Machine Learning based Early Prediction of End-stage Renal Disease in Patients with Diabetic Kidney Disease using Clinical Trials Data

AimTo predict end‐stage renal disease (ESRD) in patients with type 2 diabetes by using machine‐learning models with multiple baseline demographic and clinical characteristics.Materials and methodsIn total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine‐learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models.ResultsThe feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76‐0.87), 0.81 (0.75‐0.86) and 0.84 (0.79‐0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state‐of‐the‐art performance for predicting long‐term ESRD.ConclusionsDespite large inter‐patient variability, non‐linear machine‐learning models can be used to predict long‐term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high‐risk patients who could benefit from therapy in clinical practice.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163629/2/dom14178.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163629/1/dom14178_am.pd

Effects of nickel hyperaccumulation in Alyssum pintodasilvae on model arthropods representatives of two trophic levels

Abstract An experimental assessment of the defence hypothesis of nickel (Ni) hyperaccumulation in Alyssum was lacking. Also, to date no study had investigated the effects of hyperaccumulator litter on a detritivore species. We performed several experiments with model arthropods representatives of two trophic levels: Tribolium castaneum (herbivore) and Porcellio dilatatus (detritivore). In no-choice trials using artificial food disks with different Ni concentrations, T. castaneum fed significantly less as Ni concentration increased and totally rejected disks with the highest Ni concentration. In choice tests, insects preferred disks without Ni. In the no-choice experiment, mortality was low and did not differ significantly among treatments. Hence, this suggested a deterrent effect of high Ni diet. Experiments with P. dilatatus showed that isopods fed A. pintodasilvae litter showed significantly greater mortality (83%) than isopods fed litter from the non-hyperaccumulator species Iberis procumbens (8%), Micromeria juliana (no mortality) or Alnus glutinosa (no mortality). Also, isopods consumed significantly greater amounts of litter from the non-hyperaccumulator plant species. The behaviour of isopods fed A. pintodasilvae litter suggested an antifeedant effect of Ni, possibly due to post-ingestive toxic effects. Our results support the view that Ni defends the Portuguese serpentine hyperaccumulator A. pintodasilvae against herbivores, indicating that Ni can account both for feeding deterrence and toxic effects. The effects of hyperaccumulator litter on the detritivore P. dilatatus suggest that the activity of these important organisms may be significantly impaired with potential consequences on the decomposition processes