ArticleGerman medical science : GMS e-journal2025
Evaluating lipid-driven insulin resistance via TyG index in breast cancer patients: Toward effective secondary prevention.
Article in German medical science : GMS e-journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Correlation between TyG index and its modified index with breast cancer risk in women a study based on NHANES database and real-world data.Frontiers in oncology · 2026Article
- Evaluating lipid-driven insulin resistance via TyG index in breast cancer patients: Toward effective secondary prevention.German medical science : GMS e-journal · 2025Article
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Abstract
Objective: Breast cancer is the most commonly diagnosed malignancy worldwide. Insulin resistance (IR) plays a key role in its progression by activating oncogenic signaling pathways. The triglyceride-glucose (TyG) index is a validated, cost-effective surrogate marker for IR. This study aims to evaluate the prevalence of IR in female breast cancer patients using the TyG index and to identify lipid parameters associated with increased IR, thereby supporting strategies for secondary prevention. Methods: A cross-sectional study was conducted among non-diabetic, histopathologically confirmed female breast cancer patients. Demographic data, lipid profiles, and fasting glucose levels were collected. Participants were stratified into high-risk (TyG≥8.87) and low-risk (TyG<8.87) groups based on their TyG index. Logistic regression analysis was performed to identify significant predictors of elevated TyG index. Results: Among 122 patients, 44.3% demonstrated elevated insulin resistance. Triglycerides (TG), total cholesterol (TC), very low-density lipoprotein cholesterol (VLDL-C), and the TC/high-density lipoprotein-cholesterol (HDL-C) ratio were significantly higher in the high-risk group. Logistic regression identified TC, TC/HDL-C ratio, and low-density lipoprotein cholesterol (LDL-C) as significant predictors of elevated IR (p<0.05). The model is represented as: Logit(P)=-13.941+0.145X Conclusion: Monitoring lipid parameters and managing insulin resistance are crucial for enhancing breast cancer prognosis and potentially reducing progression.
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