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Background <p>The study aims to investigate the potential of glycolipid metabolism 6 factors (GLM6) and glycolipid metabolism 7 factors (GLM7) indexes in identifying high-risk populations for various diseases during routine screening, and to evaluate the potential of GLM6 for long-term dynamic monitoring of disease risk.</p> Methods <p>Research data were obtained from the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS). Logistic regression, Cox regression, and restricted cubic spline (RCS) curve analysis were employed to examine correlations between exposure variables and outcome variables. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm was used to establish models evaluating the predictive efficacy of GLM7 and GLM6 indicators. Subgroup analyses were conducted based on age, gender, smoking status, and drinking status.</p> Results <p>The composite indicators GLM7 and GLM6 showed significant positive correlations with the risk of 9 disease categories, 14 specific diseases, and mortality, with no association observed only for hyperthyroidism. The XGBoost model built on GLM6 performs comparably to the GLM7 model in terms of predictive ability for most diseases and has the potential to identify high-risk populations for various diseases during routine examinations. GLM7 and GLM6 demonstrate sound predictive capabilities for cardiovascular categories and metabolic diseases in the XGBoost model. In the cohort, the baseline GLM6 index, cumulative GLM6 index, and GLM6 dynamic clustering group were significantly positively correlated with cardiovascular disease (CVD) and diabetes (DM). The highest tertile of the cumulative GLM6 index, combined with an unfavorable GLM6 dynamic clustering group, was associated with the highest risk of new-onset CVD and DM, demonstrating a high risk of CVD and DM associated with long-term high GLM6 exposure. Furthermore, female exhibited higher disease risk under prolonged high GLM6 exposure.</p> Conclusions <p>GLM6 and GLM7 show potential as biomarkers for identifying individuals at high risk of CVD and DM in large-scale population screenings. They can help establish long-term disease risk monitoring networks, and following future multicenter validation studies, they are expected to support the optimization of clinical decision-making and promote the targeted allocation of public health resources.</p> Clinical trial number <p>Not applicable.</p>

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Association of glycolipid metabolism 6 factors index with cardiovascular and metabolic diseases, and its predictive value in disease progression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Jiahui Qi,
  • Lingyu Liu,
  • Yijin Pan,
  • Yihan Wang,
  • Xitong Xu,
  • Haipeng Wang,
  • Yiyan Li,
  • Qian Liu,
  • Songwen Liu,
  • Chen Li,
  • Yuqing He

摘要

Background

The study aims to investigate the potential of glycolipid metabolism 6 factors (GLM6) and glycolipid metabolism 7 factors (GLM7) indexes in identifying high-risk populations for various diseases during routine screening, and to evaluate the potential of GLM6 for long-term dynamic monitoring of disease risk.

Methods

Research data were obtained from the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS). Logistic regression, Cox regression, and restricted cubic spline (RCS) curve analysis were employed to examine correlations between exposure variables and outcome variables. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm was used to establish models evaluating the predictive efficacy of GLM7 and GLM6 indicators. Subgroup analyses were conducted based on age, gender, smoking status, and drinking status.

Results

The composite indicators GLM7 and GLM6 showed significant positive correlations with the risk of 9 disease categories, 14 specific diseases, and mortality, with no association observed only for hyperthyroidism. The XGBoost model built on GLM6 performs comparably to the GLM7 model in terms of predictive ability for most diseases and has the potential to identify high-risk populations for various diseases during routine examinations. GLM7 and GLM6 demonstrate sound predictive capabilities for cardiovascular categories and metabolic diseases in the XGBoost model. In the cohort, the baseline GLM6 index, cumulative GLM6 index, and GLM6 dynamic clustering group were significantly positively correlated with cardiovascular disease (CVD) and diabetes (DM). The highest tertile of the cumulative GLM6 index, combined with an unfavorable GLM6 dynamic clustering group, was associated with the highest risk of new-onset CVD and DM, demonstrating a high risk of CVD and DM associated with long-term high GLM6 exposure. Furthermore, female exhibited higher disease risk under prolonged high GLM6 exposure.

Conclusions

GLM6 and GLM7 show potential as biomarkers for identifying individuals at high risk of CVD and DM in large-scale population screenings. They can help establish long-term disease risk monitoring networks, and following future multicenter validation studies, they are expected to support the optimization of clinical decision-making and promote the targeted allocation of public health resources.

Clinical trial number

Not applicable.