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Background <p>Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality, with metabolic disorders playing crucial roles in its pathogenesis. Glycated hemoglobin (HbA1c) and high-density lipoprotein cholesterol (HDL-C) are established markers for glucose and lipid metabolism. However, the clinical significance of their ratio in relation to prevalent CVD is still incompletely understood.</p> Methods <p>We analyzed data from 47,704 participants aged ≥ 20&#xa0;years in the National Health and Nutrition Examination Survey (1999–2018). The aim was to investigate the association between the HbA1c/HDL-C ratio and prevalent CVD. Weighted logistic regression models were used with comprehensive adjustment for demographic, lifestyle, and clinical covariates. Restricted cubic spline analysis and two-piecewise linear regression were performed to assess non-linear relationships.</p> Results <p>We identified a significant non-linear relationship between the HbA1c/HDL-C ratio and odds of prevalent CVD. Each unit increase in the ratio was associated with 8% higher odds of prevalent CVD (adjusted OR: 1.08; 95% CI 1.05–1.11), while individuals in the highest ratio quartile demonstrated 56% higher odds compared to those in the lowest quartile (OR: 1.56; 95% CI 1.35–1.80). A saturation pattern was observed in this cross-sectional analysis, with the strongest association within the ratio range of 3.26–6.24 (OR: 1.20; 95% CI 1.11–1.30). The AUC of 81.15% was achieved by a model combining age, sex, and the HbA1c/HDL-C ratio in this cross-sectional discrimination analysis, but the ratio alone did not outperform HbA1c alone.</p> Conclusion <p>In this cross-sectional study, the HbA1c/HDL-C ratio was independently associated with prevalent CVD and showed a non-linear pattern with data-driven inflection points. ROC/AUC results reflect cross-sectional discrimination (classification) rather than prospective prediction, and model performance requires validation before clinical application.</p>

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Association between HbA1c to HDL cholesterol ratio and prevalent cardiovascular disease in NHANES 1999 to 2018

  • 期刊论文

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  • 发表日期：

    2026年5月11日

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  • Ce Zhou,
  • Niannian Shuai,
  • Jiaxiu Zhou,
  • Xiaofeng Zhang,
  • Xin Kuang

摘要

Background

Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality, with metabolic disorders playing crucial roles in its pathogenesis. Glycated hemoglobin (HbA1c) and high-density lipoprotein cholesterol (HDL-C) are established markers for glucose and lipid metabolism. However, the clinical significance of their ratio in relation to prevalent CVD is still incompletely understood.

Methods

We analyzed data from 47,704 participants aged ≥ 20 years in the National Health and Nutrition Examination Survey (1999–2018). The aim was to investigate the association between the HbA1c/HDL-C ratio and prevalent CVD. Weighted logistic regression models were used with comprehensive adjustment for demographic, lifestyle, and clinical covariates. Restricted cubic spline analysis and two-piecewise linear regression were performed to assess non-linear relationships.

Results

We identified a significant non-linear relationship between the HbA1c/HDL-C ratio and odds of prevalent CVD. Each unit increase in the ratio was associated with 8% higher odds of prevalent CVD (adjusted OR: 1.08; 95% CI 1.05–1.11), while individuals in the highest ratio quartile demonstrated 56% higher odds compared to those in the lowest quartile (OR: 1.56; 95% CI 1.35–1.80). A saturation pattern was observed in this cross-sectional analysis, with the strongest association within the ratio range of 3.26–6.24 (OR: 1.20; 95% CI 1.11–1.30). The AUC of 81.15% was achieved by a model combining age, sex, and the HbA1c/HDL-C ratio in this cross-sectional discrimination analysis, but the ratio alone did not outperform HbA1c alone.

Conclusion

In this cross-sectional study, the HbA1c/HDL-C ratio was independently associated with prevalent CVD and showed a non-linear pattern with data-driven inflection points. ROC/AUC results reflect cross-sectional discrimination (classification) rather than prospective prediction, and model performance requires validation before clinical application.