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AbstractObjectives Membranous nephropathy (MN) and minimal change disease (MCD) are two common types of nephrotic syndrome that have similar clinical presentations but require different treatment strategies. Currently, the definitive diagnosis for these conditions relies on invasive renal biopsy, which can be limited in clinical practice.Methods In this study, we aimed to differentiate idiopathic MN (IMN) from MCD using clinical data and gut microbiota. We collected clinical data and stool samples from 115 healthy individuals, 115 IMN, and 45 MCD at the onset of disease and performed 16S rRNA sequencing. Through machine learning methods including random forest, logistic regression, and support vector machine, a classifier to differentiate IMN from MCD was constructed.Results Baseline clinical data comparing the IMN and MCD groups showed that the MCD had higher levels of hemoglobin, uric acid, cystatin C, β2-microglobulin, α1-microglobulin, total cholesterol, and low-density lipoprotein and lower levels of albumin and CD4+ T-cell counts. The gut microbiota of the two groups differed at all levels of the phylum and genus. Differential gut microbiota may disturb the integrity of the intestinal wall and lead to the passage of inflammatory mediators through the intestinal barrier, causing kidney injury. We constructed a noninvasive classifier with a discrimination efficacy of 0.939 that combined the clinical data and gut microbiota information to identify IMN and MCD.Conclusions The classifier of the gut microbiota combined with clinical indicators has achieved good performance in identifying IMN and MCD, which provides a new approach for the noninvasive discrimination of different pathological types of kidney disease.

Diabetic glomerulopathy (DG) remains the prevalent microvascular complication and leading cause of shortened lifespan in type-2 diabetes mellitus (T2DM) despite improvement in hyperglycemia control. Considering the pivotal role of kidney in metabolism, using untargeted metabolomic techniques to globally delineate the serum metabolite profiles will help advance understanding pathogenetic underpinnings of renal biopsy-confirmed DG from the perspective of metabolism specifically. Fourteen pathologically diagnosed DG patients secondary to T2DM and 14 age- and gender-matched healthy controls (HCs) were recruited for study. We employed mass spectrometry-based untargeted metabolomic methods to reveal the metabolite profiles of serum samples collected from all included subjects. We identified a total of 334 and 397 metabolites in positive and negative ion mode respectively. One hundred and eighty-two important differential metabolites whose variable importance in projection (VIP) > 1 and p value

Background Diabetic kidney disease (DKD) and membranous nephropathy (MN) are the two major causes of end-stage renal disease (ESRD). Increasing evidence has shown that intestinal dysbiosis is associated with many diseases. The aim of this study was to explore the composition of the gut microbiome in DKD and MN patients. Methods 16S rRNA gene sequencing was performed on 271 fecal samples (DKD = 129 and MN = 142), and taxonomic annotation of microbial composition and function was completed. Results We observed distinct microbial communities between the two groups, with MN samples exhibiting more severe dysbiosis than DKD samples. Relative increases in genera producing short-chain fatty acids (SCFAs) in DKD and a higher proportion of potential pathogens in MN were the main contributors to the microbiome alterations in the two groups. Five-fold cross-validation was performed on a random forest model, and four operational taxonomic unit (OTU)-based microbial markers were selected to distinguish DKD from MN. The results showed 92.42% accuracy in the training set and 94.52% accuracy in the testing set, indicating high potential for these microbiome-based markers in separating MN from DKD. Overexpression of several amino acid metabolic pathways, carbohydrate metabolism and lipid metabolism was found in DKD, while interconversion of pentose/glucoronate and membrane transport in relation to ABC transporters and the phosphotransferase system were increased in MN. Conclusion The composition of the gut microbiome appears to differ considerably between patients with DKD and those with MN. Thus, microbiome-based markers could be used as an alternative tool to distinguish DKD and MN.