Clinical Guideline for Preimplantation Genetic Testing in Inherited Cardiac Diseases

BACKGROUND

Preimplantation genetic testing (PGT) is a reproductive technology that selects embryos without (familial) genetic variants. PGT has been applied in inherited cardiac disease and is included in the latest American Heart Association/American College of Cardiology guidelines. However, guidelines selecting eligible couples who will have the strongest risk reduction most from PGT are lacking. We developed an objective decision model to select eligibility for PGT and compared its results with those from a multidisciplinary team.

METHODS

All couples with an inherited cardiac disease referred to the national PGT center were included. A multidisciplinary team approved or rejected the indication based on clinical and genetic information. We developed a decision model based on published risk prediction models and literature, to evaluate the severity of the cardiac phenotype and the penetrance of the familial variant in referred patients. The outcomes of the model and the multidisciplinary team were compared in a blinded fashion.

RESULTS

Eighty-three couples were referred for PGT (1997-2022), comprising 19 different genes for 8 different inherited cardiac diseases (cardiomyopathies and arrhythmias). Using our model and proposed cutoff values, a definitive decision was reached for 76 (92%) couples, aligning with 95% of the multidisciplinary team decisions. In a prospective cohort of 11 couples, we showed the clinical applicability of the model to select couples most eligible for PGT.

CONCLUSIONS

The number of PGT requests for inherited cardiac diseases increases rapidly, without the availability of specific guidelines. We propose a 2-step decision model that helps select couples with the highest risk reduction for cardiac disease in their offspring after PGT.

Clustering of Cardiac Transcriptome Profiles Reveals Unique

Dilated cardiomyopathy is a heterogeneous disease characterized by multiple genetic and environmental etiologies. The majority of patients are treated the same despite these differences. The cardiac transcriptome provides information on the patient's pathophysiology, which allows targeted therapy. Using clustering techniques on data from the genotype, phenotype, and cardiac transcriptome of patients with early- and end-stage dilated cardiomyopathy, more homogeneous patient subgroups are identified based on shared underlying pathophysiology. Distinct patient subgroups are identified based on differences in protein quality control, cardiac metabolism, cardiomyocyte function, and inflammatory pathways. The identified pathways have the potential to guide future treatment and individualize patient care.

The HFA-PEFF score identifies 'early-HFpEF' phenogroups associated with distinct biomarker profiles

AIMS

The HFA-PEFF score was developed to optimize diagnosis and to aid in early recognition of heart failure (HF) with preserved ejection fraction (HFpEF) in patients who present with HF-like symptoms. Recognizing early-HFpEF phenogroups is essential to better understand progression towards overt HFpEF and pave the way for early intervention and treatment. Whether the HFA-PEFF domain scores can identify 'early-HFpEF' phenogroups remains unknown. The aims of this pilot study are to (i) identify distinct phenogroups by cluster analysis of HFA-PEFF domain scores in subjects that present with HF-like symptoms and (ii) study whether these phenogroups may be associated with distinct blood proteome profiles.

METHODS AND RESULTS

Subjects referred to the Cardiology Centers of the Netherlands, location Utrecht, with non-acute possibly cardiac-related symptoms (such as dyspnoea or fatigue) were prospectively enrolled in the HELPFul cohort (N = 507) and were included in the current analysis. Inclusion criteria for this study were (i) age ≥ 45 years and (ii) a left ventricular ejection fraction (LVEF) ≥ 50%, in the absence of a history of HF, coronary artery disease, congenital heart disease, or any previous cardiac interventions. Multinominal-based clustering with latent class model using the HFA-PEFF domain scores (functional, structural, and biomarker scores) as input was used to detect distinct phenotypic clusters. For each bootstrapping run, the 92 Olink proteins were analysed for their association with the identified phenogroups. Four distinct phenogroups were identified in the current analysis (validated by bootstrapping 1000×): (i) no left ventricular diastolic dysfunction (no LVDD, N = 102); (ii) LVDD with functional left ventricular (LV) abnormalities (N = 204); (iii) LVDD with functional and structural LV abnormalities (N = 204); and (iv) LVDD with functional and structural LV abnormalities and elevated BNP (N = 107). The HFA-PEFF total score risk categories significantly differed between the phenogroups (P < 0.001), with an increase of the HFA-PEFF score from Phenogroup 1 to 4 (low/intermediate/high HFA-PEFF risk score: Phenogroup 1: 88%/12%/0%; Phenogroup 2: 9%/91%/0%; Phenogroup 3: 0%/92%/8%; Phenogroup 4: 5%/83%/12%). Thirty-two out of the 92 Olink protein biomarkers significantly differed among the phenogroups. The top eight biomarkers-N-terminal prohormone brain natriuretic peptide, growth differentiation factor-15, matrix metalloproteinase-2, osteoprotegerin, tissue inhibitor of metalloproteinase-4, chitinase-3-like protein 1, insulin-like growth factor-binding protein 2, and insulin-like growth factor-binding protein 7-are mainly involved in inflammation and extracellular matrix remodelling, which are currently proposed key processes in HFpEF pathophysiology.

CONCLUSIONS

This study identified distinct phenogroups by using the HFA-PEFF domain scores in ambulant subjects referred for HF-like symptoms. The newly identified phenogroups accompanied by their circulating biomarkers profile might aid in a better understanding of the pathophysiological processes involved during the early stages of the HFpEF syndrome.

84 Left atrial reverse remodelling in dilated cardiomyopathy

Aims

Left atrial (LA) dilation is associated a with worse prognosis in several cardiovascular settings but therapies can promote LA reverse remodelling. Characterizing and defining the prognostic implications of LA volume (LAVI) reduction in dilated cardiomyopathy (DCM).

Methods and results

Consecutive DCM patients from two tertiary care centres, with available echocardiography at baseline and at 1 year follow-up, were analysed. LA dilation was defined as LAVI &gt;34 ml/m2, Delta (Δ)LAVI was defined as the 1 year relative LAVI reduction. The outcome was a composite of death/heart transplantation/heart failure hospitalization (D/HTx/HFH). Five hundred sixty patients were included [age 52 ± 13 years; left ventricular ejection fraction (LVEF) 31 ± 10%, LAVI 45 ± 18 ml/m2]. Baseline LAVI had a non-linear association with the risk of D/HTx/HFH, independently from LVEF (P &lt; 0.001). At 1 year follow-up, LAVI decreased in 374 patients (67%, median ΔLAVI 24%, interquartile range 37% 11%). Factors independently associated with ΔLAVI were higher baseline LAVI and lower baseline LVEF. After adjustment for confounders, ΔLAVI showed a linear association with the risk of D/HTx/HHF (HR: 0.92, 95% CI: 0.86 0.99 per 5% decrease, P &lt; 0.001). At 1 year Follow-up, patients with a ≥ 15% reduction in ΔLAVI or LAVI normalization (i.e. Follow-up LAVI ≤34ml/m2) (42% of the cohort) were at lower risk of D/HTx/HFH (HR: 0.49, 95% CI: 0.33 0.74, P &lt; 0.001).

Conclusions

In a large cohort of DCM, 1 year reduction in LAVI is observed in the majority of patients. The association between reduction in LAVI and D/HTx/HHF candidates LA reverse remodelling as complementary early therapeutic goal in DCM.