Research ArticleOpen Access cc icon

Opioid medication use and blood DNA methylation: epigenome-wide association meta-analysis

Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA

Search for more papers by this author

Roby Joehanes

https://orcid.org/0000-0001-5549-9054

Department of Health and Human Services, Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA

Search for more papers by this author

Daniel L McCartney

Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK

Search for more papers by this author

Minjung Kho

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA

Search for more papers by this author

Anke Hüls

Department of Epidemiology & Gangarosa, Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA

Search for more papers by this author

Annah B Wyss

Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA

Search for more papers by this author

Chunyu Liu

Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02215, USA

Framingham Heart Study, Boston University, Framingham, MA 01702, USA

Search for more papers by this author

Rosie M Walker

Centre for Clinical Brain Science, Chancellor's Building, 49 Little France Crescent, Edinburgh Bioquarter, Edinburgh, UK

School of Psychology, University of Exeter, Exeter, UK

Search for more papers by this author

Sharon L R Kardia

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA

Search for more papers by this author

Thomas S Wingo

Department of Neurology & Human Genetics, Emory University, Atlanta, GA 30322, USA

Search for more papers by this author

Adam Burkholder

Office of Environmental Science Cyberinfrastructure, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA

Search for more papers by this author

Jiantao Ma

Department of Health and Human Services, Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA

Search for more papers by this author

Archie Campbell

Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK

Search for more papers by this author

Aliza P Wingo

Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA 30322, USA

Search for more papers by this author

Tianxiao Huan

Department of Health and Human Services, Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA

Department of Ophthalmology, University of Massachusetts Medical School, Worcester, MA 01655, USA

Search for more papers by this author

Sinjini Sikdar

Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA

Department of Mathematics & Statistics, Old Dominion University, Norfolk, VA 23529, USA

Search for more papers by this author

Amena Keshawarz

Framingham Heart Study, Framingham, MA 01702, USA

Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA

Search for more papers by this author

David A Bennett

Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA

Search for more papers by this author

Jennifer A Smith

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA

Search for more papers by this author

Kathryn L Evans

Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK

Search for more papers by this author

Daniel Levy

Department of Health and Human Services, Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA

Search for more papers by this author

Stephanie J London

*Author for correspondence:

E-mail Address: london2@niehs.nih.gov

https://orcid.org/0000-0003-4911-5290

Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA

Search for more papers by this author

Published Online:26 Jan 2023https://doi.org/10.2217/epi-2022-0353

View article

Abstract

Aim: To identify differential methylation related to prescribed opioid use. Methods: This study examined whether blood DNA methylation, measured using Illumina arrays, differs by recent opioid medication use in four population-based cohorts. We meta-analyzed results (282 users; 10,560 nonusers) using inverse-variance weighting. Results: Differential methylation (false discovery rate <0.05) was observed at six CpGs annotated to the following genes: KIAA0226, CPLX2, TDRP, RNF38, TTC23 and GPR179. Integrative epigenomic analyses linked implicated loci to regulatory elements in blood and/or brain. Additionally, 74 CpGs were differentially methylated in males or females. Methylation at significant CpGs correlated with gene expression in blood and/or brain. Conclusion: This study identified DNA methylation related to opioid medication use in general populations. The results could inform the development of blood methylation biomarkers of opioid use.

Keywords:

Papers of special note have been highlighted as: • of interest

References

1. Kieffer BL, Evans CJ. Opioid receptors: from binding sites to visible molecules in vivo. Neuropharmacology 56(Suppl. 1), S205–S212 (2009).
Medline CASGoogle Scholar
2. Mattson CL, Tanz LJ, Quinn K, Kariisa M, Patel P, Davis NL. Trends and geographic patterns in drug and synthetic opioid overdose deaths – United States, 2013–2019. MMWR Morb. Mortal. Wkly Rep. 70(6), 202–207 (2021).
MedlineGoogle Scholar
3. Joehanes R, Just AC, Marioni RE et al. Epigenetic signatures of cigarette smoking. Circ. Cardiovasc. Genet. 9(5), 436–447 (2016). • A large-scale meta-analysis identifying blood DNA methylation alterations related to smoking.
Medline CASGoogle Scholar
4. Liu C, Marioni RE, Hedman AK et al. A DNA methylation biomarker of alcohol consumption. Mol. Psychiatry 23(2), 422–433 (2018). • A large-scale meta-analysis identifying blood DNA methylation alterations related to alcohol consumption.
Medline CASGoogle Scholar
5. Andersen AM, Ryan PT, Gibbons FX, Simons RL, Long JD, Philibert RA. A droplet digital PCR assay for smoking predicts all-cause mortality. J. Insur. Med. 47(4), 220–229 (2018).
MedlineGoogle Scholar
6. Chorbov VM, Todorov AA, Lynskey MT, Cicero TJ. Elevated levels of DNA methylation at the OPRM1 promoter in blood and sperm from male opioid addicts. J. Opioid Manage. 7(4), 258 (2011).
MedlineGoogle Scholar
7. Doehring A, Oertel BG, Sittl R, Lotsch J. Chronic opioid use is associated with increased DNA methylation correlating with increased clinical pain. Pain 154(1), 15–23 (2013).
Medline CASGoogle Scholar
8. Montalvo-Ortiz JL, Cheng Z, Kranzler HR, Zhang H, Gelernter J. Genomewide study of epigenetic biomarkers of opioid dependence in European-American Women. Sci. Rep. 9(1), 4660 (2019).
MedlineGoogle Scholar
9. Xu J, Wang T, Su Z et al. Opioid exposure is associated with aberrant DNA methylation of OPRM1 promoter region in a Chinese Han population. Biochem. Genet. 56(5), 451–458 (2018).
Medline CASGoogle Scholar
10. Sandoval-Sierra JV, Salgado Garcia FI, Brooks JH, Derefinko KJ, Mozhui K. Effect of short-term prescription opioids on DNA methylation of the OPRM1 promoter. Clin. Epigenetics 12(1), 76 (2020).
Medline CASGoogle Scholar
11. Ebrahimi G, Asadikaram G, Akbari H et al. Elevated levels of DNA methylation at the OPRM1 promoter region in men with opioid use disorder. Am. J. Drug Alcohol Abuse 44(2), 193–199 (2018).
MedlineGoogle Scholar
12. Craft RM. Sex differences in analgesic, reinforcing, discriminative, and motoric effects of opioids. Exp. Clin. Psychopharmacol. 16(5), 376–385 (2008).
Medline CASGoogle Scholar
13. Mazure CM, Fiellin DA. Women and opioids: something different is happening here. Lancet 392(10141), 9–11 (2018).
MedlineGoogle Scholar
14. Weiner SG, El Ibrahimi S, Hendricks MA et al. Factors associated with opioid overdose after an initial opioid prescription. JAMA Netw. Open 5(1), e2145691 (2022).
MedlineGoogle Scholar
15. Kaplovitch E, Gomes T, Camacho X, Dhalla IA, Mamdani MM, Juurlink DN. Sex differences in dose escalation and overdose death during chronic opioid therapy: a population-based cohort study. PLOS ONE 10(8), e0134550 (2015).
MedlineGoogle Scholar
16. Kelley K, Kelley T, Kaufman D, Mitchell A. The Slone Drug Dictionary: a research driven pharmacoepidemiology tool. Pharmacoepidemiol. Drug Saf. 12(Suppl. 1), S168–S169 (2003).
Google Scholar
17. Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 45(4), e22 (2017). • An extensive characterization of DNA methylation probes on the Illumina EPIC and 450K microarrays.
MedlineGoogle Scholar
18. Davidov O, Jelsema CM, Peddada S. Testing for inequality constraints in singular models by trimming or winsorizing the variance matrix. J. Am. Stat. Assoc. 113(522), 906–918 (2018).
Medline CASGoogle Scholar
19. Houseman EA, Accomando WP, Koestler DC et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012). • A method for inferring cell-type proportions using DNA methylation signatures.
MedlineGoogle Scholar
20. Rice K, Higgins JPT, Lumley T. A re-evaluation of fixed effect(s) meta-analysis. J. R. Stat. Soc. Ser. A Stat. Soc. 181(1), 205–227 (2018).
Google Scholar
21. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26(17), 2190–2191 (2010).
Medline CASGoogle Scholar
22. Benjamini Y, Hochberg Y. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57(1), 289–300 (1995).
Google Scholar
23. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated p-values. Bioinformatics 28(22), 2986–2988 (2012).
Medline CASGoogle Scholar
24. Xu Z, Xie C, Taylor JA, Niu L. ipDMR: identification of differentially methylated regions with interval p-values. Bioinformatics 37(5), 711–713 (2021).
Medline CASGoogle Scholar
25. Sidak Z. Rectangular confidence regions for the means of multivariate normal distributions. J. Am. Stat. Assoc. 62(318), 8 (1967).
Google Scholar
26. Breeze CE, Reynolds AP, Van Dongen J et al. eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data. Bioinformatics 35(22), 4767–4769 (2019). • A method useful when identifying enriched cell type-specific signals in epigenome-wide association results.
Medline CASGoogle Scholar
27. Breeze CE. Cell type-specific signal analysis in EWAS. bioRxiv doi: 10.1101/2021.05.21.445209 2021.2005.2021.445209 (2021) (Epub ahead of print).
Google Scholar
28. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W et al. Integrative analysis of 111 reference human epigenomes. Nature 518(7539), 317–330 (2015).
MedlineGoogle Scholar
29. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME Suite. Nucleic Acids Res. 43(W1), W39–W49 (2015).
Medline CASGoogle Scholar
30. Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28(4), 573–580 (2012).
Medline CASGoogle Scholar
31. Subramanian A, Tamayo P, Mootha VK et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102(43), 15545–15550 (2005).
Medline CASGoogle Scholar
32. Ren X, Kuan PF. methylGSA: a bioconductor package and shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics 35(11), 1958–1959 (2019).
Medline CASGoogle Scholar
33. Carlson M. org.Hs.eg.db: genome wide annotation for human. R package version 3.8.2. (2019). https://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html
Google Scholar
34. Huan T, Joehanes R, Song C et al. Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat. Commun. 10(1), 4267 (2019).
MedlineGoogle Scholar
35. Huls A, Robins C, Conneely KN et al. Brain DNA methylation patterns in CLDN5 associated with cognitive decline. Biol. Psychiatry 91(4), 389–398 (2022).
Medline CASGoogle Scholar
36. Min JL, Hemani G, Hannon E et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat. Genet. 53(9), 1311–1321 (2021). • A paper reporting genetic influences on DNA methylation.
Medline CASGoogle Scholar
37. Winkler TW, Kutalik Z, Gorski M, Lottaz C, Kronenberg F, Heid IM. EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data. Bioinformatics 31(2), 259–261 (2015).
Medline CASGoogle Scholar
38. Sun Z, Zhang Y, Jia J et al. H3K36me3, message from chromatin to DNA damage repair. Cell Biosc. 10, 9 (2020).
MedlineGoogle Scholar
39. McLeay RC, Bailey TL. Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 11, 165 (2010).
MedlineGoogle Scholar
40. Melik Parsadaniantz S, Rivat C, Rostene W, Reaux-Le Goazigo A. Opioid and chemokine receptor crosstalk: a promising target for pain therapy? Nat. Rev. Neurosci. 16(2), 69–78 (2015).
MedlineGoogle Scholar
41. Montague K, Malcangio M. The therapeutic potential of targeting chemokine signalling in the treatment of chronic pain. J. Neurochem. 141(4), 520–531 (2017).
Medline CASGoogle Scholar
42. Spiers H, Hannon E, Schalkwyk LC et al. Methylomic trajectories across human fetal brain development. Genome Res. 25(3), 338–352 (2015).
Medline CASGoogle Scholar
43. Assoum M, Salih MA, Drouot N et al. Rundataxin, a novel protein with RUN and diacylglycerol binding domains, is mutant in a new recessive ataxia. Brain 133(Pt. 8), 2439–2447 (2010).
MedlineGoogle Scholar
44. Seidahmed MZ, Hamad MH, Albakheet A et al. Ancient founder mutation in RUBCN: a second unrelated family confirms Salih ataxia (SCAR15). BMC Neurol. 20(1), 207 (2020).
Medline CASGoogle Scholar
45. Begemann M, Grube S, Papiol S et al. Modification of cognitive performance in schizophrenia by complexin 2 gene polymorphisms. Arch. Gen. Psychiatry 67(9), 879–888 (2010).
Medline CASGoogle Scholar
46. Harich B, Klein M, Ockeloen CW et al. From man to fly – convergent evidence links FBXO25 to ADHD and comorbid psychiatric phenotypes. J. Child Psychol. Psychiatry 61(5), 545–555 (2020).
MedlineGoogle Scholar
47. Cham KL, Soga T, Parhar IS. Expression of RING finger protein 38 in serotonergic neurons in the brain of Nile tilapia, Oreochromis niloticus. Front. Neuroanat. 12, 109 (2018).
Medline CASGoogle Scholar
48. Moriya S, Khel NB, Parhar IS. Cloning and serotonergic regulation of RING finger protein38 (rnf38) in the brain of medaka (Oryzias latipes). Neuroscience 294, 109–115 (2015).
Medline CASGoogle Scholar
49. Li M, Li Y, Qin H et al. Genome-wide DNA methylation analysis of peripheral blood cells derived from patients with first-episode schizophrenia in the Chinese Han population. Mol. Psychiatry 26(8), 4475–4485 (2021).
Medline CASGoogle Scholar
50. Xiang C, Baubet V, Pal S et al. RP58/ZNF238 directly modulates proneurogenic gene levels and is required for neuronal differentiation and brain expansion. Cell Death Differ. 19(4), 692–702 (2012).
Medline CASGoogle Scholar
51. Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics 10(11), 1024–1032 (2015).
MedlineGoogle Scholar

Vol. 14, No. 23

Supplemental Materials

Metrics

Downloaded 1,348 times

History

Received 7 October 2022

Accepted 13 January 2023

Published online 26 January 2023

Published in print December 2022

Information

Keywords

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/epi-2022-0353

Author contributions

M Lee and SJ London conceived and designed the study. M Lee, R Joehanes, DL McCartney and M Kho completed study-specific analyses. M Lee, R Joehanes, A Hüls, S Sikdar and A Burkholder performed follow-up analyses. AB Wyss, C Liu, RM Walker, SLR Kardia, TS Wingo, J Ma, A Campbell, AP Wingo, T Huan, A Keshawarz, DA Bennett, JA Smith, KL Evans, D Levy and SJ London contributed to study design and/or supervised data analysis in each cohort. M Lee wrote the first draft of the manuscript. All authors contributed to interpretation of the results and/or critical revision of the manuscript. All authors approved the final version.

Acknowledgments

We thank Frank Day of NIEHS and Jianping Jin of Westat, Inc. (Durham, NC, USA) for providing expert assistance.

Financial & competing interests disclosure

This work was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Science (Z01-ES043012). Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant HL105756. The Agricultural Lung Health Study is supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences (Z01-ES102385, Z01-ES049030, Z01-ES043012 and for ABW, contract no. HHSN273201600003I) and the National Cancer Institute (Z01-CP010119). This work was supported in part by American Recovery and Reinvestment Act (ARRA) funds through NIEHS contract N01-ES-55546. The Framingham Heart Study is funded by National Institutes of Health contract N01-HC-25195 and HHSN268201500001I. The laboratory work for this investigation was funded by the Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health. The analytical component of this project was funded by the Division of Intramural Research, National Heart, Lung, and Blood Institute, and the Center for Information Technology, National Institutes of Health, Bethesda, MD. Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006) and is currently supported by the Wellcome Trust (216767/Z/19/Z). Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL] reference 104036/Z/14/Z). The DNA methylation profiling and analysis were supported by Wellcome Investigator Award 220857/Z/20/Z and grant 104036/Z/14/Z (PI: AM McIntosh) and through funding from NARSAD (ref: 27404; awardee: DM Howard) and the Royal College of Physicians of Edinburgh (Sim Fellowship; awardee: HC Whalley). Support for the Genetic Epidemiology Network of Arteriopathy was provided by the National Heart, Lung and Blood Institute (U01 HL054457, RC1 HL100185, R01 HL087660, R01 HL119443 and R01 HL133221). ROS/MAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152, U01AG61356. ROS/MAP resources can be requested at https://www.radc.rush.edu. A Hüls is supported by the HERCULES Center (NIEHS P30ES019776). The authors declare that they have no relevant conflicts of interest.

No funded writing assistance has been used in the creation of this manuscript.

Ethical conduct of research

Each study was approved by its institutional review board. All study participants provided written informed consent.

Data sharing statement

Complete meta-analysis results can be found at Zenodo 10.5281/zenodo.7545108.

Open access

This work is licensed under the Creative Commons Attribution 4.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

PDF download