Genetic variations of low-density lipoprotein cholesterol on metabolic disorders in obstructive sleep apnea.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-06-10 DOI:10.1186/s12986-024-00805-z

Yu Peng, Hangdong Shen, Chenyang Li, Xiaoyue Zhu, Yiqing Gao, Hongliang Yi, Huajun Xu, Jian Guan, Xinyi Li, Shankai Yin

{"title":"Genetic variations of low-density lipoprotein cholesterol on metabolic disorders in obstructive sleep apnea.","authors":"Yu Peng, Hangdong Shen, Chenyang Li, Xiaoyue Zhu, Yiqing Gao, Hongliang Yi, Huajun Xu, Jian Guan, Xinyi Li, Shankai Yin","doi":"10.1186/s12986-024-00805-z","DOIUrl":null,"url":null,"abstract":"Background: The study aimed to explore the relationship between low-density lipoprotein cholesterol (LDL-C) genetic variants and obstructive sleep apnea (OSA) and its complications, including cardiovascular diseases (CVD), insulin resistance (IR), and metabolic syndrome (MS).Method: 4329 individuals with suspected OSA who underwent a comprehensive assessment of anthropometric, biochemical, and polysomnography (PSG) data, along with 30 LDL-C single nucleotide polymorphisms (SNPs) were enrolled. The 10-year Framingham CVD risk score (FRS), IR and MS were evaluated for each subject. Linear regression and logistic regression were utilized to examine the correlations among these variables.Results: After the Benjamini-Hochberg correction, linear regression results indicated positive correlations between variants rs3741297 and rs629301 with FRS (β = 0.031, PBH=0.002; β = 0.026, PBH=0.015). Logistic regression revealed that rs3741297 increased MS risk among total subjects [OR = 1.67 (95% CI:1.369-2.038), PBH=1.32 × 10- 5] and increased IR risk in females [OR = 3.475 (95% CI:1.653-7.307), PBH=0.03]. In males, rs2642438 decreased MS risk [OR = 0.81 (95% CI:0.703-0.933), PBH=0.045].Conclusions: The rs3741297 variant correlated with susceptibility to CVD, IR, and MS in the OSA population. OSA, CVD, IR and MS share a potentially common genetic background, which may promote precision medicine.Cinical trial registration: The study protocol was registered with the Chinese Clinical Trial Registry (ChiCTR1900025714).","PeriodicalId":19196,"journal":{"name":"Nutrition & Metabolism","volume":"21 1","pages":"31"},"PeriodicalIF":3.9000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163771/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12986-024-00805-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The study aimed to explore the relationship between low-density lipoprotein cholesterol (LDL-C) genetic variants and obstructive sleep apnea (OSA) and its complications, including cardiovascular diseases (CVD), insulin resistance (IR), and metabolic syndrome (MS).

Method: 4329 individuals with suspected OSA who underwent a comprehensive assessment of anthropometric, biochemical, and polysomnography (PSG) data, along with 30 LDL-C single nucleotide polymorphisms (SNPs) were enrolled. The 10-year Framingham CVD risk score (FRS), IR and MS were evaluated for each subject. Linear regression and logistic regression were utilized to examine the correlations among these variables.

Results: After the Benjamini-Hochberg correction, linear regression results indicated positive correlations between variants rs3741297 and rs629301 with FRS (β = 0.031, P_BH=0.002; β = 0.026, P_BH=0.015). Logistic regression revealed that rs3741297 increased MS risk among total subjects [OR = 1.67 (95% CI:1.369-2.038), P_BH=1.32 × 10^- 5] and increased IR risk in females [OR = 3.475 (95% CI:1.653-7.307), P_BH=0.03]. In males, rs2642438 decreased MS risk [OR = 0.81 (95% CI:0.703-0.933), P_BH=0.045].

Conclusions: The rs3741297 variant correlated with susceptibility to CVD, IR, and MS in the OSA population. OSA, CVD, IR and MS share a potentially common genetic background, which may promote precision medicine.

Cinical trial registration: The study protocol was registered with the Chinese Clinical Trial Registry (ChiCTR1900025714).

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

低密度脂蛋白胆固醇的遗传变异对阻塞性睡眠呼吸暂停症代谢紊乱的影响。

研究背景该研究旨在探讨低密度脂蛋白胆固醇（LDL-C）遗传变异与阻塞性睡眠呼吸暂停（OSA）及其并发症（包括心血管疾病（CVD）、胰岛素抵抗（IR）和代谢综合征（MS））之间的关系：方法：4329 名疑似 OSA 患者接受了人体测量、生化和多导睡眠图（PSG）数据以及 30 种低密度脂蛋白胆固醇（LDL-C）单核苷酸多态性（SNPs）的综合评估。对每个受试者的 10 年弗雷明汉心血管疾病风险评分 (FRS)、IR 和 MS 进行了评估。利用线性回归和逻辑回归研究了这些变量之间的相关性：经过本杰明-霍奇伯格校正后，线性回归结果显示变异体 rs3741297 和 rs629301 与 FRS 呈正相关（β = 0.031，PBH=0.002；β = 0.026，PBH=0.015）。逻辑回归显示，rs3741297 增加了所有受试者的 MS 风险[OR = 1.67 (95% CI:1.369-2.038), PBH=1.32 × 10-5]，并增加了女性的 IR 风险[OR = 3.475 (95% CI:1.653-7.307), PBH=0.03]。在男性中，rs2642438降低了MS风险[OR = 0.81 (95% CI:0.703-0.933), PBH=0.045]：rs3741297变异与OSA人群对心血管疾病、IR和多发性硬化症的易感性相关。OSA、心血管疾病、红外和多发性硬化可能具有共同的遗传背景，这可能会促进精准医疗的发展：研究方案已在中国临床试验注册中心注册（ChiCTR1900025714）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.