A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease.

Introduction: This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD).

Methods: Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%).

Controlling nutritional status score is associated with renal progression, cardiovascular events, and all-cause mortality in biopsy-proved diabetic kidney disease.

The Controlled Nutritional Status (CONUT) score, calculated from albumin, total cholesterol, and lymphocyte count, is a useful indicator for immune-nutritional assessment and is associated with the prognosis of various diseases. However, its relationship with renal outcomes, cardiovascular disease (CVD), and all-cause mortality in patients with diabetic kidney disease is unclear. This retrospective single-center study enrolled 336 patients with biopsy-confirmed diabetic kidney disease from August 2009 to December 2018.