Hierarchical organ aging signatures from routine abdominal CT add incremental disease risk stratification beyond blood biomarkers
Deng, Z.; Wang, Y.; Shi, Y.; Wang, L.; Qureshi, T. A.; Gaddam, S.; Javed, S.; Hsu, Y.-C.; De Righi, D. R.; Azab, L.; Diwan, G.; Yang, J. D.; Xie, Y.; Yuan, C.; Vendrami, C. L.; Rodriguez, A.; Specht, K.; Jeon, C. Y.; Chaudhry, H.; Buxbaum, J.; Pisegna, J. R.; Yaghmai, V.; Goessling, W.; Hernandez-Barco, Y. G.; Miller, F. H.; Tirkes, T.; Espinoza, S.; Musi, N.; Dey, D.; Sung, K. H.; Pandol, S. J.; Li, D.
Show abstract
Biological aging is heterogeneous across organ systems, yet whether CT-derived abdominal aging provides prognostic value beyond routine clinical data and whether organ decomposition adds beyond a unified estimate remains untested. We developed and evaluated organ-specific and ensemble biological age models from radiomic features across five abdominal organs in 68,675 CT scans from 32,883 subjects, evaluated on alignment with chronological age of healthy subjects (nested cross validation: MAE=3.68 years, R^2=0.90). In sequential analyses restricted to adults aged 20-60 years which is the stratum of strongest BAG-disease association, ensemble biological age gaps provided incremental prognostic value beyond demographic covariates for all-cause disease and mortality (Delta C-index=0.141, 0.051) and beyond routine blood biomarkers (Delta C-index=0.048), confirming CT-derived aging captures structural information beyond laboratory markers. Organ-specific biological age added incremental prognostic value beyond ensemble selectively for focal diseases: cardiovascular (aorta, Delta C-index=0.091) and hepato-pancreatic (pancreas, Delta C-index=0.096). These findings establish a hierarchical organization of CT-derived biological aging, positioning routine CT as a source that adds prognostic value to existing clinical biomarkers.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Integrative epigenetics and transcriptomics identify aging genes in human blood 96%
- Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging 96%
- Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate limit of human lifespan 95%
Similar papers in this journal
- Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs 95%
- A Novel Blood-Based Epigenetic Clock for Intrinsic Capacity Predicts Mortality and is Associated with Clinical, Immunological and Lifestyle Factors 95%
- Multi-organ and Multi-omics Biological Aging Clocks: Generation, Interpretation, and Application 95%
Similar papers in this journal
- Gompertz law based biological age (GOLD BioAge): a simple and practical measurement of biological aging to capture morbidity and mortality risks 96%
- Human brain cell-type-specific aging clocks based on single-nuclei transcriptomics 95%
- Heterogeneous metabolomic aging across the same age and prediction of health outcome 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.