Circulation: Genomic and Precision Medicine最新文献

Background: In the Myocardial Infarction Genes clinical trial (URL: https://www.clinicaltrials.gov; Unique identifier: NCT01936675), participants at intermediate risk of coronary heart disease (CHD) were randomized to receive a Framingham risk score (Framingham risk score group, n=103) or an integrated risk score (integrated risk score group [IRS_g], n=104) that additionally included a polygenic risk score. After 6 months, IRS_g participants had higher statin initiation and lower low-density lipoprotein cholesterol. We conducted a post hoc 10-year follow-up analysis to investigate whether disclosure of a polygenic risk score for CHD was associated with a reduction in major adverse cardiovascular events (MACE).

Methods: Participants were followed from randomization in October 2013 to September 2023 to ascertain MACE, testing for CHD, and changes in risk factors. The primary outcome was time to first MACE, defined as cardiovascular death, nonfatal myocardial infarction, coronary revascularization, and nonfatal stroke. Statistical analyses included Cox proportional hazards regression and linear mixed-effects models.

Results: We followed all participants who completed the trial, 100 in Framingham risk score group and 103 in IRS_g (mean age at the end of follow-up, 68.2±5.2; 48% male). During a median follow-up of 9.5 years, 9 MACEs occurred in Framingham risk score group and 2 in IRS_g (hazard ratio, 0.20 [95% CI, 0.04-0.94]; P=0.042). In Framingham risk score group, 47 (47%) underwent at least 1 diagnostic test for CHD, compared with 30 (29%) in IRS_g (hazard ratio, 0.51 [95% CI, 0.32-0.81]; P=0.004). A higher proportion of IRS_g participants were on statin therapy during the first 4 years postrandomization and had a greater reduction in low-density lipoprotein cholesterol for up to 3 years postrandomization. No significant differences were observed between 2 groups in other traditional cardiovascular risk factors during follow-up.

Conclusions: Disclosure of an integrated risk score that included a polygenic risk score to individuals at intermediate risk for CHD was associated with lower MACE incidence after 10 years, likely due to higher statin initiation, leading to lower low-density lipoprotein cholesterol levels.

Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of cardiovascular disease due to both the disease and its treatments. The rising prevalence of cancer and the availability of multiple new therapeutic options has resulted in improved survival among patients with cancer and has expanded the scope of cardio-oncology to not only short-term but also long-term cardiovascular risks resulting from both cancer and its treatments. However, there is considerable heterogeneity in cardiovascular risk, driven by the nature of the malignancy as well as each individual's unique characteristics. The use of novel therapies, such as targeted therapies and immune checkpoint inhibitors, across multiple cancer groups has also broadened the populations among which cardiotoxicity has become an important consideration of therapy. Therefore, the ability to understand and personalize cardiovascular risk management in patients with cancer is a key target for artificial intelligence, which can deduce and respond to complex patterns within the data. These advances necessitate an overview of established biomarkers of risk, spanning advanced imaging, diagnostic testing, and multi-omics, the evidence supporting their use, and the proven and proposed role of artificial intelligence in refining this risk to attain greater precision in risk prediction and management in cardio-oncologic care.

Background: It remains challenging to determine which hypertrophic cardiomyopathy (HCM) family members will subsequently develop HCM. Standard 2-dimensional and conventional Doppler echocardiography have been unable to reliably distinguish HCM genotype-positive and phenotype-negative (G+P-) from genotype-negative and phenotype-negative (G-P-) family members. We aimed to determine if advanced echocardiographic modalities can discriminate HCM G+P- from G-P- individuals.

Methods: Comprehensive echocardiography including speckle tracking evaluation of myocardial deformation and color M-mode were performed in 199 participants aged ≥16 years who had undergone genetic testing from families with a known HCM pathogenic variant: 58 G+P-, 39 G-P-, and 102 overt patients with HCM (genotype-positive and phenotype-positive). The primary analysis compared these measures in all G+P- and G-P- individuals. A secondary analysis was undertaken in younger subjects (age ≤40 years).

Results: Comparing G+P- and G-P- individuals, there were no significant differences in left ventricular ejection fraction, cavity size, wall thickness and outflow tract gradient, and tissue Doppler-derived myocardial velocities; however, septal/posterior wall thickness ratio was higher (1.06±0.09 versus 1.02±0.04, P=0.007). G+P- individuals had significantly lower color M-mode flow propagation velocity (color M-mode velocity propagation, 42.6 cm/s [interquartile range, 34.5-48.5 cm/s] versus 51.0 cm/s [interquartile range, 45.2-61.0 cm/s]; P<0.001) and higher global longitudinal strain (P=0.021), circumferential strain (P=0.003), and peak apical rotation (P=0.005). Multivariable logistic regression identified 2 independent predictors (color M-mode velocity propagation and peak apical rotation). A derived regression equation allowed reasonable discrimination of G+P- individuals with a sensitivity of 82.6% and specificity of 72.2% (P<0.0001) at the optimal cutoff. Similar findings were demonstrated when the analysis was restricted to younger subjects, although in addition to color M-mode velocity propagation and apical rotation, left ventricular ejection fraction was also independently predictive.

Conclusions: In HCM family members, color M-mode velocity propagation and apical rotation provide good sensitivity and specificity for identifying mutation carriers and may represent early disease markers before the onset of hypertrophy. Longitudinal studies involving larger cohorts are required to validate these findings.

Background: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.

Methods: A longitudinal study was performed using data from 2 cohort studies: the FOS (Framingham Offspring Study) with 3588 participants aged 19 to 57 years and the UKB (UK Biobank) with 327 837 participants aged 40 years to 70 years. A total of 134 765 and 3 831 734 person-time years were observed in FOS and UKB, respectively. Hazard ratios for CAD were calculated for polygenic risk score (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and pooled cohort equations in predicting CAD events was also evaluated by age groups.

Results: The importance of CAD PRS diminished over the life course, with a hazard ratio of 3.58 (95% CI, 1.39-9.19) at the age of 19 years in FOS and a hazard ratio of 1.51 (95% CI, 1.48-1.54) by the age of 70 years in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed pooled cohort equations in identifying subsequent CAD events in the 40- to 45-year age group, with 3.2-fold more appropriately identified events. Overall, adding PRS improved the area under the receiving operating curve of the pooled cohort equations by an average of +5.1% (95% CI, 4.9%-5.2%) across all age groups; among individuals <55 years, PRS augmented the area under the receiver operater curve (ROC) of the pooled cohort equations by 6.5% (95% CI, 5.5%-7.5%; P<0.001).

Conclusions: Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies. All results are available at https://surbut.github.io/dynamicHRpaper/index.html.

Background: While universal screening for Lipoprotein(a) [Lp(a)] is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.

Methods: We randomly sampled 100 000 patients from the Yale-New Haven Health System to evaluate the feasibility of ARISE deployment. We also evaluated Lp(a)-tested populations in the Yale-New Haven Health System (n=7981) and the Vanderbilt University Medical Center (n=10 635) to assess the association of ARISE score with elevated Lp(a). To compare the representativeness of the Lp(a)-tested population, we included 456 815 participants from the UK Biobank and 23 280 from 3 US-based cohorts of Atherosclerosis Risk in Communities, Coronary Artery Risk Development in Young Adults, and Multi-Ethnic Study of Atherosclerosis.

Results: Among 100 000 randomly selected Yale-New Haven Health System patients, 413 (0.4%) had undergone Lp(a) measurement. ARISE score could be computed for 31 586 patients based on existing data, identifying 2376 (7.5%) patients with a high probability of elevated Lp(a). A positive ARISE score was associated with significantly higher odds of elevated Lp(a) in the Yale-New Haven Health System (odds ratio, 1.87 [95% CI, 1.65-2.12]) and the Vanderbilt University Medical Center (odds ratio, 1.41 [95% CI, 1.24-1.60]). The Lp(a)-tested population significantly differed from other study cohorts in terms of ARISE features.

Conclusions: We demonstrate the feasibility of deployment of ARISE in US health systems to define the risk of elevated Lp(a), enabling a high-yield testing strategy. We also confirm the markedly low adoption of Lp(a) testing, which is also being restricted to a highly selected population.

Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.

Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System. We included patients with an Lp(a) measured between 1989 and 2022 and who had at least 1 year of electronic health record data before measurement of an Lp(a) level. The end point of interest was time to first myocardial infarction, stroke/TIA, or coronary revascularization. A random survival forest model was derived and compared with a Cox proportional hazards model derived from traditional cardiovascular risk factors (ie, the variables used to estimate the Pooled Cohort Equations for the primary prevention population and the variables used to estimate the Second Manifestations of Arterial Disease and Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention scores for the secondary prevention population). Model discrimination was evaluated using Harrell's C-index.

Results: A total of 4369 patients were included in the study (49.5% were female, mean age was 51 [SD 18] years, and mean Lp(a) level was 33.6 [38.6] mg/dL, of whom 23.7% had a prior cardiovascular event). The random survival forest model outperformed the traditional risk factor models in the test set (c-index, 0.82 [random forest model] versus 0.69 [primary prevention model] versus 0.80 [secondary prevention model]). These results were similar when restricted to a primary prevention population and under various strategies to handle competing risk. A Cox proportional hazard model based on the top 25 variables from the random forest model had a c-index of 0.80.

Conclusions: A random survival forest model outperformed a model using traditional risk factors for predicting cardiovascular events in patients with a measured Lp(a) level.

Background: Lipoprotein(a) [Lp(a)] is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).

Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies. Net reclassification improvement was determined among borderline-intermediate risk patients.

Results: We included 5902 patients aged ≥18 years (mean age 48.7±16.7 years, 51.2% women, and 7.7% Black). Our EHR model included Lp(a), age, sex, Black race/ethnicity, systolic blood pressure, total and high-density lipoprotein cholesterol, diabetes, smoking, and hypertension medication. Over a mean follow-up of 6.8 years, ASCVD event rates (per 1000 person-years) ranged from 8.7 to 16.7 across Lp(a) groups. A 25 mg/dL increment in Lp(a) was associated with an adjusted hazard ratio of 1.23 (95% CI, 1.10-1.37) for composite ASCVD. Those with Lp(a) ≥75 mg/dL had an 88% higher risk of ASCVD (hazard ratio, 1.88 [95% CI, 1.30-2.70]) and more than double the risk of incident stroke (hazard ratio, 2.55 [95% CI, 1.54-4.23]). C-statistics for our EHR and EHR+Lp(a) models in our EHR training data set were 0.7475 and 0.7556, respectively, with external validation in our pooled cohort (n=21 864) of 0.7350 and 0.7368, respectively. Among those at borderline/intermediate risk, the net reclassification improvement was 21.3%.

Conclusions: We show the feasibility of developing an improved ASCVD risk prediction model incorporating Lp(a) based on a real-world adult clinic population. The inclusion of Lp(a) in ASCVD prediction models can reclassify risk in patients who may benefit from more intensified ASCVD prevention efforts.

Background: Protein-truncating mutations in the titin gene are associated with increased risk of atrial fibrillation. However, little is known about the underlying pathophysiology.

Methods: We identified a heterozygous titin truncating variant (TTNtv) in a patient with unexplained early onset atrial fibrillation and normal ventricular function. We generated patient-specific atrial- and ventricular-like induced pluripotent stem cell-derived cardiomyocytes and engineered heart tissue to evaluate the impact of the TTNtv on electrophysiology, sarcomere structure, contractility, and gene expression.

Results: We demonstrate that the TTNtv increases susceptibility to pacing-induced arrhythmia, promotes sarcomere disorganization, and reduces contractile force in atrial induced pluripotent stem cell-derived cardiomyocytes compared with their CRISPR/Cas9-corrected isogenic controls. In ventricular induced pluripotent stem cell-derived cardiomyocytes, this variant was associated with abnormal electrophysiology and sarcomere organization without a reduction in contractile force compared with their isogenic controls. RNA-sequencing revealed an upregulation of cell adhesion and extracellular matrix genes in the presence of the TTNtv for both atrial and ventricular engineered heart tissues.

Conclusions: In a patient with unexplained atrial fibrillation, induced pluripotent stem cell-derived cardiomyocytes with a TTNtv showed structural and electrophysiological abnormalities in both atrial and ventricular models, while only atrial engineered heart tissues demonstrated reduced contractility. The observed chamber-specific effect suggests that structural disorganization and reduced contractile function may be associated with atrial myopathy in the presence of truncated titin.