Author Identifier (ORCID)
Abstract
Previous studies have demonstrated that the immunoglobulin G (IgG) N-glycome and transcriptome are potential biochemical signatures of chronological and biological ages, and several aging clocks have been developed. By integrating the IgG N-glycome and transcriptome, we propose a novel aging clock, gtAge. We developed a deep reinforcement learning-based multiomics integration method called AlphaSnake. The results showed that AlphaSnake achieved a predicted coefficient of determination (R2) value of 0.853, outperforming the concatenation-based integration method (R2 = 0.820). The gtAge estimated by AlphaSnake explained up to 85.3% of the variance in chronological age, which was higher than that in age predicted from IgG N-glycome solely (gAge; R2 = 0.290) and age predicted from transcriptome solely (tAge; R2 = 0.812). We also found that the delta age—the difference between the predicted age and chronological age—was associated with several age-related phenotypes. Both delta gtAge and tAge were negatively associated with high-density lipoprotein (p = 0.02 and p = 0.022, respectively), whereas delta gAge was positively correlated with cholesterol (p = 0.006), triglyceride (p = 0.002), fasting plasma glucose (p = 0.014), low-density lipoprotein (p = 0.006), and glycated hemoglobin (p = 0.039). These findings suggest that gtAge, tAge, and gAge are potential biomarkers for biological age.
Keywords
Aging clock, biological age, deep reinforcement learning, IgG N-glycome, multiomics integration, transcriptome
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
57
Publication Title
Engineering
Publisher
Elsevier
School
School of Science / Nutrition and Health Innovation Research Institute / School of Medical and Health Sciences
RAS ID
82324
Funding Information
This study was funded by an Australia–China International Collaborative Grant (NHMRC APP1112767-NSFC 81561128020), the European Union’s Horizon 2020 Research and Innovation Program under grant agreement (779238), the Edith Cowan University Higher Degree by Research Scholarship (ECU-HDR 10492768), the Western Australian Future Health Research and Innovation Funds (WANMA/EL2023-24/2 and WANMA/Ideas2024-25/5), and the Edith Cowan University Early-Mid Career Researcher Grant Scheme (G1006465).
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
First Page
100
Last Page
112
Recommended Citation
Xia, Y., Islam, S. M. S., Li, X., Baten, A., Tan, X., & Wang, W. (2026). Deep reinforcement learning–driven multi-omics integration for constructing gtAge: A novel aging clock from IgG N-glycome and blood transcriptome. Engineering, 57, 100-112. https://doi.org/10.1016/j.eng.2025.08.016
Comments
Xia, Y., Islam, S. M. S., Li, X., Baten, A., Tan, X., & Wang, W. (2026). Deep reinforcement learning–driven multi-omics integration for constructing gtAge: A novel aging clock from IgG N-glycome and blood transcriptome. Engineering, 57, 100-112. https://doi.org/10.1016/j.eng.2025.08.016