Braunwald: ‘Striking’ advances made in cardiometabolic care
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In recent years, cardiologists, endocrinologists and primary care physicians have observed marked advances in the use of genetics, artificial intelligence and SGLT2 inhibitors in cardiometabolic care, according to a keynote speech.
During his keynote discussion, Cardiology Today Editorial Board Member Eugene Braunwald, MD, Distinguished Hersey Professor of Medicine at Harvard Medical School and founding chairman of the TIMI Study Group at Brigham and Women’s Hospital, discussed these recent advances and how they may apply to clinical practice in the future.
Braunwald highlighted research on the intersection of genetics and cardiometabolic health.
According to the presentation, among individuals who experience early-onset MI, approximately 2% will have monogenic familial hypercholesterolemia, which increases their risk 3.8-fold. Nearly 17% will have a high genome-wide polygenic risk score and experience similarly elevated risk.
This finding could increase the population of patients who would benefit from primary prevention, Braunwald said.
In addition, an analysis of participants from the FOURIER trial published in Circulation found that among patients with high genetic risk, treatment with evolocumab (Repatha, Amgen) mitigated the excess risk and reduced CV event rate to that of individuals with low genetic risk.
Healio previously reported on the main results of FOURIER at the American College of Cardiology Scientific Session in 2017.
“This begins to show how genetics and genetic risk scores can be used to help with therapy,” Braunwald said during the presentation. “These are expensive drugs and you have to know what the genetic risk score is to decide whether to go full blast, like you would have in the [high genetic risk group], or would you consider other options in the low and intermediate groups. It has not been settled, but I'm using this as an example of how polygenic risk scores can be used.”
Braunwald also discussed three studies that assessed the utility of AI in atrial fibrillation, ventricular dysfunction and HF with preserved ejection fraction.
Researchers developed an AI-enabled ECG algorithm for the identification of patients with AF during sinus rhythm and published their findings in The Lancet.
“There is a very strong group at Mayo Clinic who have been working on this,” Braunwald said during the presentation. “They were able to analyze on an ECG strip in sinus rhythm that this is a patient who has had atrial fibrillation in the past, and that obviously makes them at much higher risk of developing atrial fibrillation in the future and makes a very good screening test, it is very inexpensive and it alerts you.”
The same group at Mayo Clinic also evaluated AI-enabled ECG for the screening of cardiac contractile dysfunction.
According to the presentation, researchers found that patients who were positively identified by the AI algorithm were at nearly four times greater risk for developing future ventricular dysfunction.
Braunwald also reviewed research published in the European Journal of Heart Failure, where investigators used machine learning-based unsupervised cluster analysis to evaluate 61 phenotypic variables and identify three phenogroups of patients with HFpEF.
According to the presentation, the algorithm identified the groups as such:
- Phenogroup 1: higher burden of comorbidities, natriuretic peptides and abnormalities in left ventricular structure and function;
- Phenogroup 2: lower prevalence of comorbidities but higher burden of diastolic dysfunction; and
- Phenogroup 3: lower natriuretic peptide levels and favorable diastolic function profile.
Researcher observed that participants in phenogroup 1 experienced the greatest prevalence of major adverse CV events and HF hospitalization, and phenogroup 3 the lowest prevalence.
Braunwald also reviewed the benefits of SGLT2 inhibitors in patients with HF, with and without diabetes. In particular, he underlined the findings from the DAPA-CKD trial presented at the virtual ESC Congress, which Healio previously reported during the meeting in August.
Investigators found that treatment with dapagliflozin (Farxiga, AstraZeneca) reduced renal failure, CV death, HF hospitalization and improved survival among patients with chronic kidney disease. These findings were consistent whether diabetes was present or not.
“The results were quite striking. The primary outcome of sustained 50% reduction of [glomerular filtration rate], end-stage kidney disease and renal or CV death showed a 39% reduction,” Braunwald said during the presentation. “All-cause mortality, amazingly, showed a 31% reduction.”
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Braunwald E. Evolving paradigms in cardiovascular research and care: Reflections and perspectives. Presented at: Cardiometabolic Health Congress; Oct. 21-24, 2020 (virtual meeting).
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