High-density lipoprotein cholesterol modifies the association between blood lead and uric acid: results from NHANES 2005–2016

Zou, Xiaoting; Zhao, Zifan; Huang, Wei-Chao; Chen, Yue; Zhang, Wanlin; Luo, Jianxun; Zhao, Tianyi; Wu, Luying; Ma, Xiaoman; Guo, Xu‐Guang

doi:10.1007/s00420-022-01944-7

Cited by 1 publication

(1 citation statement)

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“…Elevate TG can lead to a high level of free fatty acid production and utilization, which could accelerate the decomposition of adenosine triphosphate, and this process increased the production of uric acid and causes its abnormal rise in blood [38]. Second, many studies have confrmed that HDL-C is a protective factor against serum uric acid elevation in general [39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Higher Atherogenic Index of Plasma Is Associated with Hyperuricemia: A National Longitudinal Study

Xu,

Ma,

Wang

et al. 2024

International Journal of Endocrinology

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Background. The association between atherogenic index of plasma (AIP) and hyperuricemia remains indistinct. This study was aimed to examine the relationship between AIP and hyperuricemia among the middle-aged and the elderly Chinese population. Methods. Datasets were retrieved from the China Health and Retirement Longitudinal Study (CHARLS) survey conducted in 2011 and 2015. 13,021 participants in the CHARLS in 2011 and 7,017 participants involved both in 2011 and 2015 were included, respectively. The measurement of AIP and hyperuricemia was based on the test of fasting blood. Association between AIP and hyperuricemia was assessed by logistic regression, and the nonlinear association was examined by restricted cubic splines (RCS). The cutoff point of AIP was calculated using receiver operator curve (ROC). 1 : 1 propensity score matching (PSM) was adopted to further explore the relationship between AIP and hyperuricemia. Results. In the section of a cross-sectional study, a positive association between AIP and hyperuricemia was found. The odds ratios (ORs) of hyperuricemia were 1.00 (reference), 1.52 (1.10–2.10), 1.80 (1.31–2.47), and 3.81 (2.84–5.11). Nonlinear association was not detected using RCS analysis. There were 664 hyperuricemia cases during the four years follow-up. The hyperuricemia prevalence was 9.5%. In the fully adjusted longitudinal analysis, the ORs for hyperuricemia across the quartiles of AIP were 1.00 (reference), 1.00 (0.74–1.37), 1.59 (1.20–2.11), and 2.55 (1.94–3.35), respectively. In the longitudinal analysis after PSM, the OR of hyperuricemia were 1.91 (1.45, 2.51) and 1.92 (1.45, 2.54) in the univariate and multivariate model, respectively. Conclusion. AIP can predict the prevalence of hyperuricemia in the Chinese middle-aged and elderly population.

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