DOI: 10.5061/dryad.kd51c5bjn
title: Data from: Evaluating lipid-driven insulin resistance via TyG index in breast cancer patients: Toward effective secondary prevention

description: 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. 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 &lt; 8.87) groups based on
 their TyG index. Logistic regression analysis was performed to identify
 significant predictors of elevated TyG index. Among 122 patients, 44.3%
 demonstrated elevated insulin resistance. Triglycerides (TG), total
 cholesterol (TC), VLDL-C, and the TC/HDL-C ratio were significantly higher
 in the high-risk group. Logistic regression identified TC, TC/HDL-C ratio,
 and LDL-C as significant predictors of elevated IR (p &lt; 0.05). The
 model is represented as: Logit(P) = −13.941 + 0.145X₁ + 1.558X₂ − 0.178X₃,
 where X₁, X₂, and X₃ correspond to TC, TC/HDL-C ratio, and LDL-C,
 respectively. The predictive model achieved 90.2% accuracy with an area
 under the ROC curve (AUROC) of 0.927. Monitoring lipid parameters and
 managing insulin resistance are crucial for enhancing breast cancer
 prognosis and potentially reducing progression.
description: # Data from: Evaluating lipid-driven insulin resistance via TyG index in
 breast cancer patients: Toward effective secondary prevention Dataset DOI:
 [10.5061/dryad.kd51c5bjn](10.5061/dryad.kd51c5bjn) ## Description of the
 data and file structure This dataset contains clinical, biochemical, and
 lifestyle parameters relevant to cardiometabolic health. The variables are
 structured in a tabular format, where each row represents a single
 individual and each column corresponds to a clinical, biochemical, or
 derived parameter. The dataset is intended for research purposes in the
 fields of cardiovascular disease risk assessment, metabolic health,
 epidemiology, and machine learning–based health prediction. ## Variables
 and Definitions #### File: data.csv 1. **Age (years)** – Chronological age
 of the participant. 2. **Cholesterol (mg/dL)** – Total serum cholesterol
 level. 3. **Triglyceride (mg/dL)** – Serum triglyceride concentration. 4.
 **HDL (mg/dL)** – High-density lipoprotein cholesterol ("good
 cholesterol"). 5. **LDL (mg/dL)** – Low-density lipoprotein
 cholesterol ("bad cholesterol"). Calculated or measured. 6.
 **VLDL (mg/dL)** – Very low-density lipoprotein cholesterol. 7. **CH/HDL
 ratio** – Total cholesterol to HDL ratio, an indicator of cardiovascular
 risk. 8. **HDL/CH ratio** – HDL to total cholesterol ratio, often used as
 a protective marker. 9. **LDL/HDL ratio** – Ratio of LDL to HDL
 cholesterol, reflecting atherogenic balance. 10. **VLDL/HDL ratio** –
 Ratio of VLDL to HDL cholesterol. 11. **HDL/VLDL ratio** – Ratio of HDL to
 VLDL cholesterol. 12. **TG/HDL ratio** – Triglyceride-to-HDL ratio,
 associated with insulin resistance and cardiometabolic risk. 13. **Fasting
 blood glucose (mg/dL)** – Plasma glucose level after overnight fasting.
 14. **TyG index** – Triglyceride-glucose index, a logarithmic function
 combining fasting glucose and triglycerides, used as a surrogate marker
 for insulin resistance. 15. **TyG category (low/medium/high)** – Risk
 category classification based on TyG index cut-off values. 16.
 **Hypertension (Yes/No)** – Presence of hypertension (self-reported or
 clinically diagnosed). 17. **Smoking history (Yes/No)** – History of
 tobacco smoking. 18. **Family history (Yes/No)** – Positive family history
 of cardiovascular or metabolic disease. ## Code/software The dataset is
 provided as a **comma-separated values (CSV)** file. It can be opened
 directly in: * **Microsoft Excel** (any recent version) * Free
 alternatives such as **LibreOffice Calc (≥7.0)** or **Google Sheets** No
 proprietary or additional software is required to view the file.\ No
 scripts or code are included with this submission. ## Access information
 Other publicly accessible locations of the data: * Not applicable Data was
 derived from the following sources: * Not applicable ## Human subjects
 data We have obtained the explicit consent from the participants
 to publish the de-identified data in the public domain by taking only 3
 indirect identifiers and removed the personal details of the
 participants.