Agnieszka Mickiewicz1, Marta Futema2, Agnieszka Ćwiklinska3, Agnieszka Kuchta3, Maciej Jankowski3, Mariusz Kaszubowski4, Magdalena Chmara5, Bartosz Wasąg5, Marcin Fijałkowski1, Miłosz Jaguszewski1, Steve E Humphries6, Marcin Gruchała1. 1. Department of Cardiology I, Medical University of Gdansk, Dębinki 7, 80-211 Gdańsk, Poland. 2. Centre for Heart Muscle Disease, Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK. 3. Department of Clinical Chemistry, Medical University of Gdansk, Dębinki 7, 80-211 Gdańsk, Poland. 4. Institute of Statistics, Department of Economic Sciences, Faculty of Management and Economics, Gdansk University of Technology, 80-233 Gdańsk, Poland. 5. Department of Biology and Genetics, Medical University of Gdansk, Dębinki 1, 80-211 Gdańsk, Poland. 6. Centre for Cardiovascular Genetics, British Heart Foundation Laboratories, Institute of Cardiovascular Science, the Rayne Building University College London, London WC1E 6JF, UK.
Abstract
Background: The monogenic defect in familial hypercholesterolemia (FH) is detected in ∼40% of cases. The majority of mutation-negative patients have a polygenic cause of high LDL-cholesterol (LDL-C). We sought to investigate whether the underlying monogenic or polygenic defect is associated with the response to rosuvastatin. METHODS: FH Individuals were tested for mutations in LDLR and APOB genes. A previously established LDL-C-specific polygenic risk score (PRS) was used to examine the possibility of polygenic hypercholesterolemia in mutation-negative patients. All of the patients received rosuvastatin and they were followed for 8 ± 2 months. A propensity score analysis was performed to evaluate the variables associated with the response to treatment. RESULTS: Monogenic subjects had higher mean (±SD) baseline LDL-C when compared to polygenic (7.6 ± 1.5 mmol/L vs. 6.2 ± 1.2 mmol/L; p < 0.001). Adjusted model showed a lower percentage of change in LDL-C after rosuvastatin treatment in monogenic patients vs. polygenic subjects (45.9% vs. 55.4%, p < 0.001). The probability of achieving LDL-C targets in monogenic FH was lower than in polygenic subjects (0.075 vs. 0.245, p = 0.004). Polygenic patients were more likely to achieve LDL-C goals, as compared to those monogenic (OR 3.28; 95% CI: 1.23-8.72). CONCLUSION: Our findings indicate an essentially higher responsiveness to rosuvastatin in FH patients with a polygenic cause, as compared to those carrying monogenic mutations.
Background: The monogenic defect in familial hypercholesterolemia (FH) is detected in ∼40% of cases. The majority of mutation-negative patients have a polygenic cause of high LDL-cholesterol (LDL-C). We sought to investigate whether the underlying monogenic or polygenic defect is associated with the response to rosuvastatin. METHODS:FH Individuals were tested for mutations in LDLR and APOB genes. A previously established LDL-C-specific polygenic risk score (PRS) was used to examine the possibility of polygenic hypercholesterolemia in mutation-negative patients. All of the patients received rosuvastatin and they were followed for 8 ± 2 months. A propensity score analysis was performed to evaluate the variables associated with the response to treatment. RESULTS: Monogenic subjects had higher mean (±SD) baseline LDL-C when compared to polygenic (7.6 ± 1.5 mmol/L vs. 6.2 ± 1.2 mmol/L; p < 0.001). Adjusted model showed a lower percentage of change in LDL-C after rosuvastatin treatment in monogenic patients vs. polygenic subjects (45.9% vs. 55.4%, p < 0.001). The probability of achieving LDL-C targets in monogenic FH was lower than in polygenic subjects (0.075 vs. 0.245, p = 0.004). Polygenic patients were more likely to achieve LDL-C goals, as compared to those monogenic (OR 3.28; 95% CI: 1.23-8.72). CONCLUSION: Our findings indicate an essentially higher responsiveness to rosuvastatin in FHpatients with a polygenic cause, as compared to those carrying monogenic mutations.
Authors: Filip M Szymański; Agnieszka Mickiewicz; Grzegorz Dzida; Iwona Gorczyca-Głowacka; Dariusz Kozłowski; Krystyna Widecka; Zbigniew Krasiński; Adam Kobayashi; Dagmara Hering; Katarzyna Mizia-Stec; Jarosław D Kasprzak; Tomasz Zubilewicz; Krzysztof Narkiewicz; Marek Koziński; Anna E Płatek; Anna Ryś-Czaporowska; Beata Chełstowska; Stefan Grajek; Marcin Wełnicki; Artur Mamcarz; Marcin Barylski; Beata Wożakowska-Kapłon; Miłosz J Jaguszewski; Marcin Gruchała; Krzysztof J Filipiak Journal: Cardiol J Date: 2021-11-23 Impact factor: 2.737