ArticleFrontiers in endocrinology2024
L-shaped relationship between atherogenic index of plasma with uric acid levels and hyperuricemia risk.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Decoding the lipid etiology of atherogenic index of plasma and gout: establishing the causal role of triglycerides through NHANES, Mendelian randomization, and network pharmacology.Cardiovascular diabetology. Endocrinology reports · 2026Article
- Serum Uric Acid in the PAMELA Study: Main Findings and Association with the Atherogenic Index of Plasma.Metabolites · 2025Review
- Red blood cell distribution width to albumin ratio is associated with increased depression: the mediating role of atherogenic index of plasma.Frontiers in psychiatry · 2025Article
- Lymphocyte-to-Monocyte Ratio and All-Cause Mortality in Populations With Abdominal Aortic Calcification: A Prospective Cohort Study.Mediators of inflammation · 2025Article
- Identifying metabolic parameters as key indicators of hyperuricemia and ischemic stroke comorbidity via interpretable Clinlabomics models.Frontiers in endocrinology · 2025Article
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Authors and funding
5 authors.
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Abstract
Background: Hyperuricemia is a major risk factor for cardiovascular disease. This study aimed to investigate the relationship between the atherogenic index of plasma (AIP) and serum uric acid (SUA) levels, as well as the risk of hyperuricemia. Methods: Utilizing data from the National Health and Nutrition Examination Survey (NHANES), we conducted a cross-sectional study involving 9,439 participants aged 18 years and above with complete triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) data. AIP was calculated as the logarithm of the ratio of TG to HDL-C. Weighted linear regression, weighted logistic regression, subgroup analysis, generalized additive model, restricted cubic spline and two-part linear and logistic regression were utilized to examine the relationships between AIP and SUA levels and hyperuricemia risk. Results: We identified a non-linear and L-shaped relationship between AIP and both SUA levels and hyperuricemia prevalence, with significant increasing observed up to a saturation point (0.588 for uric acid levels and 0.573 for hyperuricemia prevalence). Below these thresholds, the odds ratios (OR) for increased SUA and hyperuricemia were 0.854 (95% confidence interval [CI]: 0.762, 0.946) and 4.4 (95% CI: 3.528, 5.488), respectively (P<0.001). Beyond these points, the associations were not statistically significant. Conclusion: Our findings suggest that AIP is significantly and non-linear associated with SUA levels and hyperuricemia risk, with a saturation effect observed beyond specific thresholds. These insights could inform clinical strategies for managing cardiovascular and metabolic risks associated with elevated AIP. Further longitudinal studies are warranted to confirm these associations and elucidate the underlying mechanisms.
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