Association of FTO methylation level with incident type 2 diabetes mellitus: a nested case–control study within the Rural Chinese Cohort Study

Objectives This study aimed to investigate the association of FTO methylation level with type 2 diabetes mellitus (T2DM). Methods We conducted a nested case – control study for DNA methylation of FTO in Chinese pople identied from the Rural Chinese Cohort Study with a 6-year follow-up. Controls were matched to the cases on a 1:1 basis by age, sex, ethnicity, marital status, and residence. Unconditional multivariate logistic regression models were used to calculate odds ratios (ORs) and 95% condence intervals (CIs) for association with Tag-single nucleotide polymorphisms (SNPs) and cytosine guanine (CpG) locus. Haploview was used to analyze the association of possible haplotypes with T2DM. Generalized Multifactor Dimensionality Reduction was used to explore the potential interaction.


Introduction
The world's population with type 2 diabetes mellitus (T2DM) was predicted to increase to 642 million by 2040, especially in low and middle-income countries such as China [1], because of urbanization [2] and transition to a western lifestyle [3]. According to the IDF diabetes atlas 2017, more than 114.4 million individuals experienced T2DM in 2017 and the number would reach 129.7 million in 2045 in China [2]. T2DM has reached epidemic proportions among general adults in China, which has led to severe social, medical, and economical burdens. Thus, it is important to explore the causes and intervention of T2DM.
The fat mass and obesity-associated gene (FTO) is the rst gene shown to contribute to non-syndromic human obesity [4,5]. It was widely veri ed with variety of obesity traits throughout the life courses and across diverse ancestries [6]. However, the association with T2DM remains controversial [7][8][9]. A prospective population-based cohort study recruiting 25,631 participants in Norwich suggested FTO variants associated with T2DM [10], whereas a case-control study including 3,040 participants in Taiwan suggested no association between FTO variants and T2DM [11]. Furthermore, few studies included the epigenetic context of FTO in the process of T2DM. DNA methylation might provide new insights into the pathways underlying T2DM [12]. A case-control study found a CpG site in the rst intron of FTO showed small (3.35%) but signi cant (P = 0.000021) hypomethylation of cases relative to controls [13], and a cross-sectional study also suggested that a methylation difference between diabetic patients and controls for one CpG locus located in FTO was apparent in young individuals [14], another case-control study suggested that the association with T2DM tended toward signi cance between T2DM patients and controls [15]. The conclusions of these studies are controversial and as far as we know, none of the studies directly analyzed the effect of FTO methylation level on T2DM incident in the rural Chinese population.
Therefore, the aim of our study was to evaluate the associations of FTO methylation level with T2DM based on a nested case-control study in a rural Chinese population.

Study population
In this nested case-control study, we explored an epigenome-wide association of DNA methylation in people with incident T2DM who were identi ed from the Rural Chinese Cohort Study with 6 follow-up years. Details about the design, participants, methods, and measurements of the cohort study have been previously described [16]. Participants with incident T2DM were de ned as those who did not have T2DM at baseline, but T2DM developed during follow-up. Controls were participants who did not have T2DM at both baseline and follow-up. After excluding participants with baseline T2DM (n = 1,499), T1DM (n = 13), cancer (n = 28), stroke (n = 372), myocardial infarction (n = 183), chronic obstructive pulmonary diseases (n = 353), and chronic kidney disease (n = 294), 14,523 non-diabetic participants at baseline were the target participants for the current analysis. During the follow-up, 707 new cases of T2DM were ascertained. Among the new-onset 707 diabetic cases and 13,816 non-diabetic participants, 293 pairs of study population were selected using the case-control matching method for DNA methylation and SNPs measurement [17]. Controls were matched to cases on a 1:1 basis by age (birth at the same year), sex, ethnicity, marital status, and residence (live in the same village). Finally, 293 pairs of cases and controls with complete information and blood samples were included in this nested case-control study ( Figure   S1).

Data Collection And Relevant De nitions And Diagnosis Criteria
We used a standard questionnaire to collect information on demographics (age, sex, ethnicity, residence and marital status), lifestyle behaviors (smoking, alcohol drinking, and physical activity), and personal and family medical history at baseline. Smoking was de ned as ever smoking at least 100 cigarettes during the lifetime [18]. Alcohol drinking was de ned as consuming drinking at least 12 times during the last year [18]. Physical activity level was classi ed as low, moderate and high according to the International Physical Activity Questionnaire [19]. The detailed information on body weight, height, waist circumference (WC), body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were described as previously published [20]. BMI was calculated as weight(kg)/height(m) 2 . Low-density lipoprotein cholesterol (LDL-C) was calculated by the Fried Ewald formula [21].
T2DM was de ned as fasting glucose ≥7.0 mmol/L and/or the use of insulin or oral hypoglycemic agents, and/or a self-reported history of diabetes, which agreed with the diagnostic criteria of T2DM at both baseline and follow-up examination [22].
Quantitative DNA Methylation Measurement DNA samples were extracted from baseline fasting peripheral-blood leucocytes by the automated nucleic acid extraction system (BioTeke Corp., Beijing). The target sequence covered 428 bp (Chr16:53703509-53703936) located in the promoter region of FTO. The sequenom® EpiDesigner system was used to design the primer sequences: forward 5'-aggaagagagTTTGTAGGATTTGGATAGAGATGGT-3' and reverse 3'-cagtaatacgactcactatagggagaaggctAAATCCAAAAAAAACTACATTTCCC-5'. After genomic DNA was treated with bisul te, PCR was used to amplify the target sequence, followed by removing 5'-and 3'phosphate groups from the products by using shrimp alkaline phosphate. EpiTYPER biochemistry began with bisul te treatment of genomic DNA, followed by PCR ampli cation of target regions, then DNA transcription in vitro, and Uracil-speci c DNA cleavage. Finally, the mass spectra of cleavage products were analyzed by using MALDI-TOF mass spectrometry based on the MassARRAY System (Bio Miao Biological Technology, Beijing), and the level of DNA methylation was measured by MassARRAY EpiTYPER analysis (Agena Bioscience, San Diego, CA). DNA methylation was detected in case-control pairs.

Tag-SNP Selection And Genotyping
The Tag-SNPs were selected from an extensive review of the literature and from the HapMap and NCBI databases. The selection criterion was minor allele frequency (MAF) > 0.01. Eleven SNPs in FTO (rs72803657, rs9939609, rs1121980, rs17817449, rs8050136, rs9940128, rs9926289, rs11076023, rs1558902, rs1421085, and rs9941349) were selected for our study. All SNPs met Hardy-Weinberg equilibrium (HWE). Finally, four Tag-SNPs were selected in our study based on the linkage disequilibrium (LD) analysis (r 2 ≥ 0.8). Speci c primers were designed by using Assay design3.1 software (Sequenom Inc). Genotyping and PCR involved using a MassARRAY Genotyping system (Agena Inc). We re-genotyped approximately 4% of random samples to control the quality. The agreement of the genotypes determined for the blind control samples was 100%.

Statistical analysis
Baseline characteristics were compared between T2DM cases and controls. Continuous variables are shown as median (interquartile range) for data with skewed distribution, and the Wilcoxon Rank Sum Test was used to assess differences in these data. Continuous variables with normal distribution are shown as mean (SD) and were analyzed by t-test. Categorical variables are shown as number (%) and were analyzed by chi-square test. Chi-square test was used to test for HWE among controls. The association between SNPs and T2DM was assessed by multiple logistic regression models with a dominant genetic model of Tag-SNPs. Haploview was used for analysis of LD and haplotype analyses was used to explore the association of possible haplotypes with T2DM. The methylation level was non-normally distributed and was compared by Wilcoxon Rank Sum Test. Considering important nonmatching variables between cases and controls apart from age, sex, marital status and residence region [8], we used unconditional logistic regression models to evaluate the association between methylation level and risk of T2DM. We adopted three models: 1) unadjusted; 2) adjusted for smoking, alcohol drinking, physical activity, SBP, FPG; and 3) adjusted for smoking, drinking, physical activity, SBP, BMI, and FPG, TG and HDL-C levels.
Spearman correlation analysis was used to explore the association with T2DM-related quantitative phenotypes. Wilcoxon-Rank Test was used to compare the methylation level in different SNP alleles.
Finally, we used Generalized Multifactor Dimensionality Reduction (GMDR) to explore potential interactions of Tag-SNPs, anthropometry indexes (BMI, WC, and TC, TG, HDL-C and LDL-C levels), environmental factors (smoking, alcohol drinking, physical activity) with CpG locus.

Results
Baseline demographic characteristics of the 293 incident T2DM cases and 293 controls were shown in Table 1. The mean age was 52.29 ± 9.50 years and the proportion of females was 65.53%. Physical activity, smoking, alcohol drinking, and levels of TC and LDL-C did not differ between cases and controls; family history of diabetes, BMI, SBP, DBP, and FPG, TG levels were signi cantly higher and HDL-C level was lower for cases than controls. : T-test.
Cases and controls signi cantly differed in CpG9 methylation level (Fig. 1). As compared with methylation level < 75%, CpG9 level ≥ 75% was associated with T2DM: OR 1.64 (95% CI, 1.13-2.38) ( Table 2). After adjusting for smoking, drinking, physical activity, SBP, BMI, and FPG, TG and HDLC levels, the association remained robust (OR = 1.84, 95% CI, 1.21-2.80). To explore the possible mechanisms of the CpG locus on T2DM susceptibility, we tested the association of CpG9 with the T2DM-related quantitative phenotypes (BMI, WC, and levels of TC, LDL-C, HDL-C, and TG) by Spearman correlation coe cients in the combined sample (normal glucose tolerance [NGT] + T2DM). However, we found no association between methylation level and obesity associated indexes (Table S1) or between Tag-SNPs and methylation level of CpG9 (Table S2). Allele frequencies of the Tag-SNPs and P value of HWE deviation are in Table 3. After adjusting for smoking, alcohol drinking, physical activity, SBP, FPG, and BMI, we still found no signi cant association between Tag-SNPs and T2DM. Figure S2 illustrates the linkage disequilibrium analysis of the 11 SNPs. Strong linkage disequilibrium was observed in block 1. Furthermore, we did not nd any possible haplotype associated with T2DM (Table S3). GMDR used to explore potential interactions revealed no signi cant interaction of Tag-SNPs, anthropometry indexes (BMI, WC, and TC, TG, HDL-C and LDL-C levels), or environmental factors (smoking, alcohol drinking, physical activity) with CpG locus ( Figure S3).

Discussion
Our study found higher methylation level of CpG9 on the promotor region of FTO signi cantly associated with T2DM, and the association remained signi cant after adjusting for potential confounders. In this study, we found no signi cant association between the Tag-SNPs and T2DM. Furthermore, possible haplotypes of FTO were not associated with T2DM. We found no signi cant interaction between Tag-SNPs, anthropometry indexes, environmental factors and CpG locus in this study. CpG9 on the promotor of FTO may be associated with T2DM independent of the potential confounders listed in this study.
T2DM is the archetype of a complex disease, in uenced by genetic, epigenetic and environmental factors [23]. Obesity is a major predictor of future risk of T2DM [24]. And FTO is the rst gene identi ed contributing to common forms of human obesity [4]. However, the association between FTO variants and T2DM was controversial. Consistent with two previous case-control studies [25,26], we found no signi cant association of FTO variants with T2DM. Also, we did not observe any possible haplotype associated with T2DM in the strong linkage disequilibrium region (block 1). While some other studies showed there were strong association between FTO variants and T2DM [10,[27][28][29][30][31]. The different ethnicities and race, cross-sectional design, and small sample size may lead to inconsistent results.
Further study is needed to explore the association of FTO variants with T2DM in the real world. Epigenetic factors were also taken into consideration in this study because growing evidence demonstrates that epigenetic mechanisms play an important role in the pathogenesis of T2DM [15,32]. Studies also show that ambiguous genetic results from complex disease phenotypes could be more salient if considered in an epigenetic context [4,13]. Consistent with previous case control studies [13][14][15], we found methylation level of FTO was signi cant associated with T2DM. Another review showed that methylated sites in FTO are potentially associated with T2DM and their predictive powers may hold irrespective of different genetic backgrounds and different lifestyle or environmental pressures. Methylation level play a very important role in gene expression [33]. Two previous studies found that FTO expression level was associated with insulin secretion, which may lead T2DM [34,35].
We further analyzed the association between these genetic and epigenetic polymorphisms but found no association (Table S3). Thus, we concluded that the SNP sequences cannot account for the observed methylation difference between cases and controls. Our results are consistent with a theory that genetic and epigenetic sites are independently associated with the disease because of disconnected genetic and epigenetic mechanisms that independently affect the function of a control region in which they both reside [13]. Dysfunction of lipid metabolism is also one of the important pathogenic factors for T2DM [36]. However, we observed no association between methylation and obesity indexes and serum lipid levels (Table S2). These results suggest that the observed association with T2DM was not mediated by obesity. This is notable, because the association between the FTO alleles and obesity is well established as previous described [4]. Therefore, the particular epigenetic variation we studied in the promotor region of FTO might be connected with T2DM by a mechanism different from obesity.
We note the following limitations that could have affected our ndings. First, the association of Tag-SNPs and methylation level with T2DM was estimated in a rural Chinese population, which requires validation with replicated studies in other ethnical populations to ensure statistical power for exploring additional factors across FTO genotypes, methylation status and association with T2DM. Second, the association between CpG and T2DM might be in uenced by other unmeasured confounding factors because some lifestyle factors have been found to in uence the association between FTO with T2DM [7,[37][38][39][40]. In addition, we lacked serum transcription data, which needs further study.
Despite the above limitations, our study has several signi cant strengths. Primarily, considering that DNA methylation level is not stable over time, this nested case-control design was able to capture methylation level and other exposure characters before disease onset, so we may have eliminated the time sequence criterion for causality inherent in cohort studies. Moreover, all controls were randomly matched by age, gender, sex, marital status, and residence in the same cohort, which would eliminate some potential confounders. Furthermore, SNPs and CpG locus were analyzed together, so we may have a better understanding of the association of FTO with T2DM and obesity.

Conclusion
In conclusion, our study found that a CpG locus on the promotor region of FTO may be associated with T2DM independent of BMI, and FTO variants were not associated with T2DM. Our study provides a new perspective for the screening and prevention of T2DM and has important public health implications.

Declarations
Ethics approval and consent to participate Each study participant gave their signed informed consent and the study protocol was approved by Shenzhen University Medical Ethics Committee.

Consent for publication
Not applicable.

Availability of data and materials
Please contact the corresponding author for data requests.

Competing interests
The authors declare that they have no competing interests.