Multidimensional Pancreatic Islet β-cell Function (PIF) Assessment Improves Predictive Effect of Diabetes Risk Scores

Qi Fu; Hao Dai; Jiachen Wang; Lei Liu; Lilian Fernandes Silva; Hemin Jiang; Yu Qian; Zhenzhen Fu; Ruyi Peng; Zhijie Xia; Xiaomeng Chu; Markku Laakso; Xianyong Yin; Tao Yang

doi:10.1210/clinem/dgaf372

Multidimensional Pancreatic Islet β-cell Function (PIF) Assessment Improves Predictive Effect of Diabetes Risk Scores

J Clin Endocrinol Metab. 2025 Jun 23:dgaf372. doi: 10.1210/clinem/dgaf372. Online ahead of print.

Authors

Qi Fu¹, Hao Dai¹, Jiachen Wang¹, Lei Liu², Lilian Fernandes Silva³, Hemin Jiang¹, Yu Qian¹, Zhenzhen Fu¹, Ruyi Peng², Zhijie Xia², Xiaomeng Chu², Markku Laakso³, Xianyong Yin², Tao Yang^{1

4

5}

Affiliations

¹ Department of Endocrinology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
² Department of Epidemiology, School of Public Health, Nanjing Medical University, Jiangsu, China.
³ Institute of Clinical Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
⁴ The Fourth Affiliated Hospital of Nanjing Medical University.
⁵ The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214043, China.

PMID: 40581372
DOI: 10.1210/clinem/dgaf372

Abstract

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 three dimensions: stationary-baseline (PIF-S), load-peak (PIF-L), and accelerated-slope (PIF-A). The system was evaluated in 814 JR Cohort volunteers (195 metabolically healthy, 619 abnormal), 12 Botnia clamp study participants, 3394 type 2 diabetes patients, and 6345 METSIM cohort study participants. Restricted Cubic Spline (RCS) 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 distinct meaning of three PIF indices. Cluster analysis in metabolically abnormal individuals identified three 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, Cluster 1 showing the lowest diabetes risk, with hazard ratios for Clusters 2 and 3 at 2.499 (95% 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 three-dimensional PIF indices surpass previous indicators in predicting diabetes. Combined with existing diabetes risk scores, novel PIFs also significantly improved their predictive efficiency.

Conclusions: 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.

Keywords: diabetes; metabolic disease risk; multidimensional indices; pancreatic islet β-cell function; personalized diabetes management.

© The Author(s) 2025. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.