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Mol. Cells 2014; 37(6): 467-472

Published online June 30, 2014

https://doi.org/10.14348/molcells.2014.0073

© The Korean Society for Molecular and Cellular Biology

Effect of Body Mass Index on Global DNA Methylation in Healthy Korean Women

Yeon Kyung Na1, Hae Sook Hong1, Duk Hee Lee2, Won Kee Lee2, and Dong Sun Kim*

1Department of Anatomy and Brain Korea 21 Plus KNU Biomedical Convergence Program, College of Nursing, Kyungpook National University, Daegu 702-422, Korea, 2Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu 702-422, Korea

Correspondence to : *Correspondence: doskim@knu.ac.kr

Received: March 27, 2014; Revised: May 21, 2014; Accepted: May 26, 2014

Obesity is known to be strongly associated with cardiovascular disease and cancer, the leading causes of mortality worldwide, and develops owing to interactions between genes and the environment. DNA methylation can act as a downstream effector of environmental signals, and analysis of this process therefore holds substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. Global DNA methylation of peripheral blood cells has recently been proposed as a potential biomarker for disease risk. Repetitive element DNA methylation has been shown to be associated with prominent obesity-related chronic diseases, but little is known about its relationship with weight status. In this study, we quantified the methylation of Alu elements in the peripheral blood DNA of 244 healthy women with a range of body mass indexes (BMIs) using pyrosequencing technology. Among the study participants, certain clinical laboratory parameters, including hemoglobin, serum glutamic oxaloacetic transaminase, serum glutamic- pyruvic transaminase, total cholesterol, and triglyceride levels were found to be strongly associated with BMI. Moreover, a U-shaped association between BMI and Alu methylation was observed, with the lowest methylation levels occurring at BMIs of between 23 and 30 kg/m2. However, there was no significant association between Alu methylation and age, smoking status, or alcohol consumption. Overall, we identified a differential influence of BMI on global DNA methylation in healthy Korean women, indicating that BMI-related changes in Alu methylation might play a complex role in the etiology and pathogenesis of obesity. Further studies are required to elucidate the mechanisms underlying this relationship.

Keywords Alu, BMI, methylation, pyrosequencing, U-shape

DNA methylation is a core epigenetic process that influences a wide variety of biological mechanisms, including gene expression, chromosomal stability, imprinting, and cellular differentiation (Bernstein et al., 2007). Methylation can mediate environmental influences on gene expression and can modulate disease risk associated with genetic variation (Foley et al., 2009). Abnormal DNA methylation patterns (including genome-wide hypomethylation and gene-specific hypo- and hypermethylation) have been shown to be associated with a range of health outcomes (Ozanne and Constancia, 2007). In cancer and certain other diseases, most of these changes have been observed at the tissue level (Esteller et al., 2001; Rodenhister and Mann, 2006). Data relating to whether DNA methylation changes in peripheral blood cells can serve as useful, informative biomarkers for different health outcomes is much more limited, but new information is rapidly emerging (Heyn and Esteller, 2012). A number of methods are available for the analysis of global DNA methylation levels [total content of 5-methylcytosine (5-mC)] (Terry et al., 2011). There are over 500,000 long-interspersed nuclear elements (LINEs) and 1,500,000 short-interspersed nuclear elements (SINEs) across the human genome. Alu elements are the most common SINE; comprising 11% the human genome and containing a third of all methylation sites (Deininger and Batzer, 1999). The substantial representation of these repetitive elements throughout the genome makes their effectiveness as proxies for global DNA methylation highly evident. Indeed, methylation levels in Alu and LINE-1 repeats have been shown in previous studies to be associated with total genomic methylation content (Weisenberger et al., 2005; Yang et al., 2004). Estimates of these are increasingly being used in epidemiological investigations owing to their relatively low-cost, high-throughput, and the fact that they can offer quantitative results (Weisenberger et al., 2005). Interestingly, global methylation measures in blood DNA have been shown to vary in relation to demographic and lifestyle characteristics, including age, sex, smoking status, alcohol consumption, physical activity, and diet (Heyn and Esteller, 2012; Zhu et al., 2012). However, there are still few studies of the relationship between DNA methylation and weight status.

Obesity is simply defined as a condition of abnormal or excessive fat accumulation in adipose tissue, to the extent that health may be impaired. Obesity is the result of the interplay between external (environmental) and internal (genetic) factors. It has been cited as the epidemic of our time, with obesity rates sharply and steadily rising in many parts of the world, leading to increased morbidity and mortality due to type 2 diabetes and cardiovascular disease (Danaei et al., 2009). Obesity-related mortality by far exceeds mortality caused by other common diseases (Anderson and Caswell, 2009). Moreover, about 20% of all cancers are caused by overweight, and obesity has been shown to be associated with increased risk and worse outcomes following diagnosis (Wolin et al., 2010). Furthermore, the incidence of some types of cancer have been shown to be strongly associated with body mass index (BMI), which has historically been used to define obesity (Renehan et al., 2008). Our understanding of how and why obesity develops is incomplete. To date, several studies concerning DNA methylation and obesity have focused primarily on gene-specific methylation and had relatively small sample sizes (Carless et al., 2013; Feinberg et al., 2010; Fujiki et al., 2009; Milagro et al., 2012; Wang et al., 2010). Currently, studies of global methylation levels in blood DNA and BMI have yielded inconsistent results (Kim et al., 2009; Perng et al., 2013; Piyathilake et al., 2011; Wang et al., 2010; Zhang et al., 2011a;2011b; Zhu et al., 2012). BMI is now considered to be an important determinant of methylation biomarkers in the blood of women of reproductive age (van Driel et al., 2009). Interestingly, recent evidence has shown that alterations in global DNA methylation may be an important contributor to incidence risk of cancer and cardiovascular disease (Baccarelli et al., 2010; Terry et al., 2011). In this study, in order to investigate the specific effects of BMI on global DNA methylation and develop blood-based biomarker as initial screening test for obesity-related disease, we quantified the methylation of Alu elements in the peripheral blood DNA of 244 women with a range of BMIs using pyrosequencing technology.

Study population

Study subjects were 244 apparently healthy women aged between 20 and 51 years (mean 32 ± 7.9 years). This study was approved by the Institutional Review Board of Kyungpook National University Hospital. Additionally, informed written consent was obtained from all subjects before they participated in the study. Demographic information and lifestyle factors were determined for all participants by trained interviewers using a standardized questionnaire via face-to-face interviews. Height and bodyweight were measured using standard methods with participants wearing light clothes. BMI is easy to obtain [calculated by weight divided by height squared (kg/m2)] and is considered to be an acceptable proxy for under and overweight. BMI has also been shown to be directly related to health risks and mortality in many populations. According to the current international standard (WHO 1998), a BMI of over 25 kg/m2 is considered to indicate overweight and a BMI of over and 30 kg/m2 is considered to indicate obesity. However, the BMI cutoff values for overweight and obesity vary in Asian populations from 22 to 25 kg/m2 and the increased risk of co-morbidities conferred by obesity have been shown to occur at a lower BMI in several Asian populations (Low et al., 2009). We therefore divided the participants into 3 categories based on BMI normal weight (BMI < 23 kg/m2), overweight (23 kg/m2 ≤ BMI < 30 kg/m2), and obese (BMI ≥ 30 kg/m2). Blood samples were obtained via venipuncture after overnight fasting, and serum samples were separated by centrifugation and transferred to uncontaminated bottles with Teflon-coated caps. All samples were kept frozen at -70°C until analyses were conducted. Clinical laboratory values were determined by standard biochemical automatic or semi-automatic methods.

Blood DNA extraction and bisulfite treatment

Genomic DNA was extracted from whole-blood samples using a QIAamp DNA Blood Kit (Qiagen, USA). One microgram DNA was bisulfite-modified using an EZ DNA Methylation-Gold Kit (Zymo Research, USA) according to the manufacturer’s instructions. Final elution was performed with 30μl M-Elution Buffer (Zymo Research) and was stored at -70°C until analyzed. Built-in analysis of non-CpG cytosine residues provided an internal control for the completeness of bisulfate treatment. Following treatment with bisulfite, the conversion of this C into T is expected to be 100%. It is possible to insert a C/T singlenucleotide polymorphism into the sequence to be analyzed, and this will result in 100% Ts if conversion is efficient.

Assay of Alu methylation

Alu methylation analysis was quantitatively performed on the bisulfite-treated DNA using pyrosequencing with the polymerase chain reaction (PCR) primers and conditions previously described (Bollati et al., 2007). In brief, the bisulfite-treated samples (50 ng) were amplified with a biotin-labeled primer via PCR, which enables the conversion of the PCR product to a single-stranded DNA template suitable for pyrosequencing. Confirmation of the quality of the PCR products and their freedom from contamination was established on 2% agarose gels with ethidium bromide staining. After purification of PCR products using Sepharose beads on PyroMark Vacuum Prep Workstation (Qiagen), pyrosequencing was carried out using the PyroMark Q96MD System (Qiagen) according to manufacturer’s instructions, including a single-strand binding protein. The percentage of methylatation was expressed for each DNA locus as % 5-mC divided by the sum of methylated and unmethylated cytosines. We tested each marker 3 times and used the average percentage in the statistical analysis. Three controls were included in every pyrosequencing run. One well was filled with water to ensure no contamination, and 2 wells were filled with CpGenome Universal methylated and unmethylated DNA (Chemicon, USA) to evaluate the repeatability of the assay.

Statistical analysis

Statistical analysis and plotting was performed using R version 2.15.3 (http://www.r-project.org). Continuous variables were compared using one-way analysis of variance or analysis of covariance and pairwise post hoc comparison identified statistical differences following Bonferroni adjustment. Categorical variables were compared using a chi-squared test.

Growing evidence indicates that there are sex differences in global DNA methylation in the blood (Terry et al., 2011; Zhu et al., 2012). We have chosen healthy women as study subjects. First, overweight and obesity pose more serious health challenge to women than men. Obesity is a common disorder affecting approximately 1 in 3 women and the prevalence of extreme obesity is about 50% higher among women than men (Ryan and Braverman-Panza, 2014). Moreover, prevalence of metabolic syndrome is associated with obesity and its escalation has been steeper in women, particularly in young women (Regitz-Zagrosek et al., 2007). Second, recent result has shown that BMI is a strong determinant of methylation biomarkers in women of child-bearing age (van Driel et al., 2009), indicating a greater likelihood of BMI effects on global DNA methylation in women. Finally, previous studies investigating the effect of BMI on global DNA methylation include mainly male populations (Zhu et al., 2012). In addition, we have found that Alu assays may be a more sensitive method for detecting global DNA hypomethylation in peripheral blood leukocytes in the general population than LINE-1 assays (Kim et al., 2009). Most studies have focused primarily on LINE-1 methylation as a surrogate for global DNA methylation (Perng et al., 2013; Piyathilake et al., 2011; Zhang et al., 2011a;2011b). In this study, we determined Alu methylation levels in a population of Korean women. The characteristics of the study population are shown in Table 1. Obese participants had higher baseline hemoglobin, serum glutamic-oxaloacetic transaminase (GOT), serum glutamicpyruvic transaminase (GPT), total cholesterol (TC), and triglyceride (TG) levels than normal weight and overweight participants. Scatter plot and linear regression analyses showed that these biochemical parameters were positively correlated with BMI (Fig. 1), which is consistent with recent observations that GOT and GPT levels are strongly associated with BMI, TC, and TG in Asian populations (Lin et al., 2010; Sull et al., 2009).

Taken together, our data support the hypothesis that bodyweight may be the major factor in determining serum liver enzyme levels. Moreover, a significantly lower level of Alu methylation was found in overweight participants compared with normal weight or obese participants (p < 0.0001), but no significant difference was observed between the latter 2 groups, indicating a U-shaped association between Alu methylation and BMI (Table 2 and Fig. 2). After adjusting for other clinical covariates, this association was still found to be significant (Table 2). However, age, smoking status, and alcohol consumption were not significantly associated with Alu methylation levels in blood cells (data not shown). Similarly, previous studies have reported no association between smoking, alcohol consumption, and leukocyte DNA methylation (Hsiung et al., 2007; Kim et al., 2009; Terry et al., 2011; Zhu et al., 2012).

To date, studies of global methylation levels of blood DNA and BMI have yielded inconsistent results (Kim et al., 2009; Perng et al., 2013; Piyathilake et al., 2011; Wang et al., 2010; Zhang et al., 2011a;2011b; Zhu et al., 2012). Most previous studies reported no significant association between BMI and global DNA methylation (Kim et al., 2009; Wang et al., 2010; Zhang et al., 2011a;2011b; Zhu et al., 2012). In contrast, higher BMI has been reported to be associated with lower LINE-1 methylation (Perng et al., 2013; Piyathilake et al., 2011). Notably, we observed that participants with a BMI of 25 to 30 kg/m2 had the lowest methylation levels compared with the other groups, and that Alu methylation was elevated among those with a BMI higher or lower than that range. To the best of our knowledge, this is the first study to report a novel U-shaped association between BMI and Alu methylation. Interestingly, the association between BMI and all-cause mortality has previously been shown to be U-shaped, with the concave region corresponding to BMIs of between 22 and 26 kg/m2 (Berrington de Gonzalez et al., 2010; Whitlock et al., 2009; Zheng et al., 2011). Recently, we have observed that persistent organic pollutants, polychlorinated biphenyls are related to MGMT hypermethylation with an inverted U-shaped curve (P for quadratic term < 0.01). The prevalence of hypermethylation was highest in the subjects in the 2nd quintile (28.4%); however the subjects in the 5th quintile had a low prevalence of hypermethylation (8.2%) (unpublished data). Taken together, these data suggest the biphasic (hermetic) dose response model in which adaptive responses to low doses otherwise harmful conditions improve the functional ability of the cells and organism, resulting in a U or inverted U-shaped dose response (Calabrese, 2010). Accordingly, several lines of evidence are presented that DNA methylation may be an attractive candidate molecular mechanism for hormesis-like response (Vaiserman, 2011).

Although the exact mechanism underlying the elevated Alu methylation observed in the blood cells of obese people is not known, several possible explanations have been suggested. First, it is reasonable to speculate that sleeping disturbance might contribute to increases in Alu methylation in obese people. Obstructive sleep apnea (OSA) is highly prevalent among obese patients (Young et al., 1993) and methylation of the endothelial nitric oxide synthase and forkhead box P3 genes in blood DNA has been shown to be linearly associated with the severity of OSA (Kheirandish-Gozal et al., 2013; Kim et al., 2012). This suggests that global methylation can be influenced by OSA and other sleep disorders. In support of this theory, we found that scored sleep management was higher in normal and overweight subjects compared with obese ones (data not known), indicating that obese individuals may have poorer sleep quality than non-obese people. Second, although BMI does not differentiate between muscle mass and adipose tissue, people with high BMI can be inferred to have high levels of body fat, causing higher levels of inflammatory markers such as interleukin (IL)-6 (Ramkumar et al., 2004). Moreover, persistent inflammation may cause global DNA hypermethylation in peripheral blood cells via IL-6 signaling (Stenvinkel et al., 2007). Thus, it is likely that elevated Alu methylation in people with high BMI is a consequence of a low but continuous inflammatory drive of obesity. Third, Kamei et al. (2010) demonstrated that the expression of DNA methyltransferase 3a is markedly upregulated in the adipose tissue of obese mice. Similarly, a significantly higher frequency of RASSF1A, p16 and hMLH1 methylation is observed in nonmalignant tissues of obese patients (Peters et al., 2007; Ye et al., 2006). In light of evidence that tissue-specific methylation profiles are reflected in circulating cell epigenomics (Yang et al., 2010), it is tempting to speculate that obesity can increase global methylation levels in peripheral blood DNA. Fourth, there is compelling evidence that heterogeneity in the number of specific leukocytes has the potential to confound methylation measurements using whole blood DNA and that increased total leukocytes are associated with obesity (Talens et al., 2010; Veronelli et al., 2004). Thus, a plausible hypothesis is that a progressively uprising trend at high BMI might be due to differential lymphocyte counts associated with adiposity development. In this respect, it is noteworthy that DNA methylation is closely related to lymphocyte count (Zhu et al., 2012). Finally, these divergent results may be due to the specific racial/ethnic characteristics of the study population or the small sample size. Studies with a larger sample size are therefore required to confirm these findings. Further studies to confirm the U-shaped association between Alu methylation and BMI by measuring LINE-1 methylation and to understand the biological mechanisms underlying observed relationship are also required.

Collectively, the present study showed a novel U-shaped association between Alu methylation and BMI, suggesting that BMI-global DNA methylation relationship might play a convoluted role in etiology and pathogenesis of obesity as a hermetic dose response. In addition, the correlation could be a potential biomarker for the initial screening of other obesity-related disease. Furthermore, our results showed magnitude of obesity in general healthy population as Korean reproductive aged women.

Fig. 1. Scatter plot and simple regression of population characteristics and body mass indexes (BMIs). (A) hemoglobin, (B) serum glutamic oxaloacetic transaminase (GOT), (C) serum glutamic pyruvic transaminase (GPT), (D) total cholesterol, (E) triglyceride levels.
Fig. 2. Graphic representation of body mass index (BMI)-related global DNA methylation. (A) Scatter plot of BMI and Alu methylation, (B) Percentage of Alu methylation of blood cells in normal weight, overweight/pre-obese, and obese subjects.
Table 1.

Characteristics of study participants

Normal weight (n = 92)Overweight (n = 79)Obese (n = 73)F/X2P-valuePost hoc comparison using the Duncan method
Age, years (SD)31.52 (8.84)31.70 (5.52)32.93 (8.98)0.720.487
Smoking status, n (%)
?Non-smoker67 (72.8))60 (75.9)52 (71.2)
?Former smoker6 (6.5)6 (7.6)2 (2.7)3.510.477
?Current smoker19 (20.7)13 (16.5)19 (26.1)2
Alcohol consumption, n (%)
?Non-drinker22 (23.9)23 (29.1)30 (41.1)7.580.270
?Ever drinker70 (76.1)56 (70.9)43 (58.9)
Hemoglobin, % (SD)11.97 (1.05)12.23 (0.99)12.57 (1.22)6.180.0023 > 2, 1
Creatinine, mg/dl (SD)0.87 (0.12)0.86 (0.09)0.87 (0.10)0.530.587
Glucose, mg/dl (SD)93.72 (6.17)95.57 (23.19)32.93 (8.98)93.18 (14.18)0.611
GOT, units/l (SD)17.50 (4.48)19.67 (7.17)24.27 (16.43)9.08<0.0013 > 2, 1
GPT, units/l (SD)13.36 (8.08)18.48 (12.58)29.92 (27.62)18.80<0.0013 > 2, 1
Total bilirubin, mg/dl (SD)0.62 (0.26)0.59 (0.30)0.58 (0.27)0.710.492
Total cholesterol, mg/dl (SD)179.59 (31.66)190.94 (47.89)199.08 (30.35)5.680.0043 > 2 > 1
Triglyceride, mg/dl (SD)83.93 (46.07)116.57 (86.88)168.48 (128.18)17.92<0.0013 > 2 ≥ 1

SD, standard deviation; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase


Table 2.

Correlation between Alu methylation and BMI

GeneCrude means
Adjusted means*
MeanFP-valueMeanFP-value
Alu methylation
?Normal weight26.27 ± 0.8267.46< 0.00126.28 ± 0.0967.72< 0.001
?Overweight24.96 ± 0.4524.95 ± 0.12
?Obese25.99 ± 0.9425.96 ± 0.10

*Adjusted for clinical laboratory values including hemoglobin, serum glutamic oxaloacetic transa


  1. Anderson, A.S., and Caswell, S. (2009). Obesity management-an opportunity for cancer prevention. Surgeon. 7, 282-285.
    CrossRef
  2. Baccarelli, A., Wright, R., Bollati, V., Litonjua, A., Zanobetti, A., Tarantini, L., Sparrow, D., Vokonas, P., and Schwartz, J. (2010). Ischemic heart disease and stroke in relation to blood DNA methylation. Epidemiology. 21, 819-828.
    Pubmed KoreaMed CrossRef
  3. Bernstein, B.E., Meissner, A., and Lander, E.S. (2007). The mammalian epigenome. Cell. 128, 669-681.
    Pubmed CrossRef
  4. Berrington de Gonzalez, A., Hartge, P., Cerhan, J.R., Flint, A.J., Hannan, L., Maclnnis, R.J., Moore, S.C., Tobias, G.S., Anton-Culver, H., and Freeman, L.B. (2010). Body-mass index and mortality among 1.46 million white adults. N. Engl. J. Med.. 363, 2211-2219.
  5. Bollati, V., Baccarelli, A., Hou, L., Bonzini, M., Fustinoni, S., Cavallo, D., Byun, H.M., Jiang, J., Marinelli, B., and Pesatori, A.C. (2007). Changes in DNA methylation patterns in subjects exposed to low-dose benzene. Cancer Res. 67, 876-880.
    Pubmed CrossRef
  6. Calabrese, E.J. (2010). Hormesis is central to toxicology, pharmacology and risk assessment. Hum. Exp. Toxicol.. 29, 249-261.
    Pubmed CrossRef
  7. Carless, M.A., Kulkarni, H., Kos, M.Z., Charlesworth, J., Peralta, J.M., Goring, H.H., Curran, J.E., Almasy, L., Dyer, T.D., and Comuzzie, A.G. (2013). Genetic effects on DNA methylation and its potential relevance for obesity in Mexican Americans. PLoS One. 8, .
    Pubmed KoreaMed CrossRef
  8. Danaei, G., Ding, E.L., Mozaffarian, D., Taylor, B., Rehm, J., Murray, C.J., and Ezzati, M. (2009). The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 6, .
    Pubmed KoreaMed CrossRef
  9. Deininger, P.L., and Batzer, M.A. (1999). Alu repeats and human disease. Mol. Genet. Metab.. 67, 183-193.
    Pubmed CrossRef
  10. Esteller, M., Corn, P.G., Baylin, S.B., and Herman, J.G. (2001). A gene hypermethylation profile of human cancer. Cancer Res. 61, 3225-3229.
  11. Feinberg, A.P., Irizarry, R.A., Fradin, D., Aryee, M.J., Murakami, P., Aspelund, T., Eiriksdottir, G., Harris, T.B., Launer, L., and Gudnason, V. (2010). Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci. Transl. Med.. 2, 49-67.
    CrossRef
  12. Foley, D.L., Craig, J.M., Morley, R., Olsson, C.J., Dwyer, T., Smith, K., and Saffery, R. (2009). Prospects for epigenetic epidemiology. Am. J. Epidemiol.. 169, 389-400.
  13. Fujiki, K., Kano, F., Shiota, K., and Murata, M. (2009). Expression of the peroxisome proliferator activated receptor gene is repressed by DNA methylation in visceral adipose tissue of mouse models of diabetes. BMC Biol. 7, 38.
    Pubmed KoreaMed CrossRef
  14. Heyn, H., and Esteller, M. (2012). DNA methylation profiling in the clinic: applications and challenges. Nat. Rev. Genet.. 13, 679-692.
    Pubmed CrossRef
  15. Hsiung, D, Marsit, C, Houseman, E, Eddy, K, Furniss, C, McClean, M, and Kelsey, KT (2007). Global DNA methylation level in whole blood as a biomarker in head and neck squamous cell carcinoma. Cancer Epidemiol. Biomarkers Prev.. 16, 108-114.
    Pubmed CrossRef
  16. Kamei, Y., Suganami, T., Ehara, T., Kanai, S., Hayashi, K., Yamamoto, Y., Miura, S., Ezaki, O., Okano, M., and Ogawa, Y. (2010). Increased expression of DNA methyltransferase 3a in obese adipose tissue: studies with transgenic mice. Obesity. 18, 314-321.
    Pubmed CrossRef
  17. Kheirandish-Gozal, L., Khalyfa, A., Gozal, D., Bhattacharjee, R., and Wang, Y. (2013). Endothelial dysfunction in children with obstructive sleep apnea is associated with epigenetic changes in the eNOS gene. Chest. 143, 971-977.
    Pubmed KoreaMed CrossRef
  18. Kim, K.Y., Kim, D.S., Lee, S.K., Lee, I.K., Kang, J.H., Chang, Y.S., Jacobs, D.R., Steffes, M., and Lee, D.H. (2009). Association of low-dose exposure to persistent organic pollutants with global DNA hypomethylation in healthy Koreans. Environ. Health Perspect. 118, 370-374.
    Pubmed KoreaMed CrossRef
  19. Kim, J., Bhattacharjee, R., Khalyfa, A., Kheirandish-Gozal, L., Capdevila, O.S., Wang, Y., and Gozal, D. (2012). DNA methylation in inflammatory genes among children with obstructive sleep apnea. Am. J. Respir. Crit. Care Med.. 185, 330-338.
    Pubmed KoreaMed CrossRef
  20. Lin, J.D., Lin, P.Y., Chen, L.M., Fang, W.H., Lin, L.P., and Loh, C.H. (2010). Serum gutamic-oxaloacetic transaminase (GOT) and glutamic-pyruvic transaminase (GPT) levels in children and adolescents with intellectual disabilities. Res. Dev. Disabil.. 31, 172-177.
    Pubmed CrossRef
  21. Low, S., Chin, M.C., Ma, S., Heng, D., and Deurenberg-Yap, M. (2009). Rationale for redifining obesity in Asians. Ann. Acad. Med. Singapore. 38, 66-69.
  22. Milagro, F.I., Gomez-Abellan, P., Campion, J., Martinez, J.A., Ordovas, J.M., and Garaulet, M. (2012). CLOCK, PER2 and BMAL1 DNA methylation: association with obesity and metabolic syndrome characteristics and monounsaturated fat intake. Chronobiol. Int.. 29, 1180-1194.
    Pubmed CrossRef
  23. Ozanne, S.E., and Constancia, M. (2007). Mechanisms of disease: the developmental origins of disease and the role of the epigenotype. Nat. Clin. Pract. Endocrinol. Metab.. 3, 539-546.
    Pubmed CrossRef
  24. Perng, W., Mora-Plazas, M., Marin, C., Rozek, L.S., Baylin, A., and Villamor, E. (2013). A prospective study of LINE-1 DNA methylation and development of adiposity in school-age children. PLoS One. 8, .
    CrossRef
  25. Peters, I., Vaske, B., Albrecht, K., Kuczyk, M.A., Jonas, U., and Serth, J. (2007). Adiposity and age are statistically related to enhanced RASSF1A tumor suppressor gene promoter methylation in normal autopsy kidney tissue. Cancer Epidemiol. Biomarkers Prev.. 16, 2526-2532.
    Pubmed CrossRef
  26. Piyathilake, C., Badiga, S., Johanning, G., Alvarez, R., and Patridge, E. (2011). Predictors and health consequences of epigenetic changes associated with excess bodyweight in women of childbearing age. Cancer Epidemiol. Biomarkers Prev.. 20, 719.
    CrossRef
  27. Ramkumar, N., Cheung, A.K., Pappas, L.M., Roberts, W.L., and Beddhu, S. (2004). Association of obesity with inflammation in chronic kidney disease: a cross-sectional disease. J. Ren. Nutr.. 14, 201-207.
    CrossRef
  28. Regitz-Zagrosek, V., Lehmkuhl, E., and Mahmoodzadeh, S. (2007). Gender aspects of the role of the metabolic syndrome as a risk factor for cardiovascular disease. Gend Med. 4, S162-177.
    CrossRef
  29. Renehan, A.G., Tyson, M., Egger, M., Heller, R.F., and Zwahlen, M. (2008). Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 371, 569-578.
    CrossRef
  30. Rodenhister, D., and Mann, M. (2006). Epigenetics and human disease: translating basic biology into clinical application. CMAJ. 174, 341-348.
    Pubmed KoreaMed CrossRef
  31. Ryan, D.H., and Braverman-Panza, J. (2014). Obesity in women. J. Fam. Pract.. 63, S15-20.
  32. Stenvinkel, P., Karimi, M., Johansson, S., Axelsson, J., Suliman, M., Lindholm, B., Heimb?rger, O., Barany, P., Alvestrand, A., and Nordfors, L. (2007). Impact of inflammation on epigenetic DNA methylation-a novel risk factor for cardiovascular disease?. J. Int. Med.. 261, 488-499.
    Pubmed CrossRef
  33. Sull, J.W., Yun, J.E., Lee, S.Y., Ohrr, H., Jee, S.H., Gualler, E., and Samet, J.M. (2009). Body mass index and serum aminotransferase levels in Korean men and women. J. Clin. Gastroenterol.. 43, 869-875.
    Pubmed CrossRef
  34. Talens, R.P., Boomsma, D.I., Tobi, E.W., Kremer, D., Jukema, J.W., Willemsen, G., Putter, H., Slagboom, P.E., and Heijmans, B.T. (2010). Variation, patterns, and temporal stability of DNA methylation: considerations for epigenetic epidemiology. FASEB J. 24, 3135-3144.
    Pubmed CrossRef
  35. Terry, M.B., Delgado-Cruzatak, L., Vin-Raviv, N., Wu, H.C., and Santella, R.M. (2011). DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics. 6, 828-837.
    KoreaMed CrossRef
  36. Vaiserman, A.M. (2011). Hormesis and epigenetics: Is there a link?. Aging Res. Rev.. 10, 413-421.
    CrossRef
  37. van Driel, L.M., Eijkemans, M.J., de Jonge, R., de Vries, J.H., van Meurs, J.B., Steegers, E.A., and Steegers-Theunissen, R.P. (2009). BMI is an important determinant of methylation biomarkers in blood of women of reproductive ages. J. Nutr.. 139, 2315-2321.
    Pubmed CrossRef
  38. Veronelli, A., Laneri, M., Ranieri, R., Koprivec, D., Vardaro, D., Paganelli, M., Folli, F., and Pontiroli, A.E. (2004). White blood cells in obesity and diabetes: effects of weight loss and normalization in obesity and diabetes: effects of weight loss and normalization of glucose metabolism. Diabetes Care. 27, 2501-2502.
    CrossRef
  39. Wang, X., Zhu, H., Snieder, H., Su, S., Munn, D., Harshfield, G., Maria, B., Dong, Y., Treiber, F., and Gutin, B. (2010). Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med. 8, 87.
    CrossRef
  40. Weisenberger, D.J., Campan, M., Long, T.I., Kim, M., Woods, C., Fiala, E., Ehrlich, M., and Laird, P.W. (2005). Analysis of repetitive element DNA methylation by MethyLight. Nucleic Acids Res. 33, 6823-6836.
    Pubmed KoreaMed CrossRef
  41. Whitlock, G., Lewington, S., Sherliker, P., Clarke, R., Emberson, J., Halsey, J., Qizilbash, N., Collins, R., and Peto, R. (2009). Bodymass index and cause-specific mortality in 900000 adults: collaborative analyses of 57 prospective studies. Lancet. 373, 1083-1096.
  42. Wolin, K.Y., Carson, K., and Colditz, G.A. (2010). Obesity and cancer. Oncologist. 15, 556-565.
    Pubmed KoreaMed CrossRef
  43. Yang, A.S., Estecio, M.R.H., Doshi, K., Kondo, Y., Tajara, E.H., and Issa, J.P. (2004). A simple method for estimating global DNA methylation using bisulfite PCR of repetitive DNA elements. Nucleic Acids Res. 32, .
    CrossRef
  44. Yang, HH, Hu, N, Wang, C, Ding, T, and Dunn, BK (2010). Influence of genetic background and tissue types on global DNA methylation patterns. PLoS One. 5, .
    CrossRef
  45. Ye, C., Shrubsole, M.J., Cai, Q., Ness, R., Grady, W.M., Smalley, W, Cai, H., Washington, K., and Zheng, W. (2006). Promoter methylation status of the MGMT, hMLH1, and CDKN2A/p16 genes in non-neoplastic mucosa of patients with and without colorectal adenomas. Oncol. Rep.. 16, 429-435.
    CrossRef
  46. Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., and Badr, S. (1993). The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med.. 328, 1230-1235.
    Pubmed CrossRef
  47. Zhang, F.F., Cardarelli, R., Carroll, J., Fulda, K.G., Kaur, M., Gonzalez, K., Vishwanatha, J.K., Santella, R.M., and Morabia, A. (2011a). Significant difference in global genomic methylation by gender and race/ethnicity in peripheral blood. Epigenetics. 6, 623-629.
  48. Zhang, F.F., Santella, R.M., Wolff, M., Kappil, M.A., Markowitz, S.B., and Morabia, A. (2011b). White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population. Epigenetics. 6, 606-614.
  49. Zheng, W., McLerran, D.F., Rolland, B., Zhang, X., Inoue, M., Matsio, K., He, J., Gupta, P.C., Ramadas, K., and Tsugane, S. (2011). Association between body-mass index and risk of death in more than 1 million Asians. N. Engl. J. Med.. 364, 719-729.
    Pubmed KoreaMed CrossRef
  50. Zhu, Z.Z., Hou, L., Bollati, V., Tarantini, L., Marinelli, B., Cantone, L., Yang, A.S., Vokonas, P., Lissowska, J., and Fustinoni, S. (2012). Predictors of global methylation levels in blood DNA of healthy subjects: a combined analysis. Int. J. Epidemiol.. 41, 126-139.
    Pubmed KoreaMed CrossRef

Article

Article

Mol. Cells 2014; 37(6): 467-472

Published online June 30, 2014 https://doi.org/10.14348/molcells.2014.0073

Copyright © The Korean Society for Molecular and Cellular Biology.

Effect of Body Mass Index on Global DNA Methylation in Healthy Korean Women

Yeon Kyung Na1, Hae Sook Hong1, Duk Hee Lee2, Won Kee Lee2, and Dong Sun Kim*

1Department of Anatomy and Brain Korea 21 Plus KNU Biomedical Convergence Program, College of Nursing, Kyungpook National University, Daegu 702-422, Korea, 2Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu 702-422, Korea

Correspondence to:*Correspondence: doskim@knu.ac.kr

Received: March 27, 2014; Revised: May 21, 2014; Accepted: May 26, 2014

Abstract

Obesity is known to be strongly associated with cardiovascular disease and cancer, the leading causes of mortality worldwide, and develops owing to interactions between genes and the environment. DNA methylation can act as a downstream effector of environmental signals, and analysis of this process therefore holds substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. Global DNA methylation of peripheral blood cells has recently been proposed as a potential biomarker for disease risk. Repetitive element DNA methylation has been shown to be associated with prominent obesity-related chronic diseases, but little is known about its relationship with weight status. In this study, we quantified the methylation of Alu elements in the peripheral blood DNA of 244 healthy women with a range of body mass indexes (BMIs) using pyrosequencing technology. Among the study participants, certain clinical laboratory parameters, including hemoglobin, serum glutamic oxaloacetic transaminase, serum glutamic- pyruvic transaminase, total cholesterol, and triglyceride levels were found to be strongly associated with BMI. Moreover, a U-shaped association between BMI and Alu methylation was observed, with the lowest methylation levels occurring at BMIs of between 23 and 30 kg/m2. However, there was no significant association between Alu methylation and age, smoking status, or alcohol consumption. Overall, we identified a differential influence of BMI on global DNA methylation in healthy Korean women, indicating that BMI-related changes in Alu methylation might play a complex role in the etiology and pathogenesis of obesity. Further studies are required to elucidate the mechanisms underlying this relationship.

Keywords: Alu, BMI, methylation, pyrosequencing, U-shape

INTRODUCTION

DNA methylation is a core epigenetic process that influences a wide variety of biological mechanisms, including gene expression, chromosomal stability, imprinting, and cellular differentiation (Bernstein et al., 2007). Methylation can mediate environmental influences on gene expression and can modulate disease risk associated with genetic variation (Foley et al., 2009). Abnormal DNA methylation patterns (including genome-wide hypomethylation and gene-specific hypo- and hypermethylation) have been shown to be associated with a range of health outcomes (Ozanne and Constancia, 2007). In cancer and certain other diseases, most of these changes have been observed at the tissue level (Esteller et al., 2001; Rodenhister and Mann, 2006). Data relating to whether DNA methylation changes in peripheral blood cells can serve as useful, informative biomarkers for different health outcomes is much more limited, but new information is rapidly emerging (Heyn and Esteller, 2012). A number of methods are available for the analysis of global DNA methylation levels [total content of 5-methylcytosine (5-mC)] (Terry et al., 2011). There are over 500,000 long-interspersed nuclear elements (LINEs) and 1,500,000 short-interspersed nuclear elements (SINEs) across the human genome. Alu elements are the most common SINE; comprising 11% the human genome and containing a third of all methylation sites (Deininger and Batzer, 1999). The substantial representation of these repetitive elements throughout the genome makes their effectiveness as proxies for global DNA methylation highly evident. Indeed, methylation levels in Alu and LINE-1 repeats have been shown in previous studies to be associated with total genomic methylation content (Weisenberger et al., 2005; Yang et al., 2004). Estimates of these are increasingly being used in epidemiological investigations owing to their relatively low-cost, high-throughput, and the fact that they can offer quantitative results (Weisenberger et al., 2005). Interestingly, global methylation measures in blood DNA have been shown to vary in relation to demographic and lifestyle characteristics, including age, sex, smoking status, alcohol consumption, physical activity, and diet (Heyn and Esteller, 2012; Zhu et al., 2012). However, there are still few studies of the relationship between DNA methylation and weight status.

Obesity is simply defined as a condition of abnormal or excessive fat accumulation in adipose tissue, to the extent that health may be impaired. Obesity is the result of the interplay between external (environmental) and internal (genetic) factors. It has been cited as the epidemic of our time, with obesity rates sharply and steadily rising in many parts of the world, leading to increased morbidity and mortality due to type 2 diabetes and cardiovascular disease (Danaei et al., 2009). Obesity-related mortality by far exceeds mortality caused by other common diseases (Anderson and Caswell, 2009). Moreover, about 20% of all cancers are caused by overweight, and obesity has been shown to be associated with increased risk and worse outcomes following diagnosis (Wolin et al., 2010). Furthermore, the incidence of some types of cancer have been shown to be strongly associated with body mass index (BMI), which has historically been used to define obesity (Renehan et al., 2008). Our understanding of how and why obesity develops is incomplete. To date, several studies concerning DNA methylation and obesity have focused primarily on gene-specific methylation and had relatively small sample sizes (Carless et al., 2013; Feinberg et al., 2010; Fujiki et al., 2009; Milagro et al., 2012; Wang et al., 2010). Currently, studies of global methylation levels in blood DNA and BMI have yielded inconsistent results (Kim et al., 2009; Perng et al., 2013; Piyathilake et al., 2011; Wang et al., 2010; Zhang et al., 2011a;2011b; Zhu et al., 2012). BMI is now considered to be an important determinant of methylation biomarkers in the blood of women of reproductive age (van Driel et al., 2009). Interestingly, recent evidence has shown that alterations in global DNA methylation may be an important contributor to incidence risk of cancer and cardiovascular disease (Baccarelli et al., 2010; Terry et al., 2011). In this study, in order to investigate the specific effects of BMI on global DNA methylation and develop blood-based biomarker as initial screening test for obesity-related disease, we quantified the methylation of Alu elements in the peripheral blood DNA of 244 women with a range of BMIs using pyrosequencing technology.

MATERIALS AND METHODS

Study population

Study subjects were 244 apparently healthy women aged between 20 and 51 years (mean 32 ± 7.9 years). This study was approved by the Institutional Review Board of Kyungpook National University Hospital. Additionally, informed written consent was obtained from all subjects before they participated in the study. Demographic information and lifestyle factors were determined for all participants by trained interviewers using a standardized questionnaire via face-to-face interviews. Height and bodyweight were measured using standard methods with participants wearing light clothes. BMI is easy to obtain [calculated by weight divided by height squared (kg/m2)] and is considered to be an acceptable proxy for under and overweight. BMI has also been shown to be directly related to health risks and mortality in many populations. According to the current international standard (WHO 1998), a BMI of over 25 kg/m2 is considered to indicate overweight and a BMI of over and 30 kg/m2 is considered to indicate obesity. However, the BMI cutoff values for overweight and obesity vary in Asian populations from 22 to 25 kg/m2 and the increased risk of co-morbidities conferred by obesity have been shown to occur at a lower BMI in several Asian populations (Low et al., 2009). We therefore divided the participants into 3 categories based on BMI normal weight (BMI < 23 kg/m2), overweight (23 kg/m2 ≤ BMI < 30 kg/m2), and obese (BMI ≥ 30 kg/m2). Blood samples were obtained via venipuncture after overnight fasting, and serum samples were separated by centrifugation and transferred to uncontaminated bottles with Teflon-coated caps. All samples were kept frozen at -70°C until analyses were conducted. Clinical laboratory values were determined by standard biochemical automatic or semi-automatic methods.

Blood DNA extraction and bisulfite treatment

Genomic DNA was extracted from whole-blood samples using a QIAamp DNA Blood Kit (Qiagen, USA). One microgram DNA was bisulfite-modified using an EZ DNA Methylation-Gold Kit (Zymo Research, USA) according to the manufacturer’s instructions. Final elution was performed with 30μl M-Elution Buffer (Zymo Research) and was stored at -70°C until analyzed. Built-in analysis of non-CpG cytosine residues provided an internal control for the completeness of bisulfate treatment. Following treatment with bisulfite, the conversion of this C into T is expected to be 100%. It is possible to insert a C/T singlenucleotide polymorphism into the sequence to be analyzed, and this will result in 100% Ts if conversion is efficient.

Assay of Alu methylation

Alu methylation analysis was quantitatively performed on the bisulfite-treated DNA using pyrosequencing with the polymerase chain reaction (PCR) primers and conditions previously described (Bollati et al., 2007). In brief, the bisulfite-treated samples (50 ng) were amplified with a biotin-labeled primer via PCR, which enables the conversion of the PCR product to a single-stranded DNA template suitable for pyrosequencing. Confirmation of the quality of the PCR products and their freedom from contamination was established on 2% agarose gels with ethidium bromide staining. After purification of PCR products using Sepharose beads on PyroMark Vacuum Prep Workstation (Qiagen), pyrosequencing was carried out using the PyroMark Q96MD System (Qiagen) according to manufacturer’s instructions, including a single-strand binding protein. The percentage of methylatation was expressed for each DNA locus as % 5-mC divided by the sum of methylated and unmethylated cytosines. We tested each marker 3 times and used the average percentage in the statistical analysis. Three controls were included in every pyrosequencing run. One well was filled with water to ensure no contamination, and 2 wells were filled with CpGenome Universal methylated and unmethylated DNA (Chemicon, USA) to evaluate the repeatability of the assay.

Statistical analysis

Statistical analysis and plotting was performed using R version 2.15.3 (http://www.r-project.org). Continuous variables were compared using one-way analysis of variance or analysis of covariance and pairwise post hoc comparison identified statistical differences following Bonferroni adjustment. Categorical variables were compared using a chi-squared test.

RESULTS AND DISCUSSION

Growing evidence indicates that there are sex differences in global DNA methylation in the blood (Terry et al., 2011; Zhu et al., 2012). We have chosen healthy women as study subjects. First, overweight and obesity pose more serious health challenge to women than men. Obesity is a common disorder affecting approximately 1 in 3 women and the prevalence of extreme obesity is about 50% higher among women than men (Ryan and Braverman-Panza, 2014). Moreover, prevalence of metabolic syndrome is associated with obesity and its escalation has been steeper in women, particularly in young women (Regitz-Zagrosek et al., 2007). Second, recent result has shown that BMI is a strong determinant of methylation biomarkers in women of child-bearing age (van Driel et al., 2009), indicating a greater likelihood of BMI effects on global DNA methylation in women. Finally, previous studies investigating the effect of BMI on global DNA methylation include mainly male populations (Zhu et al., 2012). In addition, we have found that Alu assays may be a more sensitive method for detecting global DNA hypomethylation in peripheral blood leukocytes in the general population than LINE-1 assays (Kim et al., 2009). Most studies have focused primarily on LINE-1 methylation as a surrogate for global DNA methylation (Perng et al., 2013; Piyathilake et al., 2011; Zhang et al., 2011a;2011b). In this study, we determined Alu methylation levels in a population of Korean women. The characteristics of the study population are shown in Table 1. Obese participants had higher baseline hemoglobin, serum glutamic-oxaloacetic transaminase (GOT), serum glutamicpyruvic transaminase (GPT), total cholesterol (TC), and triglyceride (TG) levels than normal weight and overweight participants. Scatter plot and linear regression analyses showed that these biochemical parameters were positively correlated with BMI (Fig. 1), which is consistent with recent observations that GOT and GPT levels are strongly associated with BMI, TC, and TG in Asian populations (Lin et al., 2010; Sull et al., 2009).

Taken together, our data support the hypothesis that bodyweight may be the major factor in determining serum liver enzyme levels. Moreover, a significantly lower level of Alu methylation was found in overweight participants compared with normal weight or obese participants (p < 0.0001), but no significant difference was observed between the latter 2 groups, indicating a U-shaped association between Alu methylation and BMI (Table 2 and Fig. 2). After adjusting for other clinical covariates, this association was still found to be significant (Table 2). However, age, smoking status, and alcohol consumption were not significantly associated with Alu methylation levels in blood cells (data not shown). Similarly, previous studies have reported no association between smoking, alcohol consumption, and leukocyte DNA methylation (Hsiung et al., 2007; Kim et al., 2009; Terry et al., 2011; Zhu et al., 2012).

To date, studies of global methylation levels of blood DNA and BMI have yielded inconsistent results (Kim et al., 2009; Perng et al., 2013; Piyathilake et al., 2011; Wang et al., 2010; Zhang et al., 2011a;2011b; Zhu et al., 2012). Most previous studies reported no significant association between BMI and global DNA methylation (Kim et al., 2009; Wang et al., 2010; Zhang et al., 2011a;2011b; Zhu et al., 2012). In contrast, higher BMI has been reported to be associated with lower LINE-1 methylation (Perng et al., 2013; Piyathilake et al., 2011). Notably, we observed that participants with a BMI of 25 to 30 kg/m2 had the lowest methylation levels compared with the other groups, and that Alu methylation was elevated among those with a BMI higher or lower than that range. To the best of our knowledge, this is the first study to report a novel U-shaped association between BMI and Alu methylation. Interestingly, the association between BMI and all-cause mortality has previously been shown to be U-shaped, with the concave region corresponding to BMIs of between 22 and 26 kg/m2 (Berrington de Gonzalez et al., 2010; Whitlock et al., 2009; Zheng et al., 2011). Recently, we have observed that persistent organic pollutants, polychlorinated biphenyls are related to MGMT hypermethylation with an inverted U-shaped curve (P for quadratic term < 0.01). The prevalence of hypermethylation was highest in the subjects in the 2nd quintile (28.4%); however the subjects in the 5th quintile had a low prevalence of hypermethylation (8.2%) (unpublished data). Taken together, these data suggest the biphasic (hermetic) dose response model in which adaptive responses to low doses otherwise harmful conditions improve the functional ability of the cells and organism, resulting in a U or inverted U-shaped dose response (Calabrese, 2010). Accordingly, several lines of evidence are presented that DNA methylation may be an attractive candidate molecular mechanism for hormesis-like response (Vaiserman, 2011).

Although the exact mechanism underlying the elevated Alu methylation observed in the blood cells of obese people is not known, several possible explanations have been suggested. First, it is reasonable to speculate that sleeping disturbance might contribute to increases in Alu methylation in obese people. Obstructive sleep apnea (OSA) is highly prevalent among obese patients (Young et al., 1993) and methylation of the endothelial nitric oxide synthase and forkhead box P3 genes in blood DNA has been shown to be linearly associated with the severity of OSA (Kheirandish-Gozal et al., 2013; Kim et al., 2012). This suggests that global methylation can be influenced by OSA and other sleep disorders. In support of this theory, we found that scored sleep management was higher in normal and overweight subjects compared with obese ones (data not known), indicating that obese individuals may have poorer sleep quality than non-obese people. Second, although BMI does not differentiate between muscle mass and adipose tissue, people with high BMI can be inferred to have high levels of body fat, causing higher levels of inflammatory markers such as interleukin (IL)-6 (Ramkumar et al., 2004). Moreover, persistent inflammation may cause global DNA hypermethylation in peripheral blood cells via IL-6 signaling (Stenvinkel et al., 2007). Thus, it is likely that elevated Alu methylation in people with high BMI is a consequence of a low but continuous inflammatory drive of obesity. Third, Kamei et al. (2010) demonstrated that the expression of DNA methyltransferase 3a is markedly upregulated in the adipose tissue of obese mice. Similarly, a significantly higher frequency of RASSF1A, p16 and hMLH1 methylation is observed in nonmalignant tissues of obese patients (Peters et al., 2007; Ye et al., 2006). In light of evidence that tissue-specific methylation profiles are reflected in circulating cell epigenomics (Yang et al., 2010), it is tempting to speculate that obesity can increase global methylation levels in peripheral blood DNA. Fourth, there is compelling evidence that heterogeneity in the number of specific leukocytes has the potential to confound methylation measurements using whole blood DNA and that increased total leukocytes are associated with obesity (Talens et al., 2010; Veronelli et al., 2004). Thus, a plausible hypothesis is that a progressively uprising trend at high BMI might be due to differential lymphocyte counts associated with adiposity development. In this respect, it is noteworthy that DNA methylation is closely related to lymphocyte count (Zhu et al., 2012). Finally, these divergent results may be due to the specific racial/ethnic characteristics of the study population or the small sample size. Studies with a larger sample size are therefore required to confirm these findings. Further studies to confirm the U-shaped association between Alu methylation and BMI by measuring LINE-1 methylation and to understand the biological mechanisms underlying observed relationship are also required.

Collectively, the present study showed a novel U-shaped association between Alu methylation and BMI, suggesting that BMI-global DNA methylation relationship might play a convoluted role in etiology and pathogenesis of obesity as a hermetic dose response. In addition, the correlation could be a potential biomarker for the initial screening of other obesity-related disease. Furthermore, our results showed magnitude of obesity in general healthy population as Korean reproductive aged women.

Fig 1.

Figure 1.Scatter plot and simple regression of population characteristics and body mass indexes (BMIs). (A) hemoglobin, (B) serum glutamic oxaloacetic transaminase (GOT), (C) serum glutamic pyruvic transaminase (GPT), (D) total cholesterol, (E) triglyceride levels.
Molecules and Cells 2014; 37: 467-472https://doi.org/10.14348/molcells.2014.0073

Fig 2.

Figure 2.Graphic representation of body mass index (BMI)-related global DNA methylation. (A) Scatter plot of BMI and Alu methylation, (B) Percentage of Alu methylation of blood cells in normal weight, overweight/pre-obese, and obese subjects.
Molecules and Cells 2014; 37: 467-472https://doi.org/10.14348/molcells.2014.0073

. Characteristics of study participants.

Normal weight (n = 92)Overweight (n = 79)Obese (n = 73)F/X2P-valuePost hoc comparison using the Duncan method
Age, years (SD)31.52 (8.84)31.70 (5.52)32.93 (8.98)0.720.487
Smoking status, n (%)
?Non-smoker67 (72.8))60 (75.9)52 (71.2)
?Former smoker6 (6.5)6 (7.6)2 (2.7)3.510.477
?Current smoker19 (20.7)13 (16.5)19 (26.1)2
Alcohol consumption, n (%)
?Non-drinker22 (23.9)23 (29.1)30 (41.1)7.580.270
?Ever drinker70 (76.1)56 (70.9)43 (58.9)
Hemoglobin, % (SD)11.97 (1.05)12.23 (0.99)12.57 (1.22)6.180.0023 > 2, 1
Creatinine, mg/dl (SD)0.87 (0.12)0.86 (0.09)0.87 (0.10)0.530.587
Glucose, mg/dl (SD)93.72 (6.17)95.57 (23.19)32.93 (8.98)93.18 (14.18)0.611
GOT, units/l (SD)17.50 (4.48)19.67 (7.17)24.27 (16.43)9.08<0.0013 > 2, 1
GPT, units/l (SD)13.36 (8.08)18.48 (12.58)29.92 (27.62)18.80<0.0013 > 2, 1
Total bilirubin, mg/dl (SD)0.62 (0.26)0.59 (0.30)0.58 (0.27)0.710.492
Total cholesterol, mg/dl (SD)179.59 (31.66)190.94 (47.89)199.08 (30.35)5.680.0043 > 2 > 1
Triglyceride, mg/dl (SD)83.93 (46.07)116.57 (86.88)168.48 (128.18)17.92<0.0013 > 2 ≥ 1

SD, standard deviation; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase.


. Correlation between Alu methylation and BMI.

GeneCrude means
Adjusted means*
MeanFP-valueMeanFP-value
Alu methylation
?Normal weight26.27 ± 0.8267.46< 0.00126.28 ± 0.0967.72< 0.001
?Overweight24.96 ± 0.4524.95 ± 0.12
?Obese25.99 ± 0.9425.96 ± 0.10

*Adjusted for clinical laboratory values including hemoglobin, serum glutamic oxaloacetic transa.


References

  1. Anderson, A.S., and Caswell, S. (2009). Obesity management-an opportunity for cancer prevention. Surgeon. 7, 282-285.
    CrossRef
  2. Baccarelli, A., Wright, R., Bollati, V., Litonjua, A., Zanobetti, A., Tarantini, L., Sparrow, D., Vokonas, P., and Schwartz, J. (2010). Ischemic heart disease and stroke in relation to blood DNA methylation. Epidemiology. 21, 819-828.
    Pubmed KoreaMed CrossRef
  3. Bernstein, B.E., Meissner, A., and Lander, E.S. (2007). The mammalian epigenome. Cell. 128, 669-681.
    Pubmed CrossRef
  4. Berrington de Gonzalez, A., Hartge, P., Cerhan, J.R., Flint, A.J., Hannan, L., Maclnnis, R.J., Moore, S.C., Tobias, G.S., Anton-Culver, H., and Freeman, L.B. (2010). Body-mass index and mortality among 1.46 million white adults. N. Engl. J. Med.. 363, 2211-2219.
  5. Bollati, V., Baccarelli, A., Hou, L., Bonzini, M., Fustinoni, S., Cavallo, D., Byun, H.M., Jiang, J., Marinelli, B., and Pesatori, A.C. (2007). Changes in DNA methylation patterns in subjects exposed to low-dose benzene. Cancer Res. 67, 876-880.
    Pubmed CrossRef
  6. Calabrese, E.J. (2010). Hormesis is central to toxicology, pharmacology and risk assessment. Hum. Exp. Toxicol.. 29, 249-261.
    Pubmed CrossRef
  7. Carless, M.A., Kulkarni, H., Kos, M.Z., Charlesworth, J., Peralta, J.M., Goring, H.H., Curran, J.E., Almasy, L., Dyer, T.D., and Comuzzie, A.G. (2013). Genetic effects on DNA methylation and its potential relevance for obesity in Mexican Americans. PLoS One. 8, .
    Pubmed KoreaMed CrossRef
  8. Danaei, G., Ding, E.L., Mozaffarian, D., Taylor, B., Rehm, J., Murray, C.J., and Ezzati, M. (2009). The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 6, .
    Pubmed KoreaMed CrossRef
  9. Deininger, P.L., and Batzer, M.A. (1999). Alu repeats and human disease. Mol. Genet. Metab.. 67, 183-193.
    Pubmed CrossRef
  10. Esteller, M., Corn, P.G., Baylin, S.B., and Herman, J.G. (2001). A gene hypermethylation profile of human cancer. Cancer Res. 61, 3225-3229.
  11. Feinberg, A.P., Irizarry, R.A., Fradin, D., Aryee, M.J., Murakami, P., Aspelund, T., Eiriksdottir, G., Harris, T.B., Launer, L., and Gudnason, V. (2010). Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci. Transl. Med.. 2, 49-67.
    CrossRef
  12. Foley, D.L., Craig, J.M., Morley, R., Olsson, C.J., Dwyer, T., Smith, K., and Saffery, R. (2009). Prospects for epigenetic epidemiology. Am. J. Epidemiol.. 169, 389-400.
  13. Fujiki, K., Kano, F., Shiota, K., and Murata, M. (2009). Expression of the peroxisome proliferator activated receptor gene is repressed by DNA methylation in visceral adipose tissue of mouse models of diabetes. BMC Biol. 7, 38.
    Pubmed KoreaMed CrossRef
  14. Heyn, H., and Esteller, M. (2012). DNA methylation profiling in the clinic: applications and challenges. Nat. Rev. Genet.. 13, 679-692.
    Pubmed CrossRef
  15. Hsiung, D, Marsit, C, Houseman, E, Eddy, K, Furniss, C, McClean, M, and Kelsey, KT (2007). Global DNA methylation level in whole blood as a biomarker in head and neck squamous cell carcinoma. Cancer Epidemiol. Biomarkers Prev.. 16, 108-114.
    Pubmed CrossRef
  16. Kamei, Y., Suganami, T., Ehara, T., Kanai, S., Hayashi, K., Yamamoto, Y., Miura, S., Ezaki, O., Okano, M., and Ogawa, Y. (2010). Increased expression of DNA methyltransferase 3a in obese adipose tissue: studies with transgenic mice. Obesity. 18, 314-321.
    Pubmed CrossRef
  17. Kheirandish-Gozal, L., Khalyfa, A., Gozal, D., Bhattacharjee, R., and Wang, Y. (2013). Endothelial dysfunction in children with obstructive sleep apnea is associated with epigenetic changes in the eNOS gene. Chest. 143, 971-977.
    Pubmed KoreaMed CrossRef
  18. Kim, K.Y., Kim, D.S., Lee, S.K., Lee, I.K., Kang, J.H., Chang, Y.S., Jacobs, D.R., Steffes, M., and Lee, D.H. (2009). Association of low-dose exposure to persistent organic pollutants with global DNA hypomethylation in healthy Koreans. Environ. Health Perspect. 118, 370-374.
    Pubmed KoreaMed CrossRef
  19. Kim, J., Bhattacharjee, R., Khalyfa, A., Kheirandish-Gozal, L., Capdevila, O.S., Wang, Y., and Gozal, D. (2012). DNA methylation in inflammatory genes among children with obstructive sleep apnea. Am. J. Respir. Crit. Care Med.. 185, 330-338.
    Pubmed KoreaMed CrossRef
  20. Lin, J.D., Lin, P.Y., Chen, L.M., Fang, W.H., Lin, L.P., and Loh, C.H. (2010). Serum gutamic-oxaloacetic transaminase (GOT) and glutamic-pyruvic transaminase (GPT) levels in children and adolescents with intellectual disabilities. Res. Dev. Disabil.. 31, 172-177.
    Pubmed CrossRef
  21. Low, S., Chin, M.C., Ma, S., Heng, D., and Deurenberg-Yap, M. (2009). Rationale for redifining obesity in Asians. Ann. Acad. Med. Singapore. 38, 66-69.
  22. Milagro, F.I., Gomez-Abellan, P., Campion, J., Martinez, J.A., Ordovas, J.M., and Garaulet, M. (2012). CLOCK, PER2 and BMAL1 DNA methylation: association with obesity and metabolic syndrome characteristics and monounsaturated fat intake. Chronobiol. Int.. 29, 1180-1194.
    Pubmed CrossRef
  23. Ozanne, S.E., and Constancia, M. (2007). Mechanisms of disease: the developmental origins of disease and the role of the epigenotype. Nat. Clin. Pract. Endocrinol. Metab.. 3, 539-546.
    Pubmed CrossRef
  24. Perng, W., Mora-Plazas, M., Marin, C., Rozek, L.S., Baylin, A., and Villamor, E. (2013). A prospective study of LINE-1 DNA methylation and development of adiposity in school-age children. PLoS One. 8, .
    CrossRef
  25. Peters, I., Vaske, B., Albrecht, K., Kuczyk, M.A., Jonas, U., and Serth, J. (2007). Adiposity and age are statistically related to enhanced RASSF1A tumor suppressor gene promoter methylation in normal autopsy kidney tissue. Cancer Epidemiol. Biomarkers Prev.. 16, 2526-2532.
    Pubmed CrossRef
  26. Piyathilake, C., Badiga, S., Johanning, G., Alvarez, R., and Patridge, E. (2011). Predictors and health consequences of epigenetic changes associated with excess bodyweight in women of childbearing age. Cancer Epidemiol. Biomarkers Prev.. 20, 719.
    CrossRef
  27. Ramkumar, N., Cheung, A.K., Pappas, L.M., Roberts, W.L., and Beddhu, S. (2004). Association of obesity with inflammation in chronic kidney disease: a cross-sectional disease. J. Ren. Nutr.. 14, 201-207.
    CrossRef
  28. Regitz-Zagrosek, V., Lehmkuhl, E., and Mahmoodzadeh, S. (2007). Gender aspects of the role of the metabolic syndrome as a risk factor for cardiovascular disease. Gend Med. 4, S162-177.
    CrossRef
  29. Renehan, A.G., Tyson, M., Egger, M., Heller, R.F., and Zwahlen, M. (2008). Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 371, 569-578.
    CrossRef
  30. Rodenhister, D., and Mann, M. (2006). Epigenetics and human disease: translating basic biology into clinical application. CMAJ. 174, 341-348.
    Pubmed KoreaMed CrossRef
  31. Ryan, D.H., and Braverman-Panza, J. (2014). Obesity in women. J. Fam. Pract.. 63, S15-20.
  32. Stenvinkel, P., Karimi, M., Johansson, S., Axelsson, J., Suliman, M., Lindholm, B., Heimb?rger, O., Barany, P., Alvestrand, A., and Nordfors, L. (2007). Impact of inflammation on epigenetic DNA methylation-a novel risk factor for cardiovascular disease?. J. Int. Med.. 261, 488-499.
    Pubmed CrossRef
  33. Sull, J.W., Yun, J.E., Lee, S.Y., Ohrr, H., Jee, S.H., Gualler, E., and Samet, J.M. (2009). Body mass index and serum aminotransferase levels in Korean men and women. J. Clin. Gastroenterol.. 43, 869-875.
    Pubmed CrossRef
  34. Talens, R.P., Boomsma, D.I., Tobi, E.W., Kremer, D., Jukema, J.W., Willemsen, G., Putter, H., Slagboom, P.E., and Heijmans, B.T. (2010). Variation, patterns, and temporal stability of DNA methylation: considerations for epigenetic epidemiology. FASEB J. 24, 3135-3144.
    Pubmed CrossRef
  35. Terry, M.B., Delgado-Cruzatak, L., Vin-Raviv, N., Wu, H.C., and Santella, R.M. (2011). DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics. 6, 828-837.
    KoreaMed CrossRef
  36. Vaiserman, A.M. (2011). Hormesis and epigenetics: Is there a link?. Aging Res. Rev.. 10, 413-421.
    CrossRef
  37. van Driel, L.M., Eijkemans, M.J., de Jonge, R., de Vries, J.H., van Meurs, J.B., Steegers, E.A., and Steegers-Theunissen, R.P. (2009). BMI is an important determinant of methylation biomarkers in blood of women of reproductive ages. J. Nutr.. 139, 2315-2321.
    Pubmed CrossRef
  38. Veronelli, A., Laneri, M., Ranieri, R., Koprivec, D., Vardaro, D., Paganelli, M., Folli, F., and Pontiroli, A.E. (2004). White blood cells in obesity and diabetes: effects of weight loss and normalization in obesity and diabetes: effects of weight loss and normalization of glucose metabolism. Diabetes Care. 27, 2501-2502.
    CrossRef
  39. Wang, X., Zhu, H., Snieder, H., Su, S., Munn, D., Harshfield, G., Maria, B., Dong, Y., Treiber, F., and Gutin, B. (2010). Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med. 8, 87.
    CrossRef
  40. Weisenberger, D.J., Campan, M., Long, T.I., Kim, M., Woods, C., Fiala, E., Ehrlich, M., and Laird, P.W. (2005). Analysis of repetitive element DNA methylation by MethyLight. Nucleic Acids Res. 33, 6823-6836.
    Pubmed KoreaMed CrossRef
  41. Whitlock, G., Lewington, S., Sherliker, P., Clarke, R., Emberson, J., Halsey, J., Qizilbash, N., Collins, R., and Peto, R. (2009). Bodymass index and cause-specific mortality in 900000 adults: collaborative analyses of 57 prospective studies. Lancet. 373, 1083-1096.
  42. Wolin, K.Y., Carson, K., and Colditz, G.A. (2010). Obesity and cancer. Oncologist. 15, 556-565.
    Pubmed KoreaMed CrossRef
  43. Yang, A.S., Estecio, M.R.H., Doshi, K., Kondo, Y., Tajara, E.H., and Issa, J.P. (2004). A simple method for estimating global DNA methylation using bisulfite PCR of repetitive DNA elements. Nucleic Acids Res. 32, .
    CrossRef
  44. Yang, HH, Hu, N, Wang, C, Ding, T, and Dunn, BK (2010). Influence of genetic background and tissue types on global DNA methylation patterns. PLoS One. 5, .
    CrossRef
  45. Ye, C., Shrubsole, M.J., Cai, Q., Ness, R., Grady, W.M., Smalley, W, Cai, H., Washington, K., and Zheng, W. (2006). Promoter methylation status of the MGMT, hMLH1, and CDKN2A/p16 genes in non-neoplastic mucosa of patients with and without colorectal adenomas. Oncol. Rep.. 16, 429-435.
    CrossRef
  46. Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., and Badr, S. (1993). The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med.. 328, 1230-1235.
    Pubmed CrossRef
  47. Zhang, F.F., Cardarelli, R., Carroll, J., Fulda, K.G., Kaur, M., Gonzalez, K., Vishwanatha, J.K., Santella, R.M., and Morabia, A. (2011a). Significant difference in global genomic methylation by gender and race/ethnicity in peripheral blood. Epigenetics. 6, 623-629.
  48. Zhang, F.F., Santella, R.M., Wolff, M., Kappil, M.A., Markowitz, S.B., and Morabia, A. (2011b). White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population. Epigenetics. 6, 606-614.
  49. Zheng, W., McLerran, D.F., Rolland, B., Zhang, X., Inoue, M., Matsio, K., He, J., Gupta, P.C., Ramadas, K., and Tsugane, S. (2011). Association between body-mass index and risk of death in more than 1 million Asians. N. Engl. J. Med.. 364, 719-729.
    Pubmed KoreaMed CrossRef
  50. Zhu, Z.Z., Hou, L., Bollati, V., Tarantini, L., Marinelli, B., Cantone, L., Yang, A.S., Vokonas, P., Lissowska, J., and Fustinoni, S. (2012). Predictors of global methylation levels in blood DNA of healthy subjects: a combined analysis. Int. J. Epidemiol.. 41, 126-139.
    Pubmed KoreaMed CrossRef
Mol. Cells
May 31, 2022 Vol.45 No.5, pp. 273~352
COVER PICTURE
Fe2+ ion depletion-induced expression of BΔGFP at the early stage of leaf development (Choi et al., pp. 294-305).

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