The Association Between DNA Methylation of 6p21.33 and AHRR in Blood and Coronary Heart Disease in Chinese Population

doi:10.21203/rs.3.rs-839939/v1

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Research Article

The Association Between DNA Methylation of 6p21.33 and AHRR in Blood and Coronary Heart Disease in Chinese Population

https://doi.org/10.21203/rs.3.rs-839939/v1

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Background: Early detection could significantly improve the prognosis of coronary heart disease (CHD). In-invitro diagnostic technique may provide a solution when sufficient biomarkers could be identified. Pertinent associations between blood-based aberrant DNA methylation and smoking, the pathogenesis of atherosclerosis, and CHD have been robustly demonstrated and replicated, but that studies in Chinese populations are rare. The blood-based methylation of aryl-hydrocarbon receptor repressor (AHRR) cg05575921 and 6p21.33 cg06126421 has been associated with cardiovascular mortality in Caucasians. Here, we aim to investigate whether the AHRR and 6p21.33 methylation in the blood is associated with CHD in the Chinese population.

Methods: In this case-control study,180 CHD patients and 184 controls were enrolled. Methylation intensities of 19 CpG sites, including AHRR cg05575921, 6p21.33 cg06126421, and their flanking CpG sites, were quantified by mass spectrometry. The association between methylation intensities and CHD was estimated by logistic regression analyses adjusted for covariant.

Results: Compared to the controls, lower methylation of 6p21.33_CpG_4.5/cg06126421 was independently associated with increased odds of CHD patients with heart failure and early cardiac dysfunction (OR per -10% methylation = 1.59 and 1.65; p = 0.012 and 0.020 by multiple testing corrections, respectively). No association between blood-based AHRR methylation and CHD was found.

Conclusions: 6p21.33 methylation exhibits a significant association with CHD. The combination of 6p21.33 methylation and conventional risk factors might be an intermediate step towards the early detection of CHD.

Coronary heart disease

AHRR

6p21.33

DNA Methylation

peripheral blood

Coronary heart disease (CHD) is the leading cause of morbidity and mortality worldwide [1, 2]. CHD is a gene-environment interacted disease mainly caused by atherosclerosis which is usually asymptomatic in the early stage [3–5]. Hypertension, dyslipidemia, obesity, diabetes, and smoking, have been well-known as risk factors for CHD [6, 7]. A series of biomarkers for cardiovascular disease have been identified, such as high-sensitivity C-reactive protein (hsCRP) [8], lipoprotein-associated phospholipase A2 (Lp-PLA2) [9], myeloperoxidase (MPO) [10], B-type natriuretic peptide (BNP), N-terminal prohormone BNP (NT-pro BNP) [11], leukocyte counts [12], as well as plasma metabolomics and circulating micro-RNAs [13, 14]. However, these biomarkers are inadequate for the detection of early CHD due to their insufficient clinical practice. The identification and development of novel biomarkers are necessary and urgent for the risk evaluation and early detection of CHD.

DNA methylation is the covalent methylation of cytosine C5 in CpG dinucleotide involving in inherent and acquired transcriptional inhibition of genes, which occur independently of the DNA sequence [15]. As a reversible epigenetic modification, DNA methylation is a dynamic process related to environmental exposure [16]. It also serves as an important cellular regulatory mechanism that regulates gene expression associated with inflammation and atherosclerosis [17, 18]. Previous researches have shown that aberrant DNA methylation in the leukocytes is related to inflammation and the pathogenesis of atherosclerosis, and subsequently causes increased mortality in cardiovascular diseases [17–20]. In addition, the associations between CHD and blood-based hypermethylation of several genes, such as FOXP3 (forkhead box P3), ABCG1 (ATP binding cassette subfamily G member 1) and GALNT2 (polypeptide N-acetylgalactosaminyltransferase 2), and hypomethylation of IL-6 (interleukin 6) have been reported [21–23]. Therefore, DNA methylation in blood could be a potential biomarker for CHD.

The protein encoded by the aryl-hydrocarbon receptor repressor (AHRR) gene participates in the aryl hydrocarbon receptor (AhR) signaling cascade, which mediates dioxin toxicity, and is involved in the regulation of cell proliferation and differentiation [24, 25]. Smoking can trigger the production of AhR, which mediates dioxin toxicity and other pathological effects [26, 27]. Two epigenome-wide studies by Infinium Human Methylation Illumina 450K BeadChip have revealed the association between smoking and blood-based DNA methylation in AHRR (cg05575921) and 6p21.33 (cg06126421) in the European population [28, 29]. Smoking is a major preventable risk factor for atherosclerosis and cardiovascular diseases [30, 31]. Reynolds et al., [32] further disclosed the association between smoking-responsive methylation of AHRR in the monocytes and subclinical atherosclerosis by a multi-ethnic study with 1,256 participants in the United States. In 2016, a prospective cohort study in Germany reported a strong association between cardiovascular mortality and a score based on the methylation intensity of two CpGs (AHRR cg05575921 and 6p21.33 cg06126421) [20]. Follow-up studies also suggested AHRR methylation in blood as a biomarker for cardiovascular disease in the Caucasian population, such as myocardial infarction, ischemic heart disease, ischemic stroke, and heart failure, as well as a predictor of the risk of all-cause mortality [33–36]. So far, there is no report about the association between the methylation of 6p21.33 (cg06126421) and the risk of cardiovascular disease in any population.

To explore the relationship between CHD and the methylation of 6p21.33 (cg06126421) and AHRR (cg05575921) in the Chinese population, we carried out a case-control study with 180 CHD patients and 184 healthy individuals, aiming to evaluate the association between the methylation intensities and the status of CHD diseases, lifestyles, and historical treatments.

Study population

A total of 180 patients with CHD and 184 controls were collected from the Chinese PLA General Hospital from 2018 to 2019. All subjects were Chinese. All the CHD patients were confirmed by coronary angiography combined with clinical manifestations. Of the 180 CHD cases, 145 had heart failure, and 78 experienced myocardial infarction (MI). New York Heart Association (NYHA) [37] cardiac function classifications were available from 161 CHD cases (NYHA Ⅰ CHD cases = 46; NYHA Ⅱ CHD cases = 78; NYHA Ⅲ CHD cases = 31; NYHA Ⅳ CHD cases = 6). Controls were recruited from people who participated in an annual health examination. All controls were self-claimed healthy, had no CHD history, no cancer history, and with normal blood accounts. No further exclusion or inclusion criteria were applied for the controls. The baseline information of the CHD cases and controls were recorded, such as the status of hypertension and diabetes, smoking, drinking, the levels of total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and the levels of low-density lipoprotein (LDL). Baseline characteristics for CHD cases and controls were listed in Table 1.

Table 1

Comparison of baseline characteristics between CHD cases and controls
Characteristics	All (N = 364)	Groups		p-value ^a
Characteristics	All (N = 364)	Controls (N = 184)	CHD cases (N = 180)	p-value ^a
Age (median, IQR)	64(58–70)	63(57–68)	66(58–73)	0.008
Male (%)	223(61.3%)	114(62.0%)	109(60.6%)	0.784
Hypertension (%)	214(58.8%)	84(45.7%)	130(72.2%)	2.00E-06
Diabetes (%)	107(29.4%)	45(24.5%)	62(34.4%)	0.063
Smoking (%)	126(34.6%)	53(28.8%)	73(40.6%)	0.027
Drinking (%)	118(32.4%)	66(35.9%)	52(28.9%)	0.116
TC, mmol/L	4.01(3.38–4.8)	4.26(3.61–5.06)	3.81(3.25–4.42)	0.001
TG, mmol/L	1.35(1.00-1.95)	1.39(1.06–2.17)	1.30(0.95–1.86)	0.147
HDL, mmol/L	1.12(0.91–1.34)	1.15(0.91–1.36)	1.09(0.91–1.31)	0.290
LDL, mmol/L	2.51(1.94–3.19)	2.69(2.06–3.40)	2.28(1.82–2.87)	2.00E-04
^a Significant p-values are in bold. CHD coronary heart disease, HDL High density lipoprotein, IQR interquartile range, LDL Low density lipoprotein, TC total cholesterol, TG triglyceride.

Sample collection and processing

The peripheral whole blood from CHD cases and healthy controls were collected by ethylene diamine tetraacetic acid (EDTA) tubes, and stored at -80℃ till DNA isolation. Genomic DNA was extracted from peripheral whole blood by the Genomic DNA Extraction Kit (Zymo Research, Orange County, United States). Subsequently, DNA was bisulfite converted with more than 99% efficiency (Supplementary Fig. 3) by the EZ-96 DNA Methylation Gold Kit according to the standard protocol (Zymo Research, Orange County, United States).

Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry

Agena matrix-assisted laser desorption/ionization (MALDI)- time-of-flight (TOF) mass spectrometry (Agena Bioscience, San Diego, California, United States) described by Yang et al., [38, 39] was used for the quantification of DNA methylation levels. In brief, the bisulfite-converted DNA was amplified by bisulfite-specific primers (no SNPs in the primers), and two PCR amplicons (6p21.33 amplicon covering 6p21.33_CpG_4/cg06126421 and 4 adjacent measurable CpG sites; AHRR amplicon covering AHRR_CpG_3/cg05575921 and 13 adjacent measurable CpG sites) were analyzed. These were obtained with the use of the primers 5’-aggaagagagGGTTGTTGAAAAGGTTAGAAATATAGG-3’ (sense) and 5’-cagtaatacgactcactatagggagaaggctACTATCCCTCCCAACCTTAAAAAA-3’ (antisense) for the 6p21.33 amplicon and primers 5’-aggaagagagGAGGGGTTTTGTTAGGATTATTTTT-3’ (sense) and 5’-cagtaatacgactcactatagggagaaggctAAACCACTCTACTCCAACCCTTACT-3’ (antisense) for the AHRR amplicon. Upper case letters present the sequence-specific primer regions, and non-specific tags are shown in lower case letters. The Sequence of the amplicon was presented in Supplementary Fig. 1. PCR products were treated according to the standard protocol of Agena EpiTyper Assay, and further cleaned by resin, and then dispensed to a 384 SpectroCHIP by a Nanodispenser. The chips were read by a MassARRAY system. Data were collected by EpiTYPER v1.2 software. For each batch of MassARRAY analysis, the same number of CHD cases and controls were treated and analyzed in parallel in all the processes.

Statistical analyses

The data were analyzed by IBM SPSS Statistics Version 25.0. The measurement data, such as the levels of TC, TG, HDL, LDL, and the methylation levels of 6p21.33 and AHRR are shown as the median (interquartile range (IQR)). The differences between CHD and control subjects were assessed using non-parametric tests (Mann-Whitney U test and Kruskal-Wallis test). Differences in the enumeration data, such as the frequencies of gender, smoking, drinking, hypertension, and diabetes between CHD and control subjects were analyzed using the chi-square (χ²) test. Bivariate correlations between variables were examined by Spearman's rank correlation coefficients. Additionally, the logistic regression results with effect ratios (odds ratio (OR) and 95% confidence intervals (CIs)) were adjusted for possible and available confounding effects. Common cardiovascular-related factors, such as total cholesterol (TC, < 5.0 mmol/L vs. ≥ 5.0 mmol/L), total triglyceride (TG, < 1.7 mmol/L vs. ≥ 1.7 mmol/L), high-density lipoprotein (HDL, < 1.0 mmol/L vs. ≥ 1.0 mmol/L) and low-density lipoprotein (LDL, < 3.0 mmol/L vs. ≥ 3.0 mmol/L), were divided by general used criteria [40]. Receiver operating characteristic (ROC) curve analysis was applied to assess the discriminatory power of methylation levels. Bonferroni correction was used for the multiple comparisons. All the statistical tests were two-sided referred to p < 0.05 as statistically significant.

Association between blood-based 6p21.33 and AHRR methylation and CHD

DNA methylation levels at 5 CpG loci (covering cg06126421) of 6p21.33 and 14 CPG sites (covering cg05575921) of AHRR were quantitatively determined by mass spectrometry in the blood from 180 CHD patients and 184 controls. The CHD patients have a median age of 66 years old (IQR: 58–73, range from 39 to 87 years old) with 109 males (60.6%) and 71 females (39.4%) (Table 1). Since the controls were recruited from the health examination center where most participants were under 70 years old, our control group was a bit younger than the CHD cases (median of age: 63, IQR: 57–68, range from 41 to 88 years old) with 114 males (62.0%) and 70 females (38.0%) (Table 1). Compared with controls, CHD patients had higher prevalence of hypertension (72.2% vs. 45.7%, p = 2 × 10^− 6) and more smokers (40.6% vs. 28.8%, p = 0.027). CHD patients had lower TC level (3.81 vs. 4.26 mmol/L, p = 0.001) and lower LDL level (2.28 vs. 2.69 mmol/L, p = 2 × 10^− 4) than controls (Table 1). The associations between 6p21.33 and AHRR methylation and the status of CHD were investigated by two logistic regression models adjusted for different covariants (Table 2). Of which, age, gender, and batch effect were adjusted in model 1, and all the baseline characteristics that had significant differences between the CHD cases and the controls (as shown in Table 1) were adjusted in the logistic regression model 2. With multiple testing corrections, 6p21.33_CpG_4.5/cg06126421 methylation was significantly associated with CHD in model 1 (p = 0.032 after Bonferroni correction) but not anymore in the more stringent model 2 (Table 2). None of the 14 measurable CpG loci in AHRR displayed any association with CHD in both model 1 and model 2 (Table 2). We also noticed that the methylation correlates better among close than among more distant CpGs. More specific, the methylation correlates better among CpGs in the same amplicon than CpGs in different amplicons which have larger distance. In addition, all CpG sites in the AHRR amplicon are positively correlated with each other, while partial CpG sites in the 6p21.33 amplicon are positively, or negatively correlated with each other (Supplementary Fig. 2).

Table 2

Methylation difference of *6p21.33* and *AHRR* comparing CHD cases and controls
CpG sites	Controls (N = 184)	CHD cases (N = 180)	OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
CpG sites	Median (IQR)	Median (IQR)	OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
6p21.33_CpG_1	1.00(0.99-1.00)	1.00(0.98-1.00)	0.96(0.59–1.54)	0.852	1.000	1.01(0.60–1.68)	0.976	1.000
6p21.33_CpG_2	0.93(0.90–0.95)	0.94(0.91–0.97)	0.59(0.36–0.94)	0.027	0.108	0.64(0.39–1.06)	0.083	0.332
6p21.33_CpG_3	0.63(0.57–0.70)	0.62(0.54–0.70)	1.11(0.94–1.31)	0.235	1.000	1.14(0.95–1.37)	0.152	0.608
6p21.33_CpG_4.5/cg06126421	0.50(0.45–0.55)	0.48(0.42–0.52)	1.42(1.10–1.83)	0.008	0.032	1.40(1.07–1.83)	0.016	0.064
AHRR_CpG_1	0.73(0.63–0.80)	0.74(0.58–0.83)	1.01(0.90–1.14)	0.846	1.000	1.00(0.87–1.14)	0.959	1.000
AHRR_CpG_2	0.87(0.74–0.96)	0.89(0.72-1.00)	1.02(0.90–1.16)	0.720	1.000	1.15(0.82–1.60)	0.419	1.000
AHRR_CpG_3/cg05575921	0.77(0.64–0.83)	0.75(0.59–0.85)	1.08(0.96–1.22)	0.201	1.000	1.02(0.86–1.20)	0.866	1.000
AHRR_CpG_4.5	0.77(0.68–0.84)	0.75(0.60–0.84)	1.12(0.99–1.27)	0.075	0.750	1.07(0.77–1.49)	0.696	1.000
AHRR_CpG_6	0.84(0.74–0.90)	0.82(0.65–0.91)	1.10(0.96–1.26)	0.177	1.000	1.03(0.89–1.18)	0.691	1.000
AHRR_CpG_7	0.66(0.53–0.76)	0.66(0.53–0.79)	1.01(0.89–1.16)	0.836	1.000	1.07(0.94–1.22)	0.341	1.000
AHRR_CpG_8.9	0.85(0.73–0.94)	0.87(0.76–0.94)	0.89(0.73–1.09)	0.270	1.000	1.15(1.00-1.32)	0.052	0.520
AHRR_CpG_10.11	0.92(0.89–0.95)	0.92(0.87–0.96)	1.25(0.91–1.71)	0.170	1.000	1.08(0.93–1.26)	0.315	1.000
AHRR_CpG_12	0.88(0.81–0.95)	0.88(0.78–0.98)	1.07(0.91–1.25)	0.420	1.000	0.99(0.85–1.14)	0.846	1.000
AHRR_CpG_14.15	0.94(0.91–0.95)	0.94(0.90–0.97)	1.08(0.80–1.47)	0.618	1.000	0.89(0.72–1.09)	0.260	1.000
^a Model 1: Logistic regression adjusted for age, gender, and batch effect. ^b Model 2: Logistic regression adjusted for age, gender, smoking, hypertension, TC, LDL, and batch effect. Significant p-values are in bold. AHRR aryl-hydrocarbon receptor repressor, CI confidence interval, CpG cytidine-phosphate-guanosine, OR odds ratio. ^{*, #} Bonferroni-corrected p values. Bold values indicated p < 0.05.

Methylation difference of 6p21.33 and AHRR between patients with heart failure, MI, non-MI CHD, and controls

Of the 180 CHD patients, 145 were suffered from heart failure, 78 had experienced MI, 102 were non-MI CHD cases. We further investigated the association between these CHD subtypes and the blood-based methylation of 6p21.33 and AHRR also by the two logistic regression models adjusted for different covariants (Table 3). Compared with the healthy controls, heart failure CHD patients have significantly decreased methylation at 6p21.33_CpG_4.5/cg06126421 by both logistic regression model 1 and model 2 (model 1: OR per − 10% methylation (95% CI) = 1.62 (1.21–2.17), p = 0.004 after Bonferroni correction; model 2: OR per − 10% methylation (95% CI) = 1.59 (1.17–2.16), p = 0.012 after Bonferroni correction, Panel A of Table 3). Since there are only 35 CHD patients without heart failure, the Mann-Whitney U test was applied to assess the 6p21.33 and AHRR methylation difference between the non-heart failure CHD cases and controls and found no significant differences (Supplementary Table 1).

Table 3

Methylation difference of *6p21.33* and *AHRR* comparing heart failure cases, MI cases, non-MI CHD cases, and controls
Panel (A). Heart failure cases vs. Controls
CpG sites	Controls (N = 184)		Heart failure cases (N = 145)	OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
CpG sites	Median (IQR)		Median (IQR)	OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
6p21.33_CpG_1	1.00(0.99-1.00)		1.00(0.99-1.00)	0.75(0.44–1.30)	0.308	1.000	0.78(0.44–1.38)	0.398	1.000
6p21.33_CpG_2	0.93(0.90–0.95)		0.94(0.91–0.96)	0.52(0.30–0.89)	0.017	0.068	0.56(0.31–0.98)	0.044	0.176
6p21.33_CpG_3	0.63(0.57–0.70)		0.61(0.53–0.69)	1.16(0.97–1.40)	0.113	0.452	1.19(0.98–1.45)	0.079	0.316
6p21.33_CpG_4.5/cg06126421	0.50(0.45–0.55)		0.48(0.42–0.52)	1.62(1.21–2.17)	0.001	0.004	1.59(1.17–2.16)	0.003	0.012
AHRR_CpG_1	0.73(0.63–0.80)		0.74(0.56–0.82)	1.04(0.91–1.18)	0.607	1.000	1.02(0.89–1.18)	0.775	1.000
AHRR_CpG_2	0.87(0.74–0.96)		0.88(0.70-1.00)	1.08(0.94–1.24)	0.267	1.000	1.10(0.94–1.28)	0.246	1.000
AHRR_CpG_3/cg05575921	0.77(0.64–0.83)		0.75(0.59–0.84)	1.09(0.96–1.24)	0.200	1.000	1.08(0.93–1.24)	0.319	1.000
AHRR_CpG_4.5	0.77(0.68–0.84)		0.74(0.59–0.84)	1.16(1.01–1.34)	0.031	0.310	1.19(1.02–1.38)	0.024	0.240
AHRR_CpG_6	0.84(0.74–0.90)		0.82(0.65–0.91)	1.12(0.96–1.30)	0.140	1.000	1.11(0.94–1.32)	0.217	1.000
AHRR_CpG_7	0.66(0.53–0.76)		0.67(0.54–0.82)	1.05(0.91–1.21)	0.529	1.000	1.02(0.87–1.18)	0.850	1.000
AHRR_CpG_8.9	0.85(0.73–0.94)		0.89(0.77–0.95)	0.95(0.77–1.18)	0.664	1.000	0.93(0.74–1.17)	0.542	1.000
AHRR_CpG_10.11	0.92(0.89–0.95)		0.92(0.88–0.95)	1.07(0.70–1.63)	0.762	1.000	0.96(0.61–1.51)	0.855	1.000
AHRR_CpG_12	0.88(0.81–0.95)		0.88(0.79–0.97)	1.03(0.85–1.24)	0.793	1.000	0.97(0.80–1.19)	0.798	1.000
AHRR_CpG_14.15	0.94(0.91–0.95)		0.94(0.90–0.97)	1.03(0.75–1.43)	0.841	1.000	1.04(0.74–1.45)	0.828	1.000
Panel (B). MI cases vs. Controls
CpG sites	Controls (N = 184)	MI cases (N = 78)		OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
CpG sites	Median (IQR)	Median (IQR)		OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
6p21.33_CpG_1	1.00(0.99-1.00)	1.00(0.97-1.00)		1.60(0.87–2.92)	0.128	0.512	1.54(0.83–2.87)	0.173	0.692
6p21.33_CpG_2	0.93(0.90–0.95)	0.94(0.89–0.97)		0.64(0.35–1.18)	0.156	0.624	0.64(0.34–1.21)	0.170	0.680
6p21.33_CpG_3	0.63(0.57–0.70)	0.62(0.53–0.70)		1.17(0.95–1.44)	0.150	0.600	1.17(0.94–1.45)	0.161	0.644
6p21.33_CpG_4.5/cg06126421	0.50(0.45–0.55)	0.48(0.40–0.51)		1.45(1.04–2.02)	0.030	0.120	1.42(1.01–1.99)	0.042	0.168
AHRR_CpG_1	0.73(0.63–0.80)	0.68(0.53–0.82)		1.07(0.92–1.24)	0.411	1.000	1.05(0.89–1.23)	0.584	1.000
AHRR_CpG_2	0.87(0.74–0.96)	0.84(0.62–0.98)		1.07(0.92–1.25)	0.394	1.000	1.06(0.90–1.25)	0.505	1.000
AHRR_CpG_3/cg05575921	0.77(0.64–0.83)	0.72(0.50–0.84)		1.10(0.96–1.27)	0.179	1.000	1.08(0.92–1.25)	0.344	1.000
AHRR_CpG_4.5	0.77(0.68–0.84)	0.74(0.54–0.83)		1.15(0.99–1.35)	0.073	0.730	1.15(0.98–1.36)	0.093	0.930
AHRR_CpG_6	0.84(0.74–0.90)	0.79(0.61–0.91)		1.15(0.97–1.35)	0.100	1.000	1.12(0.94–1.33)	0.207	1.000
AHRR_CpG_7	0.66(0.53–0.76)	0.61(0.49–0.73)		1.06(0.89–1.26)	0.526	1.000	1.03(0.86–1.23)	0.778	1.000
AHRR_CpG_8.9	0.85(0.73–0.94)	0.82(0.69–0.92)		0.99(0.77–1.29)	0.966	1.000	0.97(0.75–1.27)	0.838	1.000
AHRR_CpG_10.11	0.92(0.89–0.95)	0.92(0.85–0.96)		1.47(1.01–2.14)	0.042	0.420	1.39(0.95–2.04)	0.091	0.910
AHRR_CpG_12	0.88(0.81–0.95)	0.87(0.75-1.00)		1.12(0.92–1.37)	0.245	1.000	1.08(0.88–1.32)	0.450	1.000
AHRR_CpG_14.15	0.94(0.91–0.95)	0.94(0.87–0.97)		1.38(0.84–2.27)	0.210	1.000	1.41(0.83–2.38)	0.203	1.000
Panel (C). Non-MI CHD cases vs. Controls
CpG sites	Controls (N = 184)	Non-MI CHD cases (N = 102)		OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
CpG sites	Median (IQR)	Median (IQR)		OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
6p21.33_CpG_1	1.00(0.99-1.00)	1.00(0.99-1.00)		0.54(0.28–1.04)	0.064	0.256	0.56(0.29–1.11)	0.095	0.380
6p21.33_CpG_2	0.93(0.90–0.95)	0.94(0.92–0.97)		0.55(0.29–1.03)	0.061	0.244	0.70(0.35–1.39)	0.302	1.000
6p21.33_CpG_3	0.63(0.57–0.70)	0.62(0.55–0.70)		1.09(0.87–1.37)	0.448	1.000	1.16(0.91–1.48)	0.220	0.880
6p21.33_CpG_4.5/cg06126421	0.50(0.45–0.55)	0.48(0.44–0.52)		1.41(1.01–1.98))	0.047	0.188	1.39(0.97–1.99)	0.077	0.308
AHRR_CpG_1	0.73(0.63–0.80)	0.77(0.60–0.83)		0.96(0.81–1.12)	0.581	1.000	0.92(0.77–1.11)	0.400	1.000
AHRR_CpG_2	0.87(0.74–0.96)	0.91(0.76-1.00)		0.99(0.82–1.18)	0.863	1.000	0.99(0.81–1.22)	0.948	1.000
AHRR_CpG_3/cg05575921	0.77(0.64–0.83)	0.77(0.62–0.85)		1.06(0.90–1.25)	0.456	1.000	1.09(0.90–1.31)	0.373	1.000
AHRR_CpG_4.5	0.77(0.68–0.84)	0.76(0.62–0.85)		1.14(0.96–1.34)	0.128	1.000	1.20(0.99–1.45)	0.059	0.590
AHRR_CpG_6	0.84(0.74–0.90)	0.84(0.71–0.91)		1.05(0.87–1.26)	0.641	1.000	1.03(0.83–1.29)	0.774	1.000
AHRR_CpG_7	0.66(0.53–0.76)	0.70(0.57–0.83)		0.97(0.82–1.15)	0.753	1.000	0.95(0.78–1.14)	0.567	1.000
AHRR_CpG_8.9	0.85(0.73–0.94)	0.89(0.79–0.96)		0.81(0.62–1.05)	0.115	1.000	0.80(0.60–1.06)	0.112	1.000
AHRR_CpG_10.11	0.92(0.89–0.95)	0.92(0.88–0.96)		1.10(0.69–1.76)	0.680	1.000	1.00(0.61–1.65)	0.988	1.000
AHRR_CpG_12	0.88(0.81–0.95)	0.89(0.80–0.96)		1.07(0.86–1.34)	0.550	1.000	1.04(0.81–1.33)	0.770	1.000
AHRR_CpG_14.15	0.94(0.91–0.95)	0.94(0.92–0.97)		1.01(0.70–1.44)	0.968	1.000	1.00(0.69–1.43)	0.984	1.000
^a Model 1: Logistic regression adjusted for age, gender, and batch effect. ^b Model 2: Logistic regression adjusted for age, gender, smoking, hypertension, TC, LDL, and batch effect. Significant p-values are in bold. MI myocardial infarction. ^{*, #} Bonferroni-corrected p values. Bold values indicated p < 0.05.

Next, we assessed the association between the methylation of 6p21.33 and AHRR and the status of MI. The decreased methylation for the MI cases compared to the controls was also detected in the 6p21.33_CpG_4.5/cg06126421 by both model 1 and model 2, but not significant after Bonferroni correction (Panel B of Table 3). All the 14 AHRR CpG sites showed no association with MI by both models (Panel B of Table 3). In addition, none of the 19 measurable CpG sites in 6p21.33 and AHRR showed any association with the non-MI CHD cases by the two logistic regression models (Panel C of Table 3).

Association between blood-based 6p21.33 and AHRR methylation and early CHD cases

In our study, the cardiac function of 124 CHD cases was classified as NYHA I and NYHA II (NYHA Ⅰ CHD cases = 46, NYHA Ⅱ CHD cases = 78). Compared to the healthy controls, the methylation intensity of 6p21.33 at the 6p21.33_CpG_4.5/cg06126421 locus was also significantly decreased in NYHA Ⅰ&II CHD cases by the two logistic regression models (model 1: OR per − 10% methylation (95% CI) = 1.69 (1.22–2.34), p = 0.008 after Bonferroni correction; model 2: OR per − 10% methylation (95% CI) = 1.65 (1.17–2.34), p = 0.020 after Bonferroni correction, Table 4). No significant association between the early-stage cardiovascular dysfunction cases and the methylation changes was observed for all the 14 CpG sites of AHRR (Table 4). All the five CpG sites in 6p21.33 and half of the CpG sites in AHRR had lower methylation levels in the 37 NYHA Ⅲ&Ⅳ CHD cases than that in the 124 NYHA Ⅰ&Ⅱ CHD cases but without significance probably due to the limited sample size (Supplementary Table 2). Nevertheless, these observations indicated that the aberrant blood-based DNA methylation might be enhanced along with the progress of cardiac dysfunction.

Table 4

Methylation difference of *6p21.33* and *AHRR* comparing NYHA Ⅰ&Ⅱ CHD cases and controls
CpG sites	Controls (N = 184)	NYHA Ⅰ&Ⅱ CHD cases (N = 124)	OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
CpG sites	Median (IQR)	Median (IQR)	OR (95%CI) per-10%methylation	p-value^a	p-value^*	OR (95%CI) per-10%methylation	p-value^b	p-value^#
6p21.33_CpG_1	1.00(0.99-1.00)	1.00(1.00–1.00)	0.79(0.49–1.27)	0.328	1.000	0.78(0.48–1.27)	0.315	1.000
6p21.33_CpG_2	0.93(0.90–0.95)	0.94(0.91–0.97)	0.49(0.28–0.85)	0.011	0.044	0.54(0.30–0.97)	0.038	0.152
6p21.33_CpG_3	0.63(0.57–0.70)	0.62(0.53–0.69)	1.15(0.94–1.41)	0.164	0.656	1.21(0.98–1.50)	0.079	0.316
6p21.33_CpG_4.5/cg06126421	0.50(0.45–0.55)	0.49(0.44–0.52)	1.69(1.22–2.34)	0.002	0.008	1.65(1.17–2.34)	0.005	0.020
AHRR_CpG_1	0.73(0.63–0.80)	0.74(0.59–0.82)	1.04(0.90–1.20)	0.592	1.000	1.03(0.88–1.21)	0.736	1.000
AHRR_CpG_2	0.87(0.74–0.96)	0.89(0.72-1.00)	1.04(0.89–1.21)	0.618	1.000	1.06(0.89–1.26)	0.508	1.000
AHRR_CpG_3/cg05575921	0.77(0.64–0.83)	0.75(0.60–0.85)	1.10(0.95–1.26)	0.201	1.000	1.11(0.94–1.30)	0.211	1.000
AHRR_CpG_4.5	0.77(0.68–0.84)	0.76(0.61–0.84)	1.13(0.97–1.31)	0.115	1.000	1.16(0.98–1.37)	0.081	0.810
AHRR_CpG_6	0.84(0.74–0.90)	0.83(0.68–0.90)	1.11(0.94–1.31)	0.239	1.000	1.10(0.91–1.33)	0.316	1.000
AHRR_CpG_7	0.66(0.53–0.76)	0.69(0.55–0.82)	1.02(0.88–1.19)	0.772	1.000	1.01(0.85–1.19)	0.930	1.000
AHRR_CpG_8.9	0.85(0.73–0.94)	0.89(0.78–0.94)	0.92(0.74–1.16)	0.488	1.000	0.91(0.72–1.15)	0.436	1.000
AHRR_CpG_10.11	0.92(0.89–0.95)	0.92(0.88–0.95)	1.02(0.65–1.60)	0.929	1.000	0.93(0.57–1.51)	0.756	1.000
AHRR_CpG_12	0.88(0.81–0.95)	0.89(0.80–0.95)	1.05(0.86–1.29)	0.620	1.000	1.03(0.83–1.28)	0.792	1.000
AHRR_CpG_14.15	0.94(0.91–0.95)	0.94(0.91–0.96)	1.08(0.77–1.50)	0.670	1.000	1.09(0.76–1.57)	0.649	1.000
^a Model 1: Logistic regression adjusted for age, gender, and batch effect. ^b Model 2: Logistic regression adjusted for age, gender, smoking, hypertension, TC, LDL, and batch effect. Significant p-values are in bold. NYHA New York Heart Association. ^{*, #} Bonferroni-corrected p values. Bold values indicated p < 0.05.

The correlation between blood-based 6p21.33 and AHRR methylation and CHD-related characteristics

Methylation intensities across various strata of the CHD cases and controls respectively were shown in Table 5 for 6p21.33 cg06126421, AHRR cg05575921, and their adjacent measurable CpG sites. In agreement with previous reports [36, 41–43], smokers had significantly lower AHRR methylation than the non-smokers in both controls and CHD cases (Table 5). In opposite, the smoking-related lower 6p21.33 methylation was significant only in CHD cases but not in the controls (Table 5). Our results also suggested drinking as a causative factor for the hypomethylation of AHRR mostly in controls, but such effect is much weaker than smoking (Table 5). Drinkers showed no significant 6p21.33 methylation changes compared with non-drinkers in both controls and CHD cases. Compared to the women, men had lower methylation in 6p21.33 and AHRR in both controls and CHD cases (Table 5). Our results showed no or very weak 6p21.33 and AHRR methylation difference among people with the variant status of hypertension and diabetes, and people with different levels of TC, TG, HDL, and LDL (Table 5).

Most CHD patients have a history of medication. It seems that the intake of aspirin could significantly reverse the demethylation of 6p21.33 in the blood of CHD cases, but had no influence on the AHRR methylation (Table 6). The intake of digoxin was weakly correlated with the hypomethylation of two AHRR CpG sites (Table 6). The other 10 common cardiovascular drugs showed no obvious influence on the methylation intensities of 6p21.33 and AHRR in the blood of CHD patients (Table 6).

Previous studies have demonstrated that smoking could result in the hypomethylation of AHRR cg05575921 and 6p21.33 cg06126421 in the peripheral blood [28, 29]. The association between AHRR cg05575921 methylation and cardiovascular disease was also well addressed in Caucasians even in prospective studies [20, 33–36]. In this case-control study, we validated the strong association between smoking and the hypomethylation of AHRR cg05575921 and 6p21.33 cg06126421 in the Chinese population. More importantly, we have disclosed the significant hypomethylation of 6p21.33 cg06126421 in the blood leukocyte DNA of CHD patients, especially for CHD patients with heart failure. Our observation agreed with the report of Agha et al., [44] that the blood leukocyte DNA methylation could predict the risk of future MI and CHD. This decreased 6p21.33 methylation could be detected in the blood of patients with early cardiac dysfunction (NYHA Ⅰ&Ⅱ CHD cases) and might become more aberrant in the patients with advanced cardiac dysfunction (NYHA Ⅲ&Ⅳ CHD cases). Thus, we proposed that the altered 6p21.33 methylation in the blood leukocytes may play a role not only in the occurrence of CHD but also in the progress. 6p21.33 cg06126421 is located at chr6:30720081 (build 37/hg19). In the 100 kilobases flanking cg06126421 there are seven genes that code for proteins functioned in the protection of cells from Fas- or tumor necrosis factor type alpha-induced apoptosis (IER3), the intra-S phase and G2/M phase cell cycle checkpoints in response to DNA damage (MDC1), cell cycle progression (DHX16), cytoskeleton regulation and membrane transport (FLOT1, TUBB, PPP1R18, NRM). In particular, the expression of IER3 is involved in immune functions and the physiology of the cardiovascular system in transgenic and knock-out mouse models [45]. AHRR methylation has been associated with CHD in Caucasians, but could not be validated in the Chinese population. Other studies have shown that ethnic genetic background or lifestyle could play a role in epigenetic modifications [46–48]. Thus, DNA methylation patterns warrant validation when the different ethnic population is considered.

In our study, we confirmed the association between the behavior of smoking and the hypomethylation of AHRR cg05575921, 6p21.33 cg06126421, and their adjacent CpG sites, and supported the relationship between smoking and epigenetic regulation in atherosclerotic disease [30]. However, in lack of the data on the intensity of the smoking history (pack-years), smoking can hardly be fully adjusted by logistic regression in our study. Future studies with larger samples size and more detailed data on smoking history shall provide more robust evidence for the relationship among smoking, 6p21.33 methylation, and CHD. Our results also showed that the behavior of drinking has an effect on the methylation intensity of AHRR, but not on the 6p21.33 methylation. This observation was consistent with the reported alcohol-related DNA methylation signatures in specific genes [49]. The males had lower 6p21.33 and AHRR methylation levels than the females, whereas the decreased methylation of 6p21.33 was associated with CHD according to our results, and the hypomethylation of 6p21.33 and AHRR were considered as predictors for the increased cardiovascular mortality [20]. This may explain why males have a higher incidence and mortality of CHD than females. It seems that the blood-based 6p21.33 and AHRR methylations were not influenced by hypertension, diabetes, levels of TC, TG, HDL, and LDL. Given the above, our investigation suggested that the methylation of 6p21.33 and AHRR in blood could hardly be influenced by most of the environmental factors, especially the blood lipid index. The signatures of methylation could also be influenced by treatment [50]. Among the 12 cardiovascular drugs available, only 4 drugs showed an effect on the methylation level of individual sites on 6p21.33 and AHRR. However, these correlations should be taken with caution due to the limited sample size. To further explore the relationship between medication and CHD-related methylation, it is meaningful to conduct the study with an expanded sample size and more adequate information about treatment.

In our study, MALDI-TOF mass spectrometry was used for the quantification of DNA methylation levels. In clinical, mass spectrometry has already been used for multiplex genetic analyses, including non-invasive prenatal tests, disease-related SNP analyses, and even for the detection of Covid-19 [51]. With a semi-quantitative setting, mass spectrometry offers a self-designable, easy-to-use, high throughput, robustness, and cost-saving technique for the candidate-approached DNA methylation analyses. Recent studies have suggested multiple genes are involved in diseases including CHD. With the capacity of supporting multiplex analyses for a panel of genetic and epigenetic variations in a cost-efficient manner, mass spectrometry would have a great potential for clinical utility, as well as for the CHD diagnosis. Our study analyzed the DNA methylation in whole blood or said mainly from the leukocytes. In lack of the possibility to sort the leukocytes from hospital-based samples freshly, we could not further explore the origination of such altered methylation patterns from which cell subtype. However, given that in most clinical and epidemiological settings, it is more readily and convenient to obtain DNA from whole blood, and process it for further clinical practice. Meanwhile, our study was an exploratory investigation on the assessment of CHD risk using blood-based methylation biomarkers based on a limited subject with limited environmental and medication information, our observations need to be validated in multi-center and prospective studies. Collecting abundant lifestyle and historical treatment materials in future studies with large numbers of participants would be appreciated.

In summary, 6p21.33 methylation, especially at the 6p21.33 cg06126421 site, exhibits a significant association with CHD. This correlation is more susceptible in men and smokers, and more likely appears in CHD patients with heart failure and patients with early cardiac dysfunction. In contrast to Caucasians, the blood DNA methylation at AHRR is barely related to the status of CHD in the Chinese population. The combination of 6p21.33 methylation and conventional risk factors might be an intermediate step towards the risk evaluation and detection of CHD.

AHRR: Aryl-hydrocarbon receptor repressor; CHD: Coronary heart disease; OR: Odds ratio; HsCRP: High-sensitivity C-reactive protein; Lp-PLA2: lipoprotein-associated phospholipase A2; MPO: Myeloperoxidase; BNP: B-type natriuretic peptide; NT-pro BNP: N-terminal prohormone BNP; CpG: Cytidine-phosphate-guanosine; FOXP3: forkhead box P3; ABCG1: ATP binding cassette subfamily G member 1; GALNT2: polypeptide N-acetylgalactosaminyl - transferase 2; IL-6: Interleukin 6; AhR: Aryl hydrocarbon receptor; MI: Myocardial infarction; NYHA: New York Heart Association; TC: Total cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; EDTA: Ethylene diamine tetraacetic acid; MALDI-TOF: Matrix-assisted laser desorption/ionization time-of-flight; IQR: Interquartile range; CIs: Confidence intervals; ROC: Receiver operating characteristic; IER3: Immediate early response 3; MDC1: Mediator of DNA damage checkpoint 1; DHX16: DEAH-box helicase 16; FLOT1: Flotillin 1; TUBB: Tubulin beta class I; PPP1R18: Protein phosphatase 1 regulatory subunit 18; NRM: Nurim.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committees of the Chinese PLA General Hospital (S2018-298-02). Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Availability of data and material

All data analyzed during the current study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that there are no competing interests.

Funding

This work was supported by the Nanjing Medical University Research Support Funding (grant number: 2018RC0003), National Natural Science Foundation of China (grant number: 82001994), Chinese PLA General Hospital Clinical Research Support Funding (grant number: 2018FC-WJFWZX-1-21), and Chinese PLA General Hospital Youth Development Project (grant number: QNC19058).

Authors’ contributions

XJZ and RXY contributed to the conception and design of the study, critical revision of important intellectual content in the manuscript; LYZ and CZ were responsible for performing the experiments and drafting the manuscript; LYZ and RXY were responsible for analyzing the results; JXW and XJZ provided the materials and supervised the patient enrollment and acquisition of biological samples and clinical data. All authors read and approved the final manuscript.

Acknowledgments

The authors would like to thank Qiming Yin, Chunlan Liu, and Shuifang Lei for their kind technique support.

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Tables 5 and 6 are available in the Supplemental Files section.

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The Association Between DNA Methylation of 6p21.33 and AHRR in Blood and Coronary Heart Disease in Chinese Population

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Abstract

Introduction

Methods

Study population

Sample collection and processing

Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry

Statistical analyses

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Version 1