Salivary epigenetic biomarkers as predictors of emerging childhood obesity

Amanda Rushing, Evan C Sommer, Shilin Zhao, Eli K Po'e, Shari L Barkin, Amanda Rushing, Evan C Sommer, Shilin Zhao, Eli K Po'e, Shari L Barkin

Abstract

Background: Epigenetics could facilitate greater understanding of disparities in the emergence of childhood obesity. While blood is a common tissue used in human epigenetic studies, saliva is a promising tissue. Our prior findings in non-obese preschool-aged Hispanic children identified 17 CpG dinucleotides for which differential methylation in saliva at baseline was associated with maternal obesity status. The current study investigated to what extent baseline DNA methylation in salivary samples in these 3-5-year-old Hispanic children predicted the incidence of childhood obesity in a 3-year prospective cohort.

Methods: We examined a subsample (n = 92) of Growing Right Onto Wellness (GROW) trial participants who were randomly selected at baseline, prior to randomization, based on maternal phenotype (obese or non-obese). Baseline saliva samples were collected using the Oragene DNA saliva kit. Objective data were collected on child height and weight at baseline and 36 months later. Methylation arrays were processed using standard protocol. Associations between child obesity at 36 months and baseline salivary methylation at the previously identified 17 CpG dinucleotides were evaluated using multivariable logistic regression models.

Results: Among the n = 75 children eligible for analysis, baseline methylation of Cg1307483 (NRF1) was significantly associated with emerging childhood obesity at 36-month follow-up (OR = 2.98, p = 0.04), after adjusting for child age, gender, child baseline BMI-Z, and adult baseline BMI. This translates to a model-estimated 48% chance of child obesity at 36-month follow-up for a child at the 75th percentile of NRF1 baseline methylation versus only a 30% chance of obesity for a similar child at the 25th percentile. Consistent with other studies, a higher baseline child BMI-Z during the preschool period was associated with the emergence of obesity 3 years later, but baseline methylation of NRF1 was associated with later obesity even after adjusting for child baseline BMI-Z.

Conclusions: Saliva offers a non-invasive means of DNA collection and epigenetic analysis. Our proof of principle study provides sound empirical evidence supporting DNA methylation in salivary tissue as a potential predictor of subsequent childhood obesity for Hispanic children. NFR1 could be a target for further exploration of obesity in this population.

Keywords: Epigenetics; Hispanic children; Methylation; Obesity; Saliva.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Model Predicted Probability of Child Obesity at 36-month Follow-up as a Function of NRF1 Methylation. Figure 1 displays the logistic regression model-predicted probability of child obesity at 36 months as a function of the degree of methylation of cg01307483 (NRF1). The solid line indicates the predicted probability, and the gray shaded region represents the 95% confidence interval. As the degree of methylation of NRF1 increases, the probability of child obesity at 36 months increases significantly

References

    1. Ogden CL, Fryar CD, Hales CM, Carroll MD, Aoki Y, Freedman DS. Differences in obesity prevalence by demographics and urbanization in US children and adolescents, 2013-2016. JAMA. 2018;319(23):2410–2418. doi: 10.1001/jama.2018.5158.
    1. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, et al. Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014. JAMA. 2016;315(21):2292–2299. doi: 10.1001/jama.2016.6361.
    1. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev. 2008;9(5):474–488. doi: 10.1111/j.1467-789X.2008.00475.x.
    1. Wang Y, Lobstein T. Worldwide trends in childhood overweight and obesity. Int J Pediatr Obes. 2006;1(1):11–25. doi: 10.1080/17477160600586747.
    1. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth: United States, 2015-2016. NCHS Data Brief. 2017;288:1–8.
    1. Taveras EM, Gillman MW, Kleinman K, Rich-Edwards JW, Rifas-Shiman SL. Racial/ethnic differences in early-life risk factors for childhood obesity. Pediatrics. 2010;125(4):686–695. doi: 10.1542/peds.2009-2100.
    1. CDC National Center for Health Statistics. National Health and Nutrition Examination Survey: questionnaires, datasets, and related documents. Available from: . Accessed 1 Jan 2019.
    1. Barker DJ, Osmond C, Forsen TJ, Kajantie E, Eriksson JG. Trajectories of growth among children who have coronary events as adults. N Engl J Med. 2005;353(17):1802–1809. doi: 10.1056/NEJMoa044160.
    1. Kit BK, Kuklina E, Carroll MD, Ostchega Y, Freedman DS, Ogden CL. Prevalence of and trends in dyslipidemia and blood pressure among US children and adolescents, 1999-2012. JAMA Pediatr. 2015;169(3):272–279. doi: 10.1001/jamapediatrics.2014.3216.
    1. Chen X, Wang Y. Tracking of blood pressure from childhood to adulthood: a systematic review and meta-regression analysis. Circulation. 2008;117(25):3171–3180. doi: 10.1161/CIRCULATIONAHA.107.730366.
    1. Dubois L, Ohm Kyvik K, Girard M, Tatone-Tokuda F, Perusse D, Hjelmborg J, et al. Genetic and environmental contributions to weight, height, and BMI from birth to 19 years of age: an international study of over 12,000 twin pairs. PLoS One. 2012;7(2):e30153. doi: 10.1371/journal.pone.0030153.
    1. Bouchard L, Rabasa-Lhoret R, Faraj M, Lavoie ME, Mill J, Perusse L, et al. Differential epigenomic and transcriptomic responses in subcutaneous adipose tissue between low and high responders to caloric restriction. Am J Clin Nutr. 2010;91(2):309–320. doi: 10.3945/ajcn.2009.28085.
    1. Herrera BM, Keildson S, Lindgren CM. Genetics and epigenetics of obesity. Maturitas. 2011;69(1):41–49. doi: 10.1016/j.maturitas.2011.02.018.
    1. Godfrey KM, Sheppard A, Gluckman PD, Lillycrop KA, Burdge GC, McLean C, et al. Epigenetic gene promoter methylation at birth is associated with child's later adiposity. Diabetes. 2011;60(5):1528–1534. doi: 10.2337/db10-0979.
    1. Hochberg Z, Feil R, Constancia M, Fraga M, Junien C, Carel JC, et al. Child health, developmental plasticity, and epigenetic programming. Endocr Rev. 2011;32(2):159–224. doi: 10.1210/er.2009-0039.
    1. Mamtani M, Kulkarni H, Dyer TD, Goring HH, Neary JL, Cole SA, et al. Genome- and epigenome-wide association study of hypertriglyceridemic waist in Mexican American families. Clin Epigenetics. 2016;8:6. doi: 10.1186/s13148-016-0173-x.
    1. Kulkarni H, Kos MZ, Neary J, Dyer TD, Kent JW, Jr, Goring HH, et al. Novel epigenetic determinants of type 2 diabetes in Mexican-American families. Hum Mol Genet. 2015;24(18):5330–5344. doi: 10.1093/hmg/ddv232.
    1. King K, Murphy S, Hoyo C. Epigenetic regulation of Newborns' imprinted genes related to gestational growth: patterning by parental race/ethnicity and maternal socioeconomic status. J Epidemiol Community Health. 2015;69(7):639–647. doi: 10.1136/jech-2014-204781.
    1. Yoshizawa JM, Schafer CA, Schafer JJ, Farrell JJ, Paster BJ, Wong DT. Salivary biomarkers: toward future clinical and diagnostic utilities. Clin Microbiol Rev. 2013;26(4):781–791. doi: 10.1128/CMR.00021-13.
    1. Bonne NJ, Wong DT. Salivary biomarker development using genomic, proteomic and metabolomic approaches. Genome Med. 2012;4(10):82. doi: 10.1186/gm383.
    1. Abraham JE, Maranian MJ, Spiteri I, Russell R, Ingle S, Luccarini C, et al. Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping. BMC Med Genet. 2012;5:19. doi: 10.1186/1755-8794-5-19.
    1. Langie SAS, Moisse M, Declerck K, Koppen G, Godderis L, Vanden Berghe W, et al. Salivary DNA methylation profiling: aspects to consider for biomarker identification. Basic Clin Pharmacol Toxicol. 2017;121(Suppl 3):93–101. doi: 10.1111/bcpt.12721.
    1. Pfaffe T, Cooper-White J, Beyerlein P, Kostner K, Punyadeera C. Diagnostic potential of saliva: current state and future applications. Clin Chem. 2011;57(5):675–687. doi: 10.1373/clinchem.2010.153767.
    1. Oelsner KT, Guo Y, To SB. Non AL, Barkin SL. Maternal BMI as a predictor of methylation of obesity-related genes in saliva samples from preschool-age Hispanic children at-risk for obesity. BMC Genomics. 2017;18(1):57. doi: 10.1186/s12864-016-3473-9.
    1. Po'e EK, Heerman WJ, Mistry RS, Barkin SL. Growing right onto wellness (GROW): a family-centered, community-based obesity prevention randomized controlled trial for preschool child-parent pairs. Contemp Clin Trials. 2013;36(2):436–449. doi: 10.1016/j.cct.2013.08.013.
    1. Heerman WJ, White RO, Hotop A, Omlung K, Armstrong S, Mathieu I, et al. A tool Kit to enhance the informed consent process for community-engaged pediatric research. IRB. 2016;38(5):8–14.
    1. Barkin SL, Gesell SB, Po'e EK, Escarfuller J, Tempesti T. Culturally tailored, family-centered, behavioral obesity intervention for Latino-American preschool-aged children. Pediatrics. 2012;130(3):445–456. doi: 10.1542/peds.2011-3762.
    1. Bibikova M, Fan JB. GoldenGate assay for DNA methylation profiling. Methods Mol Biol. 2009;507:149–163. doi: 10.1007/978-1-59745-522-0_12.
    1. Dedeurwaerder S, Defrance M, Bizet M, Calonne E, Bontempi G, Fuks F. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform. 2014;15(6):929–941. doi: 10.1093/bib/bbt054.
    1. Wu Y, Zhou S, Smas CM. Downregulated expression of the secreted glycoprotein follistatin-like 1 (Fstl1) is a robust hallmark of preadipocyte to adipocyte conversion. Mech Dev. 2010;127(3–4):183–202. doi: 10.1016/j.mod.2009.12.003.
    1. Greenwood TA, Lazzeroni LC, Calkins ME, Freedman R, Green MF, Gur RE, et al. Genetic assessment of additional endophenotypes from the consortium on the genetics of schizophrenia family study. Schizophr Res. 2016;170(1):30–40. doi: 10.1016/j.schres.2015.11.008.
    1. Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet. 2012;44(5):526–531. doi: 10.1038/ng.2247.
    1. Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012;7(12):e51954. doi: 10.1371/journal.pone.0051954.
    1. Bartelt A, Widenmaier SB, Schlein C, Johann K, Goncalves RLS, Eguchi K, et al. Brown adipose tissue thermogenic adaptation requires Nrf1-mediated proteasomal activity. Nat Med. 2018;24(3):292–303. doi: 10.1038/nm.4481.
    1. Wang W, Chan JY. Nrf1 is targeted to the endoplasmic reticulum membrane by an N-terminal transmembrane domain. Inhibition of nuclear translocation and transacting function. J Biol Chem. 2006;281(28):19676–19687. doi: 10.1074/jbc.M602802200.
    1. Patti ME, Butte AJ, Crunkhorn S, Cusi K, Berria R, Kashyap S, et al. Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: potential role of PGC1 and NRF1. Proc Natl Acad Sci U S A. 2003;100(14):8466–8471. doi: 10.1073/pnas.1032913100.
    1. Atikuzzaman M, Alvarez-Rodriguez M, Vicente-Carrillo A, Johnsson M, Wright D, Rodriguez-Martinez H. Conserved gene expression in sperm reservoirs between birds and mammals in response to mating. BMC Genomics. 2017;18(1):98. doi: 10.1186/s12864-017-3488-x.
    1. McGovern DP, Gardet A, Torkvist L, Goyette P, Essers J, Taylor KD, et al. Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nat Genet. 2010;42(4):332–337. doi: 10.1038/ng.549.
    1. Wu JN, Nguyen N, Aghazarian M, Tan Y, Sevrioukov EA, Mabuchi M, et al. Grim promotes programmed cell death of Drosophila microchaete glial cells. Mech Dev. 2010;127(9–12):407–417. doi: 10.1016/j.mod.2010.06.001.
    1. Mattiotti A, Prakash S, Barnett P, van den Hoff MJB. Follistatin-like 1 in development and human diseases. Cell Mol Life Sci. 2018;75(13):2339–2354. doi: 10.1007/s00018-018-2805-0.
    1. Kato H, Nomura K, Osabe D, Shinohara S, Mizumori O, Katashima R, et al. Association of single-nucleotide polymorphisms in the suppressor of cytokine signaling 2 (SOCS2) gene with type 2 diabetes in the Japanese. Genomics. 2006;87(4):446–458. doi: 10.1016/j.ygeno.2005.11.009.
    1. Paterson AD, Waggott D, Boright AP, Hosseini SM, Shen E, Sylvestre MP, et al. A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose. Diabetes. 2010;59(2):539–549. doi: 10.2337/db09-0653.
    1. Chow Tze Jen, Tee Shiau Foon, Loh Siew Yim, Yong Hoi Sen, Abu Bakar Abdul Kadir, Tang Pek Yee. Variants in ZNF804A and DTNBP1 assessed for cognitive impairment in schizophrenia using a multiplex family-based approach. Psychiatry Research. 2018;270:1166–1167. doi: 10.1016/j.psychres.2018.04.051.
    1. Porcelli S, Lee SJ, Han C, Patkar AA, Albani D, Jun TY, et al. Hot genes in schizophrenia: how clinical datasets could help to refine their role. J Mol Neurosci. 2018;64(2):273–286. doi: 10.1007/s12031-017-1016-8.
    1. Bai Y, Qiu S, Li Y, Li Y, Zhong W, Shi M, et al. Genetic association between SHANK2 polymorphisms and susceptibility to autism spectrum disorder. IUBMB Life. 2018;70(8):763–776. doi: 10.1002/iub.1876.
    1. Kim R, Kim J, Chung C, Ha S, Lee S, Lee E, et al. Cell-type-specific Shank2 deletion in mice leads to differential synaptic and behavioral phenotypes. J Neurosci. 2018;38(17):4076–4092. doi: 10.1523/JNEUROSCI.2684-17.2018.
    1. Sabin MA, Werther GA, Kiess W. Genetics of obesity and overgrowth syndromes. Best Pract Res Clin Endocrinol Metab. 2011;25(1):207–220. doi: 10.1016/j.beem.2010.09.010.
    1. Sim CK, Kim SY, Brunmeir R, Zhang Q, Li H, Dharmasegaran D, et al. Regulation of white and brown adipocyte differentiation by RhoGAP DLC1. PLoS One. 2017;12(3):e0174761. doi: 10.1371/journal.pone.0174761.
    1. Liao YC, Lo SH. Deleted in liver cancer-1 (DLC-1): a tumor suppressor not just for liver. Int J Biochem Cell Biol. 2008;40(5):843–847. doi: 10.1016/j.biocel.2007.04.008.
    1. Huang Y, Zheng J, Chen D, Li F, Wu W, Huang X, et al. Transcriptome profiling identifies a recurrent CRYL1-IFT88 chimeric transcript in hepatocellular carcinoma. Oncotarget. 2017;8(25):40693–40704. doi: 10.18632/oncotarget.17244.
    1. Park KS, Shin HD, Park BL, Cheong HS, Cho YM, Lee HK, et al. Putative association of peroxisome proliferator-activated receptor gamma co-activator 1beta (PPARGC1B) polymorphism with type 2 diabetes mellitus. Diabet Med. 2006;23(6):635–642. doi: 10.1111/j.1464-5491.2006.01882.x.
    1. Koelwyn GJ, Corr EM, Erbay E, Moore KJ. Regulation of macrophage immunometabolism in atherosclerosis. Nat Immunol. 2018;19(6):526–537. doi: 10.1038/s41590-018-0113-3.
    1. Shinozaki G, Potash JB. New developments in the genetics of bipolar disorder. Curr Psychiatry Rep. 2014;16(11):493. doi: 10.1007/s11920-014-0493-5.
    1. Suliman SG, Stanik J, McCulloch LJ, Wilson N, Edghill EL, Misovicova N, et al. Severe insulin resistance and intrauterine growth deficiency associated with haploinsufficiency for INSR and CHN2: new insights into synergistic pathways involved in growth and metabolism. Diabetes. 2009;58(12):2954–2961. doi: 10.2337/db09-0787.
    1. Adams JN, Raffield LM, Freedman BI, Langefeld CD, Ng MC, Carr JJ, et al. Analysis of common and coding variants with cardiovascular disease in the diabetes heart study. Cardiovasc Diabetol. 2014;13:77. doi: 10.1186/1475-2840-13-77.
    1. Vallée Marcotte Bastien, Guénard Frédéric, Cormier Hubert, Lemieux Simone, Couture Patrick, Rudkowska Iwona, Vohl Marie-Claude. Plasma Triglyceride Levels May Be Modulated by Gene Expression of IQCJ, NXPH1, PHF17 and MYB in Humans. International Journal of Molecular Sciences. 2017;18(2):257. doi: 10.3390/ijms18020257.
    1. Hayes MG, Pluzhnikov A, Miyake K, Sun Y, Ng MC, Roe CA, et al. Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies. Diabetes. 2007;56(12):3033–3044. doi: 10.2337/db07-0482.
    1. Brietzke E, Trevizol AP, Fries GR, Subramaniapillai M, Kapczinski F, McIntyre RS, et al. The impact of body mass index in gene expression of reelin pathway mediators in individuals with schizophrenia and mood disorders: a post-mortem study. J Psychiatr Res. 2018;102:186–191. doi: 10.1016/j.jpsychires.2018.04.012.
    1. Diekstra FP, Van Deerlin VM, van Swieten JC, Al-Chalabi A, Ludolph AC, Weishaupt JH, et al. C9orf72 and UNC13A are shared risk loci for amyotrophic lateral sclerosis and frontotemporal dementia: a genome-wide meta-analysis. Ann Neurol. 2014;76(1):120–133. doi: 10.1002/ana.24198.
    1. Daoud H, Belzil V, Desjarlais A, Camu W, Dion PA, Rouleau GA. Analysis of the UNC13A gene as a risk factor for sporadic amyotrophic lateral sclerosis. Arch Neurol. 2010;67(4):516–517. doi: 10.1001/archneurol.2010.46.
    1. Granhall C, Park HB, Fakhrai-Rad H, Luthman H. High-resolution quantitative trait locus analysis reveals multiple diabetes susceptibility loci mapped to intervals<800 kb in the species-conserved Niddm1i of the GK rat. Genetics. 2006;174(3):1565–1572. doi: 10.1534/genetics.106.062208.

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj