Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry

Inclusion/exclusion criteria

Adult patients with DCM defined by ESC consensus criteria were studied. Specifically, (i) left ventricular ejection fraction <45% (>2 SD) and/or fractional shortening <25% (>2 SD), as ascertained by echocardiography, radionuclide scanning, or cardiac magnetic resonance imaging; and (ii) left ventricular end-diastolic diameter >117% of the predicted value corrected for age and body surface area (Henry's formula), which corresponds to 2 SD of the predicted normal limit +5%. Patients with heart failure attributable to coronary artery disease and clinically suspected or biopsy-proven myocarditis were excluded.^{20, 21}

Definitions

Familial dilated cardiomyopathy (FDCM) was defined by the presence of two or more affected individuals in a single family or the presence of an index patient with DCM and a first degree relative with documented unexplained sudden cardiac death at <35 years of age. Patients that did not meet these criteria were deemed sporadic DCM (SDCM). Patients with missing data concerning familial status (n = 315) were compared with both FDCM and SDCM for clinical differences to account for bias. Primary outcome was defined as a composite of cardiovascular death, implantation of a ventricular assist device, or heart transplantation. Secondary endpoint was hospitalization for urgent cardiac reason. Genetic testing and variant classification were planned and performed according to clinician's judgement. Genetic variants and their classifications were reported by individual researchers representing their centres. Because lab techniques and genetic coordinates were not recorded, centralized variant classification was not possible. The definitions of included variables have been listed in prior EORP publications and are included in the Supporting Information, Data S1.^{20, 22}

Statistical analysis

Univariable analysis was applied to both continuous and categorical variables. Continuous variables were reported as mean ± standard deviation and/or as median and interquartile range (IQR) when appropriate. Among-group comparisons were made using a non-parametric test (Kruskal–Wallis). Categorical variables were reported as counts and percentages. Among-group comparisons were made using a χ² test or a Fisher's exact test if any expected cell count was less than 5. Plots of Kaplan–Meier curves for primary outcome were performed. Cox proportional hazards model was used for survival estimates reporting hazard ratios (HRs) and 95% confidence intervals (95% CIs) in univariable and multivariable analysis. As the goal was to report covariates and their association to outcome rather than a clinical risk calculator, both multivariable results and variable selection (P < 0.05) were reported. To compare with external datasets, comparison of proportions was calculated using the “N-1” χ² test. A two-sided P-value of <0.05 was considered as statistically significant. For sensitivity analyses, probands of FDCM were compared with SDCM. Analyses were performed using SAS statistical software version 9.4 (SAS Institute, Inc., Cary, NC, USA).

Genetic testing

In total, 214 out of the 1260 (17%) cases were genetically tested (58 out of 707 [8%] SDCM and 114 out of 238 [48%] of FDCM). In 110 cases, the information on genetic testing was missing. Genetic testing was less frequently performed in Eastern Europe and North Africa (respectively 4% and 0%), whereas North, South, and West Europe performed genetic testing in 21–27% of their population (Table 3).

In 63 out of the 114 (55%) tested cases of FDCM, at least one disease causing variant was reported compared with 13 out of 58 (22%) in SDCM. These variants were most prevalent in sarcomere (n = 42) and nuclear genes (n = 27), with most variants being discovered in LMNA (16% yield in FDCM and 3% yield in SDCM) and MYH7 (14% yield in FDCM and 0% in SDCM) (Supporting Information, Data S2, Table 7).

Disease burden

Overall, DCM patients included in this cohort had similar demographic and clinical characteristics compared with DCM populations described in the literature.^{2, 4, 5, 12, 23} However, in contrast to some studies, we found that patients with FDCM were diagnosed at a younger age and have a less severe phenotype than patients with SDCM but have similar cardiovascular prognosis.^{2, 5} This was also seen in the prescribed medication, where we observed that patients with FDCM received less beta-blockers, diuretics and mineralocorticoid receptor antagonists. These findings may reflect of cascade family screening in patients with suspected FDCM. However, when comparing FDCM index patients with SDCM patients, the clinical differences remained significant. Because family screening has shown to effectively identify DCM patients at earlier stages of disease, potentially benefiting prognosis, genetic counselling remains an important pillar of care in patients with DCM and their relatives.³ Prognosis of DCM has been improving for the endpoint of cardiovascular death of heart transplantation/assist device implantation, arguably because of better cardiovascular care and the early identification and classification of disease.²⁴ Our observations however are more in line with earlier reports and less favourable outcome, which may be caused because of a selection bias towards more expert/tertiary centres.²³

A relevant finding of this study was that patients with SDCM had a higher burden of cardiovascular risk factors, such as hypertension, hypercholesterolaemia, and smoking. Because it is well known that these risk factors are more prevalent with increasing age, and our patients with SDCM were significantly older than FDCM, this result might be expected.²⁵ However, the age difference between SDCM and FDCM does not fully account for the observed increase in risk factors. When comparing our patients with SDCM with external cohorts from the same age group, we still observe a significantly increased burden of cardiovascular factors (P < 0.01) in our patients with SDCM.^{25, 26} In addition, the presence of common cardiovascular risk factors is also associated with worse outcome in SDCM. Smoking, for instance, independently increased sudden cardiac death in SDCM²⁷ and diabetes confers a two-fold to five-fold added risk for heart failure development, even after adjustment for other traditional risk factors such as coronary heart disease.²² Alcohol is also known to be the both sole cause of cardiomyopathy and may negatively affect cardiac function in mutation carriers. In a recent report including 716 DCM cases, excessive alcohol consumption resulted in an absolute LVEF reduction of ±9% in patients with a truncating TTN variant.¹⁸ These results support the hypothesis that cardiovascular risk factors and external toxic substances interplay with genetic frailty, (auto)immunity, or endocrinological disorders in the DCM phenotype.¹ Whether modification of cardiovascular risk factors provide opportunities for treatment remains to be investigated.

Differences in prognosis between FDCM and SDCM remain controversial with most follow-up studies of DCM not dividing populations in familial and non-familial categories.^{3, 28, 29} Conflicting results of prior studies may be due to the absence of reliable clinical or morphological parameters to differentiate between FDCM and SDCM.^{1, 3, 28, 29} Genotype–phenotype associations affecting prognosis in DCM have been reported: mutations in LMNA and RBM20 predispose for more/younger heart transplantations.⁶ Mutations in FLNC, LMNA, and PLN have been associated with sustained ventricular arrhythmias in DCM.³⁰ Nonetheless, our analysis did not yield a significant difference in outcome between FDCM and SDCM.

Genetic mutations in dilated cardiomyopathy

Mutations in over 40 genes are causally related to DCM and explain up to 61% of cases in FDCM and up to 25% in SDCM.^{1, 8, 14, 16, 31} Our data confirm the higher yield in FDCM.⁸ At present, genetic screening is advised in patients with family history of DCM or a personal history of atypical features such as conduction/rhythm-disturbances,^{7, 8, 11, 31} and yet less than 50% of all cases in the registry were tested. Moreover, the fact that heterogeneous screening strategies were used may have impacted on the yield of testing.²⁰ For instance, mutations in Titin (TTN) have been implicated in up to 22% of SDCM, but this gene was only tested in 19% of our patients with SDCM.^{32, 33} The selective nature of genetic testing may explain the high prevalence of mutations in genes coding for sarcomere genes and LMNA.

Genetic testing in Europe

We observed differences in genetic testing between European centres. Genetic testing was most prevalent in Northern, Southern, and Western Europe (around 20% of the cohort) and least common in Eastern Europe and North Africa. Importantly, Southern Europe included a majority (54%) of the patients with FDCM in this cohort, which could have led to sampling bias.^{34, 35} These shortcomings should be accounted for in terms of external validity of this study. Furthermore, the regional differences in our study as well as differences described by prior EORP studies provide opportunities to improve guideline adherence in Europe.^{20, 35, 36} Even though guideline adherence is highest in secondary and tertiary centres and higher for cardiologists than other specialities, care may be fragmented in highly complex diseases such as inherited cardiomyopathies.³⁷ This fragmentation can cause unwanted protocol deviations that may deteriorate quality of care. Several strategies have been suggested to improve guideline adherence. These strategies include common care pathways, ongoing education, and focus groups.³⁷ In terms of genetic testing, actively looking for familial manifestations of disease in patients and relative and acquiring a detailed family history are quintessential and may improve genetic yield dramatically.

Study limitations

In contrast to controlled clinical studies, our data are likely to be heterogeneous given the nature of the study design. As the structure of the registry requested to introduce patients from expert/tertiary centres, this may have caused a selection bias. Second limitation is that not all genes were tested in all patients, and centralized variant classification was not repeated in this study. Given that genetic testing was unknown in 110 patients, this might constitute another limitation. Even though myocarditis is formally an exclusion criterion, inflammation-caused/mediated reasons for DCM are very difficult to diagnose. Therefore, we cannot exclude with certainty that a proportion of non-familial DCM might be suspected for inflammatory diseases. As the EORP study was not designed for inter-regional comparisons, and these analyses were not planned, such comparisons may be arbitrary and unrepresentative. Furthermore, sub-analyses using ethnicity/race to provide insight into heterogeneity of populations are limited by poor accuracies of ethnicity/race in electronic health records. Importantly, even though we performed sensitivity analyses with and without patients with unknown classification, this still might have led to biased results. Lastly, the fact that no difference in outcome was detected in our DCM populations may be due to sample size and requires confirmation in larger studies.