Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach
Joint Authors
Alya al-Ansari
Zadjali, Fahd
Abd al-Salam, Abd al-Hamid
Zidoum, Hamzah
Hedjam, Rashid
Bayoumi, Riad
al-Yahyai, Said
al-Barwani, Sulayma
Source
Sultan Qaboos University Medical Journal
Issue
Vol. 21, Issue 4 (30 Nov. 2021), pp.604-612, 9 p.
Publisher
Sultan Qaboos University College of Medicine and Health Sciences
Publication Date
2021-11-30
Country of Publication
Oman
No. of Pages
9
Main Subjects
Abstract EN
-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.
American Psychological Association (APA)
Alya al-Ansari& Bayoumi, Riad; Bayoumi, Riad& al-Yahyai, Said& Hasan, Muhammad Usamah& al-Barwani, Sulayma…[et al.]. 2021. Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach. Sultan Qaboos University Medical Journal،Vol. 21, no. 4, pp.604-612.
https://search.emarefa.net/detail/BIM-1366415
Modern Language Association (MLA)
Hasan, Muhammad Usamah…[et al.]. Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach. Sultan Qaboos University Medical Journal Vol. 21, no. 4 (Nov. 2021), pp.604-612.
https://search.emarefa.net/detail/BIM-1366415
American Medical Association (AMA)
Alya al-Ansari& Bayoumi, Riad; Bayoumi, Riad& al-Yahyai, Said& Hasan, Muhammad Usamah& al-Barwani, Sulayma…[et al.]. Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach. Sultan Qaboos University Medical Journal. 2021. Vol. 21, no. 4, pp.604-612.
https://search.emarefa.net/detail/BIM-1366415
Data Type
Journal Articles
Language
English
Notes
Record ID
BIM-1366415