[
    {
        "id": "osp-10456",
        "type": "article-journal",
        "title": "Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach",
        "author": [
            {
                "family": "Abdesselam",
                "given": "Abdelhamid"
            },
            {
                "family": "Zidoum",
                "given": "Hamza"
            },
            {
                "family": "Zadjali",
                "given": "Fahd"
            },
            {
                "family": "Hedjam",
                "given": "Rachid"
            },
            {
                "family": "Al Ansari",
                "given": "Aliya"
            },
            {
                "family": "Bayoumi",
                "given": "Riad"
            },
            {
                "family": "Al-Yahyaee",
                "given": "Said"
            },
            {
                "family": "Hassan",
                "given": "Mohammed"
            },
            {
                "family": "Albarwani",
                "given": "Sulayma"
            }
        ],
        "URL": "https://omanscience.com/en/articles/estimate-of-the-homa-ir-cut-off-value-identifying-subjects-at-risk-of-insulin-resistance-using-a-machine-learning-approach",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2025
                ]
            ]
        },
        "container-title": "Sultan Qaboos University Medical Journal",
        "volume": "21",
        "issue": "4",
        "page": "604-612",
        "DOI": "10.18295/squmj.4.2021.030",
        "publisher": "Sultan Qaboos University",
        "ISSN": "2075-051X",
        "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."
    }
]