Health Technology/clinical-trials

Large-Scale Psoriasis Study Develops Risk Prediction Tool for Insulin Resistance — Clinical Implications for Dermatologists and Endocrinologists

247 Health News Editorial

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14 May 2026

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4 min read

·clinical-trials

Large-scale Chinese study of 1,265 psoriasis patients develops machine learning tool to predict insulin resistance risk. Research addresses critical gap in early identification of metabolic complications in psoriasis patients.

Originally reported by ClinicalTrials.gov

References

  1. 1.Chinese PLA General Hospital. (2024). Identification of risk factors and development of an interpretable machine learning model for predicting insulin resistance in patients with psoriasis. ClinicalTrials.gov. NCT07321288. https://clinicaltrials.gov/study/NCT07321288
  2. 2.Simental-Mendía, L. E., Rodríguez-Morán, M., & Guerrero-Romero, F. (2008). The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metabolism, 57(7), 940-945. https://doi.org/10.1016/j.metabol.2008.01.013
  3. 3.Armstrong, A. W., Harskamp, C. T., & Armstrong, E. J. (2013). Psoriasis and metabolic syndrome: a systematic review and meta-analysis of observational studies. Journal of the American Academy of Dermatology, 69(5), 698-708. https://doi.org/10.1016/j.jaad.2012.12.008
  4. 4.Boehncke, W. H., & Schön, M. P. (2015). Psoriasis. The Lancet, 386(9997), 983-994. https://doi.org/10.1016/S0140-6736(14)61909-7
  5. 5.Azfar, R. S., Seminara, N. M., Shin, D. B., Troxel, A. B., Margolis, D. J., & Gelfand, J. M. (2012). Increased risk of diabetes mellitus and likelihood of receiving diabetes mellitus treatment in patients with psoriasis. Archives of Dermatology, 148(9), 995-1000. https://doi.org/10.1001/archdermatol.2011.2513
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