文献类型： 外文期刊

作者： Zheng, Q. ¹ ; Li, W. ³ ; Zhang, Y. ⁶ ; Liu, X. ³ ; Fu, Y. ⁸ ; Luo, S. ³ ; Deng, X. ³ ; Zeng, C. ¹ ;

作者机构： 1.Chinese Acad Sci, Beijing Inst Genom, CAS Key Lab Genom & Precis Med, 11 Beitucheng West Rd, Beijing 100101, Peoples R China

2.China Natl Ctr Bioinformat, 11 Beitucheng West Rd, Beijing 100101, Peoples R China

3.Peking Univ, Sch & Hosp Stomatol, Dept Geriatr Dent, Beijing Lab Biomed Mat, 22 South Ave Zhongguancun, Beijing 100081, Peoples R China

4.Natl Med Prod Adm, Key Lab Dent Mat, Beijing, Peoples R China

5.Peking Univ, Inst Med Technol, Hlth Sci Ctr, Beijing, Peoples R China

6.Henan Acad Agr Sci, Zhengzhou, Henan, Peoples R China

7.Peking Univ, Sch & Hosp Stomatol, Cent Lab, Beijing, Peoples R China

8.Hebei Med Univ, Sch & Hosp Stomatol, Hebei Clin Res Ctr Oral Dis, Dept Periodontol 2,Hebei Key Lab Stomatol, Shijiazhuang, Hebei, Peoples R China

9.Univ Chinese Acad Sci, Savaid Med Sch, Beijing, Peoples R China

关键词： mendelian randomization analysis; metabolomics; genome wide association study; causality; oral health; metabolic pathways

期刊名称：JOURNAL OF DENTAL RESEARCH （ 影响因子：7.6； 五年影响因子：7.6 ）

ISSN： 0022-0345

年卷期： 2023 年

页码：

收录情况： SCI

摘要： It is of great importance to uncover causal biomarkers to gain insight into the pathogenesis of oral diseases and identify novel treatment targets for prevention and treatment thereof. This study aimed to systematically evaluate the causal effects of hundreds of metabolites on 10 dental traits using a 2-sample Mendelian randomization (MR) approach. Genetic variants from genome-wide association studies of 309 known metabolites were used as instrumental variables. We selected 10 dental traits, including clinical measures of dental diseases, from the Gene-Lifestyle Interactions in Dental Endpoints Consortium and self-reported oral health data from the UK Biobank. The causal relationships between metabolites and dental traits were inferred using the inverse variance-weighted approach and further controlled for horizontal pleiotropy using 5 additional MR methods. After correcting for multiple tests, 5 metabolites were identified as causal biomarkers. Genetically predicted increased levels of mannose were associated with lower risk of bleeding gums (odds ratio [OR] = 0.72; 95% confidence interval [CI], 0.61-0.85; P = 9.9 x 10-5). MR also indicated 4 metabolites on the causal pathway to dentures, with fructose (OR = 0.50; 95% CI, 0.36-0.70; P = 5.2 x 10-5) and 1-palmitoleoyl-glycerophosphocholine (OR = 0.67; 95% CI, 0.56-0.81; P = 4.8 x 10-5) as potential protective factors and glycine (OR = 1.22; 95% CI, 1.11-1.35; P = 5.6x10-5) and 1,5-anhydroglucitol (OR = 1.32; 95% CI, 1.14-1.52; P = 1.5 x 10-4) as risk factors. The causal associations were robust in various sensitivity analyses. We further observed some shared metabolites among different dental traits, implying similar biological mechanisms underlying the pathogenic processes. Finally, the pathway analysis revealed several significant metabolic pathways that may be involved in the development of dental disorders. Our study provides novel insights into the combination of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for dental disorders. It highlighted 5 metabolites and several pathways as causal candidates, warranting further investigation.