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Transition of peritoneal mesothelial cells to a mesenchymal phenotype plays an integral role in the angiogenic and fibrotic changes seen in the peritoneum of patients receiving long-term peritoneal dialysis. While signaling by transforming growth factor (TGF)-beta through Smad proteins likely causes these changes, it is possible that non-Smad pathways may also play a role. Here, we found that Smad3-deficient mice were protected from peritoneal fibrosis and angiogenesis caused by adenovirus-mediated gene transfer of active TGF-beta1 to mesothelial cells; however, mesothelial transition occurred in this setting, suggesting involvement of non-Smad mechanisms. The phosphatidyl inositol 3 kinase (PI3K) target, Akt, was upregulated in both Smad-deficient and wild-type mice after exposure to TGF-beta1. In vivo inhibition of the mammalian target of rapamycin (mTOR) by rapamycin completely abrogated the transition response in Smad3-deficient but not in wild-type mice. Rapamycin blocked nuclear localization of beta-catenin independent of glycogen synthase kinase 3beta activity. Further, in Smad3-deficient mice rapamycin reduced the expression of alpha-smooth muscle actin, which is an epithelial-to-mesenchymal transition-associated gene. Hence, we conclude that TGF-beta1 causes peritoneal injury through Smad-dependent and Smad-independent pathways; the latter involves redundant mechanisms inhibited by rapamycin, suggesting that suppression of both pathways may be necessary to abrogate mesothelial transition.

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Rates of chronic kidney disease (CKD) progression, end stage kidney disease (ESKD), all-cause mortality, and cardiovascular (CVD) events among individuals with CKD vary widely across countries. Well-characterized demographic, comorbidity, and laboratory markers captured for prospective cohorts may explain, in part, such differences. To investigate whether core characteristics of individuals with CKD explain differences in rates of outcomes, we conducted an individual-level analysis of eight studies that are part of iNET-CKD, an international network of CKD cohort studies. Overall, the rate of CKD progression was 40 events/1000 person-year (95% confidence interval 39-41), 28 (27-29) for ESKD, 41 (40-42) for death, and 29 (28-30) for CVD events. However, standardized rates were highly heterogeneous across studies (over 92.5%). Interactions by study group on the association between baseline characteristics and outcomes were then identified. For example, the adjusted hazard ratio for CKD progression was 0.44 (95% confidence interval 0.35-0.56) for women vs. men among the Japanese (CKD-JAC), while it was 0.66 (0.59-0.75) among the Uruguayan (NRHP). The adjusted hazard ratio for ESKD was 2.02 (95% CI 1.88-2.17) per 10 units lower baseline eGFR among Americans (CRIC), while it was 3.01 (2.57-3.53) among Canadians (CanPREDDICT) (significant interaction for comparisons across all studies). The risks of CKD progression, ESKD, death, and CVD vary across countries even after accounting for the distributions of age, sex, comorbidities, and laboratory markers. Thus, our findings support the need for a better understanding of specific factors in different populations that explain this variation.

Background and objectivesIntradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.Design, setting, participants, & measurementsWe obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (i.e., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.ResultsThe recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (P<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87–0.87) and 0.79 (interquartile range, 0.79–0.79), respectively, which were also higher than those obtained using the other models (P≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.ConclusionsOur deep learning model can be used to predict the real-time risk of intradialytic hypotension.