AI-Based Hematological Age Predictors and the Association Between Biological Age Acceleration and Type 2 Diabetes Mellitus - Chongqing Municipality, China, 2015-2021.

INTRODUCTION

Biological age (BA) can represent the actual state of human aging more accurately than chronological age (CA).

METHODS

Using hematological data from 112,925 participants in southwestern China, collected between 2015 and 2021, this study constructed BA predictors using 7 machine learning (ML) methods (tailored separately for male and female populations). This study then analyzed the association between BA acceleration and type 2 diabetes mellitus (T2DM) within this data using logistic regression. Additionally, it examined the impact of glycemic control on BA in individuals with diabetes.

RESULTS

Among all ML models, deep neural networks (DNN) delivered the best performance in male [mean absolute error (MAE)=6.89, r=0.75] and female subsets (MAE=6.86, r=0.74). BA acceleration showed positive correlations with T2DM in both male [odds ratio (): 2.22, 95% confidence interval (): 1.77-2.77] and female subsets (: 3.10, 95% : 2.16-4.46), while BA deceleration showed negative correlations in both male (: 0.32, 95% : 0.27-0.39) and female subsets (: 0.42, 95% : 0.33-0.53). Individuals with diabetes with normal fasting glucose had significantly lower BAs than those with impaired fasting glucose in all CA groups except for patients older than 80.

DISCUSSION

Artificial intelligence (AI)-based hematological BA predictors show promise as advanced tools for assessing aging in epidemiological studies. Implementing AI-based BA predictors in public health initiatives could facilitate proactive aging management and disease prevention.