AUTHOR=Yang Xiuming , Chen Qiuyun , Zhang Qingyu , Yu Zongliang 

TITLE=Lipoprotein cholesterol ratios and cardiovascular disease risk in US adults: a cross-sectional study

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1529223

DOI=10.3389/fnut.2025.1529223

ISSN=2296-861X

ABSTRACT=BackgroundThe ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) has been introduced as a novel indicator to evaluate lipid metabolism. The study explored the association between NHHR and cardiovascular disease (CVD).MethodsA cross-sectional study was achieved by utilizing data obtained from the NHANES (2003–2016). The association between NHHR and CVD was assessed by multivariate logistic regression analysis (LRA) and the restricted cubic spline (RCS) analysis. Also, interaction tests and subgroup analyses were employed to explore whether the associations differ by subgroups. Then, threshold analysis were conducted for interval delineation and detection of threshold effects with two-segment piecewise LR model.ResultsA cohort of 11,471 individuals was involved. The results indicated that the linear relationship between NHHR and CVD was not significant (P for trend >0.05). The RCS analysis revealed a non-linear J-shaped association of NHHR with CVD risk. A two-segment LR model was established to assess the threshold effect of the NHHR. A log-likelihood ratio test (P < 0.001) suggested that the two-segment LR model exhibited better performances compared with the single-line LR model. Additionally, a tangent point of the NHHR occurred at 2.82, and the likelihood of CVD increased by 21% as the NHHR increased by one unit (OR = 1.21, 95% CI = 1.10–1.34).ConclusionsA J-shaped association was detected between NHHR and the prevalence of CVD, suggesting that NHHR could serve as a novel assessment marker for identifying high-risk CVD populations. However, further cohort studies are needed to confirm this finding.