Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study

• Published in: The journal of clinical endocrinology and metabolism Vol. 103; no. 5; pp. 1850 - 1855
• Main Authors: Liu, Li, Wen, Yan, Zhang, Lei, Xu, Peng, Liang, Xiao, Du, Yanan, Li, Ping, He, Awen, Fan, QianRui, Hao, Jingcan, Wang, Wenyu, Guo, Xiong, Shen, Hui, Tian, Qing, Zhang, Feng, Deng, Hong-Wen
• Format: Journal Article
• Language: English
• Published: Washington, DC Endocrine Society 01.05.2018; Copyright Oxford University Press; Oxford University Press
• Subjects: Absorptiometry, Photon; Adult; Aged; Biomarkers - analysis; Biomarkers - blood; Biomarkers - metabolism; Blood; Bone Density - genetics; Bone diseases; Bone mineral density; Clinical s; Correlation analysis; Dual energy X-ray absorptiometry; Editor's Choice; Female; Gene polymorphism; Genetic Predisposition to Disease; Genome-Wide Association Study; Genomes; Genotype; Genotyping; Hip; Humans; Male; Mendelian Randomization Analysis; Metabolites; Methionine; Middle Aged; Osteoporosis; Osteoporosis - blood; Osteoporosis - epidemiology; Osteoporosis - genetics; Osteoporosis - metabolism; Phenotype; Phenotypes; Phenotypic variations; Polymorphism, Single Nucleotide; Random Allocation; Replication; Single-nucleotide polymorphism; Theophylline; Whole genome sequencing
• ISSN: 0021-972X; 1945-7197; 1945-7197
• DOI: 10.1210/jc.2017-01719

Abstract Context Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive. Objective To explore the relationship between blood metabolites and osteoporosis. Design and Methods We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate. Results We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery < 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249). Conclusions Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis. We calculated a genetic risk score for each metabolite to assess the effects of blood metabolites on osteoporosis and obesity-related phenotypes using the Mendelian randomization analysis.