An explainable machine learning estimated biological age based on morphological parameters of the spine

Abstract

Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data is rarely reported. In this study, a total of 2,364 participants from the National Health and Nutrition Examination Survey were enrolled, and morphological parameters of the spine were collected from lateral radiographs scanned by dual energy X-ray absorptiometry. The biological age of the spine, called SpineAge, was calculated with the parameters by machine learning models. The SHapley Additive exPlanation was used for better interpreting each parameter's contribution. Besides, an Accelerated Aging Index (AAI) was defined as SpineAge minus chronological age and was used to quantify the accelerating aging degree of the spine. The results indicated that the SpineAge performed better than chronological age did in predicting 2-year and 5-year all-cause mortality. After adjusting all covariates, there was a significant association between AAI and all-cause mortality risk. Specifically, each 1-year increase in AAI was associated with a 25.9% increase in all-cause mortality risk (Hazards ratio, 1.259; 95% CI, 1.087–1.457; P < 0.001). Considering the first quartile of AAI as a reference, the mortality risks for the second, third, and fourth quartiles were 2.389 (95% CI, 1.064–5.364; P = 0.035), 5.911 (95% CI, 2.241–15.590; P < 0.001) and 22.925 (95% CI, 4.744–110.769; P < 0.001) times higher, respectively. Our study developed a novel and highly applicable biological-age predictor for predicting individualized long-term prognosis and facilitating personalized care.

Graphical Abstract