Imaging-based Organ-specific Aging Clock Predicts Human Diseases and Mortality
Poster No:
1964
Submission Type:
Abstract Submission
Authors:
Peng Ren1, Wenjing Su1, Jia You1, Ying Liang1, Weikang Gong1, Wei Cheng1
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
First Author:
Peng Ren
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Co-Author(s):
Wenjing Su
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Jia You
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Ying Liang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Weikang Gong
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Wei Cheng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Human aging, commonly characterized as biological age, is a heterogeneous process that differs not only between individuals but also between organs[1]. By comparing biological age to chronological age, i.e., the age gap, insights could be given to whether specific organs exhibit accelerated or delayed aging compared to normal peers. Recent organ-specific biological ages have been developed using either empirical organ markers[2] or organ-enriched molecules derived from in vitro tissues[3,4], which could be refined by leveraging more objective organ markers. Specifically, in vivo imaging is inherently organ-specific and delineates structures and functions more objectively. However, there is a lack of comprehensive evaluations of imaging-based aging clocks across organs, regarding their shared and distinct molecular bases, and contributions to diseases and mortality.
Methods:
In this study, the prediction of chronological age for seven organs were constructed in 35,000 UKB participants using multimodal imaging-derived phenotypes (IDPs) of the corresponding organs, including brain grey matter (GM), brain white matter (WM), heart, body composition, kidney, liver, pancreas and eye. LASSO regression models were trained in 5,000 healthy participants to predict the chronological age of participants, after which the models were applied to the remaining participants to obtain the predicted age of each organ. The difference between predicted age of each organ and chronological age were regarded as organ-specific age gap. The associations between organ-specific age gap and incident of 13 categories of diseases and mortality were tested with Cox proportional hazards models. Furthermore, LightGBM predictive model was constructed to predict future risk of diseases and mortality. In resolving the microscale molecular basis of organ age gap, generalized linear models were employed to examine the association between organ age gap and proteins, with functional annotation performed with tissue and GO biological process enrichment analysis.
Results:
High accuracy was achieved for brain GM (MAE = 3.61), brain WM (MAE = 3.82), heart (MAE = 4.11) and body composition (MAE = 4.11), with no obvious sex difference observed (Fig 1A). The organ-specific age gaps were most significantly associated with the disease that primarily affecting the organ (Fig 1B and 1C). Specifically, the age gap of brain GM and WM showed strongest association with the risk of dementia, the age gap of heart was most strongly linked to the risk of heart-related diseases. The age gap of kidney was strongest associated with renal failure. Moreover, the prediction models incorporating information about organ-specific biological age also outperformed the baseline model with only chronological age and sex (Fig 1D). The proteins exhibiting negative associations were significantly enriched in the corresponding tissue, as well as the biological processes that the corresponding organ are responsible for. In particular, proteins exhibited negative association with GM and WM were enriched in over-expressed genes of brain tissue, the negatively associated proteins of body composition were enriched in over-expressed genes of muscle (Fig 2).
·Figure 1
·Figure 2
Conclusions:
This study represents the first comprehensive evaluation of organ-specific aging clocks based on multimodal imaging. The imaging-based aging clocks established in this study not only exhibit organ specificity at both macro and micro scales but are also highly predictive of incident diseases that affect the corresponding organs, highlighting their potential for clinical application. Moreover, we identified organ-specific molecular bases that could inspire innovative strategies for slowing aging.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Lifespan Development:
Aging 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Diffusion MRI
Multi-Modal Imaging 1
Keywords:
Aging
Machine Learning
MRI
Other - Aging clock
1|2Indicates the priority used for review
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2. Tian, Y.E. (2023). Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine, 29, 1221–1231.
3. Oh, H.SH. (2023). Organ aging signatures in the plasma proteome track health and disease. Nature, 624, 164–172.
4. Goeminne, Ludger J E. (2025). Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems. Cell metabolism, S1550-4131(24)00401-7.