Urinary Profiles Research Articles

Integrated Multi-Omics Analysis for Inferring Molecular Players in Inclusion Body Myositis.

Inclusion body myositis (IBM) is an acquired inflammatory myopathy affecting proximal and distal muscles that leads to weakness in patients over 50. It is diagnosed based on clinical and histological findings in muscle related to inflammation, degeneration, and mitochondria. In relation to IBM, a shortage of validated disease models and a lack of biomarkers and effective treatments constitute an unmet medical need. To overcome these hurdles, we performed an omics analysis of multiple samples from IBM patients (saliva, fibroblasts, urine, plasma, and muscle) to gain insight into the pathophysiology of IBM. Degeneration was evident due to the presence of amyloid β peptide 1-42 (Aβ1-42) in the saliva of the analyzed IBM patients. The presence of metabolic disarrangements in IBM was indicated by an imbalanced organic acid profile in fibroblasts and urine. Specifically, abnormal levels of L-pyroglutamic and orotic acid were supported by the abnormal expression of related metabolites in plasma and urine (glutathione and pyrimidines) and the aberrant expression of upstream gene regulators (L2HGDH, IDH2, OPLAH, and ASL) in muscle. Combined levels of L-pyroglutamic and orotic acid displayed an outstanding biomarker signature in urine with 100% sensitivity and specificity. The confirmation of systemic metabolic disarrangements in IBM and the identification of novel biomarkers reported herein unveil novel insights that require validation in larger cohorts.

Study of urinary markers of different podocytopathies by proteomic analysis

Focal segmental glomerulosclerosis (FSGS) is a primary podocytopathy characterized by primary podocyte detection and high proteinuria. The search for biomarkers and factors associated with the progression of this disease is an important task nowdays. To assess the proteomic profile of urine in patients with FSGS and to isolate urinary biomarkers of podocytopathies. The study included 41 patients diagnosed with chronic glomerulonephritis, 27 men and 14 women. According to the morphological study, 28 patients were diagnosed with FSGS, 9 with steroid-sensitive nephrotic syndrome and 14 with steroid-resistant nephrotic syndrome. The comparison group included 13 patients with membranous nephropathy. The study of the urinary proteome was carried out by targeted liquid chromatography-mass spectrometry using multiple reaction monitoring with synthetic stable isotope labelled peptide standards. The main differences in the protein profile of urine were found in the subgroups of steroid-sensitive (SS) and steroid-resistant (SR) FSGS. In the FSGS SR group, at the onset of the disease, there was a high concentration of proteins reflecting damage to the glomerular filter (apo-lipoprotein A-IV, orosomucoid, cadherin, hemopexin, vitronectin), as well as proteins associated with tubulo-interstitial inflammation and accumulation of extracellular matrix (retinol- and vitamin D-binding proteins, kininogen-1, lumican and neurophilin-2). Compared with the membranous nephropathy group, FSGS patients had significantly higher urinary concentrations of carnosinase, orosomucoid, cadherin-13, tenascin X, osteopontin, and zinc-alpha-2-glycoprotein. Thus, in patients with SR FSGS, the proteomic profile of urine includes more proteins at elevated concentrations, which reflects severe damage to various parts of the nephron compared with patients with SS FSGS and membranous nephropathy.

Clinical Features and Laboratory Diagnosis of Aminoacidopathies: A Narrative Review

Context: There are severe and rare groups of genetic disorders due to defects in metabolic pathways, and they are generally called inborn errors of metabolism. Amino acids, as the building blocks of proteins, have many important structural and functional roles in the human body. The deficiencies of functional enzymes cause defects in metabolic pathways and lead to aminoacidopathies. The diagnosis of aminoacidopathies is challenging for most physicians, as they can present with multiple overlapping symptoms. Evidence Acquisition: PubMed, Cochrane, Embase, and CINAHL were searched with MeSH terms: ‘inborn errors of metabolism' OR ‘Metabolism, Inborn Errors' (MeSH) AND ‘Humans' (MeSH) AND 'Amino Acids/therapeutic use'(MeSH) AND ‘Newborn, Child' (MeSH) OR ‘child' OR ‘newborn' AND "Neonatal Screening"(MeSH). Results: This study summarized some important issues, including clinical and laboratory diagnoses of phenylketonuria, tyrosinemia, methionine, homocysteine and cysteine, sulfite oxidase deficiency, molybdenum cofactor deficiency, tryptophan, glycine, hyperoxaluria, creatine deficiency disorders, serine, proline, glutamine, and urea cycle defect. Conclusions: The prognosis of many metabolic disorders has improved due to recent advances in diagnosis and treatment. The biochemical knowledge of clinicians should be improved to comprehend metabolic disorders. As the diagnostic methods are based on organic acids in urine and acylcarnitine profile, it is necessary to enhance biochemistry knowledge to understand the logic.

In-depth mass spectrometry analysis of rhGH administration altered energy metabolism and steroidogenesis

Growth hormone, as a proteohormone, is primarily known of its dramatic effect on longitudinal growth. Recombinant DNA technology has provided a safe, abundant and comparatively cheap supply of human GH for growth hormone-deficient individuals. However, many healthy subjects, especially athletics, administrate GH for enhanced athletic performance or strength. A better and more comprehensive understanding of rhGH effect in healthy individuals is urgent and essential. In this study, we recruited 14 healthy young male and injected rhGH once. Untargeted LC-MS metabolomics profiling of serum and urine was performed before and after the rhGH injection. The GH-induced dysregulation of energy related pathways, such as amino acid metabolism, nucleotide metabolism, glycolysis and TCA cycle, was revealed. Moreover, individuals supplemented with micro-doses of rhGH exhibited significantly changed urinary steroidal profiles, suggesting a role of rhGH in both energy metabolism and steroidogenesis. We expect that our results will be helpful to provide new evidence on the effects of rhGH injection and provide potential biomarkers for rhGH administration.

Impaired Abcb1a function and red meat in a translational colitis mouse model induces inflammation and alters microbiota composition.

Abcb1a-deficient colitis mice were fed either casein or red meat-supplemented diets to investigate potential colitis-aggravating components in red meat and their effects on host-microbiota interactions. We conducted deep label free quantitative proteomic inflammation profiling of gastrointestinal tissue (colon, ileum) and urine, and determined the overall microbiome in feces using 16S rRNA gene sequencing. Microbiota shifts by diet and protein transporter impairment were addressed by multivariate statistical analysis. Colon and systemic gut inflammation were validated through histology and immune assays, respectively. A quantitative discovery based proteomic analysis of intestinal tissue and urine revealed associations between ileum and urine proteomes in relation to Abcb1a deficiency. The absence of Abcb1a efflux pump function and diet-induced intestinal inflammation impacted multiple systemic immune processes, including extensive neutrophil extracellular trap (NET) components observed in relation to neutrophil degranulation throughout the gastrointestinal tract. The colitis model's microbiome differed significantly from that of wild-type mice, indicating the substantial influence of efflux transporter deficiency on microbiota. The proteomic and microbiota analyzes of a well-established murine model enabled the correlation of gastrointestinal interactions not readily identifiable in human cohorts. Insights into dysregulated biological pathways in this disease model might offer translational biomarkers based on NETs and improved understanding of IBD pathogenesis in human patients. Our findings demonstrate that drug transporter deficiency induces substantial changes in the microbiota, leading to increased levels of IBD-associated strains and resulting in intestinal inflammation. GRAPHICAL ABSTRACT.

Urinary Metabolomic Analysis of Prostate Cancer by UPLC-FTMS and UPLC-Ion Trap MSn.

Accumulative evidence suggests metabolic disorders correlate with prostate cancer. Metabolic profiling of urine allows the measurement of numerous metabolites simultaneously. This study set up a metabolomic platform consisting of UPLC-FTMS and UPLC-ion trap MSn for urine metabolome analysis. The platform improved retention time, mass accuracy, and signal stability. Additionally, the product ion spectrum obtained from ion trap MSn facilitated structure elucidation of candidate metabolites, especially when authentic standards were not available. Urine samples from six hernia patients and six BPH patients were used for the initial establishment of the analytic platform. This platform was further employed to analyze the urine samples of 27 PCa and 49 BPH patients. Choosing the upper and lower 16% of metabolites, 258 metabolite candidates were selected. Twenty-four of them with AUC values larger than 0.65 were further selected. Eighteen of the twenty-four features can be matched in METLIN and HMDB. Eleven of the eighteen features can be interpreted by MSn experiments. They were used for the combination achieving the best differential power. Finally, four metabolites were combined to reach the AUC value of 0.842 (CI 95, 0.7559 to 0.9279). This study demonstrates the urinary metabolomic analysis of prostate cancer and sheds light on future research.

Development of a Rapid, Targeted LC-IM-MS Method for Anabolic Steroids.

Anabolic steroids are of high biological interest due to their involvement in human development and disease progression. Additionally, they are banned in sport due to their performance-enhancing characteristics. Analytical challenges associated with their measurement stem from structural heterogeneity, poor ionization efficiency, and low natural abundance. Their importance in a variety of clinically relevant assays has prompted the consideration of integrating ion mobility spectrometry (IMS) into existing LC-MS assays, due primarily to its speed and structure-based separation capability. Herein we have optimized a rapid (2 min) targeted LC-IM-MS method for the detection and quantification of 40 anabolic steroids and their metabolites. First, a steroid-specific calibrant mixture was developed to cover the full range of retention time, mobility, and accurate mass. Importantly, this use of this calibrant mixture provided robust and reproducible measurements based on collision cross section (CCS) with interday reproducibility of <0.5%. Furthermore, the combined separation power of LC coupled to IM provided comprehensive differentiation of isomers/isobars within 6 different isobaric groups. Multiplexed IM acquisition also provided improved limits of detection, which were well below 1 ng/mL in almost all compounds measured. This method was also capable of steroid profiling, providing quantitative ratios (e.g., testosterone/epitestosterone, androsterone/etiocholanolone, etc.). Lastly, phase II steroid metabolites were probed in lieu of hydrolysis to demonstrate the ability to separate those analytes and provide information beyond total steroid concentration. This method has tremendous potential for rapid analysis of steroid profiles in human urine spanning a variety of applications from developmental disorders to doping in sport.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Urolithiasis in patients with inflammatory bowel disease: A systematic review and meta-analysis of 13,339,065 individuals.

This study aimed to summarize the current literature regarding the prevalence of renal stones in patients with inflammatory bowel disease (IBD). Moreover, we aimed to evaluate the risk factors of urolithiasis in patients with IBD and the difference between patients with IBD and healthy controls in terms of urinary profile. On February 23, 2022, a computerized search was conducted on PubMed, OVID via MEDLINE, Web of Science, and Scopus using relevant keywords. Three independent reviewers performed 2-stage screening and data extraction. The National Institutes of Health tools were employed for quality assessment. Review Manager 5.4 software was used to calculate the mean difference (MD) between IBD patients and non-IBD in terms of urine profile using the Inverse-variance model and to estimate the odds ratio of reported risk factors for renal stones with the Generic Inverse-Variance model. Thirty-two articles (n = 13,339,065 patients) were included. The overall prevalence of renal stones in patients with IBD was 6.3%, 95% Confidence interval (4.8%-8.3%). The prevalence of urolithiasis was more common in Chron's disease vs Ulcerative colitis (7.9% vs 5.6%) and in old studies (1964-2009) than in more recent studies (2010-2022) (7.3% vs 5.2%), respectively. Compared to non-IBD patients, patients with IBD were associated with significantly lower urine volume (MD = -518.84 mL/day, P < .00001), calcium 24-hour urine (MD = -28.46 mg/day, P < .0001), citrate 24-hour urine (MD = -144.35 mg/day, P < .00001), sodium 24-hour urine (MD = -23.72 mg/day, P = .04), and magnesium 24-hour urine (MD = -33.25 mg/day, P < .00001). The overall prevalence of renal stones in patients with IBD was comparable to the general population. Patients with Chron's disease were associated with a higher prevalence of urolithiasis compared to Ulcerative colitis. Drugs that induce renal calculi should be stopped in high-risk patients.

#5736 IDENTIFICATION AND CHARACTERIZATION OF URINARY AND SERUM AMINOACIDS IN DIABETIC KIDNEY DISEASE PATIENTS USING MS-HPLC

Abstract Background and Aims Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease, but few biomarkers of early identification of DKD are available. The aim of the study was to asses new urinary and plasma biomarkers in early stages of DKD, especially in normoalbuminuric type 2 diabetes mellitus (DM) patients. Method This cross-sectional pilot study performed an integrated metabolomic profiling of blood and urine in 90 patients with type 2 DM, classified into 3 stages (30 normo-, 30 micro-, and 30 macroalbuminuric, respectively) and 30 age- and gender-matched healthy controls through high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI* MS). Metabolites were discovered and processed by the advanced Metaboanalyst 5.0. online software using multi- and univariate untargeted analysis. Both multivariate and univariate bioinformatics confirmed the relevance of certain amino acids as biomarkers to be responsible for the occurrence of early renal involvement in the course of type 2 DM. Results A total of 5 metabolites, namely Taurine, Tiglylglycine, Tryptophan, Kynurenic acid, and L-acetylcarnitine were discovered as potential biomarkers for the early identification of DKD. UPLC-QTOF-ESI* MS revealed that: (1) the urinary amino acids levels were increased in normoalbuminuric type 2 DM patients as compared with healthy control subjects; (2) a number of key biomarkers have been identified in the urine, which reflect kidney injury at specific sites along the nephron, including glomerular injury and tubular damage, oxidative stress, inflammation, and activation of the intrarenal renin-angiotensin system. Conclusion High-performance liquid chromatography coupled with mass spectrometry showed that the urinary amino acids levels were increased in normoalbuminuric type 2 DM patients. The results of the study provide a particular metabolomic profile related to blood and urine metabolites which could impact both the glomeruli and the tubules, even in the early stages of DKD.

#5040 UNTARGETED METABOLOMIC ANALYSIS IDENTIFIES A SPECIFIC METABOLOMIC PROFILE IN PATIENTS WITH EARLY CHRONIC KIDNEY DISEASE

Abstract Background and Aims Chronic kidney disease (CKD) has become one of the most challenging diseases of the twenty-first century and is characterized by increased mortality and morbidity. The pathogenesis of CKD is heterogeneous and the evaluation of renal function is performed with biomarkers such as serum creatinine and blood urea, which have low specificity and sensitivity. Metabolomics, one of the omics sciences, has become of interest in the nephrology field of research. Metabolic pathways can impact both glomerular and tubular structures and can offer a better understanding of the pathogenic mechanisms of CKD. Also, metabolomics provides more sensitive biomarkers for an early detection of CKD. The aim of this study was to perform metabolomic profiling of serum and urine in CKD patients by means of untargeted metabolomics in relation with the glomerular filtration rate (eGFR) and to identify potential serum and urinary biomarkers of early CKD. Method In this cross-sectional study were included 99 patients with CKD, staged by eGFR in six subgroups, according to the KDIGO Guidelines and 20 healthy control subjects. Serum and urinary metabolomic profiling were performed by using ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry. Blood and urine samples were evaluated by multivariate analysis, followed by univariate analysis. First, the PLSDA score plot and VIP score were applied. By using the cross-validation algorithm for the first 4 molecules identified, a high accuracy, high R² values, and a significant Q2 value were obtained. Therefore, the model could be considered predictive. Biomarker analysis and prediction by Random Forest analysis was performed. The specificity and sensibility of the molecules identified as potential biomarkers were evaluated by applying the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC). Results Significant direct correlations with eGFR were demonstrated for serum levels of Oleoyl glycine (p &amp;lt; 0.05, Log2 = 0.773), alpha-lipoic acid (p &amp;lt; 0.05), Propylthiouracil (p&amp;lt; 0.05), and L-cysteine (p&amp;lt; 0.05, Log2 = 0.363). Interestingly, serum levels of 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinylglycine, Propenoylcarnitine, and Uridine increased gradually from C group to subgroups G5. Also, 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinylglycine, Propenoylcarnitine, Uridine, and All-trans retinoic acid levels correlated negatively with eGFR (p&amp;lt; 0.05). Moreover, in urine samples the majority of molecules were increased in group G4 and G5 versus G1-G3b and C groups, respectively. Urinary levels of Indoxyl sulfate, Glycylprolylarginine, and Butenoylcarnitine correlated negatively with eGFR (p &amp;lt; 0.05). Conclusion Amino acids, antioxidants, uremic toxins, and acylcarnitines are increased in all CKD stages. The impact of this metabolites on the glomerular and tubular structures, even in the early stages of CKD, can be explained by their dual serum and urinary variation. The study provided a particular metabolomic profile that can offer new biomarkers useful for the evaluation of early CKD and its progression, as well as potential therapeutic targets.

#2556 BIOLOGICAL EFFECTS OF A SHORT PERIOD OF NORMOTHERMIC MACHINE PERFUSION IN MARGINAL KIDNEYS: RESULTS OF A PROTEOMICS ANALYSIS

Abstract Background and Aims Renal normothermic machine perfusion (NMP) is an organ preservation method based on the circulation of a warm (35–37°C) perfusion solution through the renal vasculature to deliver oxygen and nutrients. However, the biological effects of this technique on the marginal kidneys are uncertain. We consequently used mass spectrometry to define the proteomic profile of kidney tissue and urine from eight organs, considered unsuitable for transplantation, reconditioned for 120 min using a Kidney Assist device. Method Biopsies were taken during the pre-implantation histological evaluation (T-1), at the start of back table preparation (T0), and after 60 and 120 min of perfusion (T60, T120). Urine was collected at T0, T30, T60, and T120. Multiple algorithms, support vector machine (SVM) learning and partial least squares discriminant analysis (PLS-DA) were used to select the most discriminative proteins during NMP. Results Statistical analysis revealed the upregulation of 169 proteins and the downregulation of 196 in kidney tissue during NMP. Machine learning algorithms recognized the top 50 most discriminative proteins, five of which were concomitantly upregulated (LXN, ETFB, NUDT3, CYCS and UQCRC1) and six downregulated (CFHR3, C1S, CFI, KNG1, SERPINC1, and F9) in the kidney and urine after NMP. Functional analysis showed that the most upregulated proteins were involved in the oxidative phosphorylation system and ATP synthesis, whereas the downregulated proteins were involved in the complement system and coagulation cascade. Results were, then, validated by classical and highly conservative biomolecular lab techniques. Conclusion Our proteomic analysis revealed that even brief periods of NMP induce substantial metabolic and biochemical changes in marginal organs, which supports the use of this promising technique in the clinic.

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.

Dilute-and-Shoot-Liquid Chromatography-Quadrupole Time of Flight-Mass Spectrometry for Pteridine Profiling in Human Urine and Its Association with Different Pathologies

Pteridines are a group of compounds synthesised by many living organisms that are involved in the metabolism of many cofactors and vitamins. Their concentration in biological fluids may be altered by various pathologies such as cancer or inflammatory bowel disease, urine being the main route of excretion. In this study, three lumazines and ten pterins were analysed in their native oxidation state using high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Their high concentration in urine samples and their good ionisation behaviour allow the use of the dilute-and-shoot method by simple filtration of the urine prior to chromatographic analysis. The final method offers excellent linearity, sensitivity and precision parameters, and a total of 135 urine samples were analysed from patients with some relevant information such as faecal calprotectin (FCP) levels, common diseases such as diabetes, hypertension and dyslipidaemia and immunological diseases such as inflammatory bowel disease (IBD). The pteridine profile was related to FCP levels without showing any correlation. In addition, pteridine levels were compared between healthy subjects and IBD, diabetic, hypertensive and dyslipidaemic patients, and significant differences were found between the two groups for some of the pteridines.

Effect of preoperative metabolic profiling to reduce the risk of kidneystones after bariatric surgery in patients with a historyofstoneformation.

Roux-en-Y gastric bypass (RYGB) is associated with an increased risk of kidney stone formation. This is not observed after sleeve gastrectomy (SG). Aim of this study was to assess whether preoperative metabolic profiling is helpful in selecting the most optimal bariatric procedure for patients with a kidney stone history. General hospital, the Netherlands. Patients with a kidney stone history and in the run up to bariatric surgery were screened with non-contrast abdominal computed tomography (CT), serum profiling, and 24-hour urine analysis. Those with stones on radiologic imaging and/or high preoperative urinary oxalate were advised to undergo SG instead of RYGB. Pre- and postoperative urine and serum profile differences between both groups were evaluated retrospectively. Postoperatively, RYGB (N = 28, M:F = 8:20) was associated with a 23.5% reduction in urinary volume, a 85% increase in urinary oxalate excretion with a 230% increase in calcium oxalate (CaOx) supersaturation and a 62% decrease in urinary citrate. Although SG (N = 30, M:F = 12:18) was also associated with a reduction in urinary volume, it had no adverse effects on urinary oxalate and citrate excretion, nor on calcium oxalate supersaturation (CaOx-SS). Both RYGB and SG showed favorable effects on postoperative sodium, calcium, uric acid, and phosphate excretion. This study indicates that preoperative metabolic profiling is important to select the optimal bariatric procedure in patients with an a priori increased risk of kidney stone development. These patients should be strongly encouraged to undergo SG instead of RYGB to prevent progressive or recurrent kidney stone disease.

Occurrence and probabilistic risk assessment of polycyclic aromatic hydrocarbons in blood and urine of auto-mechanics in Akure Metro, Nigeria

This study provides baseline data on the concentration of polycyclic aromatic hydrocarbons (PAHs) in blood and urine samples of auto-mechanics, using Nigeria as a case study. Eighteen auto-mechanics participated in the study excluding two controls. The concentrations for the ΣPAHs across all participants (excluding control) ranged from 1.67 to 3.30 (2.17 ± 0.58) in blood with a significantly higher (P < 0.05) range of 7.61 to 10.76 (8.69 ± 1.00) in urine. The high molecular weight PAHs (4-6 rings) dominated the PAH profile in both blood (≥ 92%) and urine (≥ 87%) across all locations. Dibenz(a)anthracene was the most distributed PAH, while acenaphthene and indeno(1,2,3-cd)pyrene were the least distributed. The dermal route constituted approximately 99% of the total chronic exposure, followed by ingestion, and the least via inhalation. The hazard index (HI) was below the safe thresholds (HI = 1), suggesting non-carcinogenic PAH effects. However, all the participants including control samples had carcinogenic risk (CR) values above the acceptable level of 10-6 in both blood and urine samples. The carcinogenic and mutagenic potencies were higher in urine than in blood. The results suggest less acute toxicity and more potential chronic effects. The computed elimination ratio (> 1) suggests low excretion in urine and a potentially harmful trend. Molecular diagnostic ratios and principal component analysis suggest mixed PAH sources. The study revealed biomonitoring solely dependent on blood analysis may greatly underestimate health risks due to PAH exposure. To the best of our knowledge, this study is the first to provide levels of PAHs in the blood and urine of Nigerian mechanics. Findings herein will support policymakers at all levels in re-focusing attention to the less prioritized professions that pre-disposes people to PAHs and other emerging pollutants in society.

Association between Systole, Diastole, Sex, Age, Blood, and Urine Profile in Older Adults with Hypertension

The main cardiovascular changes associated with aging included decreased heart rates, oxygen extraction, arterial stiffening, vasoconstriction, systolic blood pressure elevation, myocardial thickening, reduced diastolic filling rate, rhythmic rate changes, and action potential prolongation. Multifactorial risk can include the pathology of the diseased. This study aims to ascertain the relationship between systole, diastole, sex, age, blood, and urine profiles in an elderly with Hypertension. The logistic regression study found three significant associations between diastole systolic and leucocyte sedimentation with Hypertension. For the net study, various hormones are another pathological marker that can be included in hypertension screening

A new multimodal paradigm for biomarkers longitudinal monitoring: a clinical application to women steroid profiles in urine and blood

BackgroundMost current state-of-the-art strategies to generate individual adaptive reference ranges are designed to monitor one clinical parameter at a time. An innovative methodology is proposed for the simultaneous longitudinal monitoring of multiple biomarkers. The estimation of individual thresholds is performed by applying a Bayesian modeling strategy to a multivariate score integrating several biomarkers (compound concentration and/or ratio). This multimodal monitoring was applied to data from a clinical study involving 14 female volunteers with normal menstrual cycles receiving testosterone via transdermal route, as to test its ability to detect testosterone administration. The study samples consisted of urine and blood collected during 4 weeks of a control phase and 4 weeks with a daily testosterone gel application. ResultsIntegrating multiple biomarkers improved the detection of testosterone gel administration with substantially higher sensitivity compared with the distinct follow-up of each biomarker, when applied to selected urine and serum steroid biomarkers, as well as the combination of both. Among the 175 known positive samples, 38% were identified by the multimodal approach using urine biomarkers, 79% using serum biomarkers and 83% by combining biomarkers from both biological matrices, whereas 10%, 67% and 64% were respectively detected using standard unimodal monitoring. Significance and noveltyThe detection of abnormal patterns can be improved using multimodal approaches. The combination of urine and serum biomarkers reduced the overall number of false-negatives, thus evidencing promising complementarity between urine and blood sampling for doping control, as highlighted in the case of the use of transdermal testosterone preparations. The generation in a multimodal setting of adaptive and personalized reference ranges opens up new opportunities in clinical and anti-doping profiling. The integration of multiple parameters in a longitudinal monitoring is expected to provide a more complete evaluation of individual profiles generating actionable intelligence to further guide sample collection, analysis protocols and decision-making in clinics and anti-doping.

Metabolic Alterations in Streptozotocin-nicotinamide-induced Diabetic Rats Treated with Muntingia calabura Extract via 1H-NMR-based Metabolomics.

Diabetes mellitus (DM) is a metabolic endocrine disorder caused by decreased insulin concentration or poor insulin response. Muntingia calabura (MC) has been used traditionally to reduce blood glucose levels. This study aims to support the traditional claim of MC as a functional food and blood-glucose-lowering regimen. The antidiabetic potential of MC is tested on a streptozotocin-nicotinamide (STZ-NA)-induced diabetic rat model by using the 1H-NMR-based metabolomic approach. Serum biochemical analyses reveal that treatment with 250 mg/kg body weight (bw) standardized freeze-dried (FD) 50% ethanolic MC extract (MCE 250) shows favorable serum creatinine (37.77 ± 3.53 µM), urea (5.98 ± 0.84 mM) and glucose (7.36 ± 0.57 mM) lowering capacity, which was comparable to the standard drug, metformin. The clear separation between diabetic control (DC) and normal group in principal component analysis indicates the successful induction of diabetes in the STZ-NA-induced type 2 diabetic rat model. A total of nine biomarkers, including allantoin, glucose, methylnicotinamide, lactate, hippurate, creatine, dimethylamine, citrate and pyruvate are identified in rats' urinary profile, discriminating DC and normal groups through orthogonal partial least squares-discriminant analysis. Induction of diabetes by STZ-NA is due to alteration in the tricarboxylic acid (TCA) cycle, gluconeogenesis pathway, pyruvate metabolism and nicotinate and nicotinamide metabolism. Oral treatment with MCE 250 in STZ-NA-induced diabetic rats shows improvement in the altered carbohydrate metabolism, cofactor and vitamin metabolic pathway, as well as purine and homocysteine metabolism.

Integrative network fusion-based multi-omics study for biomarker identification and patient classification of rheumatoid arthritis

BackgroundCold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy.MethodsAn integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot.ResultsThe main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot.ConclusionsThe current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy.