Kidney Age Index (KAI): A novel age-related biomarker to estimate kidney function in patients with diabetic kidney disease using machine learning

A B S T R A C T Background and objective: With aging, patients with diabetic kidney disease (DKD) show progressive de-crease in kidney function. We investigated whether the deviation of biological age (BA) from the chrono-logical age (CA) due to DKD can be used (denoted as Kidney Age Index; KAI) to quantify kidney function using machine learning algorithms. Methods: Three large datasets were used in this study to develop KAI. The machine learning algorithms were trained on PREVEND dataset with healthy subjects ( N = 7963) using 13 clinical markers to pre-dict the CA. The trained model was then used to predict the BA of patients with DKD using RENAAL ( N = 1451) and IDNT ( N = 1706). The performance of four traditional machine learning algorithms were evaluated and the KAI = BA-CA was estimated for each patient. Results: The neural network model achieved the best performance and predicted the CA of healthy sub-jects in PREVEND dataset with a mean absolute deviation (MAD) = 6.5 +/- 3.5 years and pearson corre-lation = 0.62. Patients with DKD showed a significant higher KAI of 15.4 +/- 11.8 years and 13.6 +/- 12.3 years in RENAAL and IDNT datasets, respectively. Conclusions: Our findings suggest that for a given CA, patients with DKD shows excess BA when com-pared to their healthy counterparts due to disease severity. With further improvement, the proposed KAI can be used as a complementary easy-to-interpret tool to give a more inclusive idea into disease state. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )