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RATIONALE & OBJECTIVE: The diagnosis and prognostication of chronic kidney disease (CKD) largely rely on glomerular measures that may not reflect tubular damage. We investigated the associations of urine kidney tubule biomarkers with estimated glomerular filtration rate (eGFR) change among middle-aged adults, when chronic diseases typically emerge. STUDY DESIGN: An observational cohort study. SETTING & PARTICIPANTS: A total of 1,145 participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study without CKD, hypertension, or cardiovascular disease at the year 20 visit. EXPOSURES: Seven different biomarkers of tubular health: urine epidermal growth factor (EGF), alpha-1-microglobulin (α1m), interleukin-18, kidney injury molecule-1, monocyte chemoattractant protein-1, uromodulin, and chitinase-3-like protein 1. OUTCOMES: Ten-year eGFR change and incident reduced eGFR (new onset of eGFR

Rationale & objectiveTubulointerstitial damage is a feature of early chronic kidney disease (CKD), but current clinical tests capture it poorly. Urine biomarkers of tubulointerstitial health may identify risk of CKD.Study designProspective cohort (Atherosclerosis Risk in Communities [ARIC]) and case-cohort (Multi-Ethnic Study of Atherosclerosis [MESA] and Reasons for Geographic and Racial Differences in Stroke [REGARDS]).Setting & participantsAdults with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 and without diabetes in the ARIC, REGARDS, and MESA studies.ExposuresBaseline urine monocyte chemoattractant protein-1 (MCP-1), alpha-1-microglobulin (α1m), kidney injury molecule-1, epidermal growth factor, and chitinase-3-like protein 1.OutcomeIncident CKD or end-stage kidney disease.Analytical approachMultivariable Cox proportional hazards regression for each cohort; meta-analysis of results from all 3 cohorts.Results872 ARIC participants (444 cases of incident CKD), 636 MESA participants (158 cases), and 924 REGARDS participants (488 cases) were sampled. Across cohorts, mean age ranged from 60 ± 10 to 63 ± 8 years, and baseline eGFR ranged from 88 ± 13 to 91 ± 14 mL/min/1.73 m2. In ARIC, higher concentrations of urine MCP-1, α1m, and kidney injury molecule-1 were associated with incident CKD. In MESA, higher concentration of urine MCP-1 and lower concentration of epidermal growth factor were each associated with incident CKD. In REGARDS, none of the biomarkers were associated with incident CKD. In meta-analysis of all 3 cohorts, each 2-fold increase α1m concentration was associated with incident CKD (HR, 1.19; 95% CI, 1.08-1.31).LimitationsObservational design susceptible to confounding; competing risks during long follow-up period; meta-analysis limited to 3 cohorts.ConclusionsIn 3 combined cohorts of adults without prevalent CKD or diabetes, higher urine α1m concentration was independently associated with incident CKD. 4 biomarkers were associated with incident CKD in at least 1 of the cohorts when analyzed individually. Kidney tubule health markers might inform CKD risk independent of eGFR and albuminuria.

Lupus nephritis (LN), a leading cause of death in Systemic Lupus Erythematosus (SLE) patients, presents significant diagnostic and prognostic challenges. Although renal pathology offers critical insights regarding the diagnosis, classification, and therapy for LN, its clinical utility is constrained by the invasive nature and limited reproducibility of renal biopsies. Moreover, the continuous monitoring of renal pathological changes through repeated biopsies is impractical. Consequently, there is a growing interest in exploring urine as a non-invasive, easily accessible, and dynamic "liquid biopsy" alternative to guide clinical management. This paper examines novel urinary biomarkers from a renal pathology perspective, encompassing cellular components, cytokines, adhesion molecules, auto-antibodies, soluble leukocyte markers, light chain fragments, proteins, small-molecule peptides, metabolomics, urinary exosomes, and ribonucleic acids. We also discuss the application of combined models comprising multiple biomarkers in assessing lupus activity. These innovative biomarkers and models offer insights into LN disease activity, acute and chronic renal indices, fibrosis, thrombotic microangiopathy, podocyte injury, and other pathological changes, potentially improving the diagnosis, management, and prognosis of LN. These urinary biomarkers or combined models may serve as viable alternatives to traditional renal pathology, potentially revolutionizing the method for future LN diagnosis and observation. [ABSTRACT FROM AUTHOR]

Rationale & Objective: The diagnosis and prognostication of chronic kidney disease (CKD) largely rely on glomerular measures that may not reflect tubular damage. We investigated the associations of urine kidney tubule biomarkers with estimated glomerular filtration rate (eGFR) change among middle-aged adults, when chronic diseases typically emerge. Study Design: An observational cohort study. Setting & Participants: A total of 1,145 participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study without CKD, hypertension, or cardiovascular disease at the year 20 visit. Exposures: Seven different biomarkers of tubular health: urine epidermal growth factor (EGF), alpha-1-microglobulin (α1m), interleukin-18, kidney injury molecule-1, monocyte chemoattractant protein-1, uromodulin, and chitinase-3-like protein 1. Outcomes: Ten-year eGFR change and incident reduced eGFR (new onset of eGFR

Background and Aims: Differentiation of nonobstructive dilatation (NOD) from ureteropelvic junction obstruction (UPJO) is a challenge in children with antenatally detected hydronephrosis. The aim of this study is to compare the utility of urinary biomarkers: carbohydrate antigen (CA 19-9), neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule (KIM) in diagnosing UPJO. Methods: A prospective study was conducted after obtaining ethical clearance between 2021 and 2022. Group 1 -- control group (n = 30): children with normal antenatal ultrasound with no urinary symptoms. Group 2 -- study group (n = 48): children with unilateral hydronephrosis: Group 2a -- NOD (n = 24): children stable on ultrasound and diuretic renogram and Group 2b -- UPJO (n = 24): children who worsened to Grade 4 hydronephrosis on ultrasound/worsening of differential renal function (10% drop) on renogram who underwent pyeloplasty. Urinary biomarkers NGAL, KIM-1, and CA 19-9 were measured using the enzyme-linked immune absorbent assay method. Results: The urine CA 19-9 level was 128.05 ± 4.08 U/mL in the UPJO group, and this was significantly higher (P = 0.001) than NOD, 70.29 ± 4.41, and controls, 1.91 ± 1.57. The urine NGAL level was 21.41 ± 4.44 pg/mL in UPJO, and this was significantly higher than controls, 2.669 ± 0.513, but not NOD, 24.55 ± 2.67. The urine KIM level was 817 ± 15.84 pg/mL in the UPJO group, and this was significantly higher than controls, 285 ± 8.10, but not NOD, 768.23 ± 15.12. Receiver operating characteristic analysis of CA 19-9 revealed a urine biomarker cutoff of 95 U/mL for diagnosing UPJO (sensitivity 95%; specificity 96%; and area under the curve 0.99). Conclusions: CA 19-9 is a superior marker compared to NGAL and KIM in differentiating UPJO from NOD. Further studies with larger numbers are warranted. [ABSTRACT FROM AUTHOR]

Bladder cancer (BCa) is a significant health issue and poses a healthcare burden on patients, highlighting the importance of an effective detection method. Here, we developed a urine DNA methylation diagnostic panel for distinguishing between BCa and non-BCa. In the discovery stage, an analysis of the TCGA database was conducted to identify BCa-specific DNA hypermethylation markers. In the validation phase, DNA methylation levels of urine samples were measured with real-time quantitative methylation-specific PCR (qMSP). Comparative analysis of the methylation levels between BCa and non-BCa, along with the receiver operating characteristic (ROC) analyses with machine learning algorithms (logistic regression and decision tree methods) were conducted to develop practical diagnostic panels. The performance evaluation of the panel shows that the individual biomarkers of ZNF671, OTX1, and IRF8 achieved AUCs of 0.86, 0.82, and 0.81, respectively, while the combined yielded an AUC of 0.91. The diagnostic panel using the decision tree algorithm attained an accuracy, sensitivity, and specificity of 82.6%, 75.0%, and 90.9%, respectively. Our results show that the urine-based DNA methylation diagnostic panel provides a sensitive and specific method for detecting and stratifying BCa, showing promise as a standard test that could enhance the diagnosis and prognosis of BCa in clinical settings. [ABSTRACT FROM AUTHOR]

The value of Extracellular vesicles (EVs) diagnostic markers is widely recognized. However, current research on EV DNA remains limited. This study investigates the biological properties, preprocessing factors, and diagnostic potential of EV DNA. We found that DNA positive vesicles account for 23.3% ± 6.7% of the urine total EV, with a large amount of DNA attached to the outside. EV DNA fragments are large, there is no significant effect on uEV DNA when store urine less than 6 h at 4°C. In addition, the influence of different EV extraction methods on methylation detection is also minor. More importantly, RASSF1A methylation in urine total EV DNA can distinguish between PCa and BPH, with an AUC of 0.874. Our results suggest the potential of urine EV DNA as a novel marker for PCa diagnosis. This provides a new idea for the study of urinary tumor markers. [ABSTRACT FROM AUTHOR]

Background: Cisplatin is associated with acute kidney injury (AKI) and electrolyte abnormalities. Urine tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP-7) may be early cisplatin-AKI biomarkers. Methods: We conducted a 12-site prospective cohort study with pediatric patients treated with cisplatin (May 2013–December 2017). Blood and urine (measured for TIMP-2, IGFBP-7) were collected pre-cisplatin, 24-h post-cisplatin, and near hospital discharge during the first or second cisplatin cycle (early visit (EV)) and during second-to-last or last cisplatin cycle (late visit (LV)). Primary outcome: serum creatinine (SCr)-defined AKI (≥ stage 1). Results: At EV (median (interquartile (IQR)) age: 6 (2–12) years; 78 (50%) female), 46/156 (29%) developed AKI; at LV, 22/127 (17%) experienced AKI. At EV, TIMP-2, IGFBP-7, and TIMP-2*IGFBP-7 pre-cisplatin infusion concentrations were significantly higher in participants with vs. those without AKI. At EV and LV, biomarker concentrations were significantly lower in participants with vs. those without AKI at post-infusion and near-hospital discharge. Biomarker values normalized to urine creatinine were higher in patients with AKI compared to without (LV post-infusion, median (IQR): TIMP-2*IGFBP-7: 0.28 (0.08–0.56) vs. 0.04 (0.02–0.12) (ng/mg creatinine)2/1000; P <.001). At EV, pre-infusion biomarker concentrations had the highest area under the curves (AUC) (range: 0.61–0.62) for AKI diagnosis; at LV, biomarkers measured post-infusion and near discharge yielded the highest AUCs (range: 0.64–0.70). Conclusions: TIMP-2*IGFBP-7 were poor to modest at detecting AKI post-cisplatin. Additional studies are needed to determine whether raw biomarker values or biomarker values normalized to urinary creatinine are more strongly associated with patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Multiplex polymerase chain reaction (M-PCR) has increased sensitivity for microbial detection compared with standard urine culture (SUC) in cases diagnosed as urinary tract infections (UTIs), leading to questions whether detected microbes are likely causative of UTIs or are incidental findings. Objective: To compare infection-associated biomarker levels against M-PCR and SUC results in symptomatic cases with a presumptive diagnosis of a UTI by a urologist. Design, setting, and participants: Participants were ≥60 yr old and presented to urology clinics between January and April 2023 with symptoms of UTIs (n = 583). Urine microbial detection was by M-PCR and SUC. Three infection-associated biomarkers (neutrophil gelatinase-associated lipocalin, interleukin-8, and interleukin-1β) were measured by enzyme-linked immunosorbent assay. Symptomatic cases with elevated biomarkers, detection of uropathogens, and a specialist clinical diagnosis of a UTI were considered definitive UTI cases. Outcome measurements and statistical analysis: Distributions were compared using two-sample Wilcoxon rank sum test, with two-tailed p values of

Rationale & objectiveThe associations of the glomerular markers of kidney disease, estimated glomerular filtration rate (eGFR) and albuminuria, with frailty and cognition are well established. However, the relationship of kidney tubule injury and dysfunction with frailty and cognition is unknown.Study designObservational cross-sectional study.Setting & participants2,253 participants with eGFR

Abstract Background Early diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically. Results Experiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers. Conclusion A new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients.

The literature lacks consensus on the minimum microbial density required for diagnosing urinary tract infections (UTIs). This study categorized the microbial densities of urine specimens from symptomatic UTI patients aged ≥ 60 years and correlated them with detected levels of the immune response biomarkers neutrophil gelatinase-associated lipocalin (NGAL), interleukin-8 (IL-8), and interleukin-1-beta (IL-1β). The objective was to identify the microbial densities associated with significant elevation of these biomarkers in order to determine an optimal threshold for diagnosing symptomatic UTIs. Biobanked midstream voided urine samples were analyzed for microbial identification and quantification using standard urine culture (SUC) and multiplex-polymerase chain reaction (M-PCR) testing, while NGAL, IL-8, and IL-1β levels were measured via enzyme-linked immunosorbent assay (ELISA). NGAL, IL-8, and IL-1β levels were all significantly elevated at microbial densities ≥ 10,000 cells/mL when measured via M-PCR (p < 0.0069) or equivalent colony-forming units (CFUs)/mL via SUC (p < 0.0104) compared to samples with no detectable microbes. With both PCR and SUC, a consensus of two or more elevated biomarkers correlated well with microbial densities > 10,000 cells/mL or CFU/mL, respectively. The association between ≥10,000 cells and CFU per mL with elevated biomarkers in symptomatic patients suggests that this lower threshold may be more suitable than 100,000 CFU/mL for diagnosing UTIs. [ABSTRACT FROM AUTHOR]

For precision in clinical oncology practice, detection of tumor-derived peptides and proteins in urine offers an attractive and noninvasive alternative for diagnostic or screening purposes. In this study, we report comparative quantitative proteomic profiling of urine samples from patients with gastric cancer and healthy controls using tandem mass tags-based multiplexed mass spectrometry approach. We identified 1504 proteins, of which 246 were differentially expressed in gastric cancer cases. Notably, ephrin A1 (EFNA1), pepsinogen A3 (PGA3), sortilin 1 (SORT1), and vitronectin (VTN) were among the upregulated proteins, which are known to play crucial roles in the progression of gastric cancer. We also found other overexpressed proteins, including shisa family member 5 (SHISA5), mucin like 1 (MUCL1), and leukocyte cell derived chemotaxin 2 (LECT2), which had not previously been linked to gastric cancer. Using a novel approach for targeted proteomics, SureQuant, we validated changes in abundance of a subset of proteins discovered in this study. We confirmed the overexpression of vitronectin and sortilin 1 in an independent set of urine samples. Altogether, this study provides molecular candidates for biomarker development in gastric cancer, and the findings also support the promise of urinary proteomics for noninvasive diagnostics and personalized/precision medicine in the oncology clinic. [ABSTRACT FROM AUTHOR]

Introduction: Over two thirds of individuals with low back pain (LBP) may experience recurrent or persistent symptoms in the long term. Yet, current data do not allow to predict who will develop chronic low back pain and who will recover from an acute episode. Elevated serum levels of the proinflammatory cytokine tumor necrosis factor-a (TNF-α) have been associated with poor recovery and persistent pain following an acute episode of LBP. Inflammatory cytokines may also mediate mechanisms involved in nociplastic pain, and thus, have significant implications in chronic primary low back pain (CPLBP). Methods: This study aimed to investigate the potential of urinary TNF-α levels for predicting outcomes and characterizing clinical features of CPLBP patients. Twenty-four patients with CPLBP and 24 sex- and age-matched asymptomatic controls were recruited. Urinary TNF-α concentrations were measured at baseline and after 4 weeks, during which CPLBP patients underwent spinal manipulative therapy (SMT). Results: Concentrations of TNF-α were found to be elevated in baseline urine samples of CPLBP patients compared to asymptomatic controls. Moreover, these values differed among patients depending on their pain trajectory. Patients with persistent pain showed higher levels of TNF-α, when compared to those with episodic CPLBP. Furthermore, baseline TNF-α concentrations and their changes after 4 weeks predicted alterations in pain intensity and disability following SMT in patients with CPLBP. Discussion: These findings warrant further research on the potential use of urinary TNF-α concentrations as a prognostic biomarker for CPLBP. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria—uACR < 30 mg/g; microalbuminuria—uACR 30–300 mg/g; macroalbuminuria—uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum—arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine—p-cresyl sulfate. [ABSTRACT FROM AUTHOR]

The use of advanced preclinical models has become increasingly important in drug development. This is particularly relevant in bladder cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (chemotherapy, radiotherapy, immunotherapy, and/or surgery) are largely absent. Patient-derived and clinically relevant models including patient-derived xenografts (PDX), organoids, and conditional reprogramming (CR) of cell cultures efficiently generate numerous models and are being used in both basic and translational cancer biology. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have utilized CR technology to conduct bladder cancer research. [ABSTRACT FROM AUTHOR]

Bladder cancer (BCa) is a significant health issue and poses a healthcare burden on patients, highlighting the importance of an effective detection method. Here, we developed a urine DNA methylation diagnostic panel for distinguishing between BCa and non-BCa. In the discovery stage, an analysis of the TCGA database was conducted to identify BCa-specific DNA hypermethylation markers. In the validation phase, DNA methylation levels of urine samples were measured with real-time quantitative methylation-specific PCR (qMSP). Comparative analysis of the methylation levels between BCa and non-BCa, along with the receiver operating characteristic (ROC) analyses with machine learning algorithms (logistic regression and decision tree methods) were conducted to develop practical diagnostic panels. The performance evaluation of the panel shows that the individual biomarkers of ZNF671, OTX1, and IRF8 achieved AUCs of 0.86, 0.82, and 0.81, respectively, while the combined yielded an AUC of 0.91. The diagnostic panel using the decision tree algorithm attained an accuracy, sensitivity, and specificity of 82.6%, 75.0%, and 90.9%, respectively. Our results show that the urine-based DNA methylation diagnostic panel provides a sensitive and specific method for detecting and stratifying BCa, showing promise as a standard test that could enhance the diagnosis and prognosis of BCa in clinical settings.

Introduction: We aimed to identify urine biomarkers for screening individuals with adaptability to high-altitude hypoxia with high stamina levels. Although most non-high-altitude natives experience rapid decline in physical ability when ascending to high altitudes, some individuals with high-altitude adaptability continue to maintain high endurance levels. Methods: We divided the study population into two groups: the LC group (low change in endurance from low to high altitude) and HC group (high change in endurance from low to high altitude). We performed blood biochemistry testing for individuals at high altitudes and sea level. We used urine peptidome profiling to compare the HH (high-altitude with high stamina) and HL (high-altitude with low stamina) groups and the LC and HC groups to identify urine biomarkers. Results: Routine blood tests revealed that the concentration of white blood cells, lymphocytes and platelets were significantly higher in the HH group than in the HL group. Urine peptidome profiling showed that the proteins ITIH1, PDCD1LG2, NME1-NME2, and CSPG4 were significantly differentially expressed between the HH and HL groups, which was tested using ELISA. Urine proteomic analysis showed that LRG1, NID1, VASN, GPX3, ACP2, and PRSS8 were urine proteomic biomarkers of high stamina during high-altitude adaptation. Conclusion: This study provides a novel approach for identifying potential biomarkers for screening individuals who can adapt to high altitudes with high stamina. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) is one of the most often reported malignancies globally, with a high recurrence rate and associated morbidity and mortality, especially in advanced BC. There has been a surge in the number of molecular targets revealed for BC prognosis and treatment. However, there is still a great need to discover novel biomarkers. Consequently, the current study investigated biomarkers that might indicate the progression of bladder cancer. In this study, bioinformatics analysis was done on a single GEO dataset, and TCGA-BLCA information was connected with differentially expressed genes (DEGs). The levels of mRNA and protein expression were validated using qRT-PCR. According to our findings, CRYAB, ECM1, ALDOB, AOC, GPX3, IGFBP7, AQP2, LASS2, TMEM176A, GALNT1, and LASS2 were highly enriched in cell division, identical protein binding, and developmental process in bladder cancer patients. In addition, among the highly differentiated genes, ECM1, GALNT1, LASS2, and GPX3 showed significant molecular alterations in BC, which are crucial for marker identification. Moreover, the mRNA, CNVs, and protein levels of ECM1, GALNT1, LASS2, and GPX3 were significantly increased in BC patients. Our predictions and analysis studies stated that these four genes act as urine biomarkers and played a crucial role in disease prognosis and the therapeutic process of bladder cancer. Our outcomes showed that these four novel urine biomarkers have the potential to provide innovative diagnostics, early predictions, and disease targets, ultimately improving the BC patient's prognosis. [ABSTRACT FROM AUTHOR]

Metabolomic analysis methods were employed to determine biomarkers for various chronic kidney diseases (CKDs). Modern analytical methods were developed and applied successfully to find a specific metabolomic profile in urine samples from CKD and Balkan endemic nephropathy (BEN) patients. The aim was to explore a specific metabolomic profile defined by feasible/easy-to-identify molecular markers. Urine samples were collected from patients with CKDs and BEN, and from healthy subjects from endemic and nonendemic areas in Romania. Metabolomic analysis of urine samples, extracted by the liquid-liquid extraction (LLE) method, was performed by gas chromatography-mass spectrometry (GC-MS). The statistical exploration of the results was performed through a principal component analysis (PCA) evaluation. Urine samples were statistically analyzed using a classification based on six types of metabolites. Most urinary metabolites are distributed in the center of a loading plot, meaning that these compounds do not represent significant markers for BEN. One of the most frequent and higher-concentration urinary metabolites in BEN patients was p-Cresol, a phenolic compound that implies a severe injury of the renal filtration function. The presence of p-Cresol was associated with protein-bound uremic toxins, which have specific functional groups such as indole and phenyl. In prospective studies for future investigation, prevention, and disease treatment, we suggest a larger sample size, sample extraction using other methods, and analysis using other chromatography techniques coupled with mass spectrometry, which can generate a more significant data set for statistical analysis. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically. Results: Experiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers. Conclusion: A new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

ObjectiveTo evaluate the effects of HIV preexposure prophylaxis (PrEP) with tenofovir disoproxial fumurate (TDF)/emtricitabine (FTC) on kidney function and kidney tubular health.DesignThe Iniciativa Profilaxis Pre-Exposicion open-label extension (iPrEx-OLE) study enrolled former PrEP trial participants to receive open-label TDF/FTC. This study included 123 iPrEx-OLE participants who demonstrated PrEP adherence.MethodsWe compared estimated glomerular filtration rate calculated using serum creatinine (eGFRcr), serum cystatin C (eGFRcys), and in combination (eGFRcr-cys), and a panel of 14 urine biomarkers reflecting kidney tubular health before and 6 months after PrEP initiation.ResultsAt baseline, mean eGFRcr, eGFRcys, and eGFRcr-cys were 108.3, 107.0, and 111.1 ml/min per 1.73 m, respectively. Six months after PrEP initiation, eGFRcr declined by -4% (95% CI: -5.7 to -2.4%), eGFRcys declined by -3.3% (95% CI: -8.3 to 1.9%), and eGFRcr-cys declined by -4.1% (95% CI: -7.5 to -0.7%). From the urine biomarker panel, α1-microglobulin and β2-microglobulin increased by 22.7% (95% CI: 11.8--34.7%) and 14.1% (95% CI: -6.1 to 38.6%), whereas chitinase-3-like 1 protein and monocyte chemoattractant protein-1 decreased by -37.7% (95% CI: -53.0 to -17.3%) and -15.6% (95% CI: -31.6 to 4.2%), respectively. Ten of the 14 urine biomarkers, including albumin, had estimated changes of less than 12% with wide confidence intervals.ConclusionSix months of PrEP with TDF/FTC was associated with decreases in eGFRcr and eGFRcys. We also observed for the first time changes in flour of 14 urine biomarkers reflecting kidney tubular health. These findings demonstrate that PrEP has direct effects on eGFR and the proximal tubule.

BackgroundHIV-positive persons bear an excess burden of chronic kidney disease (CKD); however, conventional methods to assess kidney health are insensitive and non-specific for detecting early kidney injury. Urinary biomarkers can detect early kidney injury, and may help mitigate the risk of overt CKD.MethodsCross-sectional study of HIV-positive persons in the Multicenter AIDS Cohort Study and the Women's Interagency HIV Study. We measured levels of 14 biomarkers, capturing multiple dimensions of kidney injury. We then evaluated associations of known CKD risk factors with urine biomarkers using separate multivariable adjusted models for each biomarker.ResultsOf the 198 participants, one third were on HAART and virally suppressed. The vast majority (95%) had preserved kidney function as assessed by serum creatinine, with a median eGFR of 103 ml/min/1.73 m2 (interquartile range (IQR): 88, 116). In our multivariable analyses, the associations of each CKD risk factor with urinary biomarker levels varied in magnitude. For example, HIV viral load was predominantly associated with elevations in interleukin(IL)-18, and albuminuria, while higher CD4 levels were associated with lower monocyte chemoattractant protein-1 (MCP-1) and β2-microglobulin. In contrast, older age was significantly associated with elevations in α1-microglobulin, kidney injury marker-1, clusterin, MCP-1, and chitinase-3-like protein-1 levels, as well as lower epidermal growth factor, and uromodulin levels.ConclusionsAmong HIV-positive persons, CKD risk factors are associated with unique and heterogeneous patterns of changes in urine biomarkers levels. Additional work is needed to develop parsimonious algorithms that integrate multiple biomarkers and clinical data to discern the risk of overt CKD and its progression.

BackgroundChronic kidney disease (CKD) occurs commonly among HIV-infected persons. Statins may delay CKD onset and progression through their cholesterol-lowering and pleiotropic effects.MethodsAmong 850 HIV-infected men from the Multicenter AIDS Cohort Study with stored urine samples (2009-2011), we evaluated cross-sectional associations of statin use with urine biomarkers of kidney damage [albumin-to-creatinine ratio (ACR), alpha-1-microglobulin, interleukin-18, kidney injury molecule-1, and procollagen type III N-terminal propeptide] using multivariable linear regression. We evaluated the longitudinal associations of statin use with annual change in estimated glomerular filtration rate by creatinine (eGFR) using linear mixed models, and with incident proteinuria and incident CKD (eGFR

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19‐9. This nested case‐control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19‐9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank‐sum and Mann‐Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19‐9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19‐9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis. [ABSTRACT FROM AUTHOR]

Background: Neurogenic lower urinary tract dysfunction requires lifelong surveillance and management for the perseveration of patients' quality of life and the prevention of significant morbidity and mortality. Urine biomarkers are an attractive noninvasive method of surveillance for these patients. The aim of this systematic review is to search for and critically appraise studies that investigate the clinical usefulness of urine biomarkers in the management of neurogenic lower urinary tract dysfunction (NLUTD) in adults. Methods: This review was conducted according to PRISMA and MOOSE guidelines. Search strategy included PubMed, CENTRAL, and Scopus (until October 2022). Studies investigating potential urine biomarkers for the management of adults with NLUTD were included. Results: Fifteen studies fulfilled the criteria. To date, a variety of different urine molecules have been investigated for the diagnosis and management of neurogenic overactive bladder and detrusor overactivity (nerve growth factor, brain-derived neurotrophic factor, prostaglandin E2, prostaglandin F2α, transformation growth factor β-1, tissue inhibitor metalloproteinase-2, matrix metalloproteinase-2, substance P, microRNA), diagnosis of vesicoureteral reflux (exosomal vitronectin), urinary tract infection (neutrophil gelatinase-associated lipocalin, interleukin 6) and bladder cancer screening (cytology, BTA stat, survivin) in neurological patients. Conclusion: Further studies are needed to specify the utility of each molecule in the management algorithm of adult NLUTD. [ABSTRACT FROM AUTHOR]

IntroductionOver two thirds of individuals with low back pain (LBP) may experience recurrent or persistent symptoms in the long term. Yet, current data do not allow to predict who will develop chronic low back pain and who will recover from an acute episode. Elevated serum levels of the proinflammatory cytokine tumor necrosis factor-α (TNF-α) have been associated with poor recovery and persistent pain following an acute episode of LBP. Inflammatory cytokines may also mediate mechanisms involved in nociplastic pain, and thus, have significant implications in chronic primary low back pain (CPLBP).MethodsThis study aimed to investigate the potential of urinary TNF-α levels for predicting outcomes and characterizing clinical features of CPLBP patients. Twenty-four patients with CPLBP and 24 sex- and age-matched asymptomatic controls were recruited. Urinary TNF-α concentrations were measured at baseline and after 4 weeks, during which CPLBP patients underwent spinal manipulative therapy (SMT).ResultsConcentrations of TNF-α were found to be elevated in baseline urine samples of CPLBP patients compared to asymptomatic controls. Moreover, these values differed among patients depending on their pain trajectory. Patients with persistent pain showed higher levels of TNF-α, when compared to those with episodic CPLBP. Furthermore, baseline TNF-α concentrations and their changes after 4 weeks predicted alterations in pain intensity and disability following SMT in patients with CPLBP.DiscussionThese findings warrant further research on the potential use of urinary TNF-α concentrations as a prognostic biomarker for CPLBP.

Botulinum toxin A (BoNT-A) is effective in reducing bladder hypersensitivity and increasing capacity through the effects of anti-inflammation in the bladder urothelium; however, studies on the treatment outcome of interstitial cystitis/bladder pain syndrome (IC/BPS) are lacking. We investigated the treatment outcome in IC/BPS patients receiving intravesical BoNT-A injections. This retrospective study included IC/BPS patients who had 100U BoNT-A intravesical injections in the past 20 years. The treatment outcomes at 6 months following the BoNT-A treatment were evaluated using the global response assessment (GRA) scale. The treatment outcomes according to the GRA scale include clinical symptoms, urodynamic parameters, cystoscopic characteristics, and urinary biomarkers, and it was these predictive factors for achieving satisfactory outcomes which were investigated. Among the 220 enrolled patients (180 women, 40 men) receiving BoNT-A injections, only 87 (40%) had significantly satisfactory treatment outcomes. The satisfactory group showed significantly larger voided volumes, and lower levels of both the urinary inflammatory protein MCP-1 and the oxidative stress biomarker 8-isoprostane in comparison to the unsatisfactory group. The IC severity and detrusor pressure are predictive factors of BoNT-A treatment outcomes. IC/BPS patients with less bladder inflammation showed satisfactory outcomes with intravesical BoNT-A injections. Patients with severe bladder inflammation might require more intravesical BoNT-A injections to achieve a satisfactory outcome. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is one of the leading causes of gynaecological cancer mortality in women worldwide. If detected at an early stage (I, II), OC has a 90% 5-year survival rate; nevertheless, symptoms are often hidden, leading to late-stage (III, IV) diagnosis and a poor prognosis. The current diagnostic procedures, such as a pelvic exam, transvaginal ultrasound, CA-125 blood tests, serum HE4 tests and multivariate index assays (MIA), are insufficient. Sadly, surgery is frequently required to confirm a positive diagnosis. Therefore, there has been an increased interest in different biomarkers using a non-invasive test as a tool for the earlier diagnosis of OC to resolve the need for precise and non-invasive diagnostic methods. This review article aims to investigate how biomarkers influence early OC detection and to emphasise the role of using a combination of serum biomarkers panel rather than a single biomarker. In addition, this review provides insights into the current serum biomarkers, urine biomarkers and other emerging biomarkers in the early detection of OC for better specificity and sensitivity and to improve the overall survival (OS) rate. [ABSTRACT FROM AUTHOR]

Multiple urinary biomarkers have been reported in differentiation of nonobstructive dilatation (NOD) from ureteropelvic junction obstruction (UPJO). In this meta-analysis, we compared the accuracy of common urinary biomarkers applicable to UPJO. A systematic literature review of electronic databases was conducted for: (UPJO) OR (NOD) AND (urinary biomarkers) AND (children) for articles published in the last decade. PRISMA guidelines were used to exclude duplicate and erroneous articles. Meta-analysis involved risk of bias analysis, heterogeneity assessment, and comparison of sensitivity/specificity by forest plot analysis using MetaXL 5.3. Among the 264 articles analyzed, 19 articles met the inclusion criteria and reported the following: neutrophil gelatinase-associated lipocalin (NGAL), monocyte chemotactic protein-1 (MCP1), carbohydrate antigen 19–9 (CA 19–9), kidney injury molecule (KIM1), epidermal growth factor (EGF), and interferon gamma induced protein-10 (IP10). There was substantial heterogeneity among articles. There was wide variation in applied cut-offs among studies. Overall sensitivity was highest at 87% for CA 19–9 while overall specificity was highest at 76% for NGAL. Overall accuracy was highest at 78% for CA 19–9 followed by 77% for NGAL and 75% for KIM1. In this meta-analysis, the overall accuracy was highest for CA 19–9 followed by NGAL and KIM1. The small number of studies for CA 19–9 and considerable heterogeneity for all should be considered while interpreting these findings. Based on the current meta-analysis, we support a panel of biomarkers combining NGAL, KIM, and CA 19–9 for the best diagnostic accuracy of UPJO in children. [ABSTRACT FROM AUTHOR]

Introduction: Individuals with focal segmental glomerular sclerosis (FSGS) typically undergo kidney biopsy only once, which limits the ability to characterize kidney cell gene expression over time. Methods: We used single-cell RNA sequencing (scRNA-seq) to explore disease-related molecular signatures in urine cells from subjects with FSGS. We collected 17 urine samples from 12 FSGS subjects and captured these as 23 urine cell samples. The inflammatory signatures from renal epithelial and immune cells were evaluated in bulk gene expression data sets of FSGS and minimal change disease (MCD) (The Nephrotic Syndrome Study Network [NEPTUNE] study) and an immune single-cell data set from lupus nephritis (Accelerating Medicines Partnership). Results: We identified immune cells, predominantly monocytes, and renal epithelial cells in the urine. Further analysis revealed 2 monocyte subtypes consistent with M1 and M2 monocytes. Shed podocytes in the urine had high expression of marker genes for epithelial-to-mesenchymal transition (EMT). We selected the 16 most highly expressed genes from urine immune cells and 10 most highly expressed EMT genes from urine podocytes as immune signatures and EMT signatures, respectively. Using kidney biopsy transcriptomic data from NEPTUNE, we found that urine cell immune signature and EMT signature genes were more highly expressed in FSGS biopsies compared with MCD biopsies. Conclusion: The identification of monocyte subsets and podocyte expression signatures in the urine samples of subjects with FSGS suggests that urine cell profiling might serve as a diagnostic and prognostic tool in nephrotic syndrome. Furthermore, this approach may aid in the development of novel biomarkers and identifying personalized therapies targeting particular molecular pathways in immune cells and podocytes.

Drug-induced kidney injury (DIKI) refers to kidney damage resulting from the administration of medications. The aim of this project was to identify reliable urinary microRNA (miRNAs) biomarkers that can be used as potential predictors of DIKI before disease diagnosis. This study quantified a panel of six miRNAs (miRs-210-3p, 423-5p, 143-3p, 130b-3p, 486-5p, 193a-3p) across multiple time points using urinary samples from a previous investigation evaluating effects of a nephrotoxicant in cynomolgus monkeys. Exosome-associated miRNA exhibited distinctive trends when compared to miRNAs quantified in whole urine, which may reflect a different urinary excretion mechanism of miRNAs than those released passively into the urine. Although further research and mechanistic studies are required to elucidate how these miRNAs regulate signaling in disease pathways, we present, for the first time, data that several miRNAs displayed strong correlations with histopathology scores, thus indicating their potential use as biomarkers to predict the development of DIKI in preclinical studies and clinical trials. Also, these findings can potentially be translated into other non-clinical species or human for the detection of DIKI. • Explored mRNA biomarkers for predicting DIKI compared to traditional biomarkers. • Quantified 6 specific miRNAs in urinary samples at various time points. • Showed distinctive trends in exosome miRNAs compared to whole urine. • Results have potential translation to other species. [ABSTRACT FROM AUTHOR]

Urinary sex hormones are investigated as potential biomarkers for the early detection of breast cancer, aiming to evaluate their relevance and applicability, in combination with supervised machine-learning data analysis, toward the ultimate goal of extensive screening. Sex hormones were determined on urine samples collected from 250 post-menopausal women (65 healthy - 185 with breast cancer, recruited among the clinical patients of Candiolo Cancer Institute FPO-IRCCS (Torino, Italy). Two analytical procedures based on UHPLC-MS/HRMS were developed and comprehensively validated to quantify 20 free and conjugated sex hormones from urine samples. The quantitative data were processed by seven machine learning algorithms. The efficiency of the resulting models was compared. Among the tested models aimed to relate urinary estrogen and androgen levels and the occurrence of breast cancer, Random Forest (RF) proved to underscore all the other supervised classification approaches, including Partial Least Squares – Discriminant Analysis (PLS-DA), in terms of effectiveness and robustness. The final optimized model built on only five biomarkers (testosterone-sulphate, alpha-estradiol, 4-methoxyestradiol, DHEA-sulphate, and epitestosterone-sulphate) achieved an approximate 98% diagnostic accuracy on replicated validation sets. To balance the less-represented population of healthy women, a Synthetic Minority Oversampling TEchnique (SMOTE) data oversampling approach was applied. By means of tunable hyperparameters optimization, the RF algorithm showed great potential for early breast cancer detection, as it provides clear biomarkers ranking and their relative efficiency, allowing to ground the final diagnostic model on a restricted selection five steroid biomarkers only, as desirable for noninvasive tests with wide screening purposes. [Display omitted] • Urinary sex hormones are potential biomarkers for early detection of breast cancer. • A UHPLC-MS/HRMS method quantifying 20 sex hormones in urine is fully validated. • Random Forest modelling outcomes the performances of other multivariate algorithms. • Non-invasive screening is delivered with 98% accuracy using only 5 biomarkers. [ABSTRACT FROM AUTHOR]

The literature lacks consensus on the minimum microbial density required for diagnosing urinary tract infections (UTIs). This study categorized the microbial densities of urine specimens from symptomatic UTI patients aged ≥ 60 years and correlated them with detected levels of the immune response biomarkers neutrophil gelatinase-associated lipocalin (NGAL), interleukin-8 (IL-8), and interleukin-1-beta (IL-1β). The objective was to identify the microbial densities associated with significant elevation of these biomarkers in order to determine an optimal threshold for diagnosing symptomatic UTIs. Biobanked midstream voided urine samples were analyzed for microbial identification and quantification using standard urine culture (SUC) and multiplex-polymerase chain reaction (M-PCR) testing, while NGAL, IL-8, and IL-1β levels were measured via enzyme-linked immunosorbent assay (ELISA). NGAL, IL-8, and IL-1β levels were all significantly elevated at microbial densities ≥ 10,000 cells/mL when measured via M-PCR (p < 0.0069) or equivalent colony-forming units (CFUs)/mL via SUC (p < 0.0104) compared to samples with no detectable microbes. With both PCR and SUC, a consensus of two or more elevated biomarkers correlated well with microbial densities > 10,000 cells/mL or CFU/mL, respectively. The association between ≥10,000 cells and CFU per mL with elevated biomarkers in symptomatic patients suggests that this lower threshold may be more suitable than 100,000 CFU/mL for diagnosing UTIs.

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria—uACR < 30 mg/g; microalbuminuria—uACR 30–300 mg/g; macroalbuminuria—uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum—arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine—p-cresyl sulfate.

BackgroundPre-exposure prophylaxis (PrEP) with tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) is becoming increasingly adopted for HIV prevention. Tenofovir can cause proximal tubular damage and chronic kidney disease in HIV-infected persons, but little is known regarding its nephrotoxic potential among HIV-uninfected persons. In this study, we evaluated the effects of PrEP on urine levels of the following: α1-microglobulin (α1m), a marker of impaired tubular reabsorption; albuminuria, a measure of glomerular injury; and total proteinuria.SettingThe Iniciativa Profilaxis Pre-Exposicion (iPrEx) study randomized HIV-seronegative men and transgender women who have sex with men to oral TDF/FTC or placebo. The iPrEx open-label extension (iPrEx-OLE) study enrolled former PrEP trial participants to receive open-label TDF/FTC.MethodsA cross-sectional analysis compared urine biomarker levels by study arm in iPrEx (N = 100 treatment arm, N = 100 placebo arm). Then, urine biomarker levels were compared before and after PrEP initiation in 109 participants of iPrEx-OLE.ResultsIn iPrEx, there were no significant differences in urine α1m, albuminuria, or proteinuria by treatment arm. In iPrEx-OLE, after 24 weeks on PrEP, urine α1m and proteinuria increased by 21% [95% confidence interval (CI): 10 to 33] and 18% (95% CI: 8 to 28), respectively. The prevalence of detectable α1m increased from 44% to 65% (P < 0.001) and estimated glomerular filtration rate declined by 4 mL/min/1.73 m (P < 0.001). There was no significant change in albuminuria (6%; 95% CI: -7% to 20%).ConclusionPrEP with TDF/FTC was associated with a statistically significant rise in urine α1m and proteinuria after 6 months, suggesting that PrEP may result in subclinical tubule dysfunction.

Mechanical ventilation is associated with significant increases in the risk of acute kidney injury (AKI). The rate of AKI due to mechanical ventilation and the associated mortality remain unacceptably high. Preventative and therapeutic strategies are clearly lacking. Ventilator-induced kidney injury is believed to occur due to changes in hemodynamics that impair renal perfusion, neurohumoral-mediated alterations in intra-renal blood flow, and systemic inflammatory mediators generated by ventilator-induced lung injury. The risk of injury to the kidney by these mechanisms may be modified by open lung protective ventilation with low tidal volumes and high positive end expiratory pressure. However, these strategies may also increase the risk of injury in some settings, and clinicians have limited means to identify the optimal ventilator strategy for each specific patient. Novel urinary biomarkers have demonstrated the ability to predict AKI prior to classic clinical signs such as decreased urine output and increased creatinine. These biomarkers may serve as an early indication to intensivists of an injurious ventilator strategy and failure of traditional management.

Summary: Background: Early detection of gastric cancer (GC) remains challenging. We aimed to examine urine proteomic signatures and identify protein biomarkers that predict the progression of gastric lesions and risk of GC. Methods: A case–control study was initially designed, covering subjects with GC and gastric lesions of different stages. Subjects were aged 40–69 years, without prior diagnosis of renal or urological diseases. We enrolled a total of 255 subjects, with 123 in the discovery stage from Linqu, China, a high-risk area for GC and 132 in the validation stage from Linqu and Beijing. A prospective study was further designed for a subset of 60 subjects with gastric lesions, which were followed for 297–857 days. Findings: We identified 43 differentially expressed urine proteins in subjects with GC vs. mild or advanced gastric lesions. Baseline urinary levels of ANXA11, CDC42, NAPA and SLC25A4 were further positively associated with risk of gastric lesion progression. Three of them, except for SLC25A4, also had higher expression in GC than non-GC tissues. Integrating these four proteins showed outstanding performance in predicting the progression of gastric lesions (AUC (95% CI): 0.92 (0.83–1.00)) and risk of GC (AUC (95% CI): 0.81 (0.73–0.89) and 0.84 (0.77–0.92) for GC vs. mild or advanced gastric lesions respectively). Interpretation: This study revealed distinct urine proteomic profiles and a panel of proteins that may predict the progression of gastric lesions and risk of GC. These biomarkers in a non-invasive approach may have translational significance for defining high-risk populations of GC and its early detection. Funding: Funders are listed in the Acknowledgement.

Urine biomarkers are important in monitoring diseases related to human kidney function. The current processes for measuring biomarker levels in urine samples require patients to regularly visit clinical facilities, which is inconvenient and sometimes impossible for patients in rural areas. Therefore, portable analysis devices for the measurement of urine biomarkers are urgently requested. In this study, a portable platform using colorimetry, a common and simple-to-operate chemical analysis technique, was developed to measure urine biomarkers. The device, using commercial test kits as recognising reagents and a 96-well microplate as a solution container, provides quantitative measures of biomarker concentration. Moreover, the proposed device introduces a calibration method to minimise the dependence of regular maintenance. The device's performance was evaluated with urine from 73 renal patients and its results matched with clinical results well. The device has the potential for measuring urine creatinine, in addition to performing a variety of commercial assays for biomarker detection in human body fluids in general. [ABSTRACT FROM AUTHOR]

Purpose We aimed to identify, verify, and validate a multiplex urinary biomarker-based prediction model for diagnosis and surveillance of urothelial carcinoma of bladder, using high-throughput proteomics methods. Materials and Methods Label-free quantification of data-dependent and data-independent acquisition of 12 and 24 individuals was performed in each of the discovery and verification phases using mass spectrometry, simultaneously using urinary exosome and proteins. Based on five scoring system based on proteomics data and statistical methods, we selected eight proteins. Enzyme-linked immunosorbent assay on urine from 120 patients with bladder mass lesions used for validation. Using multivariable logistic regression, we selected final candidate models for predicting bladder cancer. Results Comparing the discovery and verification cohorts, 38% (50/132 exosomal differentially expressed proteins [DEPs]) and 44% (109/248 urinary DEPs) are consistent at statistically significance, respectively. The 20 out of 50 exosome proteins and 27 out of 109 urinary proteins were upregulated in cancer patients. From eight selected proteins, we developed two diagnostic models for bladder cancer. The area under the receiver operating characteristic curve (AUROC) of two models were 0.845 and 0.842, which outperformed AUROC of urine cytology. Conclusion The results showed that the two diagnostic models developed here were more accurate than urine cytology. We successfully developed and validated a multiplex urinary protein-based prediction, which will have wide applications for the rapid diagnosis of urothelial carcinoma of the bladder. External validation for this biomarker panel in large population is required. [ABSTRACT FROM AUTHOR]

Poliomavirus BK virus (BKV) is highly infective, causing asymptomatic infections during childhood. After the initial infection, a stable state of latent infection is recognized in kidney tubular cells and the uroepithelium with negligible clinical consequences. BKV is an important risk factor for BKV-associated diseases, and, in particular, for BKV-associated nephropathy (BKVN) in renal transplanted recipients (RTRs). BKVN affects up to 10% of renal transplanted recipients, and results in graft loss in up to 50% of those affected. Unfortunately, treatments for BK virus infection are restricted, and there is no efficient prophylaxis. In addition, consequent immunosuppressive therapy reduction contributes to immune rejection. Increasing surveillance and early diagnosis based upon easy and rapid analyses are resulting in more beneficial outcomes. In this report, the current status and perspectives in the diagnosis and treatment of BKV in RTRs are reviewed. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.

Aims: To discuss the role of autocrine/paracrine signaling of urothelial arginine vasopressin (AVP) on mammalian bladder capacities and micturition thresholds, impact of distension on water/urea reabsorption from the bladder, review of the literature to better characterize the central/peripheral effects of AVP, desmopressin (dAVP) toxicity, and urine biomarkers of nocturia., Methods: This review summarizes discussions during an International Consultation on Incontinence-Research Society 2024 think tank with respect to the role of urothelial AVP in aged individuals with nocturnal polyuria, impact of solute and water reabsorption by the bladder on uninterrupted sleep, central effects of AVP, pharmacological basis of dAVP toxicity, and biomarkers in nocturia/lower urinary tract dysfunction (LUTD) with neurological diseases., Results: Consensus recognized AVP function and pathways in the central nervous system (CNS), pre-proAVP localized using immunohistochemistry in bladder sections from adult/aged noncancerous human punch biopsies and rodent bladder sections is likely to accelerate the systemic uptake of water and urea from the bladder of anesthetized mice instilled with 3 H-water and 14 C-urea. Mechanisms for charged and uncharged solutes and water transport across the bladder, mechanism of dAVP toxicity, and utility of urine biomarkers in those with neurological diseases/nocturia were determined from literature reviews., Conclusion: Pre-proAVP is present in human/rodent bladders and may be involved in water reabsorption from bladder that prevents the sensation of fullness for uninterrupted sleep in healthy adults. The mechanism of action of AVP in the CNS was discussed, as was electrolyte/water transport across the bladder, the basis for dAVP toxicity, and feasibility of urine biomarkers to identify nocturia/LUTD with neurological diseases., (© 2024 The Author(s). Neurourology and Urodynamics published by Wiley Periodicals LLC.)

Lupus nephritis (LN), a leading cause of death in Systemic Lupus Erythematosus (SLE) patients, presents significant diagnostic and prognostic challenges. Although renal pathology offers critical insights regarding the diagnosis, classification, and therapy for LN, its clinical utility is constrained by the invasive nature and limited reproducibility of renal biopsies. Moreover, the continuous monitoring of renal pathological changes through repeated biopsies is impractical. Consequently, there is a growing interest in exploring urine as a non-invasive, easily accessible, and dynamic "liquid biopsy" alternative to guide clinical management. This paper examines novel urinary biomarkers from a renal pathology perspective, encompassing cellular components, cytokines, adhesion molecules, auto-antibodies, soluble leukocyte markers, light chain fragments, proteins, small-molecule peptides, metabolomics, urinary exosomes, and ribonucleic acids. We also discuss the application of combined models comprising multiple biomarkers in assessing lupus activity. These innovative biomarkers and models offer insights into LN disease activity, acute and chronic renal indices, fibrosis, thrombotic microangiopathy, podocyte injury, and other pathological changes, potentially improving the diagnosis, management, and prognosis of LN. These urinary biomarkers or combined models may serve as viable alternatives to traditional renal pathology, potentially revolutionizing the method for future LN diagnosis and observation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Guo, Qiao, Wang, Zhao, Guo, Li, Fan, Yu and Zhang.)

The use of advanced preclinical models has become increasingly important in drug development. This is particularly relevant in bladder cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (chemotherapy, radiotherapy, immunotherapy, and/or surgery) are largely absent. Patient-derived and clinically relevant models including patient-derived xenografts (PDX), organoids, and conditional reprogramming (CR) of cell cultures efficiently generate numerous models and are being used in both basic and translational cancer biology. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have utilized CR technology to conduct bladder cancer research.

Metabolomic analysis methods were employed to determine biomarkers for various chronic kidney diseases (CKDs). Modern analytical methods were developed and applied successfully to find a specific metabolomic profile in urine samples from CKD and Balkan endemic nephropathy (BEN) patients. The aim was to explore a specific metabolomic profile defined by feasible/easy-to-identify molecular markers. Urine samples were collected from patients with CKDs and BEN, and from healthy subjects from endemic and nonendemic areas in Romania. Metabolomic analysis of urine samples, extracted by the liquid-liquid extraction (LLE) method, was performed by gas chromatography-mass spectrometry (GC-MS). The statistical exploration of the results was performed through a principal component analysis (PCA) evaluation. Urine samples were statistically analyzed using a classification based on six types of metabolites. Most urinary metabolites are distributed in the center of a loading plot, meaning that these compounds do not represent significant markers for BEN. One of the most frequent and higher-concentration urinary metabolites in BEN patients was p-Cresol, a phenolic compound that implies a severe injury of the renal filtration function. The presence of p-Cresol was associated with protein-bound uremic toxins, which have specific functional groups such as indole and phenyl. In prospective studies for future investigation, prevention, and disease treatment, we suggest a larger sample size, sample extraction using other methods, and analysis using other chromatography techniques coupled with mass spectrometry, which can generate a more significant data set for statistical analysis.

Simple Summary: Bladder cancer (BC) is a common cancer that causes high morbidity and mortality among affected patients. As a carcinogen-driven cancer, molecular oncology of BC has contributed to several fundamental concepts in cancer biology. Molecular insights into the divergent pathways for low- and high-grade BC development, heterogeneity in bladder tumors regarding recurrence and progression, genetic polymorphism, and the associated risk for developing BC among smokers, driver mutations, and molecular subtyping of muscle-invasive BC for prognostic predictions find commonality with other cancers and therefore, have advanced the field of molecular oncology. Similarly, research into lineage plasticity and tumor heterogeneity has revealed both challenges and opportunities in applying molecular approaches to patient care. The quest for finding a "PSA" for BC has contributed to novel technologies in the biomarker field. This review critically assesses and offers a perspective on the areas where molecular oncology could directly contribute to improving patient care. Within the last forty years, seminal contributions have been made in the areas of bladder cancer (BC) biology, driver genes, molecular profiling, biomarkers, and therapeutic targets for improving personalized patient care. This overview includes seminal discoveries and advances in the molecular oncology of BC. Starting with the concept of divergent molecular pathways for the development of low- and high-grade bladder tumors, field cancerization versus clonality of bladder tumors, cancer driver genes/mutations, genetic polymorphisms, and bacillus Calmette-Guérin (BCG) as an early form of immunotherapy are some of the conceptual contributions towards improving patient care. Although beginning with a promise of predicting prognosis and individualizing treatments, "-omic" approaches and molecular subtypes have revealed the importance of BC stem cells, lineage plasticity, and intra-tumor heterogeneity as the next frontiers for realizing individualized patient care. Along with urine as the optimal non-invasive liquid biopsy, BC is at the forefront of the biomarker field. If the goal is to reduce the number of cystoscopies but not to replace them for monitoring recurrence and asymptomatic microscopic hematuria, a BC marker may reach clinical acceptance. As advances in the molecular oncology of BC continue, the next twenty-five years should significantly advance personalized care for BC patients. [ABSTRACT FROM AUTHOR]

Introduction: Ventilator-associated pneumonia (VAP) caused by Pseudomonas aeruginosa is a major cause of morbidity and mortality in hospital intensive care units (ICU). Rapid identification of P. aeruginosa -derived markers in easily accessible patients' samples can enable an early detection of P. aeruginosa VAP (VAP-PA), thereby stewarding antibiotic use and improving clinical outcomes. Methods: Metabolites were analysed using liquid chromatography-mass spectrometry (LC-MS) in prospectively collected urine samples from mechanically ventilated patients admitted to the Antwerp University Hospital ICU. Patients were followed from the start of mechanical ventilation (n = 100 patients) till the time of clinical diagnosis of VAP (n = 13). Patients (n = 8) in whom diagnosis of VAP was further confirmed by culturing respiratory samples and urine samples were studied for semi-quantitative metabolomics. Results: We first show that multivariate analyses highly discriminated VAP-PA from VAP–non-PA as well as from the pre-infection groups (R 2 =.97 and.98, respectively). A further univariate analysis identified 58 metabolites that were significantly elevated or uniquely present in VAP-PA compared to the VAP–non-PA and pre-infection groups (P <.05). These comprised both a known metabolite of histidine as well as a novel nicotine metabolite. Most interestingly, we identified 3 metabolites that were not only highly upregulated for, but were also highly specific to, VAP-PA, as these metabolites were completely absent in all pre-infection timepoints and in VAP–non-PA group. Conclusions: Considerable differences exist between urine metabolites in VAP-PA compared to VAP due to other bacterial aetiologies as well to non-VAP (pre-infection) timepoints. The unique urinary metabolic biomarkers we describe here, if further validated, could serve as highly specific diagnostic biomarkers of VAP-PA. [ABSTRACT FROM AUTHOR]

Abstract Background Homeostasis is a mechanism for maintaining a stable inner environment in healthy participants. Blood and urine biomarkers are indicators of the subject health status. Biomarkers apply for early diagnosis, prognosis, and treatment. Understanding the effect of pre-analytical factors on biomarker range is significant for the data quality of bio-specimens, reproducibility, and minimizing potential results errors. Water as a necessary element for the normal functioning of living beings and sampling frequency as pre-analytical factors influences the homeostatic range of parameters. The purpose of the study is to examine the effect of 9-day fluid intake and 2-time sampling on concentration changes of 7-urine (freezing point depression, potassium, sodium, chloride, urea, creatinine, urate) and 17-blood (urea, creatinine, urate, glucose, C-reactive protein, leukocytes, erythrocytes, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean platelet volume, thrombocytes, potassium, sodium, and chloride) variables. Methods Parametric and non-parametric test apply to data processing with SPSS software v23.0. Results The group of 23 healthy subjects was divide based on water intake (test: more than 1800 ml; control: less than 2000 ml) and gender (female: 10(43%); male: 13(57%)). The mean value of the amount of fluid consumed for the test group is 2183.33 ml, while the control is 1395.83 ml. Investigated parameters, freezing point depression, sodium, potassium, creatinine urea and urate in urine, urea, urate, glucose, hematocrit, and thrombocyte in blood show a significant difference (P