# A comparison between radiomic biological age and chronological age in estimating kidney function

## TL;DR

This study compares radiomic biological age with chronological age to estimate kidney function, suggesting biological age could improve personalized drug treatment.

## Contribution

The paper introduces radiomic biological age indices as a novel method to estimate kidney function.

## Key findings

- A radiomic biological age model including muscle, bone, and fat metrics modestly improved kidney function estimation.
- The best model incorporated dorsal muscle area, bone density, visceral fat, and subcutaneous fat density.
- Biological age indices showed potential for more precise kidney function estimation compared to chronological age.

## Abstract

Accurate kidney function estimation hinges on essential markers of age and body size. The relative benefit of both markers has been debated with the emerging biological age and novel body size descriptors. We used radiomic biomarkers of age-related changes in body composition to construct new biological age indices as covariates of kidney function. A retrospective cohort of hospitalized patients with plasma concentrations of aminoglycosides and computed tomography images were evaluated. Aminoglycoside clearance served as a kidney function surrogate. A population pharmacokinetic model was constructed to determine whether biological age indices improved aminoglycoside clearance estimation compared to chronological age. The final dataset included 156 patients (51.92% female) with a median (minimum-maximum) age and body weight of 58 [21, 93] years and 81 [43.8, 139.3] Kg. A 1-compartment clearance model with linear elimination, incorporating biological index, serum creatinine, and drug type best fits the concentration-time data. The best radiomic biological age model included dorsal muscle group area, bone mineral density, visceral fat area, and subcutaneous fat density. The radiomic biological age model offered a modest improvement over the chronological age model. This work offers a proof-of-concept, highlighting the potential for more precise methods for aging-related kidney function estimation to aid personalized pharmacotherapy.