Methylation of SOCS3 is inversely associated with metabolic syndrome in an epigenome-wide association study of obesity

Epigenetics

Volumn 11, Issue 9, 2016, Pages 699-707

  Ali, Omar ^a   Cerjak, Diana ^a   Kent, Jack W ^b   James, Roland ^a   Blangero, John ^c   Carless, Melanie A ^b   Zhang, Yi ^a  

^a MEDICAL COLLEGE OF WISCONSIN   (United States)

^b SOUTHWEST FOUNDATION FOR BIOMEDICAL RESEARCH   (United States)

^c University of Texas Rio Grande Valley School of Medicine   (United States)

Author keywords

BMI; childhood obesity; CpG methylation; epigenetics; EWAS; family study; metabolic syndrome; obesity

Indexed keywords

GLUCOSE; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; INSULIN; LEPTIN; LOW DENSITY LIPOPROTEIN CHOLESTEROL; MESSENGER RNA; SUPPRESSOR OF CYTOKINE SIGNALING 3; TRIACYLGLYCEROL; SOCS3 PROTEIN, HUMAN;

ADOLESCENT; ADULT; AGED; ARTICLE; BODY MASS; CHILD; CHILDHOOD OBESITY; CPG ISLAND; DNA METHYLATION; EPIGENETICS; FAT MASS; FEMALE; GENE EXPRESSION; GENE LOCUS; GENETIC ASSOCIATION; GENETIC TRANSCRIPTION; HUMAN; INSULIN RESISTANCE; LIPID BLOOD LEVEL; LIPID STORAGE; MALE; METABOLIC SYNDROME X; MICROARRAY ANALYSIS; OBESITY; PERIPHERAL BLOOD MONONUCLEAR CELL; POLYMERASE CHAIN REACTION; PYROSEQUENCING; WAIST TO HEIGHT RATIO; CASE CONTROL STUDY; CELL LINE; GENETIC EPIGENESIS; GENETICS; GENOME; GENOME-WIDE ASSOCIATION STUDY; METABOLISM; MIDDLE AGED; PEDIGREE; VERY ELDERLY;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; CASE-CONTROL STUDIES; CELL LINE; CHILD; DNA METHYLATION; EPIGENESIS, GENETIC; FEMALE; GENOME; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MALE; METABOLIC SYNDROME X; MIDDLE AGED; OBESITY; PEDIGREE; SUPPRESSOR OF CYTOKINE SIGNALING 3 PROTEIN;

EID: 84984868359     PISSN: 15592294     EISSN: 15592308     Source Type: Journal    
DOI: 10.1080/15592294.2016.1216284     Document Type: Article

References (65)

1. A.Alwan. Global Status Report on Noncommunicable Diseases 2010. Geneva, Switzerland:World Health Organization; 2011.
2. G.A.Stevens, G.M.Singh, Y.Lu, G.Danaei, J.K.Lin, M.M.Finucane, A.N.Bahalim, R.K.McIntire, H.R.Gutierrez, M.Cowan, et al. The global burden of metabolic risk factors of chronic diseases collaborating group (Body Mass Index), national, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr 2012 Nov 20; 10(1):22; PMID:23167948; http://dx.doi.org/10.1186/1478-7954-10-22
3. R.Lozano, M.Naghavi, K.Foreman, S.Lim, K.Shibuya, V.Aboyans, J.Abraham, T.Adair, R.Aggarwal, S.Y.Ahn, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010:a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380(9859):2095-128; PMID:23245604; http://dx.doi.org/10.1016/S0140-6736(12)61728-0
4. L.Guariguata, D.R.Whiting, I.Hambleton, J.Beagley, U.Linnenkamp, J.E.Shaw. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract 2014; 103(2):137-49; PMID:24630390; http://dx.doi.org/10.1016/j.diabres.2013.11.002
5. A.1.Stančáková, M.Laakso. Genetics of metabolic syndrome. Rev Endocr Metab Disord 2014 Dec; 15(4):243-52; http://dx.doi.org/10.1007/s11154-014-9293-9
6. A.Stančáková, M.Laakso. Genetics of Type 2 Diabetes. Endocr Dev 2016; 31:203-20; PMID:26824439; http://dx.doi.org/10.1159/00043941817
7. O.Ali. Genetics of type 2 diabetes. World J Diabetes 2013 Aug 15; 4(4):114-23; PMID:23961321; http://dx.doi.org/10.4239/wjd.v4.i4.114
8. P.Poulsen, K.O.Kyvik, A.Vaag, H.Beck-Nielsen. Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study, Diabetologia. 1999 Feb; 42(2):139-45; PMID:10064092; http://dx.doi.org/10.1007/s001250051131
9. Z.D.Smith, A.Meissner. DNA methylation:roles in mammalian development. Nat Rev Genet 2013; 14:204-20; PMID:23400093; http://dx.doi.org/10.1038/nrg3354
10. J.C.Jimenez-Chillaron, M.Ramon-Krauel, S.Ribo, R.Diaz. Transgenerational epigenetic inheritance of diabetes risk as a consequence of early nutritional imbalances. Proc Nutr Soc 2016 Feb; 75(1):78-89; PMID:26573376; http://dx.doi.org/10.1017/S0029665115004231
11. X.Wang, H.Zhu, H.Snieder, S.Su, D.Munn, G.Harshfield, B.L.Maria, Y.Dong, F.Treiber, B.Gutin, H.Shi. Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med 2010; 8:87; PMID:21176133; http://dx.doi.org/10.1186/1741-7015-8-87
12. F.I.Milagro, J.Campion, P.Cordero, E.Goyenechea, A.M.Gomez-Uriz, I.Abete, M.A.Zulet, J.A.Martinez. A dual epigenomic approach for the search of obesity biomarkers:DNA methylation in relation to diet-induced weight loss. FASEB J 2011; 25:1378-8; PMID:21209057; http://dx.doi.org/10.1096/fj.10-170365
13. A.Moleres, J.Campion, F.I.Milagro, A.Marcos, C.Campoy, J.M.Garagorri, S.Gomez-Martinez, J.A.Martinez, M.C.Azcona-Sanjulian, A.Marti, E.S.Group. Differential DNA methylation patterns between high and low responders to a weight loss intervention in overweight or obese adolescents:the EVASYON study. FASEB J 2013; 27:2504-12; PMID:23475851; http://dx.doi.org/10.1096/fj.12-215566
14. R.Lowe, C.Gemma, H.Beyan, M.I.Hawa, A.Bazeos, R.D.Leslie, A.Montpetit, V.K.Rakyan, S.V.Ramagopalan. Buccals are likely to be a more informative surrogate tissue than blood for epigenome-wide association studies. Epigenetics 2013 Apr; 8(4):445-54; PMID:23538714; http://dx.doi.org/10.4161/epi.24362
15. K.Lokk, V.Modhukur, B.Rajashekar, K.Märtens, R.Mägi, R.Kolde, M.Koltšina, T.K.Nilsson, J.Vilo, A.Salumets, N.Tõnisson. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol 2014 Apr 1; 15(4):r54; PMID:24690455; http://dx.doi.org/10.1186/gb-2014-15-4-r54
16. N.Esser, S.Legrand-Poels, J.Piette, A.J.Scheen, N.Paquot. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract 2014 Aug; 105(2):141-50; PMID:24798950; http://dx.doi.org/10.1016/j.diabres.2014.04.006
17. B.Hidalgo, M.R.Irvin, J.Sha, D.Zhi, S.Aslibekyan, D.Absher, H.K.Tiwari, E.K.Kabagambe, J.M.Ordovas, D.K.Arnett. Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network study. Diabetes 2014; 63:801-7; PMID:24170695; http://dx.doi.org/10.2337/db13-1100
18. X.Xu, S.Su, V.A.Barnes, C.De Miguel, J.Pollock, D.Ownby, H.Shi, H.Zhu, H.Snieder, X.Wang. A genome-wide methylation study on obesity:Differential variability and differential methylation. Epigenetics 2013; 8:522-33; PMID:23644594; http://dx.doi.org/10.4161/epi.24506
19. G.Toperoff, D.Aran, J.D.Kark, M.Rosenberg, T.Dubnikov, B.Nissan, J.Wainstein, Y.Friedlander, E.Levy-Lahad, B.Glaser, A.Hellman. Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum Mol Genet 2012; 21:371-83; PMID:21994764; http://dx.doi.org/10.1093/hmg/ddr472
20. S.Blüher, E.Molz, S.Wiegand, K.P.Otto, E.Sergeyev, S.Tuschy, D.L'Allemand-Jander, W.Kiess, R.W.Holl. Body mass index, waist circumference, and waist-to-height ratio as predictors of cardiometabolic risk in childhood obesity depending on pubertal development. Adiposity Patients Registry Initiative and German Competence Net Obesity. J Clin Endocrinol Metab 2013 Aug; 98(8):3384-93; PMID:23775352; http://dx.doi.org/10.1210/jc.2013-1389
21. B.Falkner. Cardiometabolic risks related to obesity severity in children and adolescents:a commentary. J Am Soc Hypertens 2016 Mar; 10(3):205-6; PMID:26767415; http://dx.doi.org/10.1016/j.jash.2015.12.009
22. N.S.Jensen, T.F.Camargo, D.P.Bergamaschi. Comparison of methods to measure body fat in 7-to-10-year-old children:a systematic review. Public Health 2016 Apr; 133:3-13; PMID:26774698; http://dx.doi.org/10.1016/j.puhe.2015.11.025
23. O.Ali, D.Cerjak, J.W.KentJr, R.James, J.Blangero, Y.Zhang. Obesity, central adiposity and cardiometabolic risk factors in children and adolescents:a family-based study. Pediatr Obes 2014 Jun; 9(3):e58-62; PMID:24677702; http://dx.doi.org/10.1111/j.2047-6310.2014.218.x
24. Adult BMI percentile calculator (halls.md v2, accessed May, 2016). http://halls.md/bodymass-index/av.htm
25. O.Ali, D.Cerjak, J.W.Kent, R.James, J.Blangero, M.A.Carless, Y.Zhang. An epigenetic map of age-associated autosomal loci in northern European families at high risk for the metabolic syndrome. Clin Epigenet 2015; 7(1):12; PMID:25806089; http://dx.doi.org/10.1186/s13148-015-0048-6
26. L.Almasy, J.Blangero. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998 May; 62(5):1198-211; PMID:9545414; http://dx.doi.org/10.1086/301844
27. S.M.Grundy, H.B.BrewerJr, J.I.Cleeman, S.C.SmithJr, C.Lenfant, American Heart Association; National Heart, Lung, and Blood Institute. Definition of metabolic syndrome:Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004 Jan 27; 109(3):433-8; PMID:14744958; http://dx.doi.org/10.1161/01.CIR.0000111245.75752.C6
28. A.Esteghamati, H.Ashraf, O.Khalilzadeh, A.Zandieh, M.Nakhjavani, A.Rashidi, M.Haghazali, F.Asgari. Optimal cut-off of homeostasis model assessment of insulin resistance (HOMA-IR) for the diagnosis of metabolic syndrome:third national surveillance of risk factors of non-communicable diseases in Iran (SuRFNCD-2007). Nutr Metab 2010; 7:26; PMID:20374655; http://dx.doi.org/10.1186/1743-7075-7-26
29. M.Keskin, S.Kurtoglu, M.Kendirci, M.E.Atabek, C.Yazici. Homeostasis model assessment is more reliable than the fasting glucose/insulin ratio and quantitative insulin sensitivity check index for assessing insulin resistance among obese children and adolescents. Pediatrics 2005; 115(4):e500-3; PMID:15741351; http://dx.doi.org/10.1542/peds.2004-1921
30. A.H.Kissebah, G.E.Sonnenberg, J.Myklebust, M.Goldstein, K.Broman, R.G.James, J.A.Marks, G.R.Krakower, H.J.Jacob, J.Weber, et al. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc Natl Acad Sci U S A 2000; 97:14478-83; PMID:11121050; http://dx.doi.org/10.1073/pnas.97.26.14478
31. A.N.Peiris, M.I.Hennes, D.J.Evans, C.R.Wilson, M.B.Lee, A.H.Kissebah. Relationship of anthropometric measurements of body fat distribution to metabolic profile in premenopausal women. Acta Med Scand Suppl 1988; 723:179-88; PMID:3164966
32. N.C.Dupont, K.Wang, P.D.Wadhwa, J.F.Culhane, E.L.Nelson. Validation and comparison of luminex multiplex cytokine analysis kits with ELISA:determinations of a panel of nine cytokines in clinical sample culture supernatants. J Reprod Immunol 2005; 66:175-91; PMID:16029895; http://dx.doi.org/10.1016/j.jri.2005.03.005
33. Centers for Disease Control and Prevention. BMI Percentile Calculator for Child and Teen, English Version (accessed May, 2016). https://nccd.cdc.gov/dnpabmi/calculator.aspx
34. Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey:NHANES III (1988-1994), (accessed May, 2016). http://www.cdc.gov/nchs/nhanes/nhanes3.htm
35. F.Marabita, M.Almgren, M.E.Lindholm, S.Ruhrmann, F.Fagerström-Billai, M.Jagodic, C.J.Sundberg, T.J.Ekström, A.E.Teschendorff, J.Tegnér, et al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics 2013; 8(3):333-46; PMID:23422812; http://dx.doi.org/10.4161/epi.24008
36. Y.Chen, M.Lemire, S.Choufani, D.T.Butcher, D.Grafodatskaya, B.W.Zanke, S.Gallinger, T.J.Hudson, R.Weksberg. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 2013; 8(2):203-9; PMID:23314698; http://dx.doi.org/10.4161/epi.23470
37. J.Zhuang, M.Widschwendter, A.E.Teschendorff. A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform. BMC Bioinformatics 2012; 13:59; PMID:22524302; http://dx.doi.org/10.1186/1471-2105-13-59
38. J.Blangero, J.T.Williams, L.Almasy. Variance component methods for detecting complex trait loci. Adv Genet 2001; 42:151-81; PMID:11037320; http://dx.doi.org/10.1016/S0065-2660(01)42021-9
39. A.E.Jaffe, R.A.Irizarry. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 2014; 15:R31; PMID:24495553; http://dx.doi.org/10.1186/gb-2014-15-2-r31
40. E.A.Houseman, W.P.Accomando, D.C.Koestler, B.C.Christensen, C.J.Marsit, H.H.Nelson, J.K.Wiencke, K.T.Kelsey. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012; 13:86; PMID:22568884; http://dx.doi.org/10.1186/1471-2105-13-86
41. L.E.Reinius, N.Acevedo, M.Joerink, G.Pershagen, S.E.Dahlen, D.Greco, C.Söderhäll, A.Scheynius, J.Kere. Differential DNA methylation in purified human blood cells:implications for cell lineage and studies on disease susceptibility. PLoS One 2012; 7:e41361; PMID:22848472; http://dx.doi.org/10.1371/journal.pone.0041361
42. H.H.Göring, J.E.Curran, M.P.Johnson, T.D.Dyer, J.Charlesworth, S.A.Cole, J.B.M.Jowett, L.J.Abraham, D.L.Rainwater, A.G.Comuzzie, et al. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet 2007; 39(10):1208-1216; PMID:17873875; http://dx.doi.org/10.1038/ng2119
43. M.C.Whitlock. Combining probability from independent tests:the weighted Z-method is superior to Fisher's approach. J Evol Biol 2005 Sep; 18(5):1368-73; PMID:16135132; http://dx.doi.org/10.1111/j.1420-9101.2005.00917.x
44. E.L.Huttlin, L.Ting, R.J.Bruckner, F.Gebreab, M.P.Gygi, J.Szpyt, S.Tam, G.Zarraga, G.Colby, K.Baltier, et al. The BioPlex network:a systematic exploration of the human interactome. Cell 2015 Jul 16; 162(2):425-40; PMID:26186194; http://dx.doi.org/10.1016/j.cell.2015.06.043
45. H.Peng, M.Talebzadeh-Farrooji, M.J.Osborne, J.W.Prokop, P.C.McDonald, J.Karar, Z.Hou, M.He, E.Kebebew, T.Orntoft, et al. LIMD2 is a small LIM-only protein overexpressed in metastatic lesions that regulates cell motility and tumor progression by directly binding to and activating the integrin-linked kinase. Cancer Res 2014 Mar 1; 74(5):1390-403; PMID:24590809; http://dx.doi.org/10.1158/0008-5472.CAN-13-1275
46. C.Wu. Integrin-linked kinase and PINCH:partners in regulation of cell-extracellular matrix interaction and signal transduction. J Cell Sci 1999 Dec; 112(Pt 24):4485-9; PMID:10574698
47. A.Sasaki, H.Yasukawa, A.Suzuki, S.Kamizono, T.Syoda, I.Kinjyo, M.Sasaki, J.A.Johnston, A.Yoshimura. Cytokine-inducible SH2 protein-3 (CIS3/SOCS3) inhibits Janus tyrosine kinase by binding through the N-terminal kinase inhibitory region as well as SH2 domain. Genes Cells 1999 Jun; 4(6):339-51; PMID:10421843; http://dx.doi.org/10.1046/j.1365-2443.1999.00263.x
48. J.J.Babon, I.S.Lucet, J.M.Murphy, N.A.Nicola, L.N.Varghese. The molecular regulation of Janus kinase (JAK) activation. Biochem J 2014 Aug 15; 462(1):1-13; PMID:25057888; http://dx.doi.org/10.1042/BJ20140712
49. J.J.Babon, N.A.Nicola. The biology and mechanism of action of suppressor of cytokine signaling 3. Growth Factors 2012; 30:207-219; PMID:22574771; http://dx.doi.org/10.3109/08977194.2012.687375
50. N.Balthasar, R.Coppari, J.McMinn, S.M.Liu, C.E.Lee, V.Tang, C.D.Kenny, R.A.McGovern, S.C.ChuaJr, J.K.Elmquist, et al. Leptin receptor signaling in POMC neurons is required for normal body weight homeostasis. Neuron 2004; 42:983-991; PMID:15207242; http://dx.doi.org/10.1016/j.neuron.2004.06.004
51. M.B.Allison, M.G.MyersJr. 20 years of leptin:connecting leptin signaling to biological function. J Endocrinol 2014 Oct; 223(1):T25-35; PMID:25232147; http://dx.doi.org/10.1530/JOE-14-0404
52. L.Marroquí, A.Gonzalez, P.∇eco, E.Caballero-Garrido, E.Vieira, C.Ripoll, A.Nadal, I.Quesada. Role of leptin in the pancreatic β-cell:effects and signaling pathways. J Mol Endocrinol 2012 May 29; 49(1):R9-17; PMID:22448029; http://dx.doi.org/10.1530/JME-12-0025
53. C.Naylor, W.A.PetriJr. Leptin Regulation of Immune Responses. Trends Mol Med 2016 Feb; 22(2):88-98; PMID:26776093; http://dx.doi.org/10.1016/j.molmed.2015.12.001
54. M.A.Sanchez-Garrido, M.Tena-Sempere. Metabolic control of puberty:roles of leptin and kisspeptins. Horm Behav 2013 Jul; 64(2):187-94; PMID:23998663; http://dx.doi.org/10.1016/j.yhbeh.2013.01.014
55. L.C.Freitas Lima, V.A.Braga, M.do Socorro de França Silva, J.C.Cruz, S.H.Sousa Santos, M.M.de Oliveira Monteiro, C.M.Balarini. Adipokines, diabetes and atherosclerosis:an inflammatory association. Front Physiol 2015 Nov 3; 6:304; PMID:26578976; http://dx.doi.org/10.3389/fphys.2015.00304
56. G.Fernández-Formoso, S.Pérez-Sieira, D.González-Touceda, C.Dieguez, S.Tovar. Leptin, 20 years of searching for glucose homeostasis. Life Sci 2015 Nov 1; 140:4-9; PMID:25744051; http://dx.doi.org/10.1016/j.lfs.2015.02.008
57. N.Martínez-Micaelo, N.González-Abuín, A.Ardévol, M.Pinent, E.Petretto, J.Behmoaras, M.Blay. Leptin signal transduction underlies the differential metabolic response of LEW and WKY rats to cafeteria diet. J Mol Endocrinol 2016 Jan; 56(1):1-10; PMID:26450996; http://dx.doi.org/10.1530/JME-56-A1
58. H.Mori, R.Hanada, T.Hanada, D.Aki, R.Mashima, H.Nishinakamura, T.Torisu, K.R.Chien, H.Yasukawa, A.Yoshimura. Socs3 deficiency in the brain elevates leptin sensitivity and confers resistance to diet-induced obesity. Nature Medicine 2004; 10:739-743; PMID:15208705; http://dx.doi.org/10.1038/nm1071
59. P.Kievit, J.K.Howard, M.K.Badman, N.Balthasar, R.Coppari, H.Mori, C.E.Lee, J.K.Elmquist, A.Yoshimura, J.S.Flier. Enhanced leptin sensitivity and improved glucose homeostasis in mice lacking suppressor of cytokine signaling-3 in POMC-expressing cells. Cell Metab 2006; 4:123-132; PMID:16890540; http://dx.doi.org/10.1016/j.cmet.2006.06.010
60. H.Schuett, M.Luchtefeld, C.Grothusen, K.Grote, B.Schieffer. How much is too much? Interleukin-6 and its signalling in atherosclerosis. Thromb Haemost 2009; 102:215-222; PMID:19652871; http://dx.doi.org/10.1160/TH09-05-0297
61. S.Sindhu, R.Thomas, P.Shihab, D.Sriraman, K.Behbehani, R.Ahmad. Obesity is a positive modulator of IL-6R and IL-6 expression in the subcutaneous adipose tissue:significance for metabolic inflammation. PLoS One 2015 Jul 22; 10(7):e0133494; PMID:26200663; http://dx.doi.org/10.1371/journal.pone.0133494
62. F.W.Asselbergs, Y.Guo, E.P.van Iperen, S.Sivapalaratnam, V.Tragante, M.B.Lanktree, L.A.Lange, B.Almoguera, Y.E.Appelman, J.Barnard, et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet 2012 Nov 2; 91(5):823-38; PMID:23063622; http://dx.doi.org/10.1016/j.ajhg.2012.08.032
63. Y.Y.Zheng, L.F.Wang, X.H.Fan, C.H.Wu, N.Huo, H.Y.Lu, X.Y.Xu, L.Wei. Association of suppressor of cytokine signalling 3 polymorphisms with insulin resistance in patients with chronic hepatitis C. J Viral Hepat 2013 Apr; 20(4):273-80; PMID:23490372; http://dx.doi.org/10.1111/j.1365-2893.2012.01644.x
64. J.C.Chambers, M.Loh, B.Lehne, A.Drong, J.Kriebel, V.Motta, S.Wahl, H.R.Elliott, F.Rota, W.R.Scott, 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 Jul; 3(7):526-34; PMID:26095709; http://dx.doi.org/10.1016/S2213-8587(15)00127-8
65. W.A.Al Muftah, M.Al-Shafai, S.B.Zaghlool, A.Visconti, P.C.Tsai, P.Kumar, T.Spector, J.Bell, M.Falchi, K.Suhre. Epigenetic associations of type 2 diabetes and BMI in an Arab population. Clin Epigenetics 2016 Jan 28; 8:13; PMID:26823690; http://dx.doi.org/10.1186/s13148-016-0177-6