Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome

Prediction Model and Decision Analysis for Early Recognition of SDNS/FRNS in Children

Purpose

This study identified factors that identification of progression-predicting utility from steroid-sensitive nephrotic syndrome(SSNS) to steroid-dependent or frequently relapsing nephrotic syndrome (SDNS/FRNS) in patients and developed a corresponding predictive model.

Patients and Methods

This retrospective study analyzed clinical data from 756 patients aged 1 to 18 years, diagnosed with SSNS, who received treatment at the Department of Nephrology, Children's Hospital of Chongqing Medical University, between November 2007 and May 2023. We developed a shrinkage and selection operator (LASSO) - logistic regression model, which was visualized using a nomogram. The model's performance, validity, and clinical utility were evaluated through receiver operating characteristic curve analysis, confusion matrix, calibration plot, and decision curve analysis.

Results

The platelet-to-lymphocyte ratio (PLR) was identified as an independent risk factor for progression, with an odds ratio (OR) of 1.01 (95% confidence interval (CI) = 1.01-1.01, p = 0.009). Additionally, other significant factors included the time for urinary protein turned negative (OR = 1.17, 95% CI = 1.12-1.23, p < 0.001), estimated glomerular filtration rate(eGFR) (OR = 0.99, 95% CI = 0.98-0.99, p < 0.001), low-density lipoprotein (OR = 0.90, 95% CI = 0.83-0.97, p = 0.006), thrombin time (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003), and neutrophil absolute counts (OR = 1.10, 95% CI = 1.02-1.18, p = 0.009). The model's performance was assessed through internal validation, yielding an area under the curve of 0.78 (0.73-0.82) for the training set and 0.81 (0.75-0.87) for the validation set.

Conclusion

PLR, eGFR, the time for urinary protein turned negative, low-density lipoprotein, thrombin time, and neutrophil absolute counts may be effective predictors for identifying SSNS patients at risk of progressing to SDNS/FRNS. These findings offer valuable insights for early detection and support the use of precision medicine strategies in managing SDNS/FRNS.

The NAD+ precursor nicotinamide riboside protects against postovulatory aging in vitro

PURPOSE

Postovulatory aging (POA) of oocytes is clinically significant as it mirrors the degeneration observed in maternally aged oocytes, leading to substantial impairments in oocyte quality and the success rates of artificial reproductive technology (ART). The molecular alterations associated with POA, such as the degeneration of the first polar body, an increase in perivitelline space, reactive oxygen species (ROS) accumulation, energy depletion, and chromosomal and DNA damage, underscore the urgency of finding interventions to mitigate these effects. This study aims to identify whether nicotinamide riboside (NR) can prevent POA during the process of in vitro culture and raise the success rates of ART.

METHOD

Taking advantage of an in vitro postovulatory oocyte aging model, we examined the morphological integrity and NAD+ levels of ovulated mouse MII oocytes after 24 h of culturing. Following in vitro fertilization, we assessed the embryonic developmental potential of oocytes affected by POA. Using immunofluorescence and confocal microscopy, we measured the levels of ROS, mitochondrial function, and γH2AX. We also evaluated spindle assembly and chromosome alignment. Additionally, we detected the distribution of cortical granules to assess the metabolic and quality changes in POA oocytes with the supplementation of NR. To further our analysis, quantitative real-time PCR was conducted to measure the mRNA expression levels of antioxidant enzymes Sod1 and Gpx1 in the oocytes.

RESULTS

With 200 μM NR supplementation during in vitro culture for 24 h, the oocytes from POA demonstrated reduced signs of aging-related decline in oocyte quality, including reduced ROS accumulation, improved mitochondrial function, and corrected mis-localization of cortical granules. This improvement in oocyte quality is likely due to the inhibition of oxidative stress via the NAD+/SIRT1 signaling pathway, which also helped to restore normal spindle assembly and chromosome alignment, as well as reduce the elevated levels of γH2AX, thereby potentially enhancing the embryonic development potential.

CONCLUSION

Current research provides evidence that NR is an efficient and safe natural component that prevents the process of POA and is thus a potential ideal antiaging drug for raising the success rates of ART in clinical practice.

Serum and Urine Metabolites and Kidney Function

Key Points

We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort. Metabolic profiling in serum and urine provides distinct and complementary insights into disease.

Background

Metabolites represent a read-out of cellular processes underlying states of health and disease.

Methods

We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.

Results

At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m2 (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5).

Conclusions

Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Lipidomic profiles in serum and urine in children with steroid sensitive nephrotic syndrome

BACKGROUND

Steroid-sensitive nephrotic syndrome (SSNS) accounts for approximately 80% of cases of nephrotic syndrome. The involvement of aberrant lipid metabolism in early SSNS is poorly understood, warranting further investigation. This study aimed to explore alterations in lipid metabolism associated with SSNS pathogenesis.

METHODS

A screening cohort containing serum (50 SSNS, 37 controls) and urine samples (27 SSNS, 26 controls) was analyzed by untargeted lipidomic profiling using UHPLC-QTOF-MS. Then, a validation cohort (20 SSNS, 56 controls) underwent further analysis to check the potential clinical application by ROC curve analysis.

RESULTS

Lipidomic profiling of serum and urine samples revealed significant lipid alterations in SSNS patients, with the alterations in the serum samples being more significant. An elevated concentration of PE and PG and downregulated concentration of FA were observed in SSNS serum. A total of 38 dysregulated lipids and 5 lipid metabolic pathways were identified in the serum samples in SSNS patients. Validation in the second cohort confirmed differential regulation of nine kinds of lipids, including 5 up-regulated substances [SM d33:2 (m/z = 686.5361), SHexCer d34:1 (m/z = 779.521), PI 20:4_22:4 (m/z = 934.5558), Cer_NS d18:1_23:0 (m/z = 635.6216), and GM3 d36:1 (m/z = 1180.7431)], as well as 4 down-regulated substances: [CE 18:1 (m/z = 650.601), PE 38:6 (m/z = 763.5205), PC 17:0_20:4 (m/z = 795.5868) and EtherPC 16:2e_20:4 (m/z = 763.5498)].

CONCLUSIONS

Untargeted lipidomic analysis successfully identified specific lipid class changes in patients with SSNS, providing a deeper understanding of lipid alterations and underlying mechanisms associated with SSNS.