Diabetes research and clinical practice · 2026
Precision staging of Type 2 diabetes through Hypergraph-Based multimorbidity Profiling: From hepatic disruption to cardiovascular escalation.
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Cao Xia, Li Xin, Yu Huixin, Liu Zhonghua, Zhou Fang, Chen Zhijie, Shao Wei, Zhao Yuhong, Yang Xiaochun, Li Ping
Аннотация
BACKGROUND: Multimorbidity in Type 2 Diabetes Mellitus (T2DM) profoundly influences disease progression and outcomes, yet conventional analytic approaches fail to capture its higher-order complexity.
METHODS: Electronic health records from 6,960 inpatients with T2DM (2014-2023) were analyzed, identifying 24,481 hyperedges. Patients were stratified by HbA1c quartiles. A hypergraph framework was used to model higher-order multimorbidity, with eigenvector centrality (EVC), hyperedge impact, and quartile-based trajectories identifying dominant clusters and transitions.
RESULTS: Higher HbA1c was linked to younger age, male predominance, and greater smoking prevalence. Metabolic comorbidities-including dyslipidemia, metabolic liver disease, and vitamin D deficiency-became increasingly prevalent across glycemic strata, whereas hypertension and metabolic bone disease were more common in lower HbA1c. The multimorbidity network contracted as HbA1c rose, with fewer but more metabolically intensive hyperedges. "Dyslipidemia-metabolic liver disease" was the most prevalent cluster, while "hypertension-silent myocardial ischemia" had the highest impact. "Dyslipidemia-hyperuricemia" dominated late-stage metabolic impact, with vitamin D deficiency emerging as a key node in advanced networks.
CONCLUSIONS: Hypergraph analysis reveals T2DM multimorbidity progresses nonlinearly through distinct transition phases, marked by network consolidation and metabolic specialization. Critical transitions denote hepatic-metabolic destabilization, microvascular and neurovascular compromise, and cardiometabolic decompensation. Precision staging guided by network dynamics may enable phase-specific interventions to disrupt progression.
METHODS: Electronic health records from 6,960 inpatients with T2DM (2014-2023) were analyzed, identifying 24,481 hyperedges. Patients were stratified by HbA1c quartiles. A hypergraph framework was used to model higher-order multimorbidity, with eigenvector centrality (EVC), hyperedge impact, and quartile-based trajectories identifying dominant clusters and transitions.
RESULTS: Higher HbA1c was linked to younger age, male predominance, and greater smoking prevalence. Metabolic comorbidities-including dyslipidemia, metabolic liver disease, and vitamin D deficiency-became increasingly prevalent across glycemic strata, whereas hypertension and metabolic bone disease were more common in lower HbA1c. The multimorbidity network contracted as HbA1c rose, with fewer but more metabolically intensive hyperedges. "Dyslipidemia-metabolic liver disease" was the most prevalent cluster, while "hypertension-silent myocardial ischemia" had the highest impact. "Dyslipidemia-hyperuricemia" dominated late-stage metabolic impact, with vitamin D deficiency emerging as a key node in advanced networks.
CONCLUSIONS: Hypergraph analysis reveals T2DM multimorbidity progresses nonlinearly through distinct transition phases, marked by network consolidation and metabolic specialization. Critical transitions denote hepatic-metabolic destabilization, microvascular and neurovascular compromise, and cardiometabolic decompensation. Precision staging guided by network dynamics may enable phase-specific interventions to disrupt progression.