Abstract
Objectives: This study describes an unsupervised machine learning approach used to estimate the homeostatic model assessment-insulin resistance (HOMA-IR) cut-off for identifying subjects at risk of IR in a given ethnic group based on the clinical data of a representative sample. Methods: The approach was applied to analyse the clinical data of individuals with Arab ancestors, which was obtained from a family study conducted in Nizwa, Oman, between January 2000 and December 2004. First, HOMA-IR-correlated variables were identified to which a clustering algorithm was applied. Two clusters having the smallest overlap in their HOMA-IR values were retrieved. These clusters represented the samples of two populations, which are insulin-sensitive subjects and individuals at risk of IR. The cut-off value was estimated from intersections of the Gaussian functions, thereby modelling the HOMA-IR distributions of these populations. Results: A HOMA-IR cut-off value of 1.62 ± 0.06 was identified. The validity of this cut-off was demonstrated by showing the following: 1) that the clinical characteristics of the identified groups matched the published research findings regarding IR; 2) that a strong relationship exists between the segmentations resulting from the proposed cut-off and those resulting from the two-hour glucose cut-off recommended by the World Health Organization for detecting prediabetes. Finally, the method was also able to identify the cut-off values for similar problems (e.g. fasting sugar cut-off for prediabetes). Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of IR. Such methods can identify high-risk individuals at an early stage, which may prevent or delay the onset of chronic diseases such as type 2 diabetes.
Publication details
- DOI
- 10.18295/squmj.4.2021.030
- Journal
- Sultan Qaboos University Medical Journal, 21(4), 604-612
- Publisher
- Sultan Qaboos University
- Open access
- Gold open access
- License
- CC BY-ND 4.0
Cite this article
APA 7
Abdesselam, A., Zidoum, H., Zadjali, F., Hedjam, R., Al Ansari, A., Bayoumi, R., Al-Yahyaee, S., Hassan, M., & Albarwani, S. (2025). Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach. Sultan Qaboos University Medical Journal, 21(4), 604-612. https://doi.org/10.18295/squmj.4.2021.030
MLA 9
Abdesselam, Abdelhamid, et al. "Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach." Sultan Qaboos University Medical Journal, vol. 21, no. 4, 2025, pp. 604-612. https://doi.org/10.18295/squmj.4.2021.030.
Chicago (author–date)
Abdesselam, Abdelhamid, Hamza Zidoum, Fahd Zadjali, Rachid Hedjam, Aliya Al Ansari, Riad Bayoumi, Said Al-Yahyaee, Mohammed Hassan, and Sulayma Albarwani. 2025. "Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach." Sultan Qaboos University Medical Journal 21 (4): 604-612. https://doi.org/10.18295/squmj.4.2021.030.
Harvard
Abdesselam, A., Zidoum, H., Zadjali, F., Hedjam, R., Al Ansari, A., Bayoumi, R., Al-Yahyaee, S., Hassan, M. and Albarwani, S. (2025) 'Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach', Sultan Qaboos University Medical Journal, 21(4), pp. 604-612. doi:10.18295/squmj.4.2021.030.
Vancouver
Abdesselam A, Zidoum H, Zadjali F, Hedjam R, Al Ansari A, Bayoumi R, et al. Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach. Sultan Qaboos University Medical Journal. 2025;21(4):604-612. doi:10.18295/squmj.4.2021.030
IEEE
A. Abdesselam, H. Zidoum, F. Zadjali, R. Hedjam, A. Al Ansari, R. Bayoumi, S. Al-Yahyaee, M. Hassan, and S. Albarwani, "Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach," Sultan Qaboos University Medical Journal, vol. 21, no. 4, pp. 604-612, 2025, doi: 10.18295/squmj.4.2021.030.