator DOTA and previously reported acyclic chelators, rendering H4L3 promising for prospective radiopharmaceutical applications.

Association of maternal pyrethroid pesticides exposure during the whole pregnancy with neonate lipid metabolism: A prospective birth cohort, Yunnan, China

Dyslipidemias may emerge during the fetal period. However, the association between prenatal pyrethroid pesticides (PYRs) exposure and neonatal lipid metabolism remains uncertain. To explore the association of prenatal PYRs exposure and neonates’ lipid metabolism, pregnant women were recruited in rural Yunnan, China, and their urine samples in the first, second, and third trimester and their neonates’ cord blood samples were collected to obtain urinary PYRs metabolites (3PBA, 4F3PBA, and DBCA), cord blood TC, TG, HDL-C, LDL-C, and Non-HDL-C, AIP, CRI-I, CRI-II, AC, and LCI. We found the total PYRs detection during pregnancy was 99.6 %. High-level DBCA in the first and third trimester and high-level 3PBA in the second trimester increased risks of high AIP. High-level ∑PYRs in the third trimester enhanced risks of high levels of TG, LDL-C, Non-HDL-C, AIP, and LCI. Repeated high-level 3PBA in two trimesters and above elevated risks of high levels of TG, LDL-C, CRI-I, AIP, AC, and LCI. Repeated high-level DBCA group in two trimesters and above increased the risk of high AIP. Repeated high ∑PYRs in three trimesters intensified risks of high levels of TC, LDL-C, Non-HDL-C, and AIP. Thus, our study suggests high PYRs exposure during the whole pregnancy may increase the risk of neonate abnormal lipid metabolism. The third trimester is the most sensitive window of high prenatal PYRs exposure. The adverse effects on neonate lipid metabolism increased as the increasing of trimesters repeated high-level PYRs exposure during pregnancy. Different kinds of PYRs exposure may induce different cord blood abnormal lipids.