nach oben

Journal of Endocrinological Investigation

Erschienen in:

01.12.2022 | Original Article

DNA methylation and waist-to-hip ratio: an epigenome-wide association study in Chinese monozygotic twins

verfasst von: Y. Wu, H. Tian, W. Wang, W. Li, H. Duan, D. Zhang

Erschienen in: Journal of Endocrinological Investigation | Ausgabe 12/2022

Einloggen, um Zugang zu erhalten

Abstract

Purpose

Epigenetic signatures such as DNA methylation may be associated with specific obesity traits. We performed an epigenome-wide association study (EWAS) by combining with the waist-to-hip ratio (WHR)-discordant monozygotic (MZ) twin design in an attempt to identify genetically independent DNA methylation marks associated with abdominal obesity in Northern Han Chinese and to determine the causation underlying.

Methods

A total of 60 WHR discordant MZ twin pairs were selected from the Qingdao Twin Registry, China. Generalized estimated equation (GEE) model was used to regress the methylation level of CpG sites on WHR. The Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) was used to assess the temporal relationship between methylation and WHR. Gene expression analysis was conducted to validate the results of differentially methylated analyses.

Results

EWAS identified 92 CpG sites with the level of P < 10^– 4 which were annotated to 32 genes, especially CADPS2, TUSC5, ZCCHC14, CORO7, COL23A1, CACNA1C, CYP26B1, and BCAT1. ICE FALCON showed significant causality between DNA methylation of several genes and WHR (P < 0.05). In region-based analysis, 14 differentially methylated regions (DMRs) located at 15 genes (slk-corrected P < 0.05) were detected. The gene expression analysis identified the significant correlation between expression levels of 5 differentially methylated genes and WHR (P < 0.05).

Conclusions

Our study identifies the associations between specific epigenetic variations and WHR in Northern Han Chinese. These DNA methylation signatures may have value as diagnostic biomarkers and provide novel insights into the molecular mechanisms of pathogenesis.

Nur mit Berechtigung zugänglich

Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J et al (2019) Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 28(1):166–74. https://doi.org/10.1093/hmg/ddy327CrossRefPubMed

Rose KM, Newman B, Mayer-Davis EJ, Selby JV (1998) Genetic and behavioral determinants of waist-hip ratio and waist circumference in women twins. Obes Res. 6(6):383–92. https://doi.org/10.1002/j.1550-8528.1998.tb00369.xCrossRefPubMed

Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518(7538):187–196. https://doi.org/10.1038/nature14132CrossRefPubMedPubMedCentral

Schadt EE (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461(7261):218–23. https://doi.org/10.1038/nature08454CrossRefPubMed

Aronica L, Levine AJ, Brennan K, Mi J, Gardner C, Haile RW et al (2017) A systematic review of studies of DNA methylation in the context of a weight loss intervention. Epigenomics. 9(5):769–87. https://doi.org/10.2217/epi-2016-0182CrossRefPubMed

Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S et al (2017) Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 541(7635):81–6. https://doi.org/10.1038/nature20784CrossRefPubMed

van Dijk SJ, Tellam RL, Morrison JL, Muhlhausler BS, Molloy PL (2015) Recent developments on the role of epigenetics in obesity and metabolic disease. Clin Epigenetics. 7:66. https://doi.org/10.1186/s13148-015-0101-5CrossRefPubMedPubMedCentral

Ling C, Rönn T (2019) Epigenetics in human obesity and type 2 diabetes. Cell Metab. 29(5):1028–44. https://doi.org/10.1016/j.cmet.2019.03.009CrossRefPubMedPubMedCentral

Li W, Zhang D, Wang W, Wu Y, Mohammadnejad A, Lund J et al (2019) DNA methylome profiling in identical twin pairs discordant for body mass index. Int J Obes (Lond). 43(12):2491–9. https://doi.org/10.1038/s41366-019-0382-4CrossRef

10.

Tan Q, Christiansen L, von Bornemann HJ, Christensen K (2015) Twin methodology in epigenetic studies. J Exp Biol 218(Pt 1):134–139. https://doi.org/10.1242/jeb.107151CrossRefPubMed

11.

Wang W, Li W, Wu Y, Tian X, Duan H, Li S et al (2021) Genome-wide DNA methylation and gene expression analyses in monozygotic twins identify potential biomarkers of depression. Transl Psychiatry 11(1):416. https://doi.org/10.1038/s41398-021-01536-yCrossRefPubMedPubMedCentral

12.

Bell JT, Saffery R (2012) The value of twins in epigenetic epidemiology. Int J Epidemiol 41(1):140–150. https://doi.org/10.1093/ije/dyr179CrossRefPubMed

13.

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(23):4073–8. https://doi.org/10.1093/bioinformatics/bty532CrossRefPubMed

14.

Li S, Bui M, Hopper JL (2020) Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization. Int J Epidemiol. 49(4):1259–69. https://doi.org/10.1093/ije/dyaa065CrossRefPubMedPubMedCentral

15.

Chen Y, Kassam I, Lau SH, Kooner JS, Wilson R, Peters A et al (2021) Impact of BMI and waist circumference on epigenome-wide DNA methylation and identification of epigenetic biomarkers in blood: an EWAS in multi-ethnic Asian individuals. Clin Epigenetics 13(1):195. https://doi.org/10.1186/s13148-021-01162-xCrossRefPubMedPubMedCentral

16.

Wang BQ, Gao WJ, Li J, Yu CQ, Cao WH, Lv J et al (2016) Methylation loci associated with body mass index, waist circumference, and waist-to-hip ratio in Chinese adults: an epigenome-wide analysis. Lancet. 388:S21CrossRef

17.

Duan H, Ning F, Zhang D, Wang S, Zhang D, Tan Q et al (2013) The Qingdao twin registry: a status update. Twin Res Hum Genet: Off J Int Soc Twin Stud 16(1):79–85. https://doi.org/10.1017/thg.2012.113CrossRef

18.

Xu C, Zhang D, Tian X, Wu Y, Pang Z, Li S et al (2017) Genetic and environmental basis in phenotype correlation between physical function and cognition in aging Chinese twins. Twin Res Hum Genet: Off J Int Soc Twin Stud 20(1):60–65. https://doi.org/10.1017/thg.2016.98CrossRef

19.

Tomsey CS, Kurtz M, Kist F, Hockensmith M, Call P (2001) Comparison of PowerPlex 16, PowerPlex1.1/2.1, and ABI AmpfISTR profiler plus/COfiler for forensic use. Croat Med J. 42(3):239–43PubMed

20.

Demakakos P, Hamer M, Stamatakis E, Steptoe A (2010) Low-intensity physical activity is associated with reduced risk of incident type 2 diabetes in older adults: evidence from the English longitudinal study of ageing. Diabetologia. 53(9):1877–85. https://doi.org/10.1007/s00125-010-1785-xCrossRefPubMed

21.

Wang W, Li W, Jiang W, Lin H, Wu Y, Wen Y et al (2021) Genome-wide DNA methylation analysis of cognitive function in middle and old-aged Chinese monozygotic twins. J Psychiatr Res 136:571–580. https://doi.org/10.1016/j.jpsychires.2020.10.031CrossRefPubMed

22.

Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27(11):1571–1572. https://doi.org/10.1093/bioinformatics/btr167CrossRefPubMedPubMedCentral

23.

Hebestreit K, Dugas M, Klein H-U (2013) Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29(13):1647–1653. https://doi.org/10.1093/bioinformatics/btt263CrossRefPubMed

24.

Jaffe AE, Irizarry RA (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 15(2):R31. https://doi.org/10.1186/gb-2014-15-2-r31CrossRefPubMedPubMedCentral

25.

Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J et al (2016) Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods 13(5):443–445. https://doi.org/10.1038/nmeth.3809CrossRefPubMedPubMedCentral

26.

Wang W, Jiang W, Hou L, Duan H, Wu Y, Xu C et al (2017) Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI. BMC Genomics 18(1):872. https://doi.org/10.1186/s12864-017-4257-6CrossRefPubMedPubMedCentral

27.

Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14(4):R36. https://doi.org/10.1186/gb-2013-14-4-r36CrossRefPubMedPubMedCentral

28.

Højsgaard S, Halekoh U, Yan J (2005) The R package geepack for generalized estimating equations. J Stat Softw 15(2):1–11. https://doi.org/10.18637/jss.v015.i02CrossRef

29.

Bui M, Bjørnerem A, Ghasem-Zadeh A, Dite GS, Hopper JL, Seeman E (2013) Architecture of cortical bone determines in part its remodelling and structural decay. Bone 55(2):353–358. https://doi.org/10.1016/j.bone.2013.04.020CrossRefPubMed

30.

Li W, Baumbach J, Larsen MJ, Mohammadnejad A, Lund J, Christensen K et al (2020) Differential long noncoding RNA profiling of BMI in twins. Epigenomics 12(17):1531–1541. https://doi.org/10.2217/epi-2020-0033CrossRefPubMed

31.

Zhu Y, Strachan E, Fowler E, Bacus T, Roy-Byrne P, Zhao J (2019) Genome-wide profiling of DNA methylome and transcriptome in peripheral blood monocytes for major depression: A monozygotic discordant twin study. Transl Psychiatry 9(1):215. https://doi.org/10.1038/s41398-019-0550-2CrossRefPubMedPubMedCentral

32.

Pedersen BS, Schwartz DA, Yang IV, Kechris KJ (2012) Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics 28(22):2986–2988. https://doi.org/10.1093/bioinformatics/bts545CrossRefPubMedPubMedCentral

33.

McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28(5):495–501. https://doi.org/10.1038/nbt.1630CrossRefPubMedPubMedCentral

34.

Kaminsky Z, Petronis A, Wang SC, Levine B, Ghaffar O, Floden D et al (2008) Epigenetics of personality traits: an illustrative study of identical twins discordant for risk-taking behavior. Twin Res Hum Genet. 11(1):1–11. https://doi.org/10.1375/twin.11.1.1CrossRefPubMed

35.

Tsai PC, Bell JT (2015) Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int J Epidemiol. 44(4):1429–41. https://doi.org/10.1093/ije/dyv041CrossRefPubMedPubMedCentral

36.

Perfilyev A, Dahlman I, Gillberg L, Rosqvist F, Iggman D, Volkov P et al (2017) Impact of polyunsaturated and saturated fat overfeeding on the DNA-methylation pattern in human adipose tissue: a randomized controlled trial. Am J Clin Nutr. 105(4):991–1000. https://doi.org/10.3945/ajcn.116.143164CrossRefPubMed

37.

Eriksson A, Williams MJ, Voisin S, Hansson I, Krishnan A, Philippot G et al (2015) Implication of coronin 7 in body weight regulation in humans, mice and flies. BMC Neurosci 16:13. https://doi.org/10.1186/s12868-015-0151-9CrossRefPubMedPubMedCentral

38.

Crujeiras AB, Pissios P, Moreno-Navarrete JM, Diaz-Lagares A, Sandoval J, Gomez A et al (2018) An epigenetic signature in adipose tissue is linked to nicotinamide N-methyltransferase gene expression. Mol Nutr Food Res. https://doi.org/10.1002/mnfr.201700933CrossRefPubMed

39.

Sayols-Baixeras S, Subirana I, Fernández-Sanlés A, Sentí M, Lluís-Ganella C, Marrugat J et al (2017) DNA methylation and obesity traits: an epigenome-wide association study. the REGICOR study. Epigenetics 12(10):909–916. https://doi.org/10.1080/15592294.2017.1363951CrossRefPubMedPubMedCentral

40.

Meeks KAC, Henneman P, Venema A, Burr T, Galbete C, Danquah I et al (2017) An epigenome-wide association study in whole blood of measures of adiposity among Ghanaians: the RODAM study. Clin Epigenetics 9:103. https://doi.org/10.1186/s13148-017-0403-xCrossRefPubMedPubMedCentral

41.

Moleres A, Campión J, Milagro FI, Marcos A, Campoy C, Garagorri JM et al (2013) Differential DNA methylation patterns between high and low responders to a weight loss intervention in overweight or obese adolescents: the EVASYON study. FASEB J 27(6):2504–12. https://doi.org/10.1096/fj.12-215566CrossRefPubMed

42.

Pedram P, Zhai G, Gulliver W, Zhang H, Sun G (2017) Two novel candidate genes identified in adults from the Newfoundland population with addictive tendencies towards food. Appetite 115:71–79. https://doi.org/10.1016/j.appet.2017.01.004CrossRefPubMed

43.

Mariman ECM, Szklarczyk R, Bouwman FG, Aller EEJG, van Baak MA, Wang P (2015) Olfactory receptor genes cooperate with protocadherin genes in human extreme obesity. Genes Nutr 10(4):465. https://doi.org/10.1007/s12263-015-0465-3CrossRefPubMed

44.

Mariman E, Bouwman FG, Aller E, Baak MV, Wang P (2014) High frequency of rare variants with a moderate-to-high predicted biological effect in protocadherin genes of extremely obese. Genes Nutr 9(3):399CrossRefPubMedPubMedCentral

45.

Yang Z, Wei Z, Wu X, Yang H (2018) Screening of exosomal miRNAs derived from subcutaneous and visceral adipose tissues: determination of targets for the treatment of obesity and associated metabolic disorders. Mol Med Rep 18(3):3314–3324. https://doi.org/10.3892/mmr.2018.9312CrossRefPubMedPubMedCentral

46.

Li P, Ogino K, Hoshikawa Y, Morisaki H, Toyama K, Morisaki T et al (2013) AMP deaminase 3 plays a critical role in remote reperfusion lung injury. Biochem Biophys Res Commun. 434(1):131–6. https://doi.org/10.1016/j.bbrc.2013.03.056CrossRefPubMed

47.

Do DN, Strathe AB, Ostersen T, Jensen J, Mark T, Kadarmideen HN (2013) Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping. PLoS ONE 8(8):e71509. https://doi.org/10.1371/journal.pone.0071509CrossRefPubMedPubMedCentral

48.

Beaton N, Rudigier C, Moest H, Müller S, Mrosek N, Röder E et al (2015) TUSC5 regulates insulin-mediated adipose tissue glucose uptake by modulation of GLUT4 recycling. Mol Metab. 4(11):795–810. https://doi.org/10.1016/j.molmet.2015.08.003CrossRefPubMedPubMedCentral

49.

Yan H, Fei N, Wu G, Zhang C, Zhao L, Zhang M (2016) Regulated inflammation and lipid metabolism in colon mRNA expressions of obese germfree mice responding to enterobacter cloacae B29 combined with the high fat diet. Front Microbiol. 7:1786. https://doi.org/10.3389/fmicb.2016.01786CrossRefPubMedPubMedCentral

50.

Marrades MP, González-Muniesa P, Arteta D, Martínez JA, Moreno-Aliaga MJ (2011) Orchestrated downregulation of genes involved in oxidative metabolic pathways in obese vs. lean high-fat young male consumers. J Physiol Biochem 67(1):15–26. https://doi.org/10.1007/s13105-010-0044-4CrossRefPubMed

51.

Chen J, Meng Y, Zhou J, Zhuo M, Ling F, Zhang Y et al (2013) Identifying candidate genes for type 2 diabetes mellitus and obesity through gene expression profiling in multiple tissues or cells. J Diabetes Res. https://doi.org/10.1155/2013/970435CrossRefPubMedPubMedCentral

52.

Viguerie N, Montastier E, Maoret JJ, Roussel B, Combes M, Valle C et al (2012) Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation. PLoS Genet. https://doi.org/10.1371/journal.pgen.1002959CrossRefPubMedPubMedCentral

53.

Serrano-Marco L, Rodríguez-Calvo R, El Kochairi I, Palomer X, Michalik L, Wahli W et al (2011) Activation of peroxisome proliferator-activated receptor-β/-δ (PPAR-β/-δ) ameliorates insulin signaling and reduces SOCS3 levels by inhibiting STAT3 in interleukin-6-stimulated adipocytes. Diabetes 60(7):1990–9. https://doi.org/10.2337/db10-0704CrossRefPubMedPubMedCentral

54.

Sekine Y, Tsuji S, Ikeda O, Sato N, Aoki N, Aoyama K et al (2006) Regulation of STAT3-mediated signaling by LMW-DSP2. Oncogene 25(42):5801–6. https://doi.org/10.1038/sj.onc.1209578CrossRefPubMed

55.

van den Dungen MW, Murk AJ, Kok DE, Steegenga WT (2016) Comprehensive DNA methylation and gene expression profiling in differentiating human adipocytes. J Cell Biochem 117(12):2707–2718. https://doi.org/10.1002/jcb.25568CrossRefPubMed

56.

Pedram P, Wadden D, Amini P, Gulliver W, Randell E, Cahill F et al (2013) Food addiction: its prevalence and significant association with obesity in the general population. PLoS ONE 8(9):e74832. https://doi.org/10.1371/journal.pone.0074832CrossRefPubMedPubMedCentral

57.

Schneider DA, Sarikonda G, Montero E, von Herrath MG (2013) Combination therapy with anti-CD6 and oral insulin immunization reverses recent onset diabetes in non obese diabetic mice but fails to induce lasting tolerance. Clin Immunol 149(3):440–1. https://doi.org/10.1016/j.clim.2013.08.004CrossRefPubMed

58.

Purushothaman I, Zagon IS, Sassani JW, McLaughlin PJ (2021) Ocular surface complications in diabetes: the interrelationship between insulin and enkephalin. Biochem Pharmacol 192:114712. https://doi.org/10.1016/j.bcp.2021.114712CrossRefPubMedPubMedCentral

59.

Xu X, Su S, Barnes V, Snieder H, Wang X (2014) Abstract MP55: DNA methylation mediates the effects of obesity on insulin resistance in African American youth and young adults. Circulation. https://doi.org/10.1161/circ.129.suppl_1.mp55CrossRefPubMedPubMedCentral

60.

Demerath EW, Guan W, Grove ML, Aslibekyan S, Mendelson M, Zhou Y-H et al (2015) Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet 24(15):4464–4479. https://doi.org/10.1093/hmg/ddv161CrossRefPubMedPubMedCentral

61.

Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S et al (2014) DNA methylation and body-mass index: a genome-wide analysis. Lancet. 383(9933):1990–8. https://doi.org/10.1016/s0140-6736(13)62674-4CrossRefPubMed

62.

Slieker RC, Bos SD, Goeman JJ, Bovée JV, Talens RP, van der Breggen R et al (2013) Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin 6(1):26. https://doi.org/10.1186/1756-8935-6-26CrossRefPubMedPubMedCentral

63.

Arguelles AO, Meruvu S, Bowman JD, Choudhury M (2016) Are epigenetic drugs for diabetes and obesity at our door step? Drug Discov Today. 21(3):499–509. https://doi.org/10.1016/j.drudis.2015.12.001CrossRefPubMed

Titel: DNA methylation and waist-to-hip ratio: an epigenome-wide association study in Chinese monozygotic twins
verfasst von: Y. Wu
H. Tian
W. Wang
W. Li
H. Duan
D. Zhang
Publikationsdatum: 01.12.2022
Verlag: Springer International Publishing
Erschienen in: Journal of Endocrinological Investigation / Ausgabe 12/2022
Elektronische ISSN: 1720-8386
DOI: https://doi.org/10.1007/s40618-022-01878-4

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Innere Medizin

CAR-M-Zellen: Warten auf das große Fressen

22.05.2024 Onkologische Immuntherapie Nachrichten

Auch myeloide Immunzellen lassen sich mit chimären Antigenrezeptoren gegen Tumoren ausstatten. Solche CAR-Fresszell-Therapien werden jetzt für solide Tumoren entwickelt. Künftig soll dieser Prozess nicht mehr ex vivo, sondern per mRNA im Körper der Betroffenen erfolgen.

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

DNA methylation and waist-to-hip ratio: an epigenome-wide association study in Chinese monozygotic twins

Abstract

Purpose

Methods

Results

Conclusions

Leitlinien kompakt für die Innere Medizin

Neu im Fachgebiet Innere Medizin

CAR-M-Zellen: Warten auf das große Fressen

Chronische Verstopfung: „Versuchen Sie es mit grünen Kiwis!“

So häufig greift rheumatoide Arthritis auf Organe über

Blutdrucksenkung könnte Uterusmyome verhindern

Update Innere Medizin

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 12/2022

A prognostic nomogram based on log odds of positive lymph nodes to predict the overall survival in biliary neuroendocrine neoplasms (NENs) patients after surgery

Clinical characteristics and outcomes of pheochromocytoma crisis: a literature review of 200 cases

Acromegaly features in performing Marionette

Chinese patients with type 2 diabetes mellitus and nonalcoholic fatty liver disease have lower serum osteocalcin levels compared to individuals with type 2 diabetes mellitus and no liver disease: a single-center cross-sectional study

New insights into the comorbid conditions of Turner syndrome: results from a long-term monocentric cohort study

Adult- and late-onset male hypogonadism: the clinical practice guidelines of the Italian Society of Andrology and Sexual Medicine (SIAMS) and the Italian Society of Endocrinology (SIE)

Leitlinien kompakt für die Innere Medizin

Neu im Fachgebiet Innere Medizin

CAR-M-Zellen: Warten auf das große Fressen

Chronische Verstopfung: „Versuchen Sie es mit grünen Kiwis!“

So häufig greift rheumatoide Arthritis auf Organe über

Blutdrucksenkung könnte Uterusmyome verhindern

Update Innere Medizin