WITHDRAWN: Combined triglyceride-glucose and frailty index (TyGFI) and risk of endometrial cancer in U.S. women aged >=45: NHANES 2011-2018 analysis integrating data engineering and machine learning with logistic modeling

Based on the provided text, which is a withdrawn preprint, a detailed technical summary of the research content, methodology, and claims is as follows:

Problem Statement

The study aimed to investigate the association between a combined metric of metabolic health and physical vulnerability—the Triglyceride-Glucose (TyG) index combined with the Frailty Index (TyGFI)—and the risk of endometrial cancer. The research focused specifically on U.S. women aged 45 and older, utilizing data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2011–2018.

Methodology

The authors proposed an analytical framework that integrated:

• Data Source: NHANES 2011–2018 datasets.
• Exposure Variable: A composite index termed TyGFI, derived from the Triglyceride-Glucose (TyG) index and the Frailty Index.
• Analytical Approach: The study employed data engineering techniques, machine learning algorithms, and logistic modeling to assess the relationship between the TyGFI and endometrial cancer incidence.

Key Contributions and Claims

The paper was intended to present a novel attempt to synthesize metabolic and frailty metrics into a single predictor (TyGFI) for endometrial cancer risk, leveraging advanced computational methods (machine learning and data engineering) alongside traditional logistic regression. The authors intended to provide a comprehensive analysis of how these combined factors influence cancer risk in the specified demographic.

Results and Current Status

Crucially, this paper reports no valid results.

The manuscript has been withdrawn by the authors. The withdrawal statement explicitly cites a critical methodological failure:

• Data Limitation: The number of positive cases (women with endometrial cancer) within the original dataset was too small.
• Statistical Consequence: This scarcity of cases led to major flaws in the statistical analysis.
• Outcome: Due to these flaws, the authors concluded that the findings and conclusions are unreliable.

Significance and Author Position

The paper does not claim scientific significance in its current withdrawn state. Instead, the authors issue a formal disclaimer:

• They do not wish this work to be cited as a reference for any project.
• They acknowledge that the study cannot guide clinical practice or inform scientific understanding due to the unreliability of the data analysis.
• The authors emphasize that the preprint should not be used to guide clinical practice, consistent with the standard disclaimer for non-peer-reviewed preprints, but specifically reinforce this due to the internal data insufficiency that prompted the withdrawal.

In summary, while the paper proposed an innovative methodological approach to studying endometrial cancer risk, the authors themselves have invalidated the work, stating that the statistical foundation was insufficient to support any conclusions.