A total of 432 patients treated at a single center were enrolled in an HCM Registry from 2006 to 2014. Among them, 220 patients were excluded owing to insufficient data, follow-up loss, or declining study enrollment. Finally, 212 patients underwent genetic testing. The patients had maximal left ventricular (LV) hypertrophy greater than 13 mm and a ratio of maximal thickness to posterior wall thickness greater than 1.3 without an underlying cause of hypertrophy, such as uncontrolled hypertension or aortic stenosis. Patterns of LV hypertrophy were classified as ApHCM and non-ApHCM (asymmetrical hypertrophy, diffuse hypertrophy, and focal segmental hypertrophy). All patients underwent screening for Fabry disease and were confirmed negative for the galactosidase alpha variant. For comparison, conventional echocardiography was performed in controls. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, and it was approved by institutional review board of Gangnam Severance Hospital (3-2015-0019). Written informed consent was obtained from all participants.
Genetic testing and analysis
HCM gene panel (nDNA) and mtDNA design
HCM genes consisted of 8 validated sarcomere genes and 25 putative HCM genes. A comprehensive HCM-specific panel, consisting of 82 nuclear DNAs (nDNAs: 33 sarcomere-associated genes, 5 phenocopy genes, and 44 nuclear genes linked to mitochondrial cardiomyopathy) and 37 mitochondrial DNAs (mtDNAs), was analyzed (SupplementaryTable S1).
DNA preparation, library construction and sequencing of HCM gene panel and mtDNA
The details are described in Supplementary Method 1 to 3.
Identification of potential pathogenic mtDNA variants
Non-haplogroup-associated novel and rare variants were evaluated for their potential pathogenicity based on variant location, amino acid change, and evolutionary conservation.
Data analysis of the HCM gene panel and mitochondrial genome
The Burrows–Wheeler aligner algorithm with default parameters was used to align reads to the human reference genome sequence GRCh37. SAMTools was used to convert the sequence alignment map file to the BAM format. Duplicates were sorted and removed using the Picard tool (http://broadinstitute.github.io/picard/). The Genome Analysis Toolkit was used for indel realigning and base-quality score re-calibration. Variants were annotated with ANNOVAR and Variant Effect Predictor and were further filtered with altered allele frequency >30%, 50´ coverage, and population frequency <0.01 in the 1000 Genome Project, ESP6500, ExAC databases, and the Korean Reference Genome Database, which was constructed using whole genome sequencing data from 1,100 Korean individuals. Potential pathogenicity was predicted using Alamut® Visual software (Interactive Biosoftware, Rouen, France), Human Gene Mutation Database professional version, release 2016.1, ClinVar, and the Atlas of Cardiac Genetic Variation. The impact of missense change was predicted using SIFT and MutationTaster. Variants were classified based on the American College of Medical Genetics and Genomics standards and guidelines.[13, 14] Data analysis of mitochondrial genome are described in Supplementary Method 4.
Comprehensive echo-Doppler evaluation was performed according to the current American Society of Echocardiography guidelines. LV ejection fraction was measured by biplane Simpson’s method. LA volume was measured at the end-systole by the ellipsoidal method, and LA volume index was calculated as LA volume/body surface area (BSA). Peak early (E) and late (A) diastolic mitral inflow velocities were measured in apical four-chamber view. The filter was set to exclude high frequency signal, and the Nyquist limit was adjusted to a range of 15 to 20 cm/s. Gain and sample volume were minimized to allow for a clear tissue signal with minimal background noise. Systolic (s’), early (e’) and late (a’) diastolic velocities of the mitral annulus were measured from the apical 4-chamber view with a 2- to 5-mm sample volume placed at the septal corner of the mitral annulus. (Figure 1) The ratio of E/e¢ was calculated. LV wall thickness was measured in all cross-sectional planes. Continuous wave Doppler was used to measure the peak velocity across the LV outflow tract (LVOT), and the pressure gradient was calculated using the Bernoulli equation, as follows: 4 ´ (peak velocity across the LVOT)2. It was measured at rest and during Valsalva maneuver. LVOT obstruction was defined as a systolic pressure gradient ≥30 mmHg.
Cardiac magnetic resonance imaging (CMR) and LV chamber performance assessment
CMR was performed using a 1.5-T scanner (Magnetom Avanto®; Siemens Medical Solutions, Erlangen, Germany) with a phased array body coil. (Supplementary Method 5)
Extent of LGE assessment
From the LGE images, the LV was divided into 16 segments. Presence of LGE involvement in each segment and the total number of LGE-involving segments were measured. In addition, the percentage of LGE in LV mass were measured using dedicated quantitative analysis software (Qmass®MR 8.1, Medis, Leiden, Netherland) using LGE images with PSIR sequence. To improve the reproducibility, experienced radiologist and cardiologist with more than 10 years of experience analyzed the LGE sizes. In each short-axis slice image, the boundaries of contrast-enhanced areas were automatically traced. On LGE-MR images, myocardium with abnormal enhancement was defined as an area of hyper-enhancement more than five standard deviations from the remote myocardium. Remote myocardium was defined as non-enhanced myocardium, opposite of the hyper-enhanced myocardium. The maximum signal was determined by computer-assisted window thresholding of the enhanced area. Obvious artifacts, such as those caused by motion, were excluded using a tool from the software package. Total LGE amount was calculated by summation of all slice volumes of enhancement.
Myocardial strain analysis using feature tracking CMR
Myocardial strain analysis using feature tracking CMR was performed in 135 patients with semi-automated software (Qstrain®MR 2.0, Medis, Leiden, Netherland). LV endocardial borders were manually drawn at a reference frame. LV endocardial and epicardial borders were manually traced in 2-, 3-, 4-chamber long-axis views at end-systolic and end-diastolic phase. LV global longitudinal strain (GLS) was obtained from averaging longitudinal strains of apical 2-, 3-, and 4-chamber view. LA endocardial border was manually traced in 4-chamber long-axis view using LV end-diastole as reference phase. LA global longitudinal strain is defined as the average peak strain value. LA maximal, pre-contraction (pre-A in cases without AF) and minimal volume were measured. LA total emptying fraction was calculated as (LA maximal volume – LA minimal volume) / LA maximal volume. LA reservoir fraction, as (LA maximal volume – LA minimal volume / LA minimal volume), LA conduit fraction as (LA maximal volume – LA pre-A volume) / LA maximal volume, and LA active emptying fraction as (LA pre-A volume – LA minimal volume) / LA pre-A volume. (Figure 1)
Continuous variables with normal distributions are reported as the mean ± standard deviation or 95% confidence interval. Student’s t-tests were used to compare the means of continuous variables that were approximately normally distributed between the two groups. Normality was determined using the Shapiro–Wilk test. Categorical variables are reported as counts (or percentages) and were compared using chi-square tests. For comparison of more than two groups, analysis of variance was performed with post-hoc analysis (LSD) for subgroup comparison. For the correlates of LA function, Pearson’s correlation coefficients were determined and Pearson’s correlation analyses were performed. For the multivariable analysis, linear regression analysis was performed with variables with p<0.05 in univariate analysis to check the independence of the variables. All statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). A two-sided p-value < 0.05 was considered statistically significant.