Multiorgan Photon Counting CT and Machine Learning to Elucidate Aging Mechanisms and Interventions

Aging is systemic yet organ-specific, and we lack in-vivo tools that track how genotype, diet, and exercise reshape multiorgan trajectories. The apolipoprotein E (APOE) gene is the most common genetic modifier of Alzheimer’s disease, cardiovascular disease, and osteoporosis. Our central hypothesis is that early shifts in hepatic fat fraction, iron content, and perfusion act upstream to modulate biological aging in the heart, brain, and bone. We propose a liver-centered model of systemic aging, leveraging photon-counting computed tomography (PCCT) and machine learning to quantify organ-specific age trajectories and cross-organ influence. We will study APOE2, APOE3, and APOE4 knock-in mice with or without a humanized NOS2 allele (hNOS2), exposed to control diet, high-fat diet (HFD), voluntary exercise, or HFD + exercise. In Aim 1, we will acquire longitudinal in vivo and ex vivo PCCT scans at 6, 12, and 18 months to quantify morphologic and perfusion changes in liver, heart, brain, and bone. Deep learning–based segmentation and radiomics will extract age-sensitive features, complemented by behavioral assessments of memory, motor function, and activity. In Aim 2, we will train contrastive learning models to predict organ-specific biological age (ΔAge) from imaging and behavioral features and construct a Multiorgan Biological Age (MBA) clock using a graph neural network framework. The MBA model will quantify directional aging influences—particularly liver-to-organ pathways—and be validated against behavioral and histological outcomes. In Aim 3, we will integrate molecular profiling (targeted RNA-seq and cytokines) with imaging data to test whether hepatic normalization mediates systemic rejuvenation under exercise. We will apply structural equation modeling and cross-lagged analyses to determine whether changes in liver composition and cytokine output causally explain exercise- induced reductions in ΔAge across organs. Expected outcomes include: (1) the first APOE-stratified, multimodal atlas linking hepatic phenotypes to systemic aging; (2) an imaging-assisted MBA biomarker readily deployable with clinical PCCT systems; and (3) mechanistic insight into how diet and exercise modulate aging through hepatic reprogramming. By using procedures aligned with FDA-cleared PCCT platforms, this project enables direct translation to human studies, positioning the liver as both a biomarker source and a therapeutic target to slow age-related disease. Project Number: 2R01AG070149-04 | Fiscal Year: 2026 | NIH Institute/Center: National Institute on Aging (NIA) | Principal Investigator: CRISTIAN BADEA (+1 co-PI) | Institution: DUKE UNIVERSITY, DURHAM, NC | Award Amount: $612,746 | Activity Code: R01 | Study Section: Emerging Imaging Technologies and Applications Study Section[EITA] View on NIH RePORTER: https://reporter.nih.gov/project-details/11363773