ObjectiveLupus nephritis (LN) is one of the most severe organ manifestations of systemic lupus erythematosus (SLE). Early identification of renal disease in SLE is important. Renal biopsy is currently recognized as the gold standard for diagnosing LN, however, it is invasive and inconvenient for dynamic monitoring. Urine has been considered more promising and valuable than blood in identifying inflamed kidney tissue. Here, we determine whether the signatures of tRNA-derived small noncoding RNA (tsRNA) in urinary exosomes can serve as novel biomarkers for the diagnosis of LN.MethodstsRNA sequencing was performed in exosome extracted from pooled urine of 20 LN patients and 20 SLE without LN, and the top 10 upregulated tsRNAs were screened as candidate markers of LN. The candidate urinary exosomal tsRNAs were primarily elected by TaqMan probe-based quantitative reverse transcription-PCR (RT-PCR) in 40 samples (20 LN and 20 SLE without LN) in the training phase. In the validation phase, selected tsRNAs from the training phase were further confirmed in a larger cohort (54 LN patients and 39 SLE without LN). Receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic efficacy.ResultsUpregulated levels of tRF3-Ile-AAT-1 and tiRNA5-Lys-CTT-1 in the urinary exosomes were observed in LN compared with SLE without LN (P < 0.0001 and P < 0.001) and healthy controls (P < 0.01 and P < 0.01), with the area under the curve (AUC) of 0.777 (95% CI: 0.681-0.874, sensitivity 79.63%, specificity 66.69%) and 0.715 (95% CI: 0.610-0.820, sensitivity 66.96%, specificity 76.92%) for discriminating LN from SLE without LN patients. SLE patients with mild activity and moderate to severe activity had higher levels of urinary exosome derived tRF3-Ile AAT-1 (P = 0.035 and P < 0.001) and tiRNA5-Lys-CTT-1 (P = 0.021 and P < 0.001) compared with patients with no activity. Moreover, bioinformatics analysis revealed that both of the tsRNAs regulate the immune process by modulating metabolism and signal pathway.ConclusionIn this study, we demonstrated that urinary exosome tsRNAs can be served as noninvasive biomarkers for the efficient diagnosis and prediction of nephritis in SLE.

Background Due to the increasing ageing population, neurocognitive disorders (NCDs) have been a global public health issue, and its prevention and early diagnosis are crucial. Our previous study demonstrated that there is a significant correlation between specific populations and NCDs, but the biological characteristics of the vulnerable group predispose to NCDs are unclear. The purpose of this study is to investigate the predictors for the vulnerable group by a multi‐omics analysis. Methods Multi‐omics approaches, including metagenomics, metabolomic and proteomic, were used to detect gut microbiota, faecal metabolites and urine exosome of 8 normal controls and 13 vulnerable elders after a rigorous screening of 400 elders in Macao. The multi‐omics data were analysed using R and Bioconductor. The two‐sided Wilcoxon's rank‐sum test, Kruskal–Wallis rank sum test and the linear discriminant analysis effective size were applied to investigate characterized features. Moreover, a 2‐year follow‐up was conducted to evaluate cognitive function change of the elderly. Results Compared with the control elders, the metagenomics of gut microbiota showed that Ruminococcus gnavus , Lachnospira eligens , Escherichia coli and Desulfovibrio piger were increased significantly in the vulnerable group. Carboxylates, like alpha‐ketoglutaric acid and d ‐saccharic acid, and levels of vitamins had obvious differences in the faecal metabolites. There was a distinct decrease in the expression of eukaryotic translation initiation factor 2 subunit 1 (eIF2α) and amine oxidase A (MAO‐A) according to the proteomic results of the urine exosomes. Moreover, the compound annual growth rate of neurocognitive scores was notably decreased in vulnerable elders. Conclusions The multi‐omics characteristics of disturbed glyoxylate and dicarboxylate metabolism (bacteria), vitamin digestion and absorption and tricarboxylic acid cycle in vulnerable elders can serve as predictors of NCDs risk among the elderly of Macao. Intervention with them may be effective therapeutic approaches for NCDs, and the underlying mechanisms merit further exploration.

Objective: To explore the clinical features of patients with systemic lupus erythematosus and Sjögren’s syndrome overlap (SLE-SS) compared to concurrent SLE or primary SS (pSS) patients, we utilized a predictive machine learning-based tool to study SLE-SS. Methods: This study included SLE, pSS, and SLE-SS patients hospitalized at Nanjing Drum Hospital from December 2018 to December 2020. To compare SLE versus SLE-SS patients, the patients were randomly assigned to discovery cohorts or validation cohorts by a computer program at a ratio of 7:3. To compare SS versus SLE-SS patients, computer programs were used to randomly assign patients to the discovery cohort or the validation cohort at a ratio of 7:3. In the discovery cohort, the best predictive features were determined using a least absolute shrinkage and selection operator (LASSO) logistic regression model among the candidate clinical and laboratory parameters. Based on these factors, the SLE-SS prediction tools were constructed and visualized as a nomogram. The results were validated in a validation cohort, and AUC, calibration plots, and decision curve analysis were used to assess the discrimination, calibration, and clinical utility of the predictive models. Results: This study of SLE versus SLE-SS included 290 patients, divided into a discovery cohort (n = 203) and a validation cohort (n = 87). The five best characteristics were selected by LASSO logistic regression in the discovery cohort of SLE versus SLE-SS and were used to construct the predictive tool, including dry mouth, dry eye, anti-Ro52 positive, anti-SSB positive, and RF positive. This study of SS versus SLE-SS included 266 patients, divided into a discovery cohort (n = 187) and a validation cohort (n = 79). In the discovery cohort of SS versus SLE-SS, by using LASSO logistic regression, the eleven best features were selected to build the predictive tool, which included age at diagnosis (years), fever, dry mouth, photosensitivity, skin lesions, arthritis, proteinuria, hematuria, hypoalbuminemia, anti-dsDNA positive, and anti-Sm positive. The prediction model showed good discrimination, good calibration, and fair clinical usefulness in the discovery cohort. The results were validated in a validation cohort of patients. Conclusion: The models are simple and accessible predictors, with good discrimination and calibration, and can be used as a routine tool to screen for SLE-SS.