Background Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. Objective To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. Methods The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. Results The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. Conclusions Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice. Electronic supplementary material The online version of this article (10.1186/s12967-019-1860-0) contains supplementary material, which is available to authorized users.

Background and Purpose: Muscle protein catabolism in patients with diabetic nephropathy (DN) results in striking loss of muscle proteins, which increases morbidity and mortality risks. Evidence shows that short-chain fatty acids (SCFAs) play an important role in health maintenance and disease development. Recently, the connection between butyrate (a SCFA) and DN has been revealed, although the relationship between butyrate and muscle atrophy remains unclear.Experimental Approach: We studied changes in serum butyrate levels in DN patients using metabolomic analyses. In db/db mice, protective effects of butyrate on DN-induced muscle atrophy. were explored. Inhibition of muscle atrophy by butyrate and the underlying mechanism(s) were studied in C2C12 cells exposed to high glucose/lipopolysaccharide (HG/LPS).

One of the mechanisms in hyperuricemia (HUA)-induced renal tubular injury is the impairment of Na +-K +-ATPase (NKA) signaling, which further triggers inflammation, autophagy, and mitochondrial dysfunction and leads to cell injury. Here, we used RNA sequencing to screen the most likely regulators of NKA signaling and found that the liver kinase B1(LKB1)/adenosine monophosphate (AMP)-activated protein kinase (AMPK)/ mammalian target of rapamycin (mTOR) pathway was the most abundantly enriched pathway in HUA. AMPK is a key regulator of cell energy metabolism; hence, we examined the effect of AMPK on HUA-induced dysregulation of NKA signaling and cell injury. We first detected AMPK activation in high uric acid (UA)-stimulated proximal tubular epithelial cells (PTECs). We further found that sustained treatment with the AMPK activator 5-aminoimidazole-4-carboxamide 1-β-d-ribofuranoside (AICAR), but not the AMPK inhibitor Compound C, significantly alleviated UA-induced reductions in NKA activity and NKA α1 subunit expression on the cell membrane by reducing NKA degradation in lysosomes; sustained AICAR treatment also significantly alleviated activation of the NKA downstream molecules Src and interleukin-1β (IL-1β) in PTECs. AICAR further alleviated high UA-induced apoptosis, autophagy, and mitochondrial dysfunction. Although AMPK activation by metformin did not reduce serum UA levels in hyperuricemic rats, it significantly alleviated HUAinduced renal tubular injury and NKA signaling impairment in vivo with effects similar to those of febuxostat. Our study suggests that AMPK activation may temporarily compensate for HUA-induced renal injury. Sustained AMPK activation could reduce lysosomal NKA degradation and maintain NKA function, thus alleviating NKA downstream inflammation and protecting tubular cells from high UA-induced renal tubular injury.