Epigenetic Biomarkers of Transition from Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Phenotype: A Prospective Study
Abstract
:1. Introduction
2. Results
2.1. Principal Component Analysis
2.2. Differentially Methylated Genes
2.3. Potential Biomarker of Transition to Unhealthy State
2.4. Enrichment Analysis
2.5. Pathway Analysis
3. Discussion
4. Materials and Methods
4.1. Design and Subjects
4.2. Classification Criteria
4.3. Procedures
4.4. DNA Methylation Assay
4.5. Methylation Data Analysis
4.6. Statistical Analysis
Principal Component Analysis (PCA)
4.7. Gene Ontology and Pathway Testing
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Health Organization Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 1 May 2021).
- Kuk, J.L.; Ardern, C.I. Are metabolically normal but obese individuals at lower risk for all-cause mortality? Diabetes Care 2009, 32, 2297–2299. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rey-López, J.P.; de Rezende, L.F.; Pastor-Valero, M.; Tess, B.H. The prevalence of metabolically healthy obesity: A systematic review and critical evaluation of the definitions used. Obes. Rev. 2014, 15, 781–790. [Google Scholar] [CrossRef]
- Soriguer, F.; Gutiérrez-Repiso, C.; Rubio-Martín, E.; García-Fuentes, E.; Almaraz, M.C.; Colomo, N.; De Antonio, I.E.; De Adana, M.S.R.; Chaves, F.J.; Morcillo, S.; et al. Metabolically healthy but obese, a matter of time? Findings from the prospective pizarra study. J. Clin. Endocrinol. Metab. 2013, 98, 2318–2325. [Google Scholar] [CrossRef] [Green Version]
- Lin, L.; Zhang, J.; Jiang, L.; Du, R.; Hu, C.; Lu, J.; Wang, T.; Li, M.; Zhao, Z.; Xu, Y.; et al. Transition of metabolic phenotypes and risk of subclinical atherosclerosis according to BMI: A prospective study. Diabetologia 2020, 63, 1312–1323. [Google Scholar] [CrossRef] [PubMed]
- Iacobini, C.; Pugliese, G.; Blasetti Fantauzzi, C.; Federici, M.; Menini, S. Metabolically healthy versus metabolically unhealthy obesity. Metabolism 2019, 92, 51–60. [Google Scholar] [CrossRef] [PubMed]
- Stefan, N.; Häring, H.U.; Hu, F.B.; Schulze, M.B. Metabolically healthy obesity: Epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013, 1, 152–162. [Google Scholar] [CrossRef]
- Locke, A.E.; Kahali, B.; Berndt, S.I.; Justice, A.E.; Pers, T.H.; Day, F.R.; Powell, C.; Vedantam, S.; Buchkovich, M.L.; Yang, J.; et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015, 518, 197–206. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ling, C.; Rönn, T. Cell Metabolism Review Epigenetics in Human Obesity and Type 2 Diabetes. Cell Metab. 2019, 29, 1028–1044. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, X.; Pan, Y.; Zhu, H.; Hao, G.; Huang, Y.; Barnes, V.; Shi, H.; Snieder, H.; Pankow, J.; North, K.; et al. An epigenome-wide study of obesity in African American youth and young adults: Novel findings, replication in neutrophils, and relationship with gene expression. Clin. Epigenetics 2018, 10, 3. [Google Scholar] [CrossRef] [Green Version]
- Fradin, D.; Boëlle, P.Y.; Belot, M.P.; Lachaux, F.; Tost, J.; Besse, C.; Deleuze, J.F.; De Filippo, G.; Bougnères, P. Genome-Wide Methylation Analysis Identifies Specific Epigenetic Marks in Severely Obese Children. Sci. Rep. 2017, 7, 46311. [Google Scholar] [CrossRef] [Green Version]
- Xu, X.; Su, S.; Barnes, V.A.; De Miguel, C.; Pollock, J.; Ownby, D.; Shi, H.; Zhu, H.; Snieder, H.; Wang, X. A genome-wide methylation study on obesity: Differential variability and differential methylation. Epigenetics 2013, 8, 522–533. [Google Scholar] [CrossRef] [Green Version]
- Dahlman, I.; Sinha, I.; Gao, H.; Brodin, D.; Thorell, A.; Rydén, M.; Andersson, D.P.; Henriksson, J.; Perfilyev, A.; Ling, C.; et al. The fat cell epigenetic signature in post-obese women is characterized by global hypomethylation and differential DNA methylation of adipogenesis genes. Int. J. Obes. 2015, 39, 910–919. [Google Scholar] [CrossRef]
- Nilsson, E.; Jansson, P.A.; Perfilyev, A.; Volkov, P.; Pedersen, M.; Svensson, M.K.; Poulsen, P.; Ribel-Madsen, R.; Pedersen, N.L.; Almgren, P.; et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014, 63, 2962–2976. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jones, P.A. Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 2012, 13, 484–492. [Google Scholar] [CrossRef] [PubMed]
- Pheiffer, C.; Willmer, T.; Dias, S.; Abrahams, Y.; Louw, J.; Goedecke, J.H. Ethnic and Adipose Depot Specific Associations Between DNA Methylation and Metabolic Risk. Front. Genet. 2020, 11, 967. [Google Scholar] [CrossRef]
- Crujeiras, A.B.; Diaz-Lagares, A.; Moreno-Navarrete, J.M.; Sandoval, J.; Hervas, D.; Gomez, A.; Ricart, W.; Casanueva, F.F.; Esteller, M.; Fernandez-Real, J.M. Genome-wide DNA methylation pattern in visceral adipose tissue differentiates insulin-resistant from insulin-sensitive obese subjects. Transl. Res. 2016, 178, 13–24.e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chambers, J.C.; Loh, M.; Lehne, B.; Drong, A.; Kriebel, J.; Motta, V.; Wahl, S.; Elliott, H.R.; Rota, F.; Scott, W.R.; et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: A nested case-control study. Lancet Diabetes Endocrinol. 2015, 3, 526–534. [Google Scholar] [CrossRef] [Green Version]
- Van Otterdijk, S.D.; Binder, A.M.; Szarc Vel Szic, K.; Schwald, J.; Michels, K.B. DNA methylation of candidate genes in peripheral blood from patients with type 2 diabetes or the metabolic syndrome. PLoS ONE 2017, 12, e0180955. [Google Scholar] [CrossRef]
- Richard, C.; Wadowski, M.; Goruk, S.; Cameron, L.; Sharma, A.M.; Field, C.J. Individuals with obesity and type 2 diabetes have additional immune dysfunction compared with obese individuals who are metabolically healthy. BMJ Open Diabetes Res. Care 2017, 5, e000379. [Google Scholar] [CrossRef]
- Ip, B.C.; Hogan, A.E.; Nikolajczyk, B.S. Lymphocyte roles in metabolic dysfunction: Of men and mice. Trends Endocrinol. Metab. 2015, 26, 91–100. [Google Scholar] [CrossRef] [Green Version]
- Klimčáková, E.; Roussel, B.; Márquez-Quiñones, A.; Kováčová, Z.; Kováčiková, M.; Combes, M.; Šiklová-Vítková, M.; Hejnová, J.; Šrámková, P.; Bouloumié, A.; et al. Worsening of obesity and metabolic status yields similar molecular adaptations in human subcutaneous and visceral adipose tissue: Decreased metabolism and increased immune response. J. Clin. Endocrinol. Metab. 2011, 96, E73–E82. [Google Scholar] [CrossRef] [Green Version]
- Deng, T.; Lyon, C.J.; Minze, L.J.; Lin, J.; Zou, J.; Liu, J.Z.; Ren, Y.; Yin, Z.; Hamilton, D.J.; Reardon, P.R.; et al. Class II major histocompatibility complex plays an essential role in obesity-induced adipose inflammation. Cell Metab. 2013, 17, 411–422. [Google Scholar] [CrossRef] [Green Version]
- Zhong, H.; Yang, X.; Kaplan, L.M.; Molony, C.; Schadt, E.E. Integrating Pathway Analysis and Genetics of Gene Expression for Genome-wide Association Studies. Am. J. Hum. Genet. 2010, 86, 581–591. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Williams, R.C.; Muller, Y.L.; Hanson, R.L.; Knowler, W.C.; Mason, C.C.; Bian, L.; Ossowski, V.; Wiedrich, K.; Chen, Y.F.; Marcovina, S.; et al. HLA-DRB1 reduces the risk of type 2 diabetes mellitus by increased insulin secretion. Diabetologia 2011, 54, 1684–1692. [Google Scholar] [CrossRef] [Green Version]
- Minchenko, D.O. Insulin resistance in obese adolescents affects the expression of genes associated with immune response. Endocr. Regul. 2019, 53, 71–82. [Google Scholar] [CrossRef] [Green Version]
- Zhou, B.; Ma, Q.; Sek, W.K.; Hu, Y.; Campbell, P.H.; McGowan, F.X.; Ackerman, K.G.; Wu, B.; Zhou, B.; Tevosian, S.G.; et al. Fog2 is critical for cardiac function and maintenance of coronary vasculature in the adult mouse heart. J. Clin. Investig. 2009, 119, 1462–1476. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Azimi-Nezhad, M.; Mirhafez, S.R.; Stathopoulou, M.G.; Murray, H.; Ndiaye, N.C.; Bahrami, A.; Varasteh, A.; Avan, A.; Bonnefond, A.; Rancier, M.; et al. The Relationship Between Vascular Endothelial Growth Factor Cis- and Trans-Acting Genetic Variants and Metabolic Syndrome. Am. J. Med. Sci. 2018, 355, 559–565. [Google Scholar] [CrossRef] [PubMed]
- Salami, A.; El Shamieh, S. Association between SNPs of Circulating Vascular Endothelial Growth Factor Levels, Hypercholesterolemia and Metabolic Syndrome. Medicina 2019, 55, 464. [Google Scholar] [CrossRef] [Green Version]
- Bagu, E.T.; Layoun, A.; Calvé, A.; Santos, M.M. Friend of GATA and GATA-6 modulate the transcriptional up-regulation of hepcidin in hepatocytes during inflammation. BioMetals 2013, 26, 1051–1065. [Google Scholar] [CrossRef]
- Guo, Y.; Yu, J.; Deng, J.; Liu, B.; Xiao, Y.; Li, K.; Xiao, F.; Yuan, F.; Liu, Y.; Chen, S.; et al. A novel function of hepatic FOG2 in insulin sensitivity and lipid metabolism through PPARα. Diabetes 2016, 65, 2151–2163. [Google Scholar] [CrossRef] [Green Version]
- Stipp, M.C.; Acco, A. Involvement of cytochrome P450 enzymes in inflammation and cancer: A review. Cancer Chemother. Pharmacol. 2020, 87, 295–309. [Google Scholar] [CrossRef]
- Lu, Y.; Cederbaum, A.I. CYP2E1 and oxidative liver injury by alcohol. Free Radic. Biol. Med. 2008, 44, 723–738. [Google Scholar] [CrossRef] [Green Version]
- Teufel, U.; Peccerella, T.; Engelmann, G.; Bruckner, T.; Flechtenmacher, C.; Millonig, G.; Stickel, F.; Hoffmann, G.F.; Schirmacher, P.; Mueller, S.; et al. Detection of carcinogenic etheno-DNA adducts in children and adolescents with non-alcoholic steatohepatitis (NASH). Hepatobiliary Surg. Nutr. 2015, 4, 426–435. [Google Scholar] [CrossRef] [PubMed]
- Lucas, D.; Farez, C.; Bardou, L.G.; Vaisse, J.; Attali, J.R.; Valensi, P. Cytochrome P450 2E1 activity in diabetic and obese patients as assessed by chlorzoxazone hydroxylation. Fundam. Clin. Pharmacol. 1998, 12, 553–558. [Google Scholar] [CrossRef]
- Bondarenko, L.B.; Shayakhmetova, G.M.; Voronina, A.K.; Kovalenko, V.M. Age-dependent features of CYP3A, CYP2C, and CYP2E1 functioning at metabolic syndrome. J. Basic Clin. Physiol. Pharmacol. 2016, 27, 603–610. [Google Scholar] [CrossRef]
- Gravel, S.; Chiasson, J.L.; Turgeon, J.; Grangeon, A.; Michaud, V. Modulation of CYP450 Activities in Patients With Type 2 Diabetes. Clin. Pharmacol. Ther. 2019, 106, 1280–1289. [Google Scholar] [CrossRef]
- Massart, J.; Begriche, K.; Fromenty, B. Cytochrome P450 2E1 should not be neglected for acetaminophen-induced liver injury in metabolic diseases with altered insulin levels or glucose homeostasis. Clin. Res. Hepatol. Gastroenterol. 2020, 45, 101470. [Google Scholar] [CrossRef]
- Soriguer, F.; Rojo-Martínez, G.; Valdés, S.; Tapia, M.J.; Botas, P.; Morcillo, S.; Delgado, E.; Esteva, I.; Ruiz De Adana, M.S.; Almaraz, M.C.; et al. Factors determining weight gain in adults and relation with glucose tolerance. Clin. Endocrinol. 2013, 78, 858–864. [Google Scholar] [CrossRef] [PubMed]
- Cleeman, J.I. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). J. Am. Med. Assoc. 2001, 285, 2486–2497. [Google Scholar] [CrossRef]
- Aryee, M.J.; Jaffe, A.E.; Corrada-Bravo, H.; Ladd-Acosta, C.; Feinberg, A.P.; Hansen, K.D.; Irizarry, R.A. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 2014, 30, 1363–1369. [Google Scholar] [CrossRef] [Green Version]
- Triche, T.J.; Weisenberger, D.J.; Van Den Berg, D.; Laird, P.W.; Siegmund, K.D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013, 41, e90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Teschendorff, A.E.; Marabita, F.; Lechner, M.; Bartlett, T.; Tegner, J.; Gomez-Cabrero, D.; Beck, S. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 2013, 29, 189–196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Song, Y.; Westerhuis, J.A.; Aben, N.; Michaut, M.; Wessels, L.F.A.; Smilde, A.K. Principal component analysis of binary genomics data. Brief. Bioinform. 2019, 20, 317–329. [Google Scholar] [CrossRef] [PubMed]
- Udhaya Kumar, S.; Thirumal Kumar, D.; Siva, R.; George Priya Doss, C.; Zayed, H. Integrative bioinformatics approaches to map potential novel genes and pathways involved in ovarian cancer. Front. Bioeng. Biotechnol. 2019, 7, 391. [Google Scholar] [CrossRef] [Green Version]
Title 1 | Baseline | 11-Year Follow-Up | ||||
---|---|---|---|---|---|---|
Stable MHO (n = 9) | Unstable MHO (n = 9) | p-Value | Stable MHO (n = 9) | Unstable MHO (n = 9) | p-Value | |
Age | 45 ± 11 | 53 ± 9 | NS | |||
Gender (Male/Female) | 2/7 | 3/6 | NS | |||
Fasting glucose (mg/dl) | 103.5 ± 13.3 | 106.6 ± 10.3 | NS | 90 ± 4.9 | 108.4 ± 16.6 | 0.01 |
BMI | 28.2 ± 1.6 | 29 ± 4.3 | NS | 29.9 ± 3.4 | 31.1 ± 4.0 | NS |
Triglycerides (mg/dl) | 52.7 ± 12.3 | 92.7 ± 37.8 | 0.01 | 82 ± 25.2 | 100.33 ± 53.0 | NS |
HDL-cholesterol | 57.8 ± 12.3 | 51.4 ± 9.6 | NS | 60.7 ± 6.3 | 53.6 ± 8.4 | 0.06 |
DBP (mm Hg) | 81.4 ± 7.5 | 88.6 ± 16.8 | NS | 75.7 ± 9.3 | 90.5 ± 11.5 | 0.015 |
SBP (mm Hg) | 121 ± 16.9 | 138.6 ± 26.3 | NS | 126.3 ± 19.3 | 153.8 ± 23.2 | 0.024 |
HTA treatment (%) | 0 | 22.2 | NS | 0 | 55.6 | 0.015 |
Probe ID | Location |
Gene Symbol | Gene Name | p-Value | Hypermethylated |
---|---|---|---|---|---|
cg20707527 | Chr: 8 q23.1 | ZFPM2 | Zinc Finger Protein. FOG Family Member 2 | 0.0001 | Stable MHO |
cg15084585 | Chr: 8 q23.1 | ZFPM2 | Zinc Finger Protein. FOG Family Member 2 | 0.0001 | Stable MHO |
cg20022036 | Chr: 6 p21.32 | HLA-DRB1 | Major Histocompatibility Complex. Class II. DR Beta 1 | 0.0015 | Stable MHO |
cg20239921 | Chr: 7 q 11.23 | DTX2P1-UPK3BP1-PMS2P11 | DTX2P1-UPK3BP1-PMS2P11 | 0.0015 | Stable MHO |
cg26805839 | Chr: 9p24.2 | SLC1A1 | Solute Carrier Family 1 Member 1 | 0.0035 | Stable MHO |
cg11445109 | Chr: 10 q26.3 | CYP2E1 | Cytochrome P450 Family 2 Subfamily E Member 1 | 0.0046 | Unstable MHO |
Cg07180987 | Chr: 6 p21.32 | HLA-DQB2 | Major Histocompatibility Complex. Class II. DQ Beta 2 | 0.0057 | Stable MHO |
cg05194426 | Chr: 10 q26.3 | CYP2E1 | Cytochrome P450 Family 2 Subfamily E Member 1 | 0.0057 | Unstable MHO |
cg25828445 | Chr: 12 p13.31 | NIFKP3 | Nucleolar Protein Interacting with The FHA Domain | 0.0067 | Unstable MHO |
cg07458466 | Chr: 12 p 12.2 | PLCZ1 | Phospholipase C Zeta 1 | 0.0083 | Stable MHO |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gutiérrez-Repiso, C.; Linares-Pineda, T.M.; Gonzalez-Jimenez, A.; Aguilar-Lineros, F.; Valdés, S.; Soriguer, F.; Rojo-Martínez, G.; Tinahones, F.J.; Morcillo, S. Epigenetic Biomarkers of Transition from Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Phenotype: A Prospective Study. Int. J. Mol. Sci. 2021, 22, 10417. https://doi.org/10.3390/ijms221910417
Gutiérrez-Repiso C, Linares-Pineda TM, Gonzalez-Jimenez A, Aguilar-Lineros F, Valdés S, Soriguer F, Rojo-Martínez G, Tinahones FJ, Morcillo S. Epigenetic Biomarkers of Transition from Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Phenotype: A Prospective Study. International Journal of Molecular Sciences. 2021; 22(19):10417. https://doi.org/10.3390/ijms221910417
Chicago/Turabian StyleGutiérrez-Repiso, Carolina, Teresa María Linares-Pineda, Andres Gonzalez-Jimenez, Francisca Aguilar-Lineros, Sergio Valdés, Federico Soriguer, Gemma Rojo-Martínez, Francisco J. Tinahones, and Sonsoles Morcillo. 2021. "Epigenetic Biomarkers of Transition from Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Phenotype: A Prospective Study" International Journal of Molecular Sciences 22, no. 19: 10417. https://doi.org/10.3390/ijms221910417