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DNA Methylation Mediated the Association of Body Mass Index With Blood Pressure in Chinese Monozygotic Twins

Published online by Cambridge University Press:  31 January 2024

Jie Yao ,
Feng Ning ,
Weijing Wang  and
Dongfeng Zhang Open the ORCID record for Dongfeng Zhang [Opens in a new window]
Jie Yao
Affiliation:
Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China Jiangsu Health Development Research Center, Nanjing, Jiangsu Province, China
Feng Ning
Affiliation:
Qingdao Centers for Disease Control and Prevention/Qingdao Institute of Preventive Medicine, Qingdao, Shandong, China
Weijing Wang
Affiliation:
Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
Dongfeng Zhang*
Affiliation:
Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
*
Corresponding author: Dongfeng Zhang; Email: zhangdf1961@126.com
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Abstract

Obesity is an established risk factor for hypertension, but the mechanisms are only partially understood. We examined whether body mass index (BMI)-related DNA methylation (DNAm) variation would mediate the association of BMI with blood pressure (BP). We first conducted a genomewide DNA methylation analysis in monozygotic twin pairs to detect BMI-related DNAm variation and then evaluated the mediating effect of DNAm on the relationship between BMI and BP levels using the causal inference test (CIT) method and mediation analysis. Ontology enrichment analysis was performed for CpGs using the GREAT tool. A total of 60 twin pairs for BMI and systolic blood pressure (SBP) and 58 twin pairs for BMI and diastolic blood pressure (DBP) were included. BMI was positively associated with SBP (β = 1.86, p = .0004). The association between BMI and DNAm of 85 CpGs reached p < 1×10–4 level. Eleven BMI-related differentially methylated regions (DMRs) within LNCPRESS1, OGDHL, RNU1-44P, NPHS1, ECEL1P2, LLGL2, RNY4P15, MOGAT3, PHACTR3, and BAI2 were found. Of the 85 CpGs, 9 mapped to C10orf71-AS1, NDUFB5P1, KRT80, BAI2, ABCA2, PEX11G and FGF4 were significantly associated with SBP levels. Of the 9 CpGs, 2 within ABCA2 negatively mediated the association between BMI and SBP, with a mediating effect of −0.24 (95% CI [−0.65, −0.01]). BMI was also positively associated with DBP (β = 0.60, p = .0495). The association between BMI and DNAm of 193 CpGs reached p < 1×10−4 level. Twenty-five BMI-related DMRs within OGDHL, POU4F2, ECEL1P2, TTC6, SMPD4, EP400, TUBA1C and AGAP2 were found. Of the 193 CpGs, 33 mapped to ABCA2, ADORA2B, CTNNBIP1, KDM4B, NAA60, RSPH6A, SLC25A19 and STIL were significantly associated with DBP levels. Of the 33 CpGs, 12 within ABCA2, SLC25A19, KDM4B, PTPRN2, DNASE1, TFCP2L1, LMNB2 and C10orf71-AS1 negatively mediated the association between BMI and DBP, with a total mediation effect of −0.66 (95% CI [−1.07, −0.30]). Interestingly, BMI might also negatively mediate the association between the DNAm of most CpG mediators mentioned above and BP. The mediating effect of DNAm was also found when stratified by sex. In conclusion, DNAm variation may partially negatively mediate the association of BMI with BP. Our findings may provide new clues to further elucidate the pathogenesis of obesity to hypertension and identify new diagnostic biomarkers and therapeutic targets for hypertension.

Keywords

BMIBlood pressureDNA methylationMediationTwin studies
Type
Article
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Twin Research and Human Genetics , First View , pp. 1 - 12
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© The Author(s), 2024. Published by Cambridge University Press on behalf of International Society for Twin Studies

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References

Benjamini, Y., & Hochberg, Y. (2018). Controlling the False Discovery Rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x%J Google Scholar
Chen, C., Xiang, Q., Liu, W., Liang, S., Yang, M., & Tao, J. (2021). Co-expression network revealed roles of RNA m(6)A methylation in human β-cell of type 2 diabetes mellitus. Frontiers in Cell and Developmental Biology, 9, 651142. https://doi.org/10.3389/fcell.2021.651142 CrossRefGoogle ScholarPubMed
Chen, J., Wang, W., Li, Z., Xu, C., Tian, X., & Zhang, D. (2021). Heritability and genome-wide association study of blood pressure in Chinese adult twins. Molecular Genetics & Genomic Medicine, 9, e1828. https://doi.org/10.1002/mgg3.1828 CrossRefGoogle ScholarPubMed
Cheng, Y., Yuan, Q., Vergnes, L., Rong, X., Youn, J. Y., Li, J., Yu, Y., Liu, W., Cai, H., Lin, J. D., Tontonoz, P., Hong, C., Reue, K., & Wang, C. Y. (2018). KDM4B protects against obesity and metabolic dysfunction. Proceedings of the National Academy of Sciences of the United States of America, 115, 5566–5575. https://doi.org/10.1073/pnas.1721814115 Google ScholarPubMed
Demura, M., & Saijoh, K. (2017). The role of DNA methylation in hypertension. Advances in Experimental Medicine and Biology, 956, 583–598. https://doi.org/10.1007/5584_2016_80 CrossRefGoogle ScholarPubMed
DiNicolantonio, J. J., Liu, J., & O’Keefe, J. H. (2018). Thiamine and cardiovascular disease: A literature review. Progress in Cardiovascular Diseases, 61, 27–32. https://doi.org/10.1016/j.pcad.2018.01.009 CrossRefGoogle ScholarPubMed
Du, P., Zhang, X., Huang, C. C., Jafari, N., Kibbe, W. A., Hou, L., & Lin, S. M. (2010). Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics, 11, 587. https://doi.org/10.1186/1471-2105-11-587 CrossRefGoogle ScholarPubMed
Duan, H., Ning, F., Zhang, D., Wang, S., Zhang, D., Tan, Q., Tian, X., & Pang, Z. (2013). The Qingdao Twin Registry: A status update. Twin Research and Human Genetics, 16, 79–85. https://doi.org/10.1017/thg.2012.113 CrossRefGoogle ScholarPubMed
Duan, H., Pang, Z., Zhang, D., Li, S., Kruse, T. A., Kyvik, K. O., Christensen, K., & Tan, Q. (2011). Genetic and environmental dissections of sub-phenotypes of metabolic syndrome in the Chinese population: A twin-based heritability study. Obesity Facts, 4, 99–104. https://doi.org/10.1159/000327735 CrossRefGoogle ScholarPubMed
Franceschini, N., Fox, E., Zhang, Z., Edwards, T. L., Nalls, M. A., Sung, Y. J., Tayo, B. O., Sun, Y. V., Gottesman, O., Adeyemo, A., Johnson, A. D., Young, J. H., Rice, K., Duan, Q., Chen, F., Li, Y., Tang, H., Fornage, M., Keene, K. L., Andrews, J. S., & Zhu, X. (2013). Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. American Journal of Human Genetics, 93, 545–554. https://doi.org/10.1016/j.ajhg.2013.07.010 CrossRefGoogle ScholarPubMed
Fu, Y., Zhu, Z., Huang, Z., He, R., Zhang, Y., Li, Y., Tan, W., & Rong, S. (2023). Association between vitamin B and obesity in middle-aged and older Chinese adults. Nutrients, 15, 483. https://doi.org/10.3390/nu15030483 CrossRefGoogle ScholarPubMed
Ganesh, S. K., Tragante, V., Guo, W., Guo, Y., Lanktree, M. B., Smith, E. N., Johnson, T., Castillo, B. A., Barnard, J., Baumert, J., Chang, Y. P., Elbers, C. C., Farrall, M., Fischer, M. E., Franceschini, N., Gaunt, T. R., Gho, J. M., Gieger, C., Gong, Y., & Asselbergs, F. W. (2013). Loci influencing blood pressure identified using a cardiovascular gene-centric array. Human Molecular Genetics, 22, 1663–1678. https://doi.org/10.1093/hmg/dds555 CrossRefGoogle ScholarPubMed
Han, L., Choudhury, S., Mich-Basso, J. D., Ammanamanchi, N., Ganapathy, B., Suresh, S., Khaladkar, M., Singh, J., Maehr, R., Zuppo, D. A., Kim, J., Eberwine, J. H., Wyman, S. K., Wu, Y. L., & Kühn, B. (2020). Lamin B2 levels regulate polyploidization of cardiomyocyte nuclei and myocardial regeneration. Developmental Cell, 53, 42–59.e11. https://doi.org/10.1016/j.devcel.2020.01.030 CrossRefGoogle ScholarPubMed
Hebestreit, K., Dugas, M., & Klein, H. U. (2013). Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics, 29, 1647–1653. https://doi.org/10.1093/bioinformatics/btt263 CrossRefGoogle ScholarPubMed
Jaffe, A. E., & Irizarry, R. A. (2014). Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology, 15, 31. https://doi.org/10.1186/gb-2014-15-2-r31 CrossRefGoogle ScholarPubMed
Kato, N., Loh, M., Takeuchi, F., Verweij, N., Wang, X., Zhang, W., Kelly, T. N., Saleheen, D., Lehne, B., Leach, I. M., Drong, A. W., Abbott, J., Wahl, S., Tan, S. T., Scott, W. R., Campanella, G., Chadeau-Hyam, M., Afzal, U., Ahluwalia, T. S., & Chambers, J. C. (2015). Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nature Genetics, 47, 1282–1293. https://doi.org/10.1038/ng.3405 CrossRefGoogle ScholarPubMed
Krueger, F., & Andrews, S. R. (2011). Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27, 1571–1572. https://doi.org/10.1093/bioinformatics/btr167 CrossRefGoogle Scholar
Li, J., Yin, L., Chen, S., Li, Z., Ding, J., Wu, J., Yang, K., & Xu, J. (2023). The perspectives of NETosis on the progression of obesity and obesity-related diseases: mechanisms and applications. Frontiers in Cell and Developmental Biology, 11, 1221361. https://doi.org/10.3389/fcell.2023.1221361 CrossRefGoogle ScholarPubMed
Li, S., Wang, W., Zhang, D., Li, W., Lund, J., Kruse, T., Mengel-From, J., Christensen, K., & Tan, Q. (2021). Differential regulation of the DNA methylome in adults born during the Great Chinese Famine in 1959–1961. Genomics, 113, 3907–3918. https://doi.org/10.1016/j.ygeno.2021.09.018 CrossRefGoogle ScholarPubMed
Li, W., Christiansen, L., Hjelmborg, J., Baumbach, J., & Tan, Q. (2018). On the power of epigenome-wide association studies using a disease-discordant twin design. Bioinformatics, 34, 4073–4078. https://doi.org/10.1093/bioinformatics/bty532 CrossRefGoogle ScholarPubMed
Li, Z., Wang, W., Tian, X., Duan, H., Xu, C., & Zhang, D. (2021). Bivariate genome-wide association study (GWAS) of body mass index and blood pressure phenotypes in northern Chinese twins. PLoS One, 16, e0246436. https://doi.org/10.1371/journal.pone.0246436 CrossRefGoogle ScholarPubMed
Liang, M. (2018). Epigenetic mechanisms and hypertension. Hypertension, 72, 1244–1254. https://doi.org/10.1161/hypertensionaha.118.11171 CrossRefGoogle ScholarPubMed
Mack, J. T., Townsend, D. M., Beljanski, V., & Tew, K. D. (2007). The ABCA2 transporter: intracellular roles in trafficking and metabolism of LDL-derived cholesterol and sterol-related compounds. Current Drug Metabolism, 8, 47–57. https://doi.org/10.2174/138920007779315044 CrossRefGoogle ScholarPubMed
Małodobra-Mazur, M., Alama, A., Bednarska-Chabowska, D., Pawelka, D., Myszczyszyn, A., & Dobosz, T. (2019). Obesity-induced insulin resistance via changes in the DNA methylation profile of insulin pathway genes. Advances in Clinical and Experimental Medicine, 28, 1599–1607. https://doi.org/10.17219/acem/110321 CrossRefGoogle ScholarPubMed
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010). GREAT improves functional interpretation of cis-regulatory regions. Nature Biotechnology, 28, 495–501. https://doi.org/10.1038/nbt.1630 CrossRefGoogle ScholarPubMed
Miao, G., Fiehn, O., Chen, M., Zhang, Y., Umans, J. G., Lee, E. T., Howard, B. V., Roman, M. J., Devereux, R. B., & Zhao, J. (2023). Longitudinal lipidomic signature of carotid atherosclerosis in American Indians: Findings from the Strong Heart Family Study. Atherosclerosis, 382, 117265. https://doi.org/10.1016/j.atherosclerosis.2023.117265 CrossRefGoogle ScholarPubMed
Millstein, J., Zhang, B., Zhu, J., & Schadt, E. E. (2009). Disentangling molecular relationships with a causal inference test. BMC Genetics, 10, 23. https://doi.org/10.1186/1471-2156-10-23 CrossRefGoogle ScholarPubMed
Mohammadnejad, A., Soerensen, M., Baumbach, J., Mengel-From, J., Li, W., Lund, J., Li, S., Christiansen, L., Christensen, K., Hjelmborg, J. V. B., & Tan, Q. (2021). Novel DNA methylation marker discovery by assumption-free genome-wide association analysis of cognitive function in twins. Aging Cell, 20, 13293. https://doi.org/10.1111/acel.13293 CrossRefGoogle ScholarPubMed
Nakwan, N., Kunhapan, P., Chaiyasung, T., Satproedprai, N., Singkhamanan, K., Mahasirimongkol, S., & Charalsawadi, C. (2023). Genome-wide association study identifies WWC2 as a possible locus associated with persistent pulmonary hypertension of the newborn in the Thai population. Translational Pediatrics, 12, 1–12. https://doi.org/10.21037/tp-22-280 CrossRefGoogle ScholarPubMed
Pedersen, B. S., Schwartz, D. A., Yang, I. V., & Kechris, K. J. (2012). Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics, 28, 2986–2988. https://doi.org/10.1093/bioinformatics/bts545 CrossRefGoogle ScholarPubMed
Rahmani, E., Zaitlen, N., Baran, Y., Eng, C., Hu, D., Galanter, J., Oh, S., Burchard, E. G., Eskin, E., Zou, J., & Halperin, E. (2016). Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nature Methods, 13, 443–445. https://doi.org/10.1038/nmeth.3809 CrossRefGoogle ScholarPubMed
Rao, A., Pandya, V., & Whaley-Connell, A. (2015). Obesity and insulin resistance in resistant hypertension: implications for the kidney. Advances in Chronic Kidney Disease, 22, 211–217. https://doi.org/10.1053/j.ackd.2014.12.004 CrossRefGoogle ScholarPubMed
Sasaki, A., Murphy, K. E., Briollais, L., McGowan, P. O., & Matthews, S. G. (2022). DNA methylation profiles in the blood of newborn term infants born to mothers with obesity. PLoS One, 17, 0267946. https://doi.org/10.1371/journal.pone.0267946 CrossRefGoogle ScholarPubMed
Tan, Q. H., Christiansen, L., Hjelmborg, J. V., & Christensen, K. (2015). Twin methodology in epigenetic studies. Journal of Experimental Biology, 218, 134–139. https://doi.org/10.1242/jeb.107151 CrossRefGoogle ScholarPubMed
Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R Package for causal mediation analysis. Journal of Statistical Software, 59, 1–38. https://doi.org/10.18637/jss.v059.i05 CrossRefGoogle Scholar
van der Linden, E. L., Halley, A., Meeks, K. A. C., Chilunga, F., Hayfron-Benjamin, C., Venema, A., Garrelds, I. M., Danser, A. H. J., van den Born, B. J., Henneman, P., & Agyemang, C. (2022). An explorative epigenome-wide association study of plasma renin and aldosterone concentration in a Ghanaian population: the RODAM study. Clinical Epigenetics, 14, 159. https://doi.org/10.1186/s13148-022-01378-5 CrossRefGoogle Scholar
van Dijk, S. J., Molloy, P. L., Varinli, H., Morrison, J. L., & Muhlhausler, B. S. (2015). Epigenetics and human obesity. International Journal of Obesity, 39, 85–97. https://doi.org/10.1038/ijo.2014.34 CrossRefGoogle ScholarPubMed
Visniauskas, B., Kilanowski-Doroh, I., Ogola, B. O., McNally, A. B., Horton, A. C., Imulinde Sugi, A., & Lindsey, S. H. (2023). Estrogen-mediated mechanisms in hypertension and other cardiovascular diseases. Journal of Human Hypertension, 37, 609–618. https://doi.org/10.1038/s41371-022-00771-0 CrossRefGoogle ScholarPubMed
Wain, L. V., Verwoert, G. C., O’Reilly, P. F., Shi, G., Johnson, T., Johnson, A. D., Bochud, M., Rice, K. M., Henneman, P., Smith, A. V., Ehret, G. B., Amin, N., Larson, M. G., Mooser, V., Hadley, D., Dörr, M., Bis, J. C., Aspelund, T., Esko, T., ¼ van Duijn, C. M. (2011). Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nature Genetics, 43, 1005–1011. https://doi.org/10.1038/ng.922 CrossRefGoogle ScholarPubMed
Wang, W., Li, W., Duan, H., Xu, C., Tian, X., Li, S., Tan, Q., & Zhang, D. (2023). Mediation by DNA methylation on the association of BMI and serum uric acid in Chinese monozygotic twins. Gene, 850, 146957. https://doi.org/10.1016/j.gene.2022.146957 CrossRefGoogle ScholarPubMed
Wang, W., Li, W., Jiang, W., Lin, H., Wu, Y., Wen, Y., Xu, C., Tian, X., Li, S., Tan, Q., & Zhang, D. (2021). Genome-wide DNA methylation analysis of cognitive function in middle and old-aged Chinese monozygotic twins. Journal of Psychiatric Research, 136, 571–580. https://doi.org/10.1016/j.jpsychires.2020.10.031 CrossRefGoogle ScholarPubMed
Wang, W., Li, W., Wu, Y., Tian, X., Duan, H., Li, S., Tan, Q., & Zhang, D. (2021). Genome-wide DNA methylation and gene expression analyses in monozygotic twins identify potential biomarkers of depression. Translational Psychiatry, 11, 416. https://doi.org/10.1038/s41398-021-01536-y CrossRefGoogle ScholarPubMed
Wang, W., Yao, J., Li, W., Wu, Y., Duan, H., Xu, C., Tian, X., Li, S., Tan, Q., & Zhang, D. (2023). Epigenome-wide association study in Chinese monozygotic twins identifies DNA methylation loci associated with blood pressure. Clinical Epigenetics, 15, 38. https://doi.org/10.1186/s13148-023-01457-1 CrossRefGoogle ScholarPubMed
Wang, W., Yao, W., Tan, Q., Li, S., Duan, H., Tian, X., Xu, C., & Zhang, D. (2023). Identification of key DNA methylation changes on fasting plasma glucose: A genome-wide DNA methylation analysis in Chinese monozygotic twins. Diabetology & Metabolic Syndrome, 15, 159. https://doi.org/10.1186/s13098-023-01136-4 CrossRefGoogle ScholarPubMed
Wang, W., Zhang, D., Xu, C., Wu, Y., Duan, H., Li, S., & Tan, Q. (2018). Heritability and genome-wide association analyses of serum uric acid in middle and old-aged Chinese twins. Frontiers in Endocrinology, 9, 75. https://doi.org/10.3389/fendo.2018.00075 CrossRefGoogle ScholarPubMed
Wang, Y., Peng, X., Nie, X., Chen, L., Weldon, R., Zhang, W., Xiao, D., & Cai, J. (2016). Burden of hypertension in China over the past decades: Systematic analysis of prevalence, treatment and control of hypertension. European Journal of Preventive Cardiology, 23, 792–800. https://doi.org/10.1177/2047487315617105 CrossRefGoogle Scholar
Wu, Y., Zhang, D., Pang, Z., Jiang, W., Wang, S., Li, S., von Bornemann Hjelmborg, J., & Tan, Q. (2015). Multivariate modeling of body mass index, pulse pressure, systolic and diastolic blood pressure in Chinese twins. Twin Research and Human Genetics, 18, 73–78. https://doi.org/10.1017/thg.2014.83 CrossRefGoogle ScholarPubMed
Zhang, M., Shi, Y., Zhou, B., Huang, Z., Zhao, Z., Li, C., Zhang, X., Han, G., Peng, K., Li, X., Wang, Y., Ezzati, M., Wang, L., & Li, Y. (2023). Prevalence, awareness, treatment, and control of hypertension in China, 2004-18: Findings from six rounds of a national survey. BMJ, 380, e071952. https://doi.org/10.1136/bmj-2022-071952 CrossRefGoogle Scholar
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