Suppr超能文献

12种醛浓度对心血管疾病的累积和单一影响:基于贝叶斯核机器回归和加权逻辑回归的分析

The Cumulative and Single Effect of 12 Aldehydes Concentrations on Cardiovascular Diseases: An Analysis Based on Bayesian Kernel Machine Regression and Weighted Logistic Regression.

作者信息

Fang Yuemei, Zhang Juan

机构信息

Medical Imaging Department, Nanjing Brain Hospital, 210029 Nanjing, Jiangsu, China.

Catheter Room of Cerebrovascular Disease Treatment Center, Nanjing Brain Hospital, 210029 Nanjing, Jiangsu, China.

出版信息

Rev Cardiovasc Med. 2024 Jun 3;25(6):206. doi: 10.31083/j.rcm2506206. eCollection 2024 Jun.

Abstract

BACKGROUND

This study investigates the individual and cumulative effects of 12 aldehydes concentrations on cardiovascular disease (CVD).

METHODS

A total of 1529 individuals from the 2013-2014 National Health and Nutrition Examination Survey were enrolled. We assessed serum concentrations of 12 aldehydes, including benzaldehyde, butyraldehyde, crotonaldehyde, decanaldehyde, heptanaldehyde, hexanaldehyde, isopentanaldehyde, nonanaldehyde, octanaldehyde, o-tolualdehyde, pentanaldehyde, and propanaldehyde. CVD patients were identified based on self-reported disease history from questionnaires. The Bayesian kernel machine regression was used to evaluate the cumulative effect of 12 aldehyde concentrations on CVD. Both weighted and unweighted logistic regression were used to assess the association of serum aldehyde concentrations with CVD, presenting effect sizes as odds ratio (OR) with 95% confidence interval (CI). Additionally, a restricted cubic spline analysis was also conducted to explore the relationship between benzaldehyde and CVD.

RESULTS

Among the participants, 111 (7.3%) were identified as having CVD. Isopentanaldehyde concentrations were notably higher in CVD patients compared to those without CVD. Bayesian kernel machine regression indicated no cumulative effect of aldehydes on CVD. Unweighted logistic regression revealed a positive association between benzaldehyde and CVD when adjusting for age and sex (OR = 1.12, 95% CI = 1.03-1.21). This association persisted after adjusting for age, sex, race, education, hypertension, diabetes, alcohol consumption, and smoking, with an OR of 1.12 (95% CI = 1.02-1.22). The restricted cubic spline showed a linear association between benzaldehyde and CVD. In the weighted logistic model, the association between benzaldehyde and CVD remains significant (OR = 1.17, 95% CI = 1.06-1.29). However, no significant association was found between other aldehydes and CVD.

CONCLUSIONS

Our study reveals the potential contributing role of benzaldehyde to CVD. Future studies should further validate these findings in diverse populations and elucidate the underlying biological mechanisms.

摘要

背景

本研究调查了12种醛浓度对心血管疾病(CVD)的个体和累积影响。

方法

纳入了2013 - 2014年国家健康与营养检查调查中的1529名个体。我们评估了12种醛的血清浓度,包括苯甲醛、丁醛、巴豆醛、癸醛、庚醛、己醛、异戊醛、壬醛、辛醛、邻甲苯醛、戊醛和丙醛。根据问卷中自我报告的疾病史确定CVD患者。采用贝叶斯核机器回归来评估12种醛浓度对CVD的累积影响。使用加权和未加权逻辑回归来评估血清醛浓度与CVD的关联,效应大小以比值比(OR)及95%置信区间(CI)表示。此外,还进行了受限立方样条分析以探索苯甲醛与CVD之间的关系。

结果

在参与者中,111人(7.3%)被确定患有CVD。与无CVD者相比,CVD患者的异戊醛浓度显著更高。贝叶斯核机器回归表明醛对CVD无累积影响。未加权逻辑回归显示,在调整年龄和性别后,苯甲醛与CVD呈正相关(OR = 1.12,95% CI = 1.03 - 1.21)。在调整年龄、性别、种族、教育程度、高血压、糖尿病、饮酒和吸烟后,这种关联仍然存在,OR为1.12(95% CI = 1.02 - 1.22)。受限立方样条显示苯甲醛与CVD之间存在线性关联。在加权逻辑模型中,苯甲醛与CVD之间的关联仍然显著(OR = 1.17,95% CI = 1.06 - 1.29)。然而,未发现其他醛与CVD之间存在显著关联。

结论

我们的研究揭示了苯甲醛对CVD的潜在促成作用。未来的研究应在不同人群中进一步验证这些发现,并阐明潜在的生物学机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f14/11270117/2cf9a5b0301c/2153-8174-25-6-206-g1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验