Integrative multi-omics investigation of sleep apnea: gut microbiome metabolomics, proteomics and phenome-wide association study.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS

Nutrition & Metabolism Pub Date : 2025-06-10 DOI:10.1186/s12986-025-00925-0

Shuxu Wei, Ronghuai Shen, Xiaojia Lu, Xinyi Li, Lingbin He, Youti Zhang, Xianxi Huang, Zhouwu Shu

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引用次数: 0

Abstract

Background: Sleep apnea (SA) is linked to various diseases. This study examines the causal link between the gut microbiome and SA, exploring potential predictive factors and target proteins using a multi-omics approach with a Phenome-wide association study (PheWAS).

Methods: Bidirectional Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC) were used to assess the genetic correlation and causal relationships between the gut microbiome and SA. Mediation analysis identified intermediate relationships involving "gut microbiome-inflammatory proteins-SA." Two-sample MR and colocalization analysis in the deCODE and UK Biobank Pharma Proteomics Project (UKB-PPP) databases identified protein quantitative trait loci (pQTL) associated with SA. Validation analysis used Fenland proteins, methylation quantitative trait loci (mQTL), and expression quantitative trait loci (eQTL). PheWAS screened 29 SA-associated SNPs and matched control SNPs (4:1 ratio) from UK Biobank data chosen through MR and LDSC analyses.

Results: Inverse-variance weighted (IVW) bidirectional MR analysis did not establish a causal link between the gut microbiome and SA. C-C motif chemokine 28 showed causal relationships in both directions (forward IVW, P = 0.0336; reverse IVW, P = 0.0336). Intermediate connections were found between the Holdemanella genus and urinary plasminogen activator levels with SA. TIMP4 protein had a significant causal relationship with SA(IVW method: P > 0.05, PH4 = 96.1%; P = 7.85 × 10^-6, PH4 in deCODE = 97.4%). PRIM1 and BMP8 A were identified as potential influencers of SA through mQTL and eQTL analyses. PheWAS suggested body impedance and predicted mass as potential predictors of SA.

Conclusion: Bidirectional causal relationships exist between SA and inflammatory proteins, with TIMP4 identified as a pathogenic factor and potential therapeutic target. PRIM1 and BMP8 A may impact SA risk. Body impedance and predicted mass predict SA significantly.

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睡眠呼吸暂停的综合多组学研究：肠道微生物组代谢组学、蛋白质组学和全现象关联研究。

背景：睡眠呼吸暂停（SA）与多种疾病有关。本研究探讨了肠道微生物组与SA之间的因果关系，利用多组学方法和全表型关联研究（PheWAS）探索了潜在的预测因素和靶蛋白。方法：采用双向孟德尔随机化（MR）和连锁不平衡评分回归（LDSC）来评估肠道微生物组与SA之间的遗传相关性和因果关系。中介分析确定了涉及“肠道微生物群-炎症蛋白- sa”的中间关系。deCODE和UK Biobank Pharma Proteomics Project （UKB-PPP）数据库的双样本MR和共定位分析发现了与SA相关的蛋白质数量性状位点（pQTL）。验证分析使用芬兰蛋白、甲基化数量性状位点（mQTL）和表达数量性状位点（eQTL）。PheWAS从通过MR和LDSC分析选择的UK Biobank数据中筛选了29个sa相关的snp和匹配的对照snp（4:1比例）。结果：反向方差加权（IVW）双向MR分析未建立肠道微生物组与SA之间的因果关系。C-C基序趋化因子28在两个方向均存在因果关系(正向IVW， P = 0.0336；反向IVW， P = 0.0336)。发现Holdemanella属和尿纤溶酶原激活剂水平与SA之间存在中间联系。TIMP4蛋白与SA有显著的因果关系(IVW法：P < 0.05, PH4 = 96.1%；P = 7.85 × 10-6, PH4 in deCODE = 97.4%)。通过mQTL和eQTL分析，PRIM1和bmp8a被确定为SA的潜在影响因子。PheWAS建议身体阻抗和预测质量作为SA的潜在预测指标。结论：SA与炎症蛋白之间存在双向因果关系，TIMP4被确定为致病因子和潜在的治疗靶点。PRIM1和bmp8a可能影响SA风险。体阻抗和预测质量显著预测SA。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.