Throughout history, human exploration of metabolic diseases has never ceased. From early clinical observations of diabetes, obesity, and endocrine disorders to the rapid development of modern molecular biology and high-throughput omics technologies, metabolic medicine has traversed a long developmental journey. According to a milestone global epidemiological analysis published in
The Lancet by the World Health Organization and the NCD Risk Factor Collaboration (NCD-RisC), the number of adults with diabetes worldwide has surged to over 820 million;[
1] while multiple large-scale epidemiological and glycemic screening studies have further demonstrated that metabolic diseases have evolved into a massive global challenge posing a severe threat to human health.[
2] For China, the epidemiological characteristics of metabolic diseases and full-lifecycle glycemic management similarly face complex dilemmas that urgently require resolution.[
3]
Faced with such a formidable clinical burden, traditional diagnostic and therapeutic paradigms are facing profound transformations. Fortunately, the emergence of single-cell sequencing, spatial omics, and multimodal medical big data has brought unprecedented opportunities to the research, diagnosis, and treatment of metabolic diseases. Studies have shown that in large-scale clinical screening and intelligent prediction for diabetes and its complications, artificial intelligence (AI)-based risk assessment models can significantly elevate the predictive sensitivity and specificity for early diabetic retinopathy and related metabolic complications to over 90%.[
4] Meanwhile, an increasing body of research has begun exploring the immense value of generative artificial intelligence and machine learning in the predictive analysis of diabetes and its complications,[
5] demonstrating broad application prospects in comprehensive diabetes care,[
6] clinical pathway optimization, and intelligent diagnosis.[
7,
8]
However, despite the new momentum injected into metabolic medicine by the development of artificial intelligence and cutting-edge intelligent technologies, their clinical translation and widespread adoption still encounter numerous challenges. Constrained by the high complexity, phenotypic diversity, and pronounced individual variability of metabolic disorders, existing AI algorithms still exhibit inadequate generalization capabilities and technical adaptability. A “gap” continues to persist between foundational research and clinical demands, restricting the clinical transformation of relevant innovative achievements. In addition, standard evaluation frameworks for AI-assisted metabolic diagnosis, dynamic glucose/lipid monitoring, and precise therapeutic regimens (such as hyperglycemic crisis management) remain imperfect.[
9] Associated issues concerning medical ethics, data security, and privacy protection also need to be scrutinized with enhanced rigor.
Against this backdrop, Intelligent Metabolism (IM) emerges as the times require. As an all-English academic journal dedicated to the full-spectrum interdisciplinary integration of artificial intelligence and metabolism, the journal aims to establish an open, high-quality platform for global researchers for academic exchange, focusing on cutting-edge technologies and innovative methodologies to fully drive the intelligent transformation, implementation, and development of metabolic medicine.
Intelligent Metabolism is committed to publishing high-impact, peer-reviewed basic, translational, and clinical research findings, covering the comprehensive application of AI and intelligent technologies in the field of metabolic diseases, and promoting innovative developments in metabolic disease diagnosis, surgical and non-surgical interventions, dynamic rehabilitation, and full-lifecycle intelligent management. The journal focuses on, but is not limited to, the following core areas:
• Artificial intelligence and metabolic data processing: AI-based metabolomics data analysis, deep learning, computer vision, radiomics, automated diagnosis, disease risk prediction, prognostic evaluation, and personalized treatment planning for metabolic diseases.
• Metabolic diseases and biomarkers: Screening, precision diagnosis, and therapeutic optimization of novel biomarkers for diabetes, obesity, dyslipidemia, bone metabolism, and endocrine rare diseases.
• Multi-omics integration and metabolic network: Deep cross-integration of multi-omics data, metabolic network modeling and regulatory mechanism mining, and the application of systems biology in complex metabolic networks.
• Drug metabolism and precision medicine: Pharmacogenomics, AI-driven drug discovery, pharmacokinetic/pharmacodynamic (PK/PD) optimization, and clinical precision medication guidance.
• Metabolic engineering and synthetic biology: Design of metabolic pathways, cell factory construction, biomanufacturing, and their translational applications in medicine and health.
• Clinical translation and frontier technologies: Safety, efficacy, standardization, regulatory science, and real-world clinical applications of artificial intelligence technologies in the metabolic domain.
We warmly welcome colleagues worldwide to actively submit their work. Article types include, but are not limited to, Original Articles, Systematic Reviews, Meta-Analyses, Case Reports, Study Protocols, Communications, and Correspondences/Letters to the Editor.
As an essential member of the “Intelligent Medical Journal Series” published by Higher Education Press, Intelligent Metabolism is supported by an international editorial board composed of leading experts in the global fields of metabolism and artificial intelligence. Herein, we sincerely invite global colleagues to contribute your exceptional research achievements to Intelligent Metabolism, and let us join hands to pioneer a new chapter in the intelligent development of metabolic medicine!
The Author(s) 2026. This article is published by Higher Education Press at journal.hep.com.cn.
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