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An explainable machine learning estimated biological age based on morphological parameters of the spine
Zi Xu1, Yunsong Peng1, Mudan Zhang1
1Department of Radiology, Guizhou Provincial Peoples Hospital, Guiyang, 550001, People's Republic of China.
Geroscience
|October 24, 2024
Summary
This study introduces SpineAge, a novel biological age predictor based on spinal morphology. SpineAge better predicts mortality risk than chronological age, highlighting its potential for personalized care.
Area of Science:
- Gerontology and Bioinformatics
- Radiology and Medical Imaging
Background:
- Estimating biological age is crucial for assessing aging and predicting health risks.
- Spine compression prevalence increases with age, yet vertebral morphological data for biological age estimation is underutilized.
Purpose of the Study:
- To develop and validate a novel biological age predictor for the spine, termed SpineAge, using machine learning models.
- To assess the association between SpineAge, an Accelerated Aging Index (AAI), and all-cause mortality risk.
Main Methods:
- Utilized data from 2,364 participants in the National Health and Nutrition Examination Survey.
- Calculated SpineAge using spinal morphological parameters from lateral radiographs analyzed via dual-energy X-ray absorptiometry.
- Employed SHapley Additive exPlanations for model interpretability and defined AAI as SpineAge minus chronological age.
Main Results:
- SpineAge demonstrated superior performance in predicting 2-year and 5-year all-cause mortality compared to chronological age.
- A significant association was found between AAI and increased all-cause mortality risk (HR, 1.259 per year).
- Higher quartiles of AAI showed substantially elevated mortality risks, with the highest quartile exhibiting a 22.9-fold increase.
Conclusions:
- SpineAge is a novel, applicable biological age predictor derived from spinal morphology.
- The developed Accelerated Aging Index (AAI) effectively quantifies spinal aging acceleration and predicts long-term mortality.
- This approach facilitates individualized prognosis and personalized healthcare strategies.
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