Causal Effects of Blood Metabolites and Obstructive Sleep Apnea: A Mendelian Randomization Study


如何引用文章

全文:

详细

Background::Obstructive sleep apnea (OSA) is one of the most common forms of sleep-disordered breathing. Studies have shown that certain changes in metabolism play an important role in the pathophysiology of OSA. However, the causal relationship between these metabolites and OSA remains unclear.

Aims::We use a mendelian randomization (MR) approach to investigate the causal associations between the genetic liability to metabolites and OSA.

Methods::We performed a 2-sample inverse-variance weighted mendelian randomization analysis to evaluate the causal effects of genetically determined 486 metabolites on OSA. Multiple sensitivity analyses were performed to assess pleiotropy. We used multivariate mendelian randomization analyses to assess confounding factors and mendelian randomization Bayesian model averaging to rank the significant biomarkers by their genetic evidence. We also conducted a metabolic pathway analysis to identify potential metabolic pathways.

Results::We identified 14 known serum metabolites (8 risk factors and 6 protective factors) and 12 unknown serum metabolites associated with OSA. These 14 known metabolites included 8 lipids( 1-arachidonoylglycerophosphoethanolamine, Tetradecanedioate, Epiandrosteronesulfate, Acetylca Glycerol3-phosphate, 3-dehydrocarnitine, Margarate17:0, Docosapentaenoaten3;22:5n3), 3 Aminoacids (Isovalerylcarnitine,3-methyl-2-oxobutyrate,Methionine), 2 Cofactors and vitamins [Bilirubin(E,ZorZ,E),X-11593--O-methylascorbate], 1Carbohydrate(1,6-anhydroglucose). We also identified several metabolic pathways that involved in the pathogenesis of OSA.

Conclusion::MR (mendelian randomization) approach was performed to identify 6 protective factors and 12 risk factors for OSA in the present study. 3-Dehydrocarnitine was the most significant risk factors for OSA. Our study also confirmed several significant metabolic pathways that were involved in the pathogenesis of OSA. Valine, leucine and isoleucine biosynthesis metabolic pathways were the most significant metabolic pathways that were involved in the pathogenesis of OSA.

作者简介

Jing-Hao Wu

Department of Neurology, Fifth Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Ying-Hao Yang

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Yun-Chao Wang

Department of Neurology, Fifth Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Wen-Kai Yu

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Shan-Shan Li

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Yun-Yun Mei

Department of neurosurgery, Fudan University Shanghai Cancer Center

Email: info@benthamscience.net

Ce-Zong

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Zi-Han Zhou

Center for Reproducyive Medicine, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Hang-Hang Zhu

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Liu-Chang He

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Xin-Yu Li

Department of Neurology, First Affiliated Hospital of Zhengzhou University

Email: info@benthamscience.net

Chang-He Shi

Department of Neurology, First Affiliated Hospital of Zhengzhou University

编辑信件的主要联系方式.
Email: info@benthamscience.net

Yu-Sheng Li

Department of Neurology, First Affiliated Hospital of Zhengzhou University

编辑信件的主要联系方式.
Email: info@benthamscience.net

参考

  1. Gottlieb DJ, Punjabi NM. Diagnosis and management of obstructive sleep apnea. JAMA 2020; 323(14): 1389-400. doi: 10.1001/jama.2020.3514 PMID: 32286648
  2. Senaratna CV, Perret JL, Lodge CJ, et al. Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Med Rev 2017; 34: 70-81. doi: 10.1016/j.smrv.2016.07.002 PMID: 27568340
  3. Watson NF. Health care savings: The economic value of diagnostic and therapeutic care for obstructive sleep apnea. J Clin Sleep Med 2016; 12(8): 1075-7. doi: 10.5664/jcsm.6034 PMID: 27448424
  4. Semelka M, Wilson J, Floyd R. Diagnosis and treatment of obstructive sleep apnea in adults. Am Fam Physician 2016; 94(5): 355-60. PMID: 27583421
  5. Gieger C, Geistlinger L, Altmaier E, et al. Genetics meets metabolomics: A genome-wide association study of metabolite profiles in human serum. PLoS Genet 2008; 4(11): e1000282. doi: 10.1371/journal.pgen.1000282 PMID: 19043545
  6. Xu H, Li X, Zheng X, et al. Pediatric obstructive sleep apnea is associated with changes in the oral microbiome and urinary metabolomics profile: A pilot study. J Clin Sleep Med 2018; 14(9): 1559-67. doi: 10.5664/jcsm.7336 PMID: 30176961
  7. Zhang X, Wang S, Xu H, Yi H, Guan J, Yin S. Metabolomics and microbiome profiling as biomarkers in obstructive sleep apnoea: A comprehensive review. Eur Respir Rev 2021; 30(160): 200220. doi: 10.1183/16000617.0220-2020 PMID: 33980666
  8. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA 2017; 318(19): 1925-6. doi: 10.1001/jama.2017.17219 PMID: 29164242
  9. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ 2018; 362: k601. doi: 10.1136/bmj.k601 PMID: 30002074
  10. Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites. Nat Genet 2014; 46(6): 543-50. doi: 10.1038/ng.2982 PMID: 24816252
  11. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012; 40(D1): D109-14. doi: 10.1093/nar/gkr988 PMID: 22080510
  12. Sun S, Jiao M, Han C, et al. Causal effects of genetically determined metabolites on risk of polycystic ovary syndrome: A mendelian randomization study. Front Endocrinol 2020; 11: 621. doi: 10.3389/fendo.2020.00621 PMID: 33013699
  13. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 2017; 36(11): 1783-802. doi: 10.1002/sim.7221 PMID: 28114746
  14. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016; 40(4): 304-14. doi: 10.1002/gepi.21965 PMID: 27061298
  15. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. Int J Epidemiol 2015; 44(2): 512-25. doi: 10.1093/ije/dyv080 PMID: 26050253
  16. Hammerton G, Munafò MR. Causal inference with observational data: The need for triangulation of evidence – CORRIGENDUM. Psychol Med 2021; 51(9): 1591. doi: 10.1017/S0033291721002634 PMID: 34236016
  17. Feng R, Lu M, Xu J, et al. Pulmonary embolism and 529 human blood metabolites: Genetic correlation and two-sample Mendelian randomization study. BMC Genomic Data 2022; 23(1): 69. doi: 10.1186/s12863-022-01082-6 PMID: 36038828
  18. Song P, Rudan D, Zhu Y, et al. Global, regional, and national prevalence and risk factors for peripheral artery disease in 2015: An updated systematic review and analysis. Lancet Glob Health 2019; 7(8): e1020-30. doi: 10.1016/S2214-109X(19)30255-4 PMID: 31303293
  19. Zuber V, Colijn JM, Klaver C, Burgess S. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat Commun 2020; 11(1): 29. doi: 10.1038/s41467-019-13870-3 PMID: 31911605
  20. Tang SN, Zuber V, Tsilidis KK. Identifying and ranking causal biochemical biomarkers for breast cancer: A Mendelian randomisation study. BMC Med 2022; 20(1): 457. doi: 10.1186/s12916-022-02660-2 PMID: 36424572
  21. Samimi M, Jamilian M, Ebrahimi FA, Rahimi M, Tajbakhsh B, Asemi Z. Oral carnitine supplementation reduces body weight and insulin resistance in women with polycystic ovary syndrome: A randomized, double‐blind, placebo‐controlled trial. Clin Endocrinol 2016; 84(6): 851-7. doi: 10.1111/cen.13003 PMID: 26666519
  22. Sun L, Liang L, Gao X, et al. Early prediction of developing type 2 diabetes by plasma acylcarnitines: A population-based study. Diabetes Care 2016; 39(9): 1563-70. doi: 10.2337/dc16-0232 PMID: 27388475
  23. Kiens O, Taalberg E, Ivanova V, et al. Apnoea-hypopnoea index of 5 events·h −1 as a metabolomic threshold in patients with sleep complaints. ERJ Open Res 2023; 9(1): 00325-2022. doi: 10.1183/23120541.00325-2022 PMID: 36632170
  24. Kaur G, Sinclair AJ, Cameron-Smith D, Barr DP, Molero-Navajas JC, Konstantopoulos N. Docosapentaenoic acid (22:5n-3) down-regulates the expression of genes involved in fat synthesis in liver cells. Prostaglandins Leukot Essent Fatty Acids 2011; 85(3-4): 155-61. doi: 10.1016/j.plefa.2011.06.002 PMID: 21807486
  25. Wanders D, Hobson K, Ji X. Methionine restriction and cancer biology. Nutrients 2020; 12(3): 684. doi: 10.3390/nu12030684 PMID: 32138282
  26. Zhang Y, Jelleschitz J, Grune T, et al. Methionine restriction - Association with redox homeostasis and implications on aging and diseases. Redox Biol 2022; 57: 102464. doi: 10.1016/j.redox.2022.102464 PMID: 36152485
  27. Nichenametla SN, Mattocks DAL, Cooke D, et al. Cysteine restriction‐specific effects of sulfur amino acid restriction on lipid metabolism. Aging Cell 2022; 21(12): e13739. doi: 10.1111/acel.13739 PMID: 36403077
  28. Vaz FM, Wanders RJA. Carnitine biosynthesis in mammals. Biochem J 2002; 361(3): 417-29. doi: 10.1042/bj3610417 PMID: 11802770
  29. Leyrolle Q, Cserjesi R, Mulders MDGH, et al. Specific gut microbial, biological, and psychiatric profiling related to binge eating disorders: A cross-sectional study in obese patients. Clin Nutr 2021; 40(4): 2035-44. doi: 10.1016/j.clnu.2020.09.025 PMID: 33023763
  30. Stocker R, Yamamoto Y, McDonagh AF, Glazer AN, Ames BN. Bilirubin is an antioxidant of possible physiological importance. Science 1987; 235(4792): 1043-6. doi: 10.1126/science.3029864 PMID: 3029864
  31. Holeček M. Role of impaired glycolysis in perturbations of amino acid metabolism in diabetes mellitus. Int J Mol Sci 2023; 24(2): 1724. doi: 10.3390/ijms24021724 PMID: 36675238
  32. Zhuang T, Liu X, Wang W, et al. Dose-related urinary metabolic alterations of a combination of quercetin and resveratrol-treated high-fat diet fed rats. Front Pharmacol 2021; 12: 655563. doi: 10.3389/fphar.2021.655563 PMID: 33935771
  33. Mohit , Tomar MS, Araniti F. et al. Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography-Mass spectrometry. Front Mol Biosci 2022; 9: 1026848.

补充文件

附件文件
动作
1. JATS XML

版权所有 © Bentham Science Publishers, 2024