Introduction

Molecular Medicine Reports

1791-2997 1791-3004

D.A. Spandidos

10.3892/mmr.2017.8039

mmr-17-01-1699

Articles

INSIG2 rs7566605 single nucleotide variant and global DNA methylation index levels are associated with weight loss in a personalized weight reduction program

Pirini

Francesca

1 Rodriguez-Torres

Sebastian

2 Ayandibu

Bola Grace

2 Orera-Clemente

María

3 Vega

Alberto Gonzalez-De La

4 Lawson

Fahcina

2 Jr

Roland J. Thorpe

5 Sidransky

David

2 Guerrero-Preston

Rafael

2 6

1Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, I-47014 Meldola, Italy 2Department of Otolaryngology, School of Medicine, Johns Hopkins University, Baltimore, MD 21231, USA 3Genetic Laboratory, University General Hospital Gregorio Marañón, 28007 Madrid, Spain 4Molecular Genetics and Clinical Genetics Department, CGC Genetics, 28016 Madrid, Spain 5Johns Hopkins University Centre for Health Disparities Solutions, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA 6Department of Obstetrics and Gynecology, School of Medicine, University of Puerto Rico, San Juan, PR 00936-5067, USA

Correspondence to: Dr Rafael Guerrero-Preston, Department of Otolaryngology, School of Medicine, Johns Hopkins University, 1550 Orleans Street, Cancer Research Building II, Room 5M06, Baltimore, MD 21231, USA, E-mail: rafael.guerrero@upr.edu

012018

14112017

17 1 1699 1709 26072017 17082017

2018

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Single nucleotide polymorphisms associated with lipid metabolism and energy balance are implicated in the weight loss response caused by nutritional interventions. Diet-induced weight loss is also associated with differential global DNA methylation. DNA methylation has been proposed as a predictive biomarker for weight loss response. Personalized biomarkers for successful weight loss may inform clinical decisions when deciding between behavioral and surgical weight loss interventions. The aim of the present study was to investigate the association between global DNA methylation, genetic variants associated with energy balance and lipid metabolism, and weight loss following a non-surgical weight loss regimen. The present study included 105 obese participants that were enrolled in a personalized weight loss program based on their allelic composition of the following five energy balance and lipid metabolism-associated loci: Near insulin-induced gene 2 (INSIG2); melanocortin 4 receptor; adrenoceptor β2; apolipoprotein A5; and G-protein subunit β3. The present study investigated the association between a global DNA methylation index (GDMI), the allelic composition of the five energy balance and lipid metabolism-associated loci, and weight loss during a 12 month program, after controlling for age, sex and body mass index (BMI). The results demonstrated a significant association between the GDMI and near INSIG2 locus, after adjusting for BMI and weight loss, and significant trends were observed when stratifying by gender. In conclusion, a combination of genetic and epigenetic biomarkers may be used to design personalized weight loss interventions, enabling adherence and ensuring improved outcomes for obesity treatment programs. Precision weight loss programs designed based on molecular information may enable the creation of personalized interventions for patients, that use genomic biomarkers for treatment design and for treatment adherence monitoring, thus improving response to treatment.

near insulin-induced gene 2 melanocortin 4 receptor adrenoceptor β2 apolipoprotein A5 G-protein subunit β3 global DNA methylation index energy balance single nucleotide variant obesity lipid metabolism personalized weight reduction program

Introduction

Obesity is a major public health concern, and contributes to morbidity and mortality rates via associations with chronic diseases (1,2), including type 2 diabetes mellitus, cardiovascular diseases, osteoarthritis and certain types of cancer (3). Although weight loss through dietary regimes and exercise is commonly prescribed, there is minimal insight into the molecular basis of weight loss, particularly concerning disparities in the extent of weight loss among individuals (4,5). The observed differences in weight loss among individuals is an important issue for clinicians and patients, and these differences are predominantly observed between males and females undergoing the same treatment courses (6).

The current understanding of the etiology of obesity and weight loss involves environmental, genetic and epigenetic factors (4,7,8). External factors in the pathogenesis of obesity include diet, physical exercise and stress. Previous studies indicate that single nucleotide polymorphisms (SNPs) are also important determinants of weight loss (8,9). It is accepted that genetic factors partially determine individual susceptibility to weight gain and obesity, however, the established genetic variants only partially explain the variation observed. As a result, interest in understanding the potential role of epigenetics as a mediator of gene-environment interactions in obesity development has increased (10). Previous studies have investigated gene-environment interactions associated with the development of obesity (4,11–13). The initial studies focused on the associations between obesity and global DNA methylation (14–16). Global DNA methylation refers to the overall level of methylcytosine in the genome as a percentage of total cytosine, while gene-specific methylation refers to the methylation status of a specific site. While numerous studies have reported a complex association between global DNA methylation and body mass index (BMI), there is no consistent evidence for an association between global DNA methylation and obesity, which may be due to the lack of a gold standard method of measuring global DNA methylation (14,17–22). Genome-wide arrays have demonstrated a concurrent loss of methylation in the non-coding areas of the genome and gain of methylation in CpG islands located on promoter regions of obese patients with a high BMI, compared with patients with a low BMI. The studies also identified multiple obesity-associated differentially methylated sites, primarily in blood cells (23–26).

Cytosine methylation in SNPs (allele-specific methylation) has previously been successfully investigated in weight loss (9). Accumulating evidence indicates that the tendency towards adult obesity has early developmental origins, which are associated with a ‘nutritional memory response’ that can take form in epigenetic modifications during a lifetime. Associations between methylation marks at birth and later life obesity were reported (14,27–36). However, to the best of our knowledge, the potential genetic and epigenetic interactions associated with weight loss have not previously been investigated (37–41).

The present study aimed to investigate the genetic and epigenetic alterations associated with weight loss in a population of obese patients participating in a personalized weight loss program, which was designed based on genetic information. Personalized weight loss diets and lifestyle modification plans were provided to study participants according to the combined SNP profiles of five genes associated with energy balance and lipid metabolism. The present study determined the association between a global DNA methylation index (GDMI) and the allelic composition of near insulin-induced gene 2 (INSIG2), melanocortin 4 receptor (MC4R), adrenoceptor β2 (ADRB2), apolipoprotein A5 (APOA5) and G-protein subunit β3 (GNB3) in 105 obese or overweight patients that participated in a 12 month program.

Materials and methods <sec> <title>Study participants

The patients were referred to the Clinical Genetics laboratory of CGC (Madrid Spain) between January 2009 and June 2010, by either an endocrinologist or a dietitian in order to establish a healthy lifestyle intervention based on the risk of obesity inherent to each polymorphism investigated. Upon arrival at the clinic, the patients answered a questionnaire regarding previous health issues, including surgery, pathologies, diabetes and cardiovascular disease, and their height and present weight was recorded. The Institutional Review Boards of the Johns Hopkins University School of Medicine (Baltimore, MD, USA) approved the protocol for the present study. All participants signed an informed consent form where it was specifically stated that the samples may be used for anonymized research studies and for publications.

Personalized weight reduction program

The patients participated in a personalized weight reduction program guided by their genotypic profile, which included SNP-associated dietary recommendations, for 12 months. The BMI in kg/m² was calculated for each participant prior to and after completion of the personalized weight reduction program. Adult obesity was defined as a BMI of ≥30 kg/m², and patients were classified as overweight if they had a BMI between 25 and 29.9 kg/m². The participants underwent a personalized weight reduction program based on their genotypic profile. This method utilized SNP data to develop dietary recommendations. Participants also provided data regarding age at maximum weight recorded, previous dietary interventions and maximum weight loss. Additionally, women provided details regarding age at menarche and pregnancy history, in addition to height and weight prior to and following menarche and pregnancy. The polymorphisms were investigated in our laboratory and the results were disclosed in a post-test genetic counseling session.

The dietary and lifestyle interventions were subsequently tailored to the genotype of each patient by their endocrinologist/dietitian, following the general recommendations provided by the laboratory, as described in Table I. Genetic information and lifestyle modification recommendations were provided during the initial counseling session and adherence was determined at follow-up by the referring endocrinologist/dietitian. Patients were provided with a personalized weight reduction program, with primary and preferential lifestyle interventions, together with supplemental activities for each SNP of the five loci in the panel. The descriptions of sequence variants presented in Table I follow the Human Genome Variation Society 2016 recommended format (42). The five loci selected for this intervention are collectively associated with energy balance and lipid metabolism in ≥1 of the following metabolic pathways or conditions: Development of obesity, high BMI, hyperphagia, hyperinsulinemia, lipolysis control, lipid metabolism homeostasis and exercise-induced weight loss (43–65).

Information on the well-established associations between each SNP and the risk of obesity was also provided to each patient. Each patient received up to three personalized dietary/lifestyle recommendations, based on their individual genotypic mosaic, which included a combination of physical activity and diets low in calories, carbohydrates and/or lipids.

DNA isolation

DNA was extracted from peripheral blood mononuclear cell (PBMC) samples obtained from all participants, and all samples were kept at −80°C until analysis. Blood was collected in 4 ml vacutainer tubes containing EDTA (BD Biosciences, Franklin Lakes, NJ, USA). Whole blood was centrifuged at 300 × g for 10 min and the leukocyte layer was separated, adding the same volume of PBS (0.01 M PO4, 0.15 M NaCl, pH 7.2). The resulting mixture was carefully placed on Ficoll-Paque (17-1440-02), followed by centrifugation at 700 × g for 30 min. The corresponding leukocyte portion was separated in another tube and centrifuged at 300 × g for 10 min. The supernatant was discarded and the material precipitated was washed with 1 ml PBS (0.01 M PO4, 0.15 M NaCl, pH 7.2) by centrifugation at 300 × g for 10 min and resuspended in 200 µl NET 100 (5 M NaCl, 1 M Tris-HCl, 0.5 M EDTA, pH 8.0) to be stored at −20°C. The concentration of cells was determined by manual cell counting using a Neubauer chamber and divided into three equal aliquots. PBMC DNA samples were sent to the Head and Neck Cancer Research Laboratory of Johns Hopkins School of Medicine where they were digested with 50 µg/ml proteinase K in the presence of 1% sodium dodecyl sulfate at 48°C for 3 days, which was followed by phenol/chloroform extraction and ethanol precipitation, and finally dissolved in 30 µl LoTE (2.5 mmol/l EDTA and 10 mmol/l Tris-HCl), as previously described (66).

Global DNA methylation assays

In total, sufficient DNA levels were obtained from 95 samples for the global DNA methylation analysis. The global DNA methylation levels were determined with an ELISA-based commercial kit (Imprint Methylated DNA Quantification kit; cat no. MDQ1; Sigma-Aldrich, St Louis, MO, USA), according to the manufacturer's protocol. The MDQ1 kit is a high-throughput molecular biology kit, which employs a 96-well plate format to provide accurate differential global DNA methylation absorbance readings with as little as 50 ng genomic DNA. In the present study, 2 µl DNA at a concentration of 100 ng/µl was diluted with 28 µl lysis and binding buffers, and incubated at 37°C for 60 min. The samples were incubated with capture and detection antibodies and absorbance was read at 450 nm. Quantification of global DNA methylation was performed by calculating the amount of methylated cytosine (5-methylcytosine) in the sample relative to the global cytidine (5-methylcytosine + deoxycytosine) in a positive control that had been previously methylated. All samples were analyzed in duplicate.

SNP analysis

DNA was genotyped using made-to-order TaqMan SNP Genotyping Assays with the following cat no. 4351379 and assay IDs: C_29404113_20 (near INSIG2-rs7566605); C_32667060_10 (MC4R-rs17782313); C_2084765_20 (ADRB2-rs1042714); C_2310403_10 (APOA5-rs662799); C_2184734_10 (GNB3-rs5443) (Thermo Fisher Scientific, Inc., Waltham, MA, USA).

The following probe sequences were used in each respective genotyping assay: i) rs7566605 (near INSIG2) consensus sequence-Chr2: 118078449 on GrCH38: 5-AAGTACTTAACAATGGATATTTGAT[C/G]GTGGTCCTTTAGGTCTGTACCAGGG-3′; ii) rs17782313 (MC4R) consensus sequence-Chr18: 60183864 on GrCH38: 5′-GTTTAAAGCAGGAGAGATTGTATCC[C/T]GATGGAAATGACAAGAAAAGCTTCA-3′; iii) rs1042714 (ADRB2) consensus sequence-Chr5: 148826910 on GrCH38: 5′-TGCGCCGGACCACGACGTCACGCAG[C/G]AAAGGGACGAGGTGTGGGTGGTGGG-3′; iv) rs662799 (APOA5) consensus sequence-Chr11: 116792991 on GrCH38: 5′-GAGCCCCAGGAACTGGAGCGAAAGT[A/G]AGATTTGCCCCATGAGGAAAAGCTG-3′; and v) rs5443 (GNB3) consensus sequence-Chr12:6845711 on GrCH38: 5′-AGAGCATCATCTGCGGCATCACGTC[C/T]GTGGCCTTCTCCCTCAGTGGCCGCC-3′.

The two probes for each locus were identical apart from the region highlighted in square brackets, where the nucleotide in this position differed between the two probes, as indicated.

Quantitative polymerase chain reactions (PCR) were performed with two allele-specific TaqMan MGB probes for each of the five SNPs tested on an ABI 7,500 Real-Time PCR instrument. Duplicate reactions were run for each assay using a 25 µl reaction volume on a 96-well plate. PCR was performed with 20 ng input DNA, 1 µmol/l each primer, 0.25 µmol/l each probe and 1× TaqMan master mix (cat no. 4371355; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The cycling program was one cycle of 50°C for 2 min, one cycle of 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min. The ROX passive reference provides an internal reference to which the reporter-dye signal can be normalized during data analysis. The Taqman master mix contained AmpliTaq Gold^® DNA Polymerase, Ultra Pure (UP) Deoxyribonucleotide triphosphates (dNTPs), ROX passive reference and buffer components optimized for tight endpoint fluorescence clusters, reproducible allelic discrimination and bench top stability. Fluorescence intensities (arbitrary units) of the two probes were plotted and genotype calling was performed using predefined calling parameters.

Statistical analysis

The SNPassoc package (https://CRAN.R-project.org/package=SNPassoc) in R (version 1.9–2) was employed to examine the association between global DNA methylation and five energy balance and lipid metabolism-associated loci. R is a language and environment for statistical computing and graphics, which is similar to the S language and environment (https://www.r-project.org/about.html).

Univariate and multivariate analyses were performed to investigate the association between each SNP and global DNA methylation adjusting for sex, age, weight, BMI and weight loss using the association function in the SNPassoc package in R. This function carries out an association analysis between a single SNP and a dependent variable (phenotype) under five different genetic models (inheritance patterns): co-dominant, dominant, recessive, over-dominant and log-additive. The only significant association between global DNA methylation and an SNP, adjusted by BMI and weight loss, was obtained when using the log additive model. P<0.05 was considered to indicate a statistically significant difference. All samples were analyzed in duplicate.

Results <sec> <title>Participant characteristics

The characteristics of the study participants are listed in Table II. The majority of the participants were females (57%). The average age of males and females was 45 and 42 years old, respectively. The obese to overweight ratio among males (73% obese and 27% overweight) was different to that observed for female participants (61% obese and 39% overweight). The mean age was 43.5 years old with a standard error of the mean (SEM) of 1.29, the median age was 45 years old, the age range was 11–67 years old and the interquartile range was 15. The mean weight was 85.6 kg with a SEM of 1.89, the median weight was 84.8 kg, the weight range was 55–139 kg and the interquartile range was 25.3. The mean height was 167.5 cm with a SEM of 0.92, the median height was 166 cm, the height range was 149–139 cm and the interquartile range was 25.3. The mean weight loss was 11.4 kg with a SEM of 1.02, the median weight loss was 12 kg, the range of weight loss observed was 0–64 kg and the interquartile range was 9. The mean BMI was 30.1 with a SEM of 0.68, the median BMI was 30, the BMI range was 17–47 and the interquartile range was 7. Furthermore, substantial weight loss was observed across the study (median average, 12 kg) however, marginally higher weight loss was observed in female (median average, 13 kg) compared with male participants (median average, 12 kg). BMI within the obese range was 30 on average, with a difference of 2 points between an average BMI of 29 for females and 31 for males.

Associations between GDMI values and loci associated with energy balance and lipid metabolism

The present study did not identify any associations between global DNA methylation and weight at baseline, BMI, sex or age (data not shown). However, an inverse association between global DNA methylation and weight loss (P<0.05; Fig. 1) was demonstrated. Furthermore, significant associations between the GDMI and INSIG2, after adjusting for BMI and weight loss (P<0.05), and significant trends when stratifying by gender (P<0.05) were also observed (data not shown). The frequency of genotypes for near INSIG2 (rs7566605; 50.53, 41.05 and 8.42% for GG, GC and CC, respectively) and the boxplots for INSIG2 genotype and their corresponding GDMI values are presented in Fig. 2. No significant associations between global DNA methylation and the other genes were observed (data not shown). The frequency of genotypes for the other four genes were as follows: ADRB2 (rs1042714), 50.53, 34.74 and 14.74% for CC, CG and GG, respectively; APOA5 (rs662799), 88.42, 10.53 and 1.05% for AA, AG and GG, respectively; GNB3 (rs5443), 41.05, 50.53 and 8.42% for CC, CT and TT, respectively; and MC4R (rs17782313), 97.89, 1.05 and 1.05% for GG, AG and AA, respectively (Table III). Boxplots of GDMI values stratified by genotype for ADRB2, GNB3, APOA5 and MC4R are presented in Fig. 3.

Discussion

The present study demonstrated an inverse association between global DNA methylation and weight loss; as weight loss increased, global DNA methylation decreased. However, no associations between global DNA methylation and weight at baseline, BMI, sex or age were observed. Therefore, global DNA methylation may have potential as a marker for weight loss potential from personalized weight reduction programs based on genotypic profiles. The association between near INSIG2 (rs7566605) and global DNA methylation indicates that genetic variants may interact with epigenetic events that are ultimately associated with weight loss potential. The present study investigated the association between global DNA methylation and the allelic composition of five genetic loci associated with energy balance and lipid metabolism, and weight loss among participants, in a personalized weight reduction program designed on the basis of genotypic information. These five loci, near INSIG2 (rs7566605), MC4R (rs17782313), ADRB2 (rs1042714), APOA5 (rs662799) and GNB3 (rs5443), are among the most well-characterized SNPs regarding their roles in obesity, energy balance and lipid metabolism.

INSIG2 is located at 2q14.2. The protein product of this gene has a high degree of similarity with the protein encoded by INSIG1, both of which are endoplasmic reticulum proteins that inhibit sterol regulatory element binding proteins (SREBP) processing. INSIG1/2 impair the processing of SREBPs as they bind to SREBP cleavage-activating protein (SCAP), which prevents SCAP from assisting the transport of SREBPs to the Golgi apparatus. Variations in INSIG2 are reported to be associated with weight gain in certain ethnic subgroups (43,67). The SNP that was investigated in the present study, which is in close proximity to INSIG2, is present in 10% of the population and predisposes to the development of obesity in the general population. The CC single SNP predisposes to the development of obesity, is associated with a higher BMI and increases the probability of developing obesity by 40% (44–46). MC4R is located at 18q22. The protein product of this gene is a membrane-bound receptor and a member of the melanocortin receptor family. MC4R is primarily expressed in the central nervous system in areas where energy intake is controlled. The absence of this receptor leads to hyperphagia, hyperinsulinemia and obesity. The protein interacts with adrenocorticotropic and melanocyte-stimulating hormones, and is mediated by G-proteins. Variations in this gene are thought to be associated with waist circumference and insulin resistance, fat mass and obesity (47,48). Heterozygotes (CT) and homozygotes (TT) exhibit a higher energy expenditure, increased glucose oxidation, reduced levels of free fatty acids, a lower BMI and almost half the probability of developing obesity. For these reasons, MC4R variants are considered to be protective factors in obesity (49,50,68,69).

ADRB2 is located at 5q31-q32. This gene encodes the β2-adrenergic receptor, which is a member of the G-protein-coupled receptor superfamily. ADRB2 has an important role in the regulation of energy balance as it leads to increases in lipolysis and thermogenesis (51). Variants in ADRB2 are associated with obesity, chronic obstructive pulmonary disease and responses to asthma treatment (70–72). The ADRB2 rs1042714 polymorphism was reported to regulate diet-induced alterations in body weight and composition; women with the CG variant that consumed diets rich in carbohydrates exhibited a 2.5-fold higher risk of developing obesity (52). Furthermore, the ADRB2 rs1042714 allele is reported to be associated with increased BMI, body fat mass, fat cell volume and waist: hip ratio, in addition to associations with type II diabetes and the inhibition of lipid oxidation (54,55,73–75).

APOA5 is located at chromosome 11q23. This protein is an apolipoprotein that is involved in the regulation of plasma triglyceride levels, is associated with the levels of high-density lipoprotein cholesterol and the susceptibility to coronary artery disease (56,57,76,77). It is a component of high-density lipoprotein and accelerates the catalysis of low-density lipoprotein via the activation of the lipase protein. APOA5 enhances the catabolism of triglyceride-rich lipoproteins and reduces the production of very low-density lipoprotein, which is the primary triglyceride carrier. The presence of one of its variants in heterozygosity (AG variant) or homozygosity (GG variant) is associated with an increased risk of cardiovascular disease and metabolic syndrome, which may lead to the development of diabetes and obesity (58–60,78). Additionally, the rs662799 SNP of APOA5, which was included in the present study, was associated with weight loss following short-term dieting (61).

GNB3 is located at 12p13. This gene is a heterotrimeric guanine nucleotide-binding protein and is also a member of the G protein-coupled receptor superfamily. The GNB3 SNP rs5443 was reported to be a predictor of successful weight loss under sibutramine therapy (62). This variant has also been associated with various metabolic conditions, including obesity, coronary artery disease, insulin resistance and diabetes, and hypertension. Female carriers of the TT variant were reported to have a 6-fold higher risk of becoming overweight in the postpartum period, though the risk was markedly reduced in women who exercised regularly (63–65,79,80). In addition, associations between rs5443 and patient responses to sildenafil have been reported (81,82).

Epigenetic alterations occur over time and throughout the lifetime of individuals. Examples of these alterations include DNA methylation (83,84) and histone modifications (85,86), which are associated with factors such as diet (87), stress (88) and other modifiable lifestyles, including smoking (89) and alcohol consumption (90). Global DNA methylation levels, measured in PBMCs with Long Interspersed Nucleotide Elements-1 (LINE)-1 as a surrogate endpoint, was reported to be significantly higher in participants with a higher degree of weight loss compared with those who exhibited low responses (<8%) to energy-restricted treatment (91). LINE-1 was reported to be positively associated with healthy energy and micronutrient intake, and inversely associated with body fat mass (92).

It has been demonstrated that weight loss induced by a hypocaloric diet in humans altered the DNA methylation status of certain genes. Baseline DNA methylation patterns have previously been employed as epigenetic markers that may allow the degree of weight loss to be predicted in obese patients (52). In another study, epigenetic scores were used to predict alterations in body weight (7), and also identified five genes (aquaporin 9, dual specificity phosphatase 22, homeodomain-interacting protein kinase 3, troponin T1 slow skeletal type and troponin I3 cardiac type) that were differentially methylated between participants with high and low responses to a weight loss intervention program. The study also reported that subjects with the highest methylation in these regions exhibited a significantly enhanced response to the weight loss treatment program. While these studies demonstrate that differential methylation at specific loci may have an effect on weight loss, the results of the present study also demonstrate that global differential DNA methylation may also be associated with weight loss.

Global and gene-specific DNA methylation alterations, which vary with age, sex and socioeconomic status, may also be predictive biomarkers of weight loss response to intervention programs (91). Global DNA methylation and inflammatory gene promoter hypermethylation are reported to be early biomarkers of adiposity and metabolic alterations (93). Global DNA methylation and hydroxymethylation may functions as biomarkers in obesity and associated comorbidities. DNA methylation patterns are reported to behave differently depending on the choice of intervention in obesity (diet or surgery) (94).

The major strength of the present study is the simultaneous analysis of SNP loci and DNA methylation in the context of weight loss in obese patients. Interactions between germline variants of genes with somatic changes in epigenetic modifications may provide insights into pathologic causality in obesity and weight loss. The present study also demonstrates that non-invasive methods of assaying molecular biomarkers, such as those employed in the current study, may translate well in the clinic. Additionally, as global DNA methylation does not appear to be associated with initial BMI, initial weight, sex or age, further studies of this type should consider including additional weight-associated variable measurements that may be associated with global and gene-specific DNA methylation levels.

The results of the current study indicate that precision weight loss programs designed based on genetic and epigenetic information, which involve the creation of personalized interventions for individuals, may be beneficial for obese patients (95,96). These personalized programs may incorporate data from previous studies that have identified associations between DNA methylation, diet and weight loss. At present, is difficult to interpret how the interaction between INSIG2 and global DNA methylation modulates the weight loss response. Therefore, additional studies should consider the concurrent associations of established and unknown energy balance and lipid metabolism SNPs to improve the understanding of the role of DNA methylation in obesity and the weight loss responses.

Acknowledgements

The present study was supported by the National Cancer Institute (grant nos. K01-CA164092 and U01-CA8498).

References 1

Jiang

Chen

Manuel

Morrison

Mao

Obesity Working Group: Quantifying the impact of obesity category on major chronic diseases in Canada

ScientificWorldJournal7121112212007

10.1100/tsw.2007.216

Huxley

James

Barzi

Patel

Lear

Suriyawongpaisal

Janus

Caterson

Zimmet

Prabhakaran

Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension

Obes Rev9Suppl 1S53S612008

10.1111/j.1467-789X.2007.00439.x

Obesity in Asia Collaboration: Is central obesity a better discriminator of the risk of hypertension than body mass index in ethnically diverse populations?

J Hypertens261691772008

10.1097/HJH.0b013e3282f16ad3

Goni

Milagro

Cuervo

Martínez

Single-nucleotide polymorphisms and DNA methylation markers associated with central obesity and regulation of body weight

Nutr Rev726736902014

10.1111/nure.12143

Lau

Douketis

Morrison

Hramiak

Sharma

Obesity Canada Clinical Practice Guidelines Expert Panel: 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children (summary)

CMAJ176S1S132007

10.1503/cmaj.070306

1839777

Stroh

Köckerling

Weiner

Horbach

Ludwig

Dressler

Lange

Loermann

Wolff

Schmidt

Are there gender-specific aspects of sleeve gastrectomy-data analysis from the quality assurance study of surgical treatment of obesity in Germany

Obes Surg22121412192012

10.1007/s11695-012-0681-5

Moleres

Campión

Milagro

Marcos

Campoy

Garagorri

Gómez-Martínez

Martínez

Azcona-Sanjulián

Martí

EVASYON Study Group: Differential DNA methylation patterns between high and low responders to a weight loss intervention in overweight or obese adolescents: The EVASYON study

FASEB J27250425122013

10.1096/fj.12-215566

Bouchard

Rabasa-Lhoret

Faraj

Lavoie

Mill

Pérusse

Vohl

Differential epigenomic and transcriptomic responses in subcutaneous adipose tissue between low and high responders to caloric restriction

Am J Clin Nutr913093202010

10.3945/ajcn.2009.28085

Mansego

Milagro

Zulet

Martinez

SH2B1 CpG-SNP is associated with body weight reduction in obese subjects following a dietary restriction program

Ann Nutr Metab66192015

10.1159/000368425

van Dijk

Tellam

Morrison

Muhlhausler

Molloy

Recent developments on the role of epigenetics in obesity and metabolic disease

Clin Epigenetics7662015

10.1186/s13148-015-0101-5

4940755

Campión

Milagro

Goyenechea

Martínez

TNF-alpha promoter methylation as a predictive biomarker for weight-loss response

Obesity (Silver Spring)17129312972009

Wang

Zhang

Wang

Glessner

Grant

Zhao

Hakonarson

Price

A genome-wide association study on obesity and obesity-related traits

PLoS One6e189392011

10.1371/journal.pone.0018939

3084240

Mansego

Garcia-Lacarte

Milagro

Marti

Martinez

GENOI members: DNA methylation of miRNA coding sequences putatively associated with childhood obesity

Pediatr Obes1219272017

10.1111/ijpo.12101

van Dijk

Molloy

Varinli

Morrison

Muhlhausler

Members of EpiSCOPE: Epigenetics and human obesity

Int J Obes (Lond)3985972015

10.1038/ijo.2014.34

Youngson

Morris

What obesity research tells us about epigenetic mechanisms

Philos Trans R Soc Lond B Biol Sci368201103372013

10.1098/rstb.2011.0337

3539363

Hong

Lee

Kim

Effect of body mass index on global DNA methylation in healthy Korean women

Mol Cells374676722014

10.14348/molcells.2014.0073

4086340

Nomura

Lambertini

Rialdi

Lee

Mystal

Grabie

Manaster

Huynh

Finik

Davey

Global methylation in the placenta and umbilical cord blood from pregnancies with maternal gestational diabetes, preeclampsia, and obesity

Reprod Sci211311372014

10.1177/1933719113492206

3857768

BLUEPRINT consortium: Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

Nat Biotechnol347267372016

10.1038/nbt.3605

Crary-Dooley

Tam

Dunaway

Hertz-Picciotto

Schmidt

LaSalle

A comparison of existing global DNA methylation assays to low-coverage whole-genome bisulfite sequencing for epidemiological studies

Epigenetics122062142017

10.1080/15592294.2016.1276680

5406214

Perng

Mora-Plazas

Marín

Rozek

Baylin

Villamor

A prospective study of LINE-1DNA methylation and development of adiposity in school-age children

PLoS One8e625872013

10.1371/journal.pone.0062587

3640064

Pearce

McConnell

Potter

Barrett

Parker

Mathers

Relton

Global LINE-1 DNA methylation is associated with blood glycaemic and lipid profiles

Int J Epidemiol412102172012

10.1093/ije/dys020

3304536

Duggan

Xiao

Terry

McTiernan

No effect of weight loss on LINE-1 methylation levels in peripheral blood leukocytes from postmenopausal overweight women

Obesity (Silver Spring)22209120962014

10.1002/oby.20806

4183146

Demerath

Guan

Grove

Aslibekyan

Mendelson

Zhou

Hedman

ÅK

Sandling

Irvin

Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci

Hum Mol Genet24446444792015

10.1093/hmg/ddv161

4492394

Aslibekyan

Demerath

Mendelson

Zhi

Guan

Liang

Sha

Pankow

Liu

Irvin

Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference

Obesity (Silver Spring)23149315012015

10.1002/oby.21111

4482015

Dick

Nelson

Tsaprouni

Sandling

Aïssi

Wahl

Meduri

Morange

Gagnon

Grallert

DNA methylation and body-mass index: A genome-wide analysis

Lancet383199019982014

10.1016/S0140-6736(13)62674-4

Kaz

Wong

Varadan

Willis

Chak

Grady

Global DNA methylation patterns in Barrett's esophagus, dysplastic Barrett's and esophageal adenocarcinoma are associated with BMI, gender, and tobacco use

Clin Epigenetics81112016

10.1186/s13148-016-0273-7

5082363

Lillycrop

Murray

Cheong

Teh

Clarke-Harris

Barton

Costello

Garratt

Cook

Titcombe

ANRIL promoter DNA methylation: A perinatal marker for later adiposity

EBioMedicine1960722017

10.1016/j.ebiom.2017.03.037

5440605

Lumey

Terry

Delgado-Cruzata

Liao

Wang

Susser

McKeague

Santella

Adult global DNA methylation in relation to pre-natal nutrition

Int J Epidemiol411161232012

10.1093/ije/dyr137

Tobi

Slieker

Stein

Suchiman

Slagboom

van Zwet

Heijmans

Lumey

Early gestation as the critical time-window for changes in the prenatal environment to affect the adult human blood methylome

Int J Epidemiol44121112232015

10.1093/ije/dyv043

4588866

Tobi

Goeman

Monajemi

Putter

Zhang

Slieker

Stok

Thijssen

Müller

DNA methylation signatures link prenatal famine exposure to growth and metabolism

Nat Commun555922014

10.1038/ncomms6592

4246417

Agha

Hajj

Rifas-Shiman

Just

Hivert

Burris

Lin

Litonjua

Oken

DeMeo

Birth weight-for-gestational age is associated with DNA methylation at birth and in childhood

Clin Epigenetics81182016

10.1186/s13148-016-0285-3

5112715

Kupers

Jankipersadsing

Vaez

la Bastide-van Gemert

Scholtens

Nolte

Richmond

Relton

Felix

DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring

Int J Epidemiol44122412372015

10.1093/ije/dyv048

4588868

Kresovich

Zheng

Cardenas

Joyce

Rifas-Shiman

Oken

Gillman

Hivert

Baccarelli

Hou

Cord blood DNA methylation and adiposity measures in early and mid-childhood

Clin Epigenetics9862017

10.1186/s13148-017-0384-9

5558655

Godfrey

Sheppard

Gluckman

Lillycrop

Burdge

McLean

Rodford

Slater-Jefferies

Garratt

Crozier

Epigenetic gene promoter methylation at birth is associated with child's later adiposity

Diabetes601528342011

10.2337/db10-0979

3115550

Richmond

Rothman

Buliung

Schwartz

Larsen

Howard

Exploring the impact of a dedicated streetcar right-of-way on pedestrian motor vehicle collisions: A quasi experimental design

Accid Anal Prev712222272014

10.1016/j.aap.2014.05.022

Stel

Legler

The role of epigenetics in the latent effects of early life exposure to obesogenic endocrine disrupting chemicals

Endocrinology156346634722015

10.1210/en.2015-1434

4588824

Shearer

Suzuki

Terry

Gelernter

Greally

Tycko

Genetic-epigenetic interactions in cis: A major focus in the post-GWAS era

Genome Biol181202017

10.1186/s13059-017-1250-y

5477265

Lang

Lin

Darbary

Krupska

Gaba

Petukhova

Vonsattel

Gallagher

Goland

Mechanisms and disease associations of haplotype-dependent allele-specific DNA methylation

Am J Hum Genet989349552016

10.1016/j.ajhg.2016.03.027

4863666

Cole

Epigenetic studies of perinatal determinants of later obesity link important, but previously unrelated, genetic and epidemiological findings

EBioMedicine2015162017

10.1016/j.ebiom.2017.05.004

5478202

Day

Coletta

Kim

Garcia

Campbell

Benjamin

Roust

De Filippis

Mandarino

Coletta

Potential epigenetic biomarkers of obesity-related insulin resistance in human whole-blood

Epigenetics12254263

10.1080/15592294.2017.1281501

Aronica

Levine

Brennan

Gardner

Haile

Hitchins

A systematic review of studies of DNA methylation in the context of a weight loss intervention

Epigenomics97697872017

10.2217/epi-2016-0182

den Dunnen

Dalgleish

Maglott

Hart

Greenblatt

McGowan-Jordan

Roux

Smith

Antonarakis

Taschner

HGVS recommendations for the description of sequence variants: 2016 update

Hum Mutat375645692016

10.1002/humu.22981

Le Hellard

Theisen

Haberhausen

Raeder

Fernø

Gebhardt

Hinney

Remschmidt

Krieg

Mehler-Wex

Association between the insulin-induced gene 2 (INSIG2) and weight gain in a German sample of antipsychotic-treated schizophrenic patients: Perturbation of SREBP-controlled lipogenesis in drug-related metabolic adverse effects?

Mol Psychiatry143083172009

10.1038/sj.mp.4002133

Prakash

Mittal

Apurva

Shally

Pranjal

Neena

Common genetic variant of insig2 gene rs7566605 polymorphism is associated with severe obesity in north India

Iran Biomed J212612692017

10.18869/acadpub.ibj.21.4.261

5459941

Apalasamy

Moy

Rampal

Bulgiba

Mohamed

Genetic associations of the INSIG2 rs7566605 polymorphism with obesity-related metabolic traits in malaysian malays

Genet Mol Res134904102014

10.4238/2014.July.4.4

Kaulfers

Deka

Dolan

Martin

Association of INSIG2 polymorphism with overweight and LDL in children

PLoS One10e01163402015

10.1371/journal.pone.0116340

4301876

Chambers

Elliott

Zabaneh

Zhang

Froguel

Balding

Scott

Kooner

Common genetic variation near MC4R is associated with waist circumference and insulin resistance

Nat Genet407167182008

10.1038/ng.156

Loos

Lindgren

Wheeler

Zhao

Prokopenko

Inouye

Freathy

Attwood

Beckmann

Common variants near MC4R are associated with fat mass, weight and risk of obesity

Nat Genet407687752008

10.1038/ng.140

2669167

Chandak

Shen

Wang

Zhou

Association between common polymorphism near the MC4R gene and obesity risk: A systematic review and meta-analysis

PLoS One7e45731

10.1371/journal.pone.0045731

3458070

Paolini

Maltese

Del Ciondolo

Tavian

Missaglia

Ciuoli

Zuntini

Cecchin

Bertelli

Pompucci

Prevalence of mutations in LEP, LEPR and MC4R genes in individuals with severe obesity

Genet Mol Res152016

10.4238/gmr.15038718

Takenaka

Nakamura

Mitsunaga

Inoue-Murayama

Udono

Suryobroto

Human-specific SNP in obesity genes, adrenergic receptor beta2 (ADRB2), Beta3 (ADRB3) and PPAR γ2 (PPARG), during primate evolution

PLoS One7e434612012

10.1371/journal.pone.0043461

3427335

Ruiz

Larrarte

Margareto

Ares

Labayen

Role of β₂-adrenergic receptor polymorphisms on body weight and body composition response to energy restriction in obese women: Preliminary results

Obesity (Silver Spring)192122152011

10.1038/oby.2010.130

Ruiz

Larrarte

Margareto

Ares

Alkorta

Labayen

Preliminary findings on the role of PLIN1 polymorphisms on body composition and energy metabolism response to energy restriction in obese women

Br J Nutr106486902011

10.1017/S0007114511000432

Large

Hellström

Reynisdottir

Lönnqvist

Eriksson

Lannfelt

Arner

Human beta-2 adrenoceptor gene polymorphisms are highly frequent in obesity and associate with altered adipocyte beta-2 adrenoceptor function

J Clin Invest100300530131997

10.1172/JCI119854

508512

Ishiyama-Shigemoto

Yamada

Yuan

Ichikawa

Nonaka

Association of polymorphisms in the beta2-adrenergic receptor gene with obesity, hypertriglyceridaemia, and diabetes mellitus

Diabetologia42981011999

10.1007/s001250051120

Martin

Nicaud

Humphries

Talmud

EARS group: Contribution of APOA5 gene variants to plasma triglyceride determination and to the response to both fat and glucose tolerance challenges

Biochim Biophys Acta16372172252003

10.1016/S0925-4439(03)00033-4

Kisfali

Mohás

Maasz

Hadarits

Markó

Horvatovich

Oroszlán

Bagosi

Bujtor

Gasztonyi

Apolipoprotein A5 IVS3+476A allelic variant associates with increased trigliceride levels and confers risk for development of metabolic syndrome in Hungarians

Circ J7240432008

10.1253/circj.72.40

Elosua

Ordovas

Cupples

Lai

Demissie

Fox

Polak

Wolf

D'Agostino

SrO'Donnell

Variants at the APOA5 locus, association with carotid atherosclerosis and modification by obesity: The Framingham Study

J Lipid Res479909962006

10.1194/jlr.M500446-JLR200

Sánchez-Moreno

Ordovás

Smith

Baraza

Lee

Garaulet

APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population

J Nutr1413803852011

10.3945/jn.110.130344

3040902

Garelnabi

Lor

Jin

Chai

Santanam

The paradox of ApoA5 modulation of triglycerides: Evidence from clinical and basic research

Clin Biochem4612192013

10.1016/j.clinbiochem.2012.09.007

Aberle

Evans

Beil

Seedorf

A polymorphism in the apolipoprotein A5 gene is associated with weight loss after short-term diet

Clin Genet681521542005

10.1111/j.1399-0004.2005.00463.x

Hauner

Meier

Jöckel

Frey

Siffert

Prediction of successful weight reduction under sibutramine therapy through genotyping of the G-protein beta3 subunit gene (GNB3) C825T polymorphism

Pharmacogenetics134534592003

10.1097/00008571-200308000-00003

Hsiao

Hwang

Liu

Chang

Lin

Association of the C825T polymorphism in the GNB3 gene with obesity and metabolic phenotypes in a Taiwanese population

Genes Nutr81371442013

10.1007/s12263-012-0304-8

Maniotis

Chantziara

Kokkoris

Papadogiannis

Andreou

Tsioufis

Vaiopoulos

Stefanadis

The AGT and the GNB3 polymorphisms and insulin resistance in prehypertension

Hormones (Athens)1379862014

Michalsen

Knoblauch

Lehmann

Grossman

Kerkhoff

Wilhelm

Moebus

Konstantinides

Binder

Heusch

Effects of lifestyle modification on the progression of coronary atherosclerosis, autonomic function and angina-the role of GNB3 C825T polymorphism

Am Heart J1518708772006

10.1016/j.ahj.2005.06.025

Hoque

Lee

Cairns

Schoenberg

Sidransky

Genome-wide genetic characterization of bladder cancer: A comparison of high-density single-nucleotide polymorphism arrays and PCR-based microsatellite analysis

Cancer Res63221622222003

Smith

Cooper

Humphries

INSIG2 gene polymorphism is not associated with obesity in Caucasian, Afro-Caribbean and Indian subjects

Int J Obes (Lond)31175317552007

10.1038/sj.ijo.0803645

Granell

Serra-Juhé

Martos-Moreno

GÁ

Díaz

Pérez-Jurado

Baldini

Argente

A novel melanocortin-4 receptor mutation MC4R-P272L associated with severe obesity has increased propensity to be ubiquitinated in the ER in the face of correct folding

PLoS One7e508942012

10.1371/journal.pone.0050894

3520997

Corella

Ortega-Azorín

Sorlí

Covas

Carrasco

Salas-Salvadó

Martínez-González

MÁ

Arós

Lapetra

Serra-Majem

Statistical and biological gene-lifestyle interactions of MC4R and FTO with diet and physical activity on obesity: New effects on alcohol consumption

PLoS One7e523442012

10.1371/journal.pone.0052344

3528751

Feigelson

Teras

Diver

Tang

Patel

Stevens

Calle

Thun

Bouzyk

Genetic variation in candidate obesity genes ADRB2, ADRB3, GHRL, HSD11B1, IRS1, IRS2 and SHC1 and risk for breast cancer in the cancer prevention study II

Breast Cancer Res10R572008

10.1186/bcr2114

2575528

Brøgger

Steen

Eiken

Gulsvik

Bakke

Genetic association between COPD and polymorphisms in TNF, ADRB2 and EPHX1

Eur Respir J276826882006

10.1183/09031936.06.00057005

Park

Yang

Park

Kim

Moon

Min

Kim

Cho

Additive role of tiotropium in severe asthmatics and Arg16Gly in ADRB2 as a potential marker to predict response

Allergy647787832009

10.1111/j.1398-9995.2008.01876.x

Sánchez

González JL

Proenza

Martínez

Larrad MT

Ramis

Pérez

Fernández C

Palou

Ríos

Serrano M

The glutamine 27 glutamic acid polymorphism of the beta2-adrenoceptor gene is associated with abdominal obesity and greater risk of impaired glucose tolerance in men but not in women: A population-based study in Spain

Clin Endocrinol (Oxf)594764812003

10.1046/j.1365-2265.2003.01871.x

Kawaguchi

Masuo

Katsuya

Sugimoto

Rakugi

Ogihara

Tuck

Beta2- and beta3-Adrenoceptor polymorphisms relate to subsequent weight gain and blood pressure elevation in obese normotensive individuals

Hypertens Res299519592006

10.1291/hypres.29.951

Masuo

Katsuya

Kawaguchi

Rakugi

Ogihara

Tuck

Beta2-adrenoceptor polymorphisms relate to obesity through blunted leptin-mediated sympathetic activation

Am J Hypertens19108410912006

10.1016/j.amjhyper.2006.02.015

Maasz

Kisfali

Jaromi

Horvatovich

Szolnoki

Csongei

Safrany

Sipeky

Hadarits

Melegh

Apolipoprotein A5 gene IVS3+G476A allelic variant confers susceptibility for development of ischemic stroke

Circ J72106510702008

10.1253/circj.72.1065

Maász

Kisfali

Szolnoki

Hadarits

Melegh

Apolipoprotein A5 gene C56G variant confers risk for the development of large-vessel associated ischemic stroke

J Neurol2556496542008

10.1007/s00415-008-0768-z

Elosua

Cupples

Fox

Polak

D'Agostino

SrWolf

O'Donnell

Ordovas

Association between well-characterized lipoprotein-related genetic variants and carotid intimal medial thickness and stenosis: The framingham heart study

Atherosclerosis1892222282006

10.1016/j.atherosclerosis.2005.12.005

Casiglia

Tikhonoff

Boschetti

Bascelli

Saugo

Guglielmi

Caffi

Rigoni

Giordano

Grasselli

The C825T GNB3 polymorphism, independent of blood pressure, predicts cerebrovascular risk at a population level

Am J Hypertens254514572012

10.1038/ajh.2011.257

Frey

Moebus

Möhlenkamp

Kälsch

Bauer

Lehmann

Nöthen

Mühleisen

Stang

Erbel

GNB3 gene 825 TT variant predicts hard coronary events in the population-based HEINZ NIXDORF RECALL study

Atherosclerosis2374374422014

10.1016/j.atherosclerosis.2014.08.025

Nishimura

Tanabe

Sekine

Kasai

Suda

Kato

Jujo

Sugiura

Shigeta

Sakao

Tatsumi

Synergistic effects of ACE Insertion/deletion and GNB3 C825T polymorphisms on the efficacy of PDE-5 inhibitor in patients with pulmonary hypertension

Respiration911321402016

10.1159/000443772

Sperling

Eisenhardt

Virchow

Hauck

Lenk

Porst

Stief

Wetterauer

Rubben

Muller

Siffert

Sildenafil response is influenced by the G protein beta 3 subunit GNB3 C825T polymorphism: A pilot study

J Urol169104810512003

10.1097/01.ju.0000058369.72348.ba

Bell

Xia

Yuan

Gao

Ward

Roos

Mangino

Hysi

Bell

Wang

Spector

Novel regional age-associated DNA methylation changes within human common disease-associated loci

Genome Biol171932016

10.1186/s13059-016-1051-8

5034469

Murray

Bryant

Titcombe

Barton

Inskip

Harvey

Cooper

Lillycrop

Hanson

Godfrey

DNA methylation at birth within the promoter of ANRIL predicts markers of cardiovascular risk at 9 years

Clin Epigenetics8902016

10.1186/s13148-016-0259-5

5010744

Pal

Tyler

Epigenetics and aging

Sci Adv2e16005842016

10.1126/sciadv.1600584

4966880

Cao

Fenech

Wang

Role of sirtuins in maintenance of genomic stability: Relevance to cancer and healthy aging

DNA Cell Biol355425752016

10.1089/dna.2016.3280

Perng

Villamor

Shroff

Nettleton

Pilsner

Liu

Diez-Roux

Dietary intake, plasma homocysteine and repetitive element DNA methylation in the Multi-Ethnic Study of Atherosclerosis (MESA)

Nutr Metab Cardiovasc Dis246146222014

10.1016/j.numecd.2013.11.011

Needham

Smith

Zhao

Wang

Mukherjee

Kardia

Shively

Seeman

Liu

Diez

Roux AV

Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: The multi-ethnic study of atherosclerosis

Epigenetics109589692015

10.1080/15592294.2015.1085139

4844216

Guerrero-Preston

Goldman

Brebi-Mieville

Ili-Gangas

Lebron

Witter

Apelberg

Hernández-Roystacher

Jaffe

Halden

Sidransky

Global DNA hypomethylation is associated with in utero exposure to cotinine and perfluorinated alkyl compounds

Epigenetics55395462010

10.4161/epi.5.6.12378

3322495

Chater-Diehl

Laufer

Castellani

Alberry

Singh

Alteration of gene expression, DNA methylation and histone methylation in free radical scavenging networks in adult mouse hippocampus following fetal alcohol exposure

PLoS One11e01548362016

10.1371/journal.pone.0154836

4852908

Garcia-Lacarte

Milagro

Zulet

Martinez

Mansego

LINE-1 methylation levels, a biomarker of weight loss in obese subjects, are influenced by dietary antioxidant capacity

Redox Rep2167742016

10.1179/1351000215Y.0000000029

Marques-Rocha

Milagro

Mansego

Mourão

Martínez

Bressan

LINE-1 methylation is positively associated with healthier lifestyle but inversely related to body fat mass in healthy young individuals

Epigenetics1149602016

10.1080/15592294.2015.1135286

4846126

Carraro

Mansego

Milagro

Chaves

Vidigal

Bressan

Martínez

LINE-1 and inflammatory gene methylation levels are early biomarkers of metabolic changes: Association with adiposity

Biomarkers216256322016

10.3109/1354750X.2016.1171904

Nicoletti

Nonino

de Oliveira

Pinhel

Mansego

Milagro

Zulet

Martinez

DNA methylation and hydroxymethylation levels in relation to two weight loss strategies: Energy-restricted diet or bariatric surgery

Obes Surg266036112016

10.1007/s11695-015-1802-8

Weiner

El-Sayes

Manger

Weiner

Lippert

Stroh

Obesity Surgery Working Group, Competence Network Obesity: Antidiabetic efficacy of obesity surgery in Germany: A quality assurance nationwide survey

Surg Obes Relat Dis103223272014

10.1016/j.soard.2013.07.007

Stroh

Weiner

Wolff

Knoll

Manger

Obesity Surgery Working Group; Competence Network Obesity: Influences of gender on complication rate and outcome after Roux-en-Y gastric bypass: Data analysis of more than 10,000 operations from the german bariatric surgery registry

Obes Surg24162516332014

10.1007/s11695-014-1252-8

Figure 1.

Inverse association between global DNA methylation and weight loss (P<0.05).

Figure 2.

(A) The allelic frequency of near INSIG2 (rs7566605) was 50.53, 41.05 and 8.42% for G/G, G/C and C/C, respectively. (B) Boxplots for each near INSIG2 (rs7566605) genotype and their corresponding global DNA methylation index values. INSIG2, insulin-induced gene 2.

Figure 3.

Boxplots of global DNA methylation index values stratified by genotype for (A) ADRB2, (B) GNB3, (C) APOA5 and (D) MC4R. ADRB2, adrenoceptor β2; GNB3, G-protein subunit β3; APOA5, apolipoprotein A5; MC4R, melanocortin 4 receptor.

Table I.

Recommended diet and lifestyle interventions for overweight patients with specific variants of five loci associated with energy balance and lipid metabolism.

		Diet

Gene (SNP) and variants	Physical activity	Low calorie	Low carbohydrate	Low lipid	HGVS name	MAF/minor allele count	Health effects
Near INSIG2 (rs7566605)					NC_000002.12:g.118078449C>G	C=0.2859/1432 (1000 Genomes project) C=0.2798/8146 (TOPMed)	Obesity, dyslipidemia, control of lipid synthesis
GG and GC	NI	NI	NI	NI
CC	^b	^a	NI	^d
MC4R (rs17782313)					NC_000018.10:g.60186834T>C	A=0.2400/1202 (1000 Genomes project) A=0.2503/7289 (TOPMed)	Obesity, obesity-related quantitative traits
TT	NI	NI	NI	NI
CT	^d	^c	NI	^d
CC	^c	^b	NI	^b
ADRB2 (rs1042714)					NC_000005.10:g.148826910G>C	G=0.3166/38431 (ExAC) G=0.2043/1023 (1000 Genomes project) G=0.3400/4422 (GO-ESP) G=0.2960/8620 (TOPMed)	Obesity, susceptibility to metabolic syndrome
CC	NI	NI	NI	NI
CG	^d	^d	^b	NI
GG	NI	^d	^b	^d
APOA5 (rs662799)					NC_000011.10:g.116792991G>A	G=0.1629/816 (1000 Genomes project) G=0.1043/3038 (TOPMed)	Obesity, triglyceride metabolism, cardiovascular disease
AA	NI	NI	NI	NI
AG and GG	^b	^d	NI	^a
GNB3 (rs5443)					NC_000012.12:g.6845711C>T	T=0.3598/43631 (ExAC) T=0.4498/5850 (GO-ESP)	Plasma triglyceride regulation, essential hypertension
CC	^d	^d	NI	^d
CT	NI	NI	NI	NI
TT	^a	^d	NI	^d

The description of sequence variants follows the HGVS 2016 recommended format. All are genomic sequences. <REFERENCE_SEQUENCE_ID>:<SEQUENCE_TYPE>.<POSITION><CHANGE>. MAF refers to the frequency at which the second most common allele occurs in a given population. The MAF/minimum allele count numbers for a specific SNP represent the frequency of the allele in a sequencing project database (i.e., 1000 Genomes) and the number of times this SNP was observed in that population. SNP information was accessed from dbSNP (https://www.ncbi.nlm.nih.gov/SNP) on July 3, 2017. SNP, single nucleotide polymorphism; HGVS, Human Genome Variation Society; MAF, minor allele frequency; INSIG2, insulin-induced gene 2; NI, no intervention; NHLBI, National Heart, Lung and Blood Institute; TOPMed, NHLBI Trans-Omics for Precision Medicine project; MC4R, melanocortin 4 receptor; ADRB2, adrenoceptor β2; ExAC, Exome Aggregation Consortium; GO-ESP, NHLBI GO Exome Sequencing Project; APOA5, apolipoprotein A5; GNB3, G-protein subunit β3.

Recommended as primary intervention

recommended as preferential intervention

recommended as primary supplemental activity

recommended as supplemental activity.

Table II.

Patient characteristics in DNA methylation study that examined the association between global DNA methylation levels and SNPs.

Sex
F (%)	60 (57%)
Age
<34	23
35-39	8
40-44	21
45-49	23
50+	30
Mean (SEM)	43.5 (1.29)
Median (range)	45 (11–67)
Interquartile range	15
Weight (kilograms)
Mean (SEM)	85.6 (1.89)
Median (range)	84.8 (55–139)
Interquartile range	25,3
Height (centimeters)
Mean (SE)	167.5 (0.92)
Median (range)	166 (149–190)
Interquartile range	15
Weight loss (kilograms)
Mean (SE)	11.4 (1.02)
Median (range)	10 (0–54)
Interquartile range	9
BMI
Mean (SE)	30.1 (0.68)
Median (range)	30 (17–47)
Interquartile range	7
Diet
Yes (%)	82 (78)
No (%)	22 (21)
Unknown (%)	1 (1)

Table III.

Frequency and percentage of alleles and genotypes at five loci associated with energy balance and lipid metabolism in study participants.

A, Frequency and percentage of alleles for each locus in study participants

Gene (SNP) and variants	Frequency	Percentage
ADRB2 (rs1042714)
C	129	67.89
G	61	32.11
APOA5 (rs662799)
A	178	93.68
G	12	6.32
GNB3 (rs5443)
C	126	66.32
T	64	33.68
Near INSIG2 (rs7566605)
G	135	71.05
C	55	28.95
MC4R (rs17782313)
C	187	98.42
T	3	1.58

B, Frequency and percentage of genotypes for each locus in study participants

Gene (SNP) and variants	Frequency	Percentage

ADRB2 (rs1042714)
CC	48	50.53
CG	33	34.74
GG	14	14.74
APOA5 (rs662799)
AA	84	88.42
AG	10	10.53
GG	1	1.05
GNB3 (rs5443)
CC	39	41.05
CT	48	50.53
TT	8	8.42
Near INSIG2 (rs7566605)
GG	48	50.53
GC	39	41.05
CC	8	8.42
MC4R (rs17782313)
CC	93	97.89
TC	1	1.05
TT	1	1.05

SNP, single nucleotide polymorphism; ADRB2, adrenoceptor β2; APOA5, apolipoprotein A5; GNB3, G-protein subunit β3; INSIG2, insulin-induced gene 2; MC4R, melanocortin 4 receptor.