Integration of PTP1B Genetic Variation and Clinical Factors in Risk Stratification of Insulin Resistance in Type 2 Diabetes Mellitus: Scoping Review

Achmad Sufa Ramdlani Al Kindi; Syahrul  Tuba; Adi Priyono; Budi Sumaryono; Endah Permata Sari

doi:10.69693/ijmst.v4i2.10472

Authors

Achmad Sufa Ramdlani Al Kindi Universitas Pertahanan
Syahrul Tuba Universitas Pertahanan
Adi Priyono Universitas Pertahanan
Budi Sumaryono Universitas Pertahanan
Endah Permata Sari Universitas Muslim Indonesia

DOI:

https://doi.org/10.69693/ijmst.v4i2.10472

Keywords:

Clinical Factors, Gene, PTP1B 1, T2DM, And Variation

Abstract

Background: Type 2 diabetes mellitus (T2DM) is a complex metabolic disease characterized by insulin resistance. The protein Tyrosine Phosphatase 1B (PTP1B) acts as a negative regulator of insulin signaling pathways, and its genetic variation is known to affect insulin sensitivity. The integration of PTP1B genomic data with clinical factors such as BMI, age, and metabolic parameters has the potential to improve the accuracy of insulin resistance risk stratification. Objective: This study aims to review the potential integration of PTP1B genetic variation with clinical factors in the risk stratification of insulin resistance in patients with T2DM. Methods: This study uses a scoping review method by searching the literature through PubMed, Scopus, and Google Scholar (2016–2025) using related keywords. Relevant articles were selected based on inclusion-exclusion criteria and analyzed qualitatively through narrative synthesis. Results: PTP1B variations such as rs6067472, rs2143511, and rs3787348 showed associations with insulin resistance, adiposity, and metabolic response. These genetic effects are influenced by clinical factors, especially BMI and physical activity, and are amplified by epigenetic mechanisms in the form of promoter methylation, which overall improves risk stratification ability. Conclusions: The integration of PTP1B genetic variation with clinical and epigenetic factors provides a comprehensive approach in stratification of insulin resistance risk in T2DM, and has the potential to support the implementation of more accurate and personalized precision medicine.

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