Genetic prediction of blood metabolites and causal relationships with pain associated with temporomandibular disorders: a Mendelian randomization study

Background: This study aims to elucidate the causal relationships between 1400 blood metabolites and pain related to temporomandibular disorders (TMD) using Mendelian Randomization (MR) analysis. Methods: Utilizing data from genome-wide association studies (GWAS), our analysis was conducted with R software using the “TwoSampleMR” package. The primary method applied was Inverse Variance Weighted (IVW) analysis, which was supplemented with MR-Egger, Weighted Median, Simple Mode and Weighted Mode methods to examine the causal impact of blood metabolites on TMD-associated pain. We also assessed heterogeneity and the presence of horizontal pleiotropy using MR-Egger regression, MR-PRESSO (MR Pleiotropy Residual Sum and Outlier) global tests, and MR-Egger intercept tests. Results: Three metabolites—Acetylcarnitine, Propionylcarnitine (c3) and X-24241—were significantly associated with TMD-related pain. Specifically, Acetylcarnitine had an odds ratio (OR) of 1.409 (95% CI (confidence intervals): 1.016–1.954, p = 0.04), Propionylcarnitine (c3) had an OR of 1.194 (95% CI: 1.036–1.377, p = 0.014), and X-24241 had an OR of 1.408 (95%CI: 1.108–1.790, p = 0.005). Conclusions: This study establishes a causal link between increased levels of these three metabolites and TMD-related pain. Our findings provide new insights into the pathogenesis of TMD and potential therapeutic targets.

Blood metabolites; Mendelian randomization; Temporomandibular joint pain; Causal relationships

[1] Velly AM, Anderson GC, Look JO, Riley JL, Rindal DB, Johnson K, et al. Management of painful temporomandibular disorders: methods and overview of the national dental practice-based research network prospective cohort study. Journal of the American Dental Association. 2022; 153: 144–157.

[2] Kapos FP, Exposto FG, Oyarzo JF, Durham J. Temporomandibular disorders: a review of current concepts in aetiology, diagnosis and management. Oral Surgery. 2020; 13: 321–334.

[3] Shrivastava M, Battaglino R, Ye L. A comprehensive review on biomarkers associated with painful temporomandibular disorders. International Journal of Oral Science. 2021; 13: 23.

[4] Wu N, Hirsch C. Temporomandibular disorders in German and Chinese adolescents. Journal of Orofacial Orthopedics. 2010; 71: 187–198.

[5] Qvintus V, Sipila K, Le Bell Y, Suominen AL. Prevalence of clinical signs and pain symptoms of temporomandibular disorders and associated factors in adult Finns. Acta Odontologica Scandinavica. 2020; 78: 515–521.

[6] Xie C, Lin M, Yang H, Ren A. Prevalence of temporomandibular disorders and its clinical signs in Chinese students, 1979–2017: a systematic review and meta-analysis. Oral Diseases. 2019; 25: 1697–1706.

[7] Shimada A, Ishigaki S, Matsuka Y, Komiyama O, Torisu T, Oono Y, et al. Effects of exercise therapy on painful temporomandibular disorders. Journal of Oral Rehabilitation. 2019; 46: 475–481.

[8] Slade GD, Fillingim RB, Sanders AE, Bair E, Greenspan JD, Ohrbach R, et al. Summary of findings from the OPPERA prospective cohort study of incidence of first-onset temporomandibular disorder: implications and future directions. Journal of Pain. 2013; 14: T116–T124.

[9] Yang Q, Vijayakumar A, Kahn BB. Metabolites as regulators of insulin sensitivity and metabolism. Nature Reviews Molecular Cell Biology. 2018; 19: 654–672.

[10] Khoo SC, Shoji Y, Teh CH, Ma NL. Discovery of biomarkers for myogenous temporomandibular disorders through salivary metabolomic profiling: a pilot study. Journal of Oral & Facial Pain and Headache. 2023; 37: 207–216.

[11] Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nature Reviews Molecular Cell Biology. 2016; 17: 451–459.

[12] Wang Z, Chen S, Zhu Q, Wu Y, Xu G, Guo G, et al. Using a two-sample mendelian randomization method in assessing the causal relationships between human blood metabolites and heart failure. Frontiers in Cardiovascular Medicine. 2021; 8: 695480.

[13] Wang Z, Yang Q. The causal relationship between human blood metabolites and the risk of visceral obesity: a Mendelian randomization analysis. Lipids in Health and Disease. 2024; 23: 39.

[14] Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization as an approach to assess causality using observational data. Journal of the American Society of Nephrology. 2016; 27: 3253–3265.

[15] Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: the STROBE-MR statement. JAMA. 2021; 326: 1614–1621.

[16] Yang J, Yan B, Zhao B, Fan Y, He X, Yang L, et al. Assessing the causal effects of human serum metabolites on 5 major psychiatric disorders. Schizophrenia Bulletin. 2020; 46: 804–813.

[17] Burgess S, Swanson SA, Labrecque JA. Are Mendelian randomization investigations immune from bias due to reverse causation? European Journal of Epidemiology. 2021; 36: 253–257.

[18] Guo JZ, Xiao Q, Gao S, Li XQ, Wu QJ, Gong TT. Review of Mendelian randomization studies on ovarian cancer. Frontiers in Oncology. 2021; 11: 681396.

[19] Xu C, Ren X, Lin P, Jin S, Zhang Z. Exploring the causal effects of sleep characteristics on TMD-related pain: a two-sample Mendelian randomization study. Clinical Oral Investigations. 2024; 28: 384.

[20] Chen X, Cheng Z, Xu J, Wang Q, Zhao Z, Jiang Q. Causal effects of educational attainment on temporomandibular disorders and the mediating pathways: a Mendelian randomization study. Journal of Oral Rehabilitation. 2024; 51: 817–826.

[21] Chen X, Cheng Z, Xu J, Wang Q, Zhao Z, Jiang Q. Causal effects of autoimmune diseases on temporomandibular disorders and the mediating pathways: a Mendelian randomization study. Frontiers in Immunology. 2024; 15: 1390516.

[22] Xiang Y, Song J, Liang Y, Sun J, Zheng Z. Causal relationship between psychiatric traits and temporomandibular disorders: a bidirectional two-sample Mendelian randomization study. Clinical Oral Investigations. 2023; 27: 7513–7521.

[23] Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nature Genetics. 2023; 55: 44–53.

[24] Cheng Q, Yang Y, Shi X, Yeung KF, Yang C, Peng H, et al. MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genomics and Bioinformatics. 2020; 2: lqaa028.

[25] Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. European Journal of Epidemiology. 2017; 32: 377–389.

[26] Du Y, Wang Q, Zheng Z, Zhou H, Han Y, Qi A, et al. Gut microbiota influence on lung cancer risk through blood metabolite mediation: from a comprehensive Mendelian randomization analysis and genetic analysis. Frontiers in Nutrition. 2024; 11: 1425802.

[27] Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology. 2016; 40: 304–314.

[28] Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017; 28: 30–42.

[29] Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology. 2013; 37: 658–665.

[30] Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology. 2015; 44: 512–525.

[31] Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics. 2018; 50: 693–698.

[32] Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-base platform supports systematic causal inference across the human phenome. eLife. 2018; 7: e34408.

[33] Schiffman E, Ohrbach R, Truelove E, Look J, Anderson G, Goulet JP, et al. Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications: recommendations of the international RDC/TMD consortium network* and orofacial pain special interest group†. Journal of Oral & Facial Pain and Headache. 2014; 28: 6–27.

[34] Reiter S, Emodi-Perlman A, Kasiel H, Abboud W, Friedman-Rubin P, Arias OW, et al. Headache attributed to temporomandibular disorders: axis I and II findings according to the diagnostic criteria for temporomandibular disorders. Journal of Oral & Facial Pain and Headache. 2021; 35: 119–128.

[35] Nguyen TT, Vanichanon P, Bhalang K, Vongthongsri S. Pain duration and intensity are related to coexisting pain and comorbidities present in temporomandibular disorder pain patients. Journal of Oral & Facial Pain and Headache. 2019; 33: 205–212.

[36] Karamat A, Smith JG, Melek LNF, Renton T. Psychologic impact of chronic orofacial pain: a critical review. Journal of Oral & Facial Pain and Headache. 2022; 36: 103–140.

[37] Jasim H, Ghafouri B, Gerdle B, Hedenberg-Magnusson B, Ernberg M. Altered levels of salivary and plasma pain related markers in temporomandibular disorders. The Journal of Headache and Pain. 2020; 21: 105.

[38] Sorenson A, Hresko K, Butcher S, Pierce S, Tramontina V, Leonardi R, et al. Expression of Interleukin-1 and temporomandibular disorder: contemporary review of the literature. CRANIO®. 2018; 36: 268–272.

[39] Ibi M. Inflammation and temporomandibular joint derangement. Biological and Pharmaceutical Bulletin. 2019; 42: 538–542.

[40] Kringel D, Lippmann C, Parnham MJ, Kalso E, Ultsch A, Lötsch J. A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes. European Journal of Pain. 2018; 22: 1735–1756.

[41] Cunha-Oliveira T, Montezinho L, Simoes RF, Carvalho M, Ferreiro E, Silva FSG. Mitochondria: a promising convergent target for the treatment of amyotrophic lateral sclerosis. Cells. 2024; 13: 248.

[42] Pourshahidi S, Shamshiri AR, Derakhshan S, Mohammadi S, Ghorbani M. The effect of Acetyl-L-Carnitine (ALCAR) on peripheral nerve regeneration in animal models: a systematic review. Neurochemical Research. 2023; 48: 2335–2344.

[43] Lucarini E, Micheli L, Toti A, Ciampi C, Margiotta F, Di Cesare Mannelli L, et al. Anti-Hyperalgesic efficacy of Acetyl L-Carnitine (ALCAR) against visceral pain induced by colitis: involvement of glia in the enteric and central nervous system. International Journal of Molecular Sciences. 2023; 24: 14841.

[44] Chiechio S, Nicoletti F. Metabotropic glutamate receptors and the control of chronic pain. Current Opinion in Pharmacology. 2012; 12: 28–34.

[45] Liao ZQ, Han X, Wang YH, Shi J, Zhang Y, Zhao H, et al. Differential metabolites in osteoarthritis: a systematic review and meta-analysis. Nutrients. 2023; 15: 4191.

[46] Longo N, Sass JO, Jurecka A, Vockley J. Biomarkers for drug development in propionic and methylmalonic acidemias. Journal of Inherited Metabolic Disease. 2022; 45: 132–143.

[47] Miettinen T, Nieminen AI, Mantyselka P, Kalso E, Lötsch J. Machine learning and pathway analysis-based discovery of metabolomic markers relating to chronic pain phenotypes. International Journal of Molecular Sciences. 2022; 23: 5085.