Differential DNA methylation of steatosis and non-alcoholic fatty liver disease in adolescence

The Raine study

The Raine Study is a longitudinal cohort study initiated 1989–1992 in Perth, Western Australia as a cohort of pregnant women (“Gen1”) and their offspring (“Gen2”). The Raine Study Gen2 cohort is representative of the general population of Western Australia, as described in detail elsewhere [11]. The current cross-sectional follow-up study was performed when the cohort had reached approximately age 17 years (Gen2-17); 1170 participants underwent assessment including (i) a detailed health questionnaire; (ii) anthropometric assessment; (iii) abdominal ultrasonography; and (iv) fasting biochemistry.

Steatosis score and NAFLD definition

NAFLD was diagnosed by ultrasound-confirmed hepatic steatosis and a daily alcohol consumption < 10 g for females and < 20 g for males [12]. Ultrasound by trained sonographers used a Siemens Antares ultrasound machine with a CH 6–2 curved array probe (Sequoia, Siemens Medical solutions, Mountain View CA), according to a standardized protocol [13]. A single radiologist interpreted images and scored hepatic steatosis severity based upon echotexture, deep attenuation, and vessel blurring (0–1 no steatosis, 2 mild steatosis, and 3–6 moderate-severe steatosis). The intra-observer reliability (κ statistics) for fatty liver was 0.78 (95% confidence interval [CI] 0.73–0.88). Testing for hepatitis B or C virus infections was not performed because notification rates were on average less than 24/100,000 and 23/100,000, respectively, for Western Australian adolescents aged 15–19 years over the study period [12].

Epigenome-wide DNA methylation profiling

DNA was extracted from blood (Puregene DNA isolation kit; Qiagen, Venlo Netherlands) [14]. Epigenome-wide DNA methylation profiles were undertaken using the Illumina (San Diego, CA) Infinium HumanMethylation 450 BeadChip array (University of British Columbia Centre for Molecular Medicine and Therapeutics; http://www.cmmt.ubc.ca).

Quality control was performed using shinymethyl [15], MethylAID [16] and RnBeads [17] as described previously [18]. Beta-mixture quantile normalization [19] was applied. Technical covariates (plate, slide, well number) were included in all statistical models to adjust for batch effects. Cell counts were estimated using the estimated Houseman method [20] for six cell types (CD8T, CD4T, NK, B cell, monocytes, granulocytes).

Statistical analysisUnivariate analysis

A total of 707 of the original 1,170 Raine Gen2 Age 17 participants who had undergone assessment for NAFLD had complete epigenome and covariate data used for statistical analysis. Univariate comparisons of continuous demographic and biochemical variables with NAFLD status were compared with Student’s t or Welch’s one-way tests if normally distributed, and Kruskal–Wallis or Wilcoxon rank sum tests if skewed. Associations of binary variables with NAFLD were assessed using t-tests for parametric variables and Mann–Whitney U tests for non-parametric variables. Measures of adiposity were BMI, and waist circumference, while liver biochemistry comprised serum γ-GGT, ALT, and AST [12]. Insulin-metabolism measures were fasting glucose and insulin, homeostasis model assessment of insulin resistance (HOMA-IR). Serum high-sensitivity C-reactive protein (hsCRP), leptin and adiponectin were measured [12].

epigenome-wide DNA methylation association analysis

For EWAS with ultrasound liver steatosis scores, we used linear mixed effects models. Four models were analysed for internal validation: (i) Model 1 adjusted for CpG, age, sex, white blood cell count, principal components derived from genome-wide genotype data, and technical covariates with plate number representing the random effect in the model; (ii) Model 2 included variables from model 1 and Houseman cell count estimates; (iii) Model 3 used all model 2 estimates without principal components; and, (iv) Model 4 included model 1 covariates with assayed white blood counts (red blood cell, neutrophils, lymphocytes, eosinophils, basophils.)

Overlap with adult CpGs identified in NAFLD meta-analysis

We investigated 22 dmCpGs previously associated with liver fat accumulation in adults [6]. A Bonferroni correction of p value < 0.05/22 = 2.3 × 10–3 was used to define statistical significance as we are hypothesis testing if the dmCpGs demonstrate signal at an earlier age.

Pyrosequencing validation

Inclusion criteria for CpG pyrosequencing were genes represented by 2 or more dmCpGs that were within the top 100 most significantly associated CpGs in statistical model 3 and that were significant across all four statistical models at p < 0.007. Four dmCpGs (cg01572694 MIR10A, cg05821571 PTPRN2, cg19537719 ANK1, cg27650870 ANK1) in three genes passed these criteria. Sodium bisulphite pyrosequencing was carried out on whole blood DNA samples at age 17 as described [21] (Supplementary Table 1). Pyrosequencing was carried out using PCR products (10 μl) to measure DNA methylation (%) of sixteen dmCpGs (Pyro-Q-CpG 1.0.9 software, Supplementary Table 2) across the three genes of interest (ANK1, MiR10A, PTPRN2). Agreement between methylation from pyrosequencing and EWAS arrays was assessed by Bland–Altman plots for four dmCpGs (cg01572694 MIR10A, cg05821571 PTPRN2, cg19537719 ANK1, cg27650870 ANK1), one-sample t-test of the difference and linear regression between mean methylation (independent variable) and difference in methylation (dependent variable).

Pyrosequencing association analysis

Three statistical models assessed association of DNA methylation of dmCpGs determined by pyrosequencing with steatosis score or NAFLD: (1) model 1 accounted for age and sex; (2) model 2 accounted for age, sex, and five Houseman cell count covariates (CD4T, CD8T, B cell, NK, and monocytes). Granulocytes were removed due to high collinearity with steatosis score and NAFLD [22]; (3) model 3 investigated whether the associated CpG was also influenced by adiposity and included waist circumference as a covariate. These dmCpGs were also investigated if associated with three markers of liver biochemistry (GGT, ALT, AST).

All analyses were performed using the statistical package R, version 3.0 or above.

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