The Journal of clinical endocrinology and metabolism · 2026
Multidimensional Pancreatic Islet β-cell Function Assessment Improves Predictive Effect of Diabetes Risk Scores.
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Fu Qi, Dai Hao, Wang Jiachen, Liu Lei, Silva Lilian Fernandes, Jiang Hemin, Qian Yu, Fu Zhenzhen, Peng Ruyi, Xia Zhijie, Chu Xiaomeng, Laakso Markku, Yin Xianyong, Yang Tao
Аннотация
AIMS/HYPOTHESIS: Comprehensive assessment of pancreatic islet β-cell function (PIF) is crucial for diabetes management. We proposed a multidimensional, relative quantification system for PIF measurement.
METHODS: Our novel approach evaluates PIF using 3 dimensions: stationary baseline (PIF-S), load peak (PIF-L), and accelerated slope (PIF-A). The system was evaluated in 814 JuRong City, Jiangsu Province cohort volunteers (195 metabolically healthy, 619 abnormal), 12 Botnia clamp study participants, 3394 type 2 diabetes patients, and 6345 Metabolic Syndrome in Men study (METSIM) cohort study participants. Restricted cubic spline modeling determined ideal values based on human physiological parameters. Each subject's actual values were compared with predicted ideals and converted into percentile indices.
RESULTS: The Botnia clamp experiment confirmed the distinct meaning of 3 PIF indices. Cluster analysis in metabolically abnormal individuals identified 3 clusters. Cluster 1, with the highest PIF-A, had the best metabolic profiles and lowest cardiovascular and renal disease risks. Cluster 3, with the highest PIF-S and PIF-L but lowest PIF-A, had the poorest metabolic profiles and highest disease risks. Type 2 diabetes patients with high PIF-S and PIF-L were more prone to complications. Similar patterns were observed in the METSIM cohort, with cluster 1 showing the lowest diabetes risk; hazard ratios for clusters 2 and 3 were 2.499 [95% confidence interval (CI) 1.932-3.233, P = 3.11E-12] and 3.185 (95% CI 2.353-4.311, P = 6.35E-12), respectively. The novel 3-dimensional PIF indices surpass previous indicators in predicting diabetes. Combined with existing diabetes risk scores, novel PIFs also significantly improved their predictive efficiency.
CONCLUSION: This novel system offers an effective method for PIF assessment, enhancing diabetes prediction and management by deepening the understanding of diabetes complexity and aiding in precise therapy.
METHODS: Our novel approach evaluates PIF using 3 dimensions: stationary baseline (PIF-S), load peak (PIF-L), and accelerated slope (PIF-A). The system was evaluated in 814 JuRong City, Jiangsu Province cohort volunteers (195 metabolically healthy, 619 abnormal), 12 Botnia clamp study participants, 3394 type 2 diabetes patients, and 6345 Metabolic Syndrome in Men study (METSIM) cohort study participants. Restricted cubic spline modeling determined ideal values based on human physiological parameters. Each subject's actual values were compared with predicted ideals and converted into percentile indices.
RESULTS: The Botnia clamp experiment confirmed the distinct meaning of 3 PIF indices. Cluster analysis in metabolically abnormal individuals identified 3 clusters. Cluster 1, with the highest PIF-A, had the best metabolic profiles and lowest cardiovascular and renal disease risks. Cluster 3, with the highest PIF-S and PIF-L but lowest PIF-A, had the poorest metabolic profiles and highest disease risks. Type 2 diabetes patients with high PIF-S and PIF-L were more prone to complications. Similar patterns were observed in the METSIM cohort, with cluster 1 showing the lowest diabetes risk; hazard ratios for clusters 2 and 3 were 2.499 [95% confidence interval (CI) 1.932-3.233, P = 3.11E-12] and 3.185 (95% CI 2.353-4.311, P = 6.35E-12), respectively. The novel 3-dimensional PIF indices surpass previous indicators in predicting diabetes. Combined with existing diabetes risk scores, novel PIFs also significantly improved their predictive efficiency.
CONCLUSION: This novel system offers an effective method for PIF assessment, enhancing diabetes prediction and management by deepening the understanding of diabetes complexity and aiding in precise therapy.