Circulation: Genomic and Precision Medicine最新文献

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 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: Lp(a; Lipoprotein[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 people-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.

Background: Females with hypertrophic cardiomyopathy present at a more advanced stage of the disease and have a higher risk of heart failure and death. The factors behind these differences are unclear. We aimed to investigate sex-related differences in clinical and genetic factors affecting adverse outcomes in the Sarcomeric Human Cardiomyopathy Registry.

Methods: Cox proportional hazard models were fit with a sex interaction term to determine if significant sex differences existed in the association between risk factors and outcomes. Models were fit separately for females and males to find the sex-specific hazard ratio (HR).

Results: After a mean follow-up of 6.4 years, females had a higher risk of heart failure (HR, 1.51 [95% CI, 1.21-1.88]; P=0.0003) but a lower risk of atrial fibrillation (HR, 0.74 [95% CI, 0.59-0.93]; P<0.0001) and ventricular arrhythmia (HR, 0.60 [95% CI, 0.38-0.94]; P=0.027) than males. No sex difference was observed for death (P=0.84). Sarcomere-positive males had higher heart failure (HR, 1.34 [95% CI, 1.06-1.69]) and death risks (HR, 1.48 [95% CI, 1.08-2.04]) not seen in females (HR, 0.85 [95% CI, 0.66-1.08] versus HR, 0.86 [95% CI, 0.71-1.21]). MYBPC3 variants lowered heart failure risk in females (HR, 0.56 [95% CI, 0.41-0.77]) but not in males (HR, 1.29 [95% CI, 0.99-1.67]). A sex difference appeared in moderate (4% to <6%) versus low risk (<4%) European Society of Cardiology hypertrophic cardiomyopathy risk score, with females at moderate risk more prone to ventricular arrhythmia (HR, 3.57 [95% CI, 1.70-7.49]), unobserved in males (HR, 1.13 [95% CI, 0.63-2.03]).

Conclusions: We found that clinical and genetic factors contributing to adverse outcomes in hypertrophic cardiomyopathy affect females and males differently. Thus, research to inform sex-specific management of hypertrophic cardiomyopathy could improve outcomes for both sexes.

Background: While universal screening for Lp(a; lipoprotein[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 scores 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 scores 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 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 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] versus 0.69 [primary prevention] versus 0.80 [secondary prevention]). 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: Transcriptional dysregulation, possibly affected by genetic variation, contributes to disease development. Due to dissimilarities in development, function, and remodeling during disease progression, transcriptional differences between the left atrial (LA) and right atrial (RA) may provide insight into diseases such as atrial fibrillation.

Methods: Lateral differences in atrial transcription were evaluated in CATCH ME (Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly) using a 2-stage discovery and replication design. The design took advantage of the availability of 32 paired samples, for which both LA and RA tissue were obtained, as a discovery cohort, and 98 LA and 69 RA unpaired samples utilized as a replication cohort.

Results: A total of 714 transcripts were identified and replicated as differentially expressed (DE) between LA and RA, as well as 98 exons in 55 genes. Approximately 50% of DE transcripts were colocated with another frequently correlated DE transcript (P_FDR ≤0.05 for 579 regions). These transcription disequilibrium blocks contained examples including side-specific differential exon usage, such as the PITX2 locus, where ENPEP showed evidence of differential exon usage. Analysis of this region in conjunction with BMP10 identified rs9790621 as associated with ENPEP transcription in LA, while rs7687878 was associated with BMP10 expression in RA. In RA, BMP10 and ENPEP were strongly correlated in noncarriers, which was attenuated in risk-allele carriers, where BMP10 and PITX2 expression were strongly correlated.

Conclusions: These results significantly expand knowledge of the intricate, tissue-specific transcriptional landscape in human atria, including DE transcripts and side-specific isoform expression. Furthermore, they suggest the existence of blocks of transcription disequilibrium influenced by genetics.