Zuoguan Chen, MD |Chinese Academy of Medical Sciences; Chengran Lu, MD; Peng Li, MD; Yuqing Miao, PhD; Xihao Zhang; Wenxin Zhao; Yaming Guo; Bowen Zhang
Competition Sponsor: Chinese Academy of Medical Sciences
Awardee Year: 2025
Ischemic lower-limb disease has a high prevalence and high severity among the elderly, and its heterogeneity makes it a major threat to healthy longevity in this population. The current diagnostic and treatment system faces three major problems: difficulty in early diagnosis, suboptimal treatment outcomes, and difficulty in prognostic prediction. The main reason is the neglect of imaging features of the muscle target organ and the multi-omics changes associated with ischemia and hypoxia. Based on prior work, the applicant originally proposed the “vascular–muscle–microenvironment imbalance” concept (awarded an NSFC general project in 2024) and, leveraging the country’s first multimodal database (VIP platform, clinical–imaging–omics data from 2,000 patients), has overcome the limitations of traditional single-dimensional assessments. First, by integrating “degree of vascular stenosis — plaque lesion characteristics — muscle target-organ injury,” a CTA imaging metric evaluation standard was constructed, emphasizing the importance of assessing the muscle target organ. Second, using plasma proteomics and metabolomics, a comprehensive assessment system for the lower-limb ischemic microenvironment was developed, clarifying the importance of evaluating systemic frailty, inflammation, and glucose–lipid metabolic microenvironment homeostasis. Finally, AI was used to build an integrated multimodal predictive model for disease risk stratification and prognostic assessment. Machine learning algorithms will provide full-cycle decision support for preoperative clinical risk stratification and postoperative rehabilitation warning, offering reliable tools for clinical decision-making and improving the diagnosis and treatment of lower-limb ischemic disease in our country.