Genetic variants of HIF1α are associated with right ventricular fibrotic load in repaired tetralogy of Fallot patients: a cardiovascular magnetic resonance study

Details about this study population have been described [17, 18]. Briefly, patients with TOF were recruited from the Cardiac Center at The Children’s Hospital of Philadelphia (CHOP) from 1992 to 2010 regardless of race or ethnicity [17]. Demographic information (e.g., gender, race/ethnicity) for patients with confirmed diagnosis of TOF was collected via patient or parent interviews. Clinical information including gestational age and details of the cardiac anatomy were ascertained at the time of consent. Additional information including the patient’s height and weight, use of palliative procedure, age at complete repair, type of surgical repair, intervening events since complete repair, pulmonary artery peak velocity, RV outflow tract gradient, and pulmonary regurgitant fraction were abstracted from medical records at the time of the CMR. All cases were screened for 22q11.2 deletion syndrome using fluorescence in situ hybridization and multiplex ligation-dependent probe amplification. Cases determined to carry the 22q11.2 deletion or any other suspected syndromes were excluded from the present study [19]. The Institutional Review Board for the Protection of Human Subjects at CHOP and the Committee for the Protection of Human Subjects at the University of Texas Health Science Center approved this study.

The subset of TOF cases with at least one post-operative CMR at CHOP was selected for this study. Cases had either a research-based or clinically-indicated CMR [18]. Complete repair for TOF was defined as closure of the ventricular septal defect and relief of right-sided outflow tract obstruction. Cases were excluded if they underwent complete repair for TOF after 2 years of age or had their first CMR after 21 years of age (i.e., to exclude those with prolonged exposure to pre-operative hypoxia and pressure overload, or chronic pulmonary regurgitation and post-operative aging). We also excluded cases when the CMR did not allow for the measurement of fibrosis.

CMRs were performed on at 1.5T (Avanto, Siemens Healthineers, Erlangen, Germany) as previously described [18]. In brief, the protocol included balanced steady-state free-precession cine CMR acquisitions in 4-chamber and long-axis planes and contiguous short-axis cine imaging from base to apex. Thickness of the slices were adjusted to obtain 8–12 slices across the ventricle with in plane resolution ranging from 1.5–2.5 mm. The number of phases ranged from 20 to 30 depending upon the heart rate. Flip angle ranged from 75 to 90 degrees. Parallel imaging was utilized with an acceleration factor of 2. All volumes were indexed to body surface area.

Cardiac fibrosis was measured using an inversion recovery technique for late gadolinium enhancement (LGE). Ten minutes after gadolinium chelate (gado-pentetate dimeglumine) was intravenously administered at a dose of 0.4 ml/kg, imaging was performed with appropriate inversion time to maximize contrast between the normal myocardium and fibrotic areas. Both magnitude and phase sensitive images were reconstructed.

RV fibrosis was quantified using two scales: indexed fibrotic volume and fibrotic score. RV fibrotic volume was calculated by identifying areas of fibrosis (excluding the ventricular septal and transannular patches) and tracing them manually on each slice; the total fibrotic load was quantified by integrating the fibrotic volume on each slice over the entire ventricle. For RV fibrotic score, we used an LGE scoring system, dividing the RV into seven regions, as described by Babu-Narayan et al. [5, 20]. The maximum score was 20. The investigator measuring fibrotic load was blinded to RV function, and the physician reporting on RV function was blinded to fibrotic load measurements.

Phase contrast CMR (PC-CMR) was utilized to determine pulmonary regurgitant fraction. An initial velocity encoding (VENC) of 150 cm/sec was utilized with a flip angle of 25 degrees; if velocities in the pulmonary artery were found to exceed the VENC, a higher VENC was used and the sequence was repeated. Slice thickness was between 4 and 6 mm with the number of phases, similar to cine imaging, a function of the heart rate (between 20 and 30 phases/cardiac cycle).

RV end-diastolic volume indexed (RVEDVI) and RV ejection fraction (RVEF) were measured as previously described [21‐24]. The RV infundibulum was included in the RV volume up to the pulmonary annulus. RV end-systolic volume was also measured in the same manner as RV end-diastolic volume to calculate RVEF. RVEF was calculated as:

Pulmonary regurgitation was measured by planimeterizing the pulmonary artery in cross section on the PC-CMR images and integrating the velocities in each voxel over the vessel across the cardiac cycle; multiplying that stroke volume by the average heart rate during imaging yielded flow. Pulmonary regurgitant fraction was calculated as:

Existing genotyped and imputed data were available for a subset of patients with TOF, as previously described [17]. Briefly, cases had been genotyped on either the Illumina Infinium™ II HumanHap550K or 610Q BeadChip at CHOP’s Center for Applied Genomics. Genotype data were used to impute additional data based on the 1000 Genomes Project. After applying post-imputation quality control procedures (e.g., excluding single nucleotide polymorphisms [SNPs] with minor allele frequencies < 5%) [17], the 3 SNPs evaluated by Jeewa et al. [16], and 45 additional SNPs were available in the region under study (HIF1α gene ±1 kb upstream and downstream).

We determined counts and frequencies for categorical variables and medians and ranges for continuous variables. For cases ≥ 20 years old, body mass index (BMI) at CMR was calculated as weight (kg) divided by height squared (m²). For cases < 20 years old, BMI percentiles at CMR were determined using age and sex-specific growth charts (https://​www.​cdc.​gov/​growthcharts/​clinical_​charts.​htm). Cases were categorized into the standard BMI categories. Cases were considered to have residual right-sided obstruction if they had both a main pulmonary artery peak velocity > 2.5 m/s and an RV outflow tract gradient > 30 mmHg on CMR; otherwise, they were considered not to have right-sided obstruction.

To study the association between RV fibrosis (assessed by indexed fibrotic volume and fibrotic score) and RV function (assessed by RVEF and RVEDVI), we used multivariable linear mixed-effects regression with a random intercept. A random intercept was used to account for repeated measurements in cases for whom we had data from two CMRs. We considered the following variables as potential confounders: age at completed surgical repair (0–4 months, 5–8 months, 9–12 months, > 12 months), type of surgical repair (transannular patch, RV to pulmonary artery conduit, nontransannular patch or ventricular septal defect closure only), time between completed surgical repair and CMR (the earliest CMR in patients with results from two CMRs), intervening events (i.e. pulmonary artery intervention, pulmonary valve replacement, revision of RV to branch pulmonary artery conduit, closure of residual septal defect(s), arrhythmias requiring medication or electrophysiologic intervention, use of cardiac medications), BMI at the time of the CMR, the degree of RV outflow tract obstruction at the time of CMR, and pulmonary regurgitation fraction at the time of the CMR [mild (0–20%), moderate (> 20–40%), severe (> 40%)]. To identify covariates to include in the model, first we ran a crude model between RVEF and indexed fibrotic volume. Next, each potential confounder was included in the model and the percent change from the crude estimate was calculated. This process was repeated using fibrotic score as the exposure. Covariates that changed the crude estimate by more than 10% in either model (indexed fibrotic volume or fibrotic score) were considered potential confounders and included in the main analyses. As the time between CMRs varied between cases, we also controlled for time between the two CMRs. Time between CMRs was set to zero for cases with only one CMR. The main analyses were repeated using RVEDVI to assess RV dilation. RVEDVI was log-transformed to meet model assumptions.

We also evaluated RVEF and RVEDVI as dichotomous traits. RVEF < 50% was considered abnormal and RVEF ≥ 50% was normal [25]. For RVEDVI, > 108 cc/m² was considered abnormal and ≤ 108 cc/m² was normal [25]. We used multivariable logistic mixed-effects regression with a random intercept, controlling for the same covariates as included in the multivariable mixed-linear regression. Analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, North Carolina, USA). For the multivariable models, p < 0.05 was considered significant.

We restricted our genetic analyses to cases that identified as being white (to limit the potential for population stratification bias) and had complete covariate data. The small number of non-white cases precluded separate analyses within other race/ethnic groups (n < 25 cases). We examined the association between each genetic variant (modeled additively for the minor allele) and each measurement of RV fibrotic load and RV function. Indexed fibrotic volume was square root transformed to meet model assumptions. For continuous outcomes (square root transformed indexed fibrotic volume, RVEF, RVEDVI), we used multivariable linear mixed-effects regression with a random intercept. For fibrotic score, we used a random intercept, multivariable generalized Poisson regression to account for underdispersion. In all models, we controlled for time between completed surgical repair and the first CMR, and time between the first and second CMR. We used a false discovery rate to account for multiple comparisons conducted for each outcome [26, 27]. For all variants, a regional association plot was constructed using LocusZoom [28], using the 1000 Genomes European population (Nov 2014). We also checked for linkage disequilibrium using SNPclip (R² = 0.8, minor allele frequency = 0.01) [29]. All variants were annotated using Combined Annotation Dependent Depletion (CADD) [30] and Genome-Wide Annotation of VAriants (GWAVA) scores [31].

After adjusting for time between completed surgical repair and first CMR, BMI, obstruction, and pulmonary regurgitant fraction, neither indexed fibrotic volume nor fibrotic score were associated with log-transformed RVEDVI. However, both were significantly associated with RVEF (modeled continuously) (Table 2). RVEF decreased by 1.6% (p = 0.04), for every one-unit increase in indexed fibrotic volume and decreased by 0.9% (p = 0.03) for every one-unit increase in fibrotic score.

When RV volume and function were analyzed as dichotomous variables (RVEDVI > 108 cc/m² and RVEF < 50% were considered abnormal, respectively), RVEDVI was not significantly associated with indexed fibrotic volume (OR = 1.21, 95% CI: 0.72, 2.03; p = 0.46) or fibrotic score (OR = 1.06, 95% CI: 0.81, 1.38; p = 0.68) (Table 2). However, the odds of having an abnormal RVEF increased by 72% for every one-unit increase in indexed fibrotic volume (95% CI: 0.99, 2.96; p = 0.05) and by 37% (95% CI: 1.06, 1.78; p = 0.02) for every one-unit increase in fibrotic score.

A total of 125 white cases had genetic data and complete information on covariates (Fig. 2). In this subset, the distribution of case characteristics at the first and second CMR is similar to the distribution reported in Table 1, except the time between complete repair and first CMR is lower at the second CMR (median of 10 years in subset vs 15 years in study population) (data not shown). None of the 48 HIFIα variants were associated with either RVEF or RVEDVI (Additional file 1: Table S1).

After accounting for multiple comparisons, none of the variants were significantly associated with square root transformed fibrotic volume (Additional file 1: Table S2). However, six HIF1α SNPs (i.e., rs76308410, rs11549465, rs74481028, rs7161527, rs10147275, and rs2057482) were significantly (FDR p < 0.05) associated with fibrotic score (Table 3). (The full results for the association between fibrotic score and each of the 48 variants are available in Additional file 1: Table S2). Based on LDlink, these six SNPs are in linkage disequilibrium and represent two unique signals. Rs76308410, rs11549465, and rs74481028 are in linkage disequilibrium with each other (i.e., highly correlated). Likewise, rs7161527, rs10147275, and rs2057482 are in linkage disequilibrium with each other. However, correlations between SNPs in the two different groups (e.g., rs76308410 and rs7161527) are low (r² < 0.80). Fig. 3 illustrates the linkage disequilibrium across all 48 SNPs.

Given the absence of T1 images on the available CMRs, we could not measure diffuse fibrosis. Instead, we used LGE, which is known to correlate well with scar tissue and provides a good estimate of focal or reactive fibrosis [39]. We included surgical approach to account for the potential extent of surgical disruption of RV myocardium, but we were unable to detail the size of surgical incisions or quantify the extent of muscle bundle resection. Most fibrosis is presumably secondary to surgical disruption of the ventricular myocardium, but we and others have observed fibrosis outside of surgical sites including the RV inferior and anterior walls, and RV septal surface and septal insertion points [5]. Because a single investigator measured fibrosis independent of RV function, biases in measuring fibrosis were probably minimal. Given that some CMRs were performed for clinical purposes, the study population might be biased toward those with more severe clinical circumstances or cardiac status. However, many patients in our practice undergo routine surveillance by CMR and the study cohort displayed a broad range of RV characteristics and fibrotic load, suggesting that the disease-spectrum was somewhat representative of a general TOF population. Our genetic analyses had a small sample size, but we included repeated CMR measures to improve power. We only studied variants from one gene, but the relationship is probably genetically more complex than SNPs within a single gene.