DOI: 10.1093/ehjvshd/xwag048 ISSN: 2977-8565

The Influence of Genotype on Clinical Outcomes in Dilated Cardiomyopathy

Douglas E Cannie, Athanasios Bakalakos, Petros Syrris, Alexandros Protonotarios, Massimiliano Lorenzini, Oliver Guttmann, Constantinos O’Mahony, Konstantinos Savvatis, Neha Sekhri, Saidi A Mohiddin, Luis R Lopes, Perry M Elliott

Abstract

Background

The risk of malignant ventricular arrhythmia (MVA) and end-stage heart failure (ESHF) in dilated cardiomyopathy (DCM) may vary by genotype. Comparative analyses remain limited.

Aim

To compare clinical outcomes and risk predictors across genotypes.

Methods and results

Carriers of pathogenic variants in DCM-associated genes were identified from a dedicated database. Clinically affected variant-carriers from the five most prevalent genotypes were compared with genotype-negative patients. A composite primary endpoint consisted of MVA or ESHF. Secondary endpoints were MVA and ESHF individually. Incidence rates and baseline variables associated with outcomes were evaluated.

Among 484 patients, 57 (11.8%) met the primary endpoint after 5 years. Genotype-negative patients had the lowest primary endpoint incidence rates (1.4 [0.7–2.1] per 100 person-years), whereas LMNA had the highest (6.2 [3.0–9.5]). With a genotype-negative reference, incidence rate ratios were 1.3 for TTN, 1.7 for FLNC, 2.0 for DSP, 2.7 for RBM20 and 4.4 for LMNA. Rates were higher in LMNA and RBM20 than genotype-negative patients (p <0.001 and p = 0.003, respectively) and higher in LMNA than TTN (p = 0.001). Results were similar for MVA. LMNA had higher ESHF rates than genotype-negative, TTN and DSP.

Left ventricular diastolic diameter, LMNA variants and late gadolinium enhancement were independently associated with the primary endpoint. Baseline predictors varied by genotype.

Conclusion

Genotype stratification in DCM reveals a risk hierarchy with risk lowest in genotype-negative and TTN patients, two-fold higher in DSP and FLNC, and three- and four-fold higher in RBM20 and LMNA, respectively. Accounting for genotype improves risk prediction.

More from our Archive