Naderian, Mohammadreza

Abstract Background Contemporary prevalence and penetrance of familial hypercholesterolemia (FH) and the associated coronary heart disease (CHD) risk across diverse genetic ancestries in the United States are unclear. Purpose To estimate prevalence of individuals harboring pathogenic/likely pathogenic (P/LP) variants in LDLR, APOB, and PCSK9, distribution of low-density-lipoprotein cholesterol (LDL-C) levels, and associated CHD risk for carriers in an ancestrally diverse cohort. Methods In 242,167 All of Us (AoU) participants with available whole-genome sequencing data, we obtained LDL-C measurements, statin prescriptions, and ascertained P/LP variants in FH-related genes and presence of CHD based on electronic-health record data. P/LP variants in LDLR, APOB, and PCSK9 were identified in AoU data by filtering for high-impact (frameshift, canonical splice site, indels, and stop gain) and predicted-deleterious missense variants, then selecting those classified as P/LP in ClinVar. Untreated LDL-C levels were estimated assuming a 30% reduction if there was a record of a statin prescription in the preceding 12 months and the highest value among multiple measurements for each individual were selected. Penetrance was defined as LDL-C>155 mg/dl. CHD was determined using a validated phenotyping algorithm based on diagnostic and procedural billing codes. Logistic regression models were used to obtain prevalence and penetrance estimates and odds ratios for CHD, along with associated 95% confidence intervals (95% CI). Results We identified 221 FH-related P/LP variants (LDLR=169, APOB=50, PCSK9=2) in the study cohort. Of 125,841 European (EUR), 53,367 African (AFR), 40,189 admixed American (AMR), and 22,770 other ancestry individuals, 1:218 (0.46%, 95% CI 0.42%-0.50%), 1:307 (0.33%,0.28%-0.38%), 1:350 (0.29%, 0.24%-0.34%), and 1:201 (0.50%, 0.41%-0.59%) were carriers of a P/LP variant. In 92,616 individuals with at least one LDL-C measurement, carriers of a P/LP variant had a higher distribution of LDL-C across all ancestries with similarly high penetrance (Figure 1). Harboring a P/LP variant in LDLR, APOB, and PCSK9 was associated with similarly increased odds of CHD across all the different ancestry groups (Figure 2). Conclusions Monogenic FH was prevalent, penetrant, and significantly associated with CHD similarly across diverse ancestry groups in the AoU cohort. These findings support a ‘genotype first’ approach to detection of FH to enable early intervention across diverse ancestry groups.Figure 1 Figure 2

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), familial hypercholesterolemia (FH), and family history (FamHx)- on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults -eMERGE IV (eIV) and All of Us (AoU). Methods: CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results: We analyzed data for 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.

Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics—recall, area under the curve (AUC), and accuracy—were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 ± 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74–0.82), accuracy = 0.81 (0.69–0.89), and recall = 0.90 (0.70–0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68–0.82), accuracy = 0.73 (0.59–0.84), and recall = 0.87 (0.50–0.98)); valve-related dysfunction (AUC = 0.73 (0.62–0.84), accuracy = 0.79 (0.57–0.91), and recall = 0.54 (0.26–0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67–0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets.

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.

Introduction: The MI-GENES randomized clinical trial (NCT01936675) assessed the effect of disclosing a polygenic risk score (PRS) for coronary heart disease (CHD), in addition to a clinical risk based on Framingham risk score (FRS), on LDL-C levels. The trial enrolled participants from Olmsted County, Minnesota, without cardiovascular disease, at intermediate CHD risk (10-y risk: 5-20%), and not on statins. There was a significant LDL-C reduction in the integrated risk score group (IRS g ; received PRS information in addition to the FRS) compared to the FRS group (FRS g ; received their risk based on FRS), due to more frequent statin initiation. Research Question: Does disclosure of an IRS for CHD lead to a lower rate of major adverse cardiovascular events (MACE)? Methods: Participants were followed from randomization beginning in October 2013 until September 2023 to ascertain cardiovascular events, testing for CHD, and risk factor changes, by blinded review of electronic health records. The primary outcome was time from randomization to the first MACE— defined as cardiovascular death, nonfatal MI, coronary revascularization, and nonfatal stroke. Analyses were done using Cox and linear mixed-effects models. Results: We followed all 203 participants, who completed the MI-GENES trial, 100 in FRS g and 103 in IRS g (mean age at the end of follow-up 68.2±5.2 years, 48% male). During a median follow-up of 9.5 years, 9 MACE occurred in FRS g and 2 in IRS g (HR, 0.20; 95% CI, 0.04 to 0.94; P =0.042, Figure 1A). In FRS g , 47 (47%) underwent at least one test for CHD, compared to 30 (29%) in IRS g (HR, 0.51; 95% CI, 0.32 to 0.81; P =0.004, Figure 1B). IRS g participants had a longer duration of statin therapy during the first four years post-randomization (Figure 1C) and a greater reduction in LDL-C for up to 3 years post-randomization (Figure 1D). No significant differences between the two groups were observed for hemoglobin A1C, blood pressure, weight, and smoking cessation rate during follow-up. Conclusion: The disclosure of 10-year CHD risk that included a PRS to those at intermediate risk was associated with lower incidence of MACE after a decade, likely due to more frequent and prolonged statin use, leading to lower LDL-C levels.

Background: Coronary heart disease (CHD) results from environmental and genetic factors. Lifestyle-psychosocial factors that capture environmental risk and polygenic risk scores (PRS) that capture genetic risk have been associated with CHD in prior studies. Aim: To assess whether higher CHD risk traditionally attributed to non-White race/ethnicty is mediated by PSS CHD or PRS CHD . Methods: In UK Biobank, we ascertained incident CHD (MI/coronary revascularization) and calculated an ancestry-specific PRS for CHD (PGS000018). Based on self-reported race/ethnicity, participants were classified as White and non-White. An age, sex, and ethnicity-adjusted polysocial score for CHD (PSS CHD ) was derived from 100 self-reported lifestyle-psychosocial factors across nine domains: Adulthood Relationship, Education, Entertainment, Housing, Income, Occupation, Physical Activity, Psychological Health, and Sleep. Those with CHD or statin use at recruitment or missing age, sex, race/ethnicity or genotype were excluded. Machine learning (elastic net model) was used to develop PSS CHD . Cox and mediation analyses were performed. Results: In 398,403 participants (56±8 y, 43% male, 96.3% White, 1.8% Asian, 1.6% Black, and 0.3% Chinese), 10,033 had an incident CHD event during a 10-year follow-up. The HRs (95% CIs) for a 1 SD increase in PSS CHD and PRS CHD for CHD risk were 1.5 (1.4–1.5, P <10 -16 ), and 1.6 (1.5 - 1.6, P <10 -16 ), respectively (Figure 1A-B). Asians and Blacks had the highest PSS CHD (mean±SD: 0.5±1, and 0.5±1, respectively), followed by Chinese (0.1±0.9), and Whites (0.0±1) (Figure 1C). After adjustment for age, sex, diabetes, hypertension, LDL-C, and smoking, non-Whites had higher CHD risk: HR (95% CI): 1.2 (1.0–1.4, P <10 -16 ), and the higher risk was mediated more substantially by PSS CHD than PRS CHD (63% (38%-100%) vs. 16% (8%-41%), both P <10 -16 ). Conclusions: The higher CHD risk observed in non-Whites in UK Biobank was mainly due to unfavorable lifestyle-psychosocial factors, rather than PRS, consistent with the paradigm that race/ethnicity is not a biological but rather a social construct. Our findings support substituting race/ethnicity with lifestyle-psychosocial factors and social determinants of health in CHD risk equations.

Introduction: The overall amount and the distribution of body fat, proxied by body-mass index (BMI) and waist-to-hip ratio (WHR), are two important measures of obesity and have significant polygenic heritability. We aimed to investigate whether polygenic scores (PGS) for these traits have additional predictive utility, beyond phenotypic characterization of obesity, for cardiovascular-kidney-metabolic (CKM) health. Methods: We derived PGS for BMI (PGS BMI ) and WHR adjusted for BMI (PGS WHRadjBMI ) using PRS-CS-auto on summary statistics from ~700k individuals of European ancestry (EA) in the GIANT consortium and selecting ~1M common variants for each score. We conducted association analyses in the Atherosclerosis Risk in Communities study, a prospective cohort including 8,631 EA individuals with ascertainment of incident diabetes, hypertension, chronic kidney disease, atrial fibrillation, coronary heart disease, ischemic stroke, and heart failure. We used nested Cox models to measure the added predictive utility of PGS to BMI and WHR for incident CKM phenotypes. Results: The study cohort (age 54.4±5.7 years, 52.4% women, follow-up 24.5±7.6 years) had a baseline BMI of 26.4 kg/m 2 (median [IQR]; 21.6-29.6) and WHR of 0.94 (0.88-0.98). PGS BMI and PGS WHRadjBMI were associated with their phenotypes (partial R 2 ; BMI 0.11 and WHR 0.04, p <0.001), although with a large phenotypic variability across the distribution of PGS (IQR for 1 st and 10 th deciles of PGS; BMI 19.6-28.3 and 22.6-35.9, WHR 0.79-1.03 and 0.87-1.05). Anthropometric measures of obesity were associated with a wider gradient of risk compared to PGS for obesity–most notable for diabetes and cardiovascular phenotypes (Table 1a). Addition of PGS BMI and PGS WHRadjBMI to BMI and WHR resulted in limited improvement in risk discrimination across incident CKM phenotypes (Table 1b). Conclusions: Observed anthropometric measures of obesity are sufficient, and for certain conditions far superior, to capture the risk of incident CKM phenotypes. These findings suggest PGS for adiposity traits likely have limited clinical utility to refine risk stratification for CKM syndrome among middle aged adults.