Method development with high-throughput enhanced matrix removal followed by UHPLC-QqQ-MS/MS for analysis of grape polyphenol metabolites in human urine.

Grape and grape derived products contain many bioactive phenolics which have a variety of impacts on health. Following oral ingestion, the phenolic compounds and their metabolites may be detectable in human urine. However, developing a reliable method for the analysis of phenolic compounds in urine is challenging. In this work, we developed and validated a new high-throughput, sensitive and reproducible analytical method for the simultaneous analysis of 31 grape phenolic compounds and metabolites using Oasis PRiME HLB cleanup for sample preparation combined with ultra-performance liquid chromatography with triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS). Using this new method, the accuracy achieved was 69.3 % ∼ 134.9 % (except for six compounds), and the recovery achieved was 52.4 % ∼ 134.7 % (except for two very polar compounds). For each of the 31 target analytes, the value of intra-day precision was less than 14.3 %. The value of inter-day precision was slightly higher than intra-day precision, with a range of 0.7 % ∼ 19.1 %. We report for the first time on the effect of gender and BMI on the accuracy and recovery of human urine samples, and results from analysis of variance (ANOVA), and principal component analysis (PCA) indicated there was no difference in the value of accuracy and recovery between different gender or BMI (>30) using our purposed cleanup and UHPLC-QqQ-MS/MS method. Overall, this newly developed method could serve as a powerful tool for analyzing grape phenolic compounds and metabolites in human urine samples.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

A single extraction 96-well method for LC-MS/MS quantification of urinary eicosanoids, steroids and drugs.

Urinary eicosanoid concentrations reflect inflammatory processes in multiple diseases and have been used as biomarkers of disease as well as suggested for patient stratification in precision medicine. However, implementation of urinary eicosanoid profiling in large-scale analyses is restricted due to sample preparation limits. Here we demonstrate a single solid-phase extraction of 300 µL urine in 96-well-format for prostaglandins, thromboxanes, isoprostanes, cysteinyl-leukotriene E4 and the linoleic acid-derived dihydroxy-octadecenoic acids (9,10- and 12,13-DiHOME). A simultaneous screening protocol was also developed for cortisol/cortisone and 7 exogenous steroids as well as 3 cyclooxygenase inhibitors. Satisfactory performance for quantification of eicosanoids with an appropriate internal standard was demonstrated for intra-plate analyses (CV = 8.5-15.1%) as well as for inter-plate (n = 35) from multiple studies (CV = 22.1-34.9%). Storage stability was evaluated at - 20 °C, and polar tetranors evidenced a 50% decrease after 5 months, while the remaining eicosanoids evidenced no significant degradation. All eicosanoids were stable over 3.5-years in urine stored at - 80 °C. This method will facilitate the implementation of urinary eicosanoid quantification in large-scale screening.

Urine Metabolite Analysis to Identify Pathomechanisms of Long COVID: A Pilot Study.

Background: Around 10% of people who had COVID-9 infection suffer from persistent symptoms such as fatigue, dyspnoea, chest pain, arthralgia/myalgia, sleep disturbances, cognitive dysfunction and impairment of mental health. Different underlying pathomechanisms appear to be involved, in particular inflammation, alterations in amino acid metabolism, autonomic dysfunction and gut dysbiosis. Aim: As routine tests are often inconspicuous in patients with Long COVID (LC), similarly to patients suffering from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), accessible biomarkers indicating dysregulation of specific pathways are urgently needed to identify underlying pathomechanisms and enable personalized medicine treatment. Within this pilot study we aimed to proof traceability of altered metabolism by urine analysis. Patients and Methods: Urine metabolome analyses were performed to investigate the metabolic signature of patients with LC (n = 25; 20 women, 5 men) in comparison to healthy controls (Ctrl, n = 8; 7 women, 1 man) and individuals with ME/CFS (n = 8; 2 women, 6 men). Concentrations of neurotransmitter precursors tryptophan, phenylalanine and their downstream metabolites, as well as their association with symptoms (fatigue, anxiety and depression) in the patients were examined. Results and Conclusion: Phenylalanine levels were significantly lower in both the LC and ME/CFS patient groups when compared to the Ctrl group. In many LC patients, the concentrations of downstream metabolites of tryptophan and tyrosine, such as serotonin, dopamine and catecholamines, deviated from the reference ranges. Several symptoms (sleep disturbance, pain or autonomic dysfunction) were associated with certain metabolites. Patients experiencing fatigue had lower levels of kynurenine, phenylalanine and a reduced kynurenine to tryptophan ratio (Kyn/Trp). Lower concentrations of gamma-aminobutyric acid (GABA) and higher activity of kynurenine 3-monooxygenase (KMO) were observed in patients with anxiety. Conclusively, our results suggest that amino acid metabolism and neurotransmitter synthesis is disturbed in patients with LC and ME/CFS. The identified metabolites and their associated dysregulations could serve as potential biomarkers for elucidating underlying pathomechanisms thus enabling personalized treatment strategies for these patient populations.

Metabolic Fate of Lignin in Birch Glucuronoxylan Extracts as Dietary Fiber Studied in a Rat Model.

SCOPE: While previously considered inert, recent studies suggest lignin metabolism with unknown metabolic fates is occurring in the gastrointestinal tract of several animal models. This study focuses on analyzing the potential metabolites of lignin. METHODS AND RESULTS: The diets of rats include relatively pure birch glucuronoxylan (pureGX) with residual lignin or lignin-rich GX (GXpoly) in their diet. Nuclear magnetic spectroscopy of the lignin isolated from the GXpoly-fed rats fecal sample shows high alteration in chemical structure, whereas lignin-carbohydrate complexes (LCCs) are enriched in fecal samples from the pureGX group. Moreover, the increased syringyl-to-guaiacyl (S/G) ratio suggests that lignin G-units are predominantly metabolized based on pyrolysis gas chromatography-mass spectrometry (pyr-GC/MS). The presence of small phenolic metabolites identified in urine samples of the GXpoly group, for example, ferulic and sinapic acids, their sulfate and glucuronide derivatives, and 4-sulfobenzylalcohol, suggests that the small fragmented lignin metabolites in the large intestine enter the plasma, and are further processed in the liver. Finally, the relative abundances of polyphenol-degrading Enterorhabdus and Akkermansia in the gut microbiota are associated with lignin metabolism. CONCLUSION: These findings give further evidence to lignin metabolism in the gut of nonruminants and provide insight to the potential microbes and metabolic routes.

Urine metabolites and viral pneumonia among children: a case-control study in China.

Background: Viral pneumonia in children is common and has grave consequences. The study aims to better understand the pathophysiological processes involved in the onset and progression of viral pneumonia and identify common effects or biomarkers across different viruses. Methods: This study collected urine samples from 96 patients with viral pneumonia including respiratory syncytial virus (RSV) (n=30), influenza virus (IV) (n=23), parainfluenza virus (PIV) (n=24), and adenovirus (ADV) (n=19), and 31 age- and sex-matched normal control (NC) subjects. The samples were analyzed using liquid chromatography coupled with mass spectrometry (LC-MS) to identify endogenous substances. The XCMS Online platform was utilized for data processing and analysis , including feature detection, retention time correction, alignment, annotation, and statistical analysis for difference between groups and biomarker identification. Results: A total of 948 typical metabolites were identified using the XCMS Online platform with the Mummichog technique. After analyzing the data, 24 metabolites were selected as potential biomarkers for viral pneumonia, of which 16 were aspartate and asparagine metabolites, byproducts of alanine, leucine, and isoleucine degradation, and butanoate metabolites. Conclusions: This study specific metabolites and altered pathways in children with viral pneumonia and propose that these findings could contribute to the discovery of new treatments and the development of antiviral drugs.

The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers.

Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.

Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial.

BACKGROUND: Objective markers of ultraprocessed foods (UPF) may improve the assessment of UPF intake and provide insight into how UPF influences health. OBJECTIVES: To identify metabolites that differed between dietary patterns (DPs) high in or void of UPF according to Nova classification. METHODS: In a randomized, crossover, controlled-feeding trial (clinicaltrials.govNCT03407053), 20 domiciled healthy participants (mean ± standard deviation: age 31 ± 7 y, body mass index [kg/m2] 22 ± 11.6) consumed ad libitum a UPF-DP (80% UPF) and an unprocessed DP (UN-DP; 0% UPF) for 2 wk each. Metabolites were measured using liquid chromatography with tandem mass spectrometry in ethylenediaminetetraacetic acid plasma, collected at week 2 and 24-h, and spot urine, collected at weeks 1 and 2, of each DP. Linear mixed models, adjusted for energy intake, were used to identify metabolites that differed between DPs. RESULTS: After multiple comparisons correction, 257 out of 993 plasma and 606 out of 1279 24-h urine metabolites differed between UPF-DP and UN-DP. Overall, 21 known and 9 unknown metabolites differed between DPs across all time points and biospecimen types. Six metabolites were higher (4-hydroxy-L-glutamic acid, N-acetylaminooctanoic acid, 2-methoxyhydroquinone sulfate, 4-ethylphenylsulfate, 4-vinylphenol sulfate, and acesulfame) and 14 were lower following the UPF-DP; pimelic acid, was lower in plasma but higher in urine following the UPF-DP. CONCLUSIONS: Consuming a DP high in, compared with 1 void of, UPF has a measurable impact on the short-term human metabolome. Observed differential metabolites could serve as candidate biomarkers of UPF intake or metabolic response in larger samples with varying UPF-DPs. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

Kinetic Profile of Urine Metabolites after Acute Intake of a Phenolic Compounds-Rich Juice of Juçara (Euterpe edulis Mart.) and Antioxidant Capacity in Serum and Erythrocytes: A Human Study.

The juçara palm tree produces a small spherical and black-purple fruit similar to açaí. It is rich in phenolic compounds, especially anthocyanins. A clinical trial evaluated the absorption and excretion of the main bioactive compounds in urine and the antioxidant capacity in serum and erythrocytes of 10 healthy subjects after juçara juice intake. Blood samples were collected before (0.0 h) and 0.5 h, 1 h, 2 h, and 4 h after a single dose (400 mL) of juçara juice, while urine was collected at baseline and 0-3 and 3-6 h after juice intake. Seven phenolic acids and conjugated phenolic acids were identified in urine deriving from the degradation of anthocyanins: protocatechuic acid, vanillic acid, vanillic acid glucuronide, hippuric acid, hydroxybenzoic acid, hydroxyphenylacetic acid, and ferulic acid derivative. In addition, kaempferol glucuronide was also found in urine as a metabolite of the parent compound in juçara juice. Juçara juice caused a decrease in the total oxidant status of serum after 0.5 h in comparison to baseline values (p < 0.05) and increased the phenolic acid metabolites excretion. This study shows the relationship between the production of metabolites of juçara juice and the total antioxidant status in human serum, indicating evidence of its antioxidant capacity.

Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review.

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer's disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.

Urinary Metabolomics Study on the Protective Role of Cocoa in Zucker Diabetic Rats via 1H-NMR-Based Approach.

Cocoa constitutes one of the richest sources of dietary flavonoids with demonstrated anti-diabetic potential. However, the metabolic impact of cocoa intake in a diabetic context remains unexplored. In this study, metabolomics tools have been used to investigate the potential metabolic changes induced by cocoa in type 2 diabetes (T2D). To this end, male Zucker diabetic fatty rats were fed on standard (ZDF) or 10% cocoa-rich diet (ZDF-C) from week 10 to 20 of life. Cocoa supplementation clearly decreased serum glucose levels, improved glucose metabolism and produced significant changes in the urine metabolome of ZDF animals. Fourteen differential urinary metabolites were identified, with eight of them significantly modified by cocoa. An analysis of pathways revealed that butanoate metabolism and the synthesis and degradation of branched-chain amino acids and ketone bodies are involved in the beneficial impact of cocoa on diabetes. Moreover, correlation analysis indicated major associations between some of these urine metabolites (mainly valine, leucine, and isoleucine) and body weight, glycemia, insulin sensitivity, and glycated hemoglobin levels. Overall, this untargeted metabolomics approach provides a clear metabolic fingerprint associated to chronic cocoa intake that can be used as a marker for the improvement of glucose homeostasis in a diabetic context.

Bile acids as drivers and biomarkers of hepatocellular carcinoma.

The prevalence of hepatocellular carcinoma (HCC) is rapidly increasing, driven not least in part by the escalating prevalence of non-alcoholic fatty liver disease. Bile acid (BA) profiles are altered in patients with HCC and there is a developing body of evidence from in vitro human cellular models as well as rodent data suggesting that BA are able to modulate fundamental processes that impact on cellular phenotype predisposing to the development of HCC including senescence, proliferation and epithelial-mesenchymal transition. Changes in BA profiles associated with HCC have the potential to be exploited clinically. Whilst excellent diagnostic and imaging tools are available, their use to screen populations with advanced liver disease at risk of HCC is limited by high cost and low availability. The mainstay for HCC screening among subjects with cirrhosis remains frequent interval ultrasound scanning. Importantly, currently available serum biomarkers add little to diagnostic accuracy. Here, we review the current literature on the use of BA measurements as predictors of HCC incidence in addition to their use as a potential screening method for the early detection of HCC. Whilst these approaches do show early promise, there are limitations including the relatively small cohort sizes, the lack of a standardized approach to BA measurement, and the use of inappropriate control comparator samples.

An observation study of urinary biomarker exploratory in Alzheimer's disease using high-resolution mass spectrometry.

Alzheimer's disease (AD) is regarded as a progressive neurodegenerative dementia, characterized by degeneration of distinct neuronal populations. A case-control study was carried out using high-resolution mass spectrometry to explore AD-associated urinary metabolic biomarkers from 30 AD patients and 30 cognitively normal (CN) individuals. In total, 49 metabolites were determined and validated as known compounds using LC/MS analysis. Using the two-sample t-test statistical analysis (P < 0.05), 19 metabolites were shown to be significantly different from AD to CN. A diagnostic model of the receiver operating characteristic curve was constructed with a combination of nine molecules out of 19 metabolites, it yielded a separation with an area under the curve value of 0.976 between the two groups. This study indicated that urinary metabolites showed a significant expression between AD and CN. AD-related metabolites enable to satisfy the diagnostic power of disease discrimination. In addition, as a noninvasive approach, urine collection is done easily in clinical diagnosis of AD.

Change in Urinary Myoinositol/Citrate Ratio Associates with Progressive Loss of Renal Function in ADPKD Patients.

INTRODUCTION: In autosomal dominant polycystic kidney disease (ADPKD) patients, predicting renal disease progression is important to make a prognosis and to support the clinical decision whether to initiate renoprotective therapy. Conventional markers all have their limitations. Metabolic profiling is a promising strategy for risk stratification. We determined the prognostic performance to identify patients with a fast progressive disease course and evaluated time-dependent changes in urinary metabolites. METHODS: Targeted, quantitative metabolomics analysis (1H NMR-spectroscopy) was performed on spot urinary samples at two time points, baseline (n = 324, 61% female; mean age 45 years, SD 11; median eGFR 61 mL/min/1.73 m2, IQR 42-88; mean years of creatinine follow-up 3.7, SD 1.3) and a sample obtained after 3 years of follow-up (n = 112). Patients were stratified by their eGFR slope into fast and slow progressors based on an annualized change of > -3.0 or ≤ -3.0 mL/min/1.73 m2/year, respectively. Fifty-five urinary metabolites and ratios were quantified, and the significant ones were selected. Logistic regression was used to determine prognostic performance in identifying those with a fast progressive course using baseline urine samples. Repeated-measures ANOVA was used to analyze whether changes in urinary metabolites over a 3-year follow-up period differed between fast and slow progressors. RESULTS: In a single urinary sample, the prognostic performance of urinary metabolites was comparable to that of a model including height-adjusted total kidney volume (htTKV, AUC = 0.67). Combined with htTKV, the predictive value of the metabolite model increased (AUC = 0.75). Longitudinal analyses showed an increase in the myoinositol/citrate ratio (p < 0.001) in fast progressors, while no significant change was found in those with slow progression, which is in-line with an overall increase in the myoinositol/citrate ratio as GFR declines. CONCLUSION: A metabolic profile, measured at a single time point, showed at least equivalent prognostic performance to an imaging-based risk marker in ADPKD. Changes in urinary metabolites over a 3-year follow-up period were associated with a fast progressive disease course.

Simultaneous determination of 16 urinary metabolites of organophosphate flame retardants and organophosphate pesticides by solid phase extraction and ultra performance liquid chromatography coupled to tandem mass spectrometry.

Organophosphate flame retardants (OPFRs) and organophosphate pesticides (OPPs), pertaining to organophosphate esters, are ubiquitous in environment and have been verified to pose noticeable risks to human health. To evaluate human exposures to OPFRs and OPPs, a fast and sensitive approach based on a solid phase extraction (SPE) followed by the ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) detection has been developed for the simultaneous analysis of multiple organophosphorus metabolites in urine. The method allows the identification and quantification of ten metabolites of the most common OPFRs and all six dialkylphosphates (DAPs) of OPPs concerning the population exposure characteristics. The method provided good linearities (R2 = 0.998-0.999), satisfactory method detection limits (MDLs) (0.030-1.129 ng/mL) and only needed a small volume (200 µL) of urine. Recovery rates ranged 73.4-127.1% at three spiking levels (2, 10 and 25 ng/mL urine), with both intra- and inter-day precision less than 14%. The good correlations for DAPs in a cross-validation test with a previous gas chromatography-mass spectrometry (GC-MS) method and a good inter-laboratory agreement for several OPFR metabolites in a standard reference material (SRM 3673) re-enforced the precision and validity of our method. Finally, the established method was successfully applied to analyze 16 organophosphorus metabolites in 35 Chinese children's urine samples. Overall, by validating the method's sensitivity, accuracy, precision, reproducibility, etc., data reliability and robustness were ensured; and the satisfactory pilot application on real urine samples demonstrated feasibility and acceptability of this method for being implemented in large population-based studies.

Model of retention time and density of gradient peak capacity for improved LC-MS method optimization: Application to metabolomics.

A general and deterministic model is derived from the fundamentals of liquid chromatography to calculate retention time, peak width, peak capacity, and density of peak capacity in gradient liquid chromatography. The calculation of these chromatographic properties accounts for 1) the presence of initial (separation of the earliest eluters) and final (column wash) isocratic steps before and after the linear gradient, respectively, 2) the pre- (flow through needle and preheater tubes) and post-column (outlet and emitter tubes before MS detection) dispersion, 3) the compression of the chromatographic band, and 4) the retention of the organic modifier onto the RPLC column. The multiple and variable method parameters may include the column dimensions, particle size, flow rate, temperature, initial and final isocratic hold times, gradient time, gradient steepness, column conditioning/sample load time, and the pre- and post-column tube dimensions. The model enables the users to perform robust multi-dimensional optimization of UHPLC-MS methods and offers the possibility to predict the expected MS feature density for increased method performance. Method optimization can be further improved by matching the observed MS feature density (number of metabolites detected as function of time) to the predicted density of peak capacity. It is directly applied to the optimization of high-throughput RPLC separation methods specifically designed for large-scale urinary metabolic phenotyping.

Protection effect of gut microbiota composition and acetate absorption against hypertension-induced damages on the longevity population in Guangxi, China.

Introduction: Recent evidence supports a role for the gut microbe-metabolites in longevity. However, the phenomenon of hypertension is more common in the longevity area and whether hypertension is associated with longevity remains unclear. Here, we hypothesize that the levels of gut microbiota, SCFAs, and urine metabolites were different between hypertension elderly and hypertension longevity. Methods: We recruited 46 elderly volunteers from Donglan County, Guangxi, and 32 were selected and included in the experiment. The subjects with hypertension were divided into two groups according to age, Hypertension Elderly (HTE, aged 70.5 ± 8.59, n = 19) and Hypertension Longevity (HTL, aged 100 ± 5.72, n = 13). The gut microbiota, SCFAs, and urine metabolites were determined by three-generation 16S rRNA full-length sequencing, GC-MS, and 1H-NMR, respectively. Results: Compared with the HTL group, the HTE group had higher levels of hypertension-related genera Klebsiella and Streptococcus, while having lower levels of the SCFA-producing genera Bacteroides, Faecalibacterium, and Alistipes. Based on LEFse analysis, Klebsiella pneumoniae, Lactobacillus gasseri, Streptococcus salivarius, Ruminococcus, Actinomyces, Rikenellaceae, f_Saccharimonadaceae, Clostridium perfringens, and Bacteroids, Faecalibacterium prausnitzii, Parabacteroides, Alistipes were biomarkers that showed significant differences between the groups. In addition, the microbial pathways associated with K. pneumoniae and E. coli may promote hypertension, while A. muciniphila may play a role in reversing the development of hypertension in long-lived elderly. Metabolomics revealed that HTL contained a lower concentration of fecal acetate and propionate than HTE, while it contained a higher concentration of serum acetate and urine acetate. Furthermore, their immune cells exhibited no significant changes in SCFAs receptors. Conclusion: Although long-lived elderly have extremely high systolic blood pressure, their unique gut microbiota composition and efficient acetate absorption in the colon may offset the damages caused by hypertension and maintain healthy homeostasis.

Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease.

INTRODUCTION: Urine tricarboxylic acid (TCA) cycle organic anions (OAs) are elevated in diabetes and may be biomarkers for diabetic kidney disease (DKD) progression. OBJECTIVES: We assessed associations of 10 urine TCA cycle OAs with estimated glomerular filtration rate (eGFR) and eGFR slope. METHODS: This study is ancillary to the Simultaneous Risk Factor Control Using Telehealth to SlOw Progression of Diabetic Kidney Disease (STOP-DKD) Trial-a randomized trial of pharmacist-led medication and behavior management in 281 patients with early to moderate DKD at Duke from 2014 to 2015. We used linear mixed models to assess associations of urine TCA cycle OAs with outcomes and modelled TCA cycle OAs as: (1) the average of z-scores for each OA; and (2) principal component (PC) scores derived by principal component analysis (PCA). Untargeted urine metabolomics were added for additional discovery. RESULTS: Among 132 participants with 24 h urine samples (50% men; 58% Black; mean age 64 years [SD 9]; mean eGFR 74 ml/min/1.73m2 [SD 21] and median urine albumin-to-creatinine [UACR] 20 mg/g [IQR 8-95]), PCA identified 3 OA metabolite PCs. Malate, fumarate, pyruvate, α-ketoglutarate, lactate, succinate and citrate/isocitrate loaded positively on PC1; methylsuccinate, ethylmalonate and succinate loaded positively on PC2; and methylmalonate, ethylmalonate and citrate/isocitrate loaded negatively on PC3. Over a median follow-up of 1.8 years (IQR, 1.2 to 2.2), higher average OA z-score was strongly associated with higher eGFR after covariate adjustment (p = 0.01), but not with eGFR slope (p = 0.9). Higher PC3, but not other PCs, was associated with lower eGFR (p < 0.001). Conditional random forests and smooth clipped absolute deviation models confirmed methylmalonate, citrate/isocitrate, and ethylmalonate, and added lactate as top ranked metabolites in models of baseline eGFR (R-squared 0.32 and 0.33, respectively). Untargeted urine metabolites confirmed association of urine TCA cycle OAs with kidney function. CONCLUSION: Thus, lower urine TCA cycle OAs, most notably lower methylmalonate, ethylmalonate and citrate/isocitrate, are potential indicators of kidney impairment in early stage DKD.

Self-Reported Medication Use and Urinary Drug Metabolites in the German Chronic Kidney Disease (GCKD) Study.

BACKGROUND: Polypharmacy is common among patients with CKD, but little is known about the urinary excretion of many drugs and their metabolites among patients with CKD. METHODS: To evaluate self-reported medication use in relation to urine drug metabolite levels in a large cohort of patients with CKD, the German Chronic Kidney Disease study, we ascertained self-reported use of 158 substances and 41 medication groups, and coded active ingredients according to the Anatomical Therapeutic Chemical Classification System. We used a nontargeted mass spectrometry-based approach to quantify metabolites in urine; calculated specificity, sensitivity, and accuracy of medication use and corresponding metabolite measurements; and used multivariable regression models to evaluate associations and prescription patterns. RESULTS: Among 4885 participants, there were 108 medication-drug metabolite pairs on the basis of reported medication use and 78 drug metabolites. Accuracy was excellent for measurements of 36 individual substances in which the unchanged drug was measured in urine (median, 98.5%; range, 61.1%-100%). For 66 pairs of substances and their related drug metabolites, median measurement-based specificity and sensitivity were 99.2% (range, 84.0%-100%) and 71.7% (range, 1.2%-100%), respectively. Commonly prescribed medications for hypertension and cardiovascular risk reduction-including angiotensin II receptor blockers, calcium channel blockers, and metoprolol-showed high sensitivity and specificity. Although self-reported use of prescribed analgesics (acetaminophen, ibuprofen) was <3% each, drug metabolite levels indicated higher usage (acetaminophen, 10%-26%; ibuprofen, 10%-18%). CONCLUSIONS: This comprehensive screen of associations between urine drug metabolite levels and self-reported medication use supports the use of pharmacometabolomics to assess medication adherence and prescription patterns in persons with CKD, and indicates under-reported use of medications available over the counter, such as analgesics.

Urine Metabolite Levels, Adverse Kidney Outcomes, and Mortality in CKD Patients: A Metabolome-wide Association Study.

RATIONALE & OBJECTIVE: Mechanisms underlying the variable course of disease progression in patients with chronic kidney disease (CKD) are incompletely understood. The aim of this study was to identify novel biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiologic mechanisms. STUDY DESIGN: Metabolome-wide association study. SETTING & PARTICIPANTS: 5,087 patients with CKD enrolled in the observational German Chronic Kidney Disease Study. EXPOSURES: Measurements of 1,487 metabolites in urine. OUTCOMES: End points of interest were time to kidney failure (KF), a combined end point of KF and acute kidney injury (KF+AKI), and overall mortality. ANALYTICAL APPROACH: Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable-adjusted Cox regression models. RESULTS: After a median follow-up of 4 years, 362 patients died, 241 experienced KF, and 382 experienced KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly associated with adverse kidney outcomes and/or mortality. Higher levels of C-glycosyltryptophan were consistently associated with all 3 main end points (hazard ratios of 1.43 [95% CI, 1.27-1.61] for KF, 1.40 [95% CI, 1.27-1.55] for KF+AKI, and 1.47 [95% CI, 1.33-1.63] for death). Metabolites belonging to the phosphatidylcholine pathway showed significant enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established Kidney Failure Risk Equation. LIMITATIONS: Findings among patients of European ancestry with CKD may not be generalizable to the general population. CONCLUSIONS: Our comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identifies metabolites that predict KF and represents a valuable resource for future studies of biomarkers of CKD progression.