Search Results (18)

Background: Polygenic risk scores (PRSs) aggregate the effects of many common genetic variants and are being investigated as tools to refine coronary artery disease (CAD) risk prediction beyond traditional clinical models. Methods and Results: We review the development of PRS from early unweighted scores to contemporary genome-wide models and summarize evidence from major studies. We identified key studies through PubMed searches using the terms “polygenic risk score,” “genetic risk prediction,” and “coronary artery disease,” supplemented by citation chaining of highly cited articles and recent reviews. Large cohorts, such as the UK Biobank, show that individuals in the highest PRS percentiles have a 3–5-fold higher risk of CAD, and may gain the greatest benefit from statin therapy. PRS can also reclassify younger adults at borderline or intermediate risk and may complement coronary artery calcium (CAC) scoring. Conclusions: PRSs hold promise for lifetime risk stratification and targeted prevention in CAD but are limited by ancestry bias in GWAS, underrepresentation of diverse populations, inconsistency in individual estimates, and lack of standardized reporting. Future research should focus on expanding multi-ancestry databases, standardizing methods, prospective validation, and effective communication strategies to support equitable and evidence-based clinical use. Full article

Background: The relationship between sarcopenia and the incidence of coronary artery disease (CAD) is not well understood. This study aimed to investigate this relationship and the modifying effect of potential risk factors. Methods: We conducted a prospective study including 439,295 individuals from the UK Biobank. The primary outcome was the incidence of CAD. The main physical capability markers for sarcopenia, grip strength and muscle mass, were investigated as risk factors of interest. Grip strength was measured using a Jamar J00105 (Lafayette, IN, USA) hydraulic hand dynamometer, while muscle mass was estimated through bioelectrical impedance. Cox proportional hazard models were employed to analyze the associations between the exposures and the risk of CAD. Results: A total of 41,564 incident cases of CAD were identified after a median follow-up of 13.15 years (IQR 12.29–13.88 years). Compared with the lowest quintile of grip strength, the adjusted HRs for incidences of CAD from the second to the fifth quintile were 0.81 (95% CI: 0.79–0.83), 0.71 (95% CI: 0.69–0.73), 0.61 (95% CI: 0.60–0.63), and 0.49 (95% CI: 0.48–0.51). The association remained significant in subgroup analysis and interactions were observed between the two exposures and sex, age, smoking status, inflammatory diseases, metabolic syndrome, and genetic predisposition (all p for interactions < 0.05). Conclusions: Physical capability markers of sarcopenia, grip strength and muscle mass, were independently associated with a dose–response decreased risk for CAD incidence, regardless of genetic predisposition and potential modifying risk factors. Full article

Sudden cardiac death (SCD) is a major cause of mortality among patients with coronary artery disease (CAD). This study aimed to identify risk factors for CAD-related SCD (SCD_CAD) through autopsy data and genetic screening with a particular emphasis on rare variants (minor allele frequency < 0.01). We included 241 SCD_CAD cases (mean age 54.6 ± 12.8 years, 74.7% male) verified by medico-legal examination and 241 silent CAD controls (mean age 53.6 ± 15.2 years, 25.3% female) who died from severe craniocerebral trauma. Information about death characteristics was obtained from questionnaires, police reports and autopsy data. Whole-exome sequencing was performed on myocardial tissue samples. Polygenic risk score (PRS) from a previously validated model was applied and rare variant pathogenicity was predicted using in silico tools. SCD_CAD victims predominantly died at night and showed higher mortality rates during summer and winter months, with more complex coronary disease. Nocturnal time (adjusted odds ratio [AOR] = 3.53, 95% CI: 2.37–5.25, p < 0.001), winter (AOR = 2.06, 95% CI: 1.33–3.20, p = 0.001), multiple vessel occlusion (AOR = 1.79, 95% CI: 1.16–2.77, p = 0.009), right coronary artery stenosis (AOR = 2.38, 95% CI: 1.54–3.68, p < 0.001) and unstable plaque (AOR = 2.17, 95% CI: 1.46–3.23, p < 0.001) were identified as risk factors of SCD_CAD. The PRS score was associated with a 60% increased risk of SCD_CAD (OR = 1.632 per SD, 95%CI: 1.631–1.633, p < 0.001). Genetic analysis identified MUC19 and CGN as being associated with SCD_CAD. We identified both hereditary and acquired risk factors that may contribute to cardiac dysfunction and precipitate SCD in CAD patients, thereby facilitating the prevention and early recognition of high-risk individuals. Full article

Background: Polygenic risk score (PRS) quantifies the cumulative effects of common genetic variants across the genome, including both coding and non-coding regions, to predict the risk of developing common diseases. In cardiovascular medicine, PRS enhances risk stratification beyond traditional clinical risk factors, offering a precision medicine approach to coronary artery disease (CAD) prevention. This study evaluates the predictive performance of a multi-ancestry PRS framework for cardiovascular risk assessment using the All of Us (AoU) short-read whole-genome sequencing dataset comprising over 225,000 participants. Methods: We developed PRSs for lipid traits (LDL-C, HDL-C, triglycerides) and cardiometabolic conditions (type 2 diabetes, hypertension, atrial fibrillation) and constructed two metaPRSs: one integrating lipid and cardiometabolic PRSs (risk factor metaPRS) and another incorporating CAD PRSs in addition to these risk factors (risk factor + CAD metaPRS). Predictive performance was evaluated separately for each trait-specific PRS and for both metaPRSs to assess their effectiveness in CAD risk prediction across diverse ancestries. Model predictive performance, including calibration, was assessed separately for each ancestry group, ensuring that all metrics were ancestry-specific and that PRSs remain generalizable across diverse populations Results: PRSs for lipids and cardiometabolic conditions demonstrated strong predictive performance across ancestries. The risk factors metaPRS predicted CAD risk across multiple ancestries. The addition of a CAD-specific PRS to the risk factors metaPRS improved predictive performance, highlighting a genetic component in CAD etiopathology that is not fully captured by traditional risk factors, whether clinically measured or genetically inferred. Model calibration and validation across ancestries confirmed the broad applicability of PRS-based approaches in multi-ethnic populations. Conclusion: PRS-based risk stratification provides a reliable, ancestry-inclusive framework for personalized cardiovascular disease prevention, enabling better targeted interventions such as pharmacological therapy and lifestyle modifications. By incorporating genetic information from both coding and non-coding regions, PRSs refine risk prediction across diverse populations, advancing the integration of genomics into precision medicine for common diseases Full article

Hypercholesterolemia, characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), along with inflammation, is a well-known risk factor for developing atherosclerosis and coronary artery disease (CAD). Many patients with hypercholesterolemia may carry inherited genetic variants that are not part of the commonly recognized mutations in the LDLR, APOB, LDLRAP1, and PCSK9 genes. These genetic variants may have cumulative effects that contribute to increased LDL-C levels and CAD development. The polygenic risk score (PRS) may provide an essential tool for evaluating an individual’s genetic predisposition to these conditions. This pilot study aimed to investigate the impact of the PRS calculated from specific single nucleotide polymorphisms (SNPs) associated with LDL cholesterol (LDL-C)—namely, CELSR2 rs629301, APOB rs1367117, ABCG8 rs6544713, LDLR rs6511720, APOE rs429358, and rs7412—on LDL-C levels in both healthy individuals with elevated LDL-C levels (>2.6 mmol/L) and those diagnosed with ST-segment elevation myocardial infarction (STEMI). A total of 61 healthy individuals with high LDL-C levels (>2.6 mmol/L) and 93 STEMI patients were selected for the study. The High-Resolution Melting Polymerase Chain Reaction (HRM PCR) method was adopted and sequencing techniques were employed to identify the specific single nucleotide polymorphisms (SNPs) of interest. The patient group exhibited a PRS of 0.824 (with a range of −0.62 to 1.174) compared to 0.674 (range: −0.176 to 0.974) in healthy individuals, indicating a higher genetic predisposition to elevated LDL-C levels (p = 0.001) in patients. Interestingly, patients had lower LDL-C concentrations than healthy individuals. Additionally, a more significant number of patients were past smokers and statin users. The PRS calculations revealed that patients with a higher PRS had increased odds of experiencing an MI, with an odds ratio of 12.044 (95% confidence interval: 1.551–93.517, p = 0.017). Similarly, smokers showed even higher odds, with an odds ratio of 24.962 (95% CI: 7.171–86.890, p < 0.001). Among healthy individuals, those with a higher PRS had increased odds of having an LDL-C concentration greater than 4.9 mmol/L (odds ratio: 20.391, 95% CI: 1.116–358.486, p = 0.039). However, no significant association was found between the PRS and LDL-C levels in the patient group during hospitalization (p = 0.782). This pilot study shows that PRS can be employed to evaluate the risk of MI and to estimate concentrations greater than 4.9 mmol/L LDL-C in healthy individuals. Full article

A lower mortality rate from coronary artery disease (CAD) and a more favourable lipid profile have been reported in Afro-Caribbeans compared with people of European ancestry. The aim of this study was to determine whether common lipid variants identified in other populations are associated with lipid levels and CAD in Afro-Caribbeans. We studied 705 Afro-Caribbeans (192 with CAD) who were genotyped for 13 lipid-associated variants. We calculated three polygenic risk scores (PRSs) for elevated LDL (LDL-PRS), decreased HDL (HDL-PRS), and elevated triglycerides (TG-PRS). LDL-PRS, HDL-PRS, and TG-PRS were associated with LDL, HDL, and TG levels, respectively. The LDL-PRS was positively associated with LDL > 2.6 mmol/L and with LDL > 3.0 mmol/L with ORs (odds ratios) of 1.33 (95% confidence interval (CI) = 1.14–1.56) and 1.40 (CI = 1.21–1.62), respectively. The HDL-PRS was associated with a low HDL category (HDL < 1.03 mmol/L) with an OR of 1.3 (CI = 1.04–1.63) and inversely associated with a high HDL category (HDL > 1.55 mmol/L) with an OR of 0.79 (CI = 0.65–0.96). The LDL-PRS was positively associated with CAD after adjustment for age, gender, hypertension, diabetes, and smoking with an OR of 1.27 (CI = 1.06–1.51) but not the HDL-PRS nor the TG-PRS. Results of the present study indicate that common lipid variants are associated with lipid levels and prevalent CAD in Afro-Caribbeans. Full article

Background/Objectives: The identification of coronary artery disease (CAD) high-risk individuals is a major clinical need for timely diagnosis and intervention. Many different polygenic scores (PGSs) for CAD risk are available today to estimate the genetic risk. It is necessary to carefully choose the score to use, in particular for studies on populations, which are not adequately represented in the large datasets of European biobanks, such as the Italian one. This work aimed to analyze which PGS had the best performance within the Italian population. Methods: We used two Italian independent cohorts: the EPICOR case–control study (576 individuals) and the Atherosclerosis, Thrombosis, and Vascular Biology (ATVB) Italian study (3359 individuals). We evaluated 266 PGS for cardiovascular disease risk from the PGS Catalog, selecting 51 for CAD. Results: Distributions between patients and controls were significantly different for 49 scores (p-value < 0.01). Only five PGS have been trained and tested for the European population specifically. PGS003727 demonstrated to be the most accurate when evaluated independently (EPICOR AUC = 0.68; ATVB AUC = 0.80). Taking into account the conventional CAD risk factors further enhanced the performance of the model, particularly in the ATVB study (p-value = 0.0003). Conclusions: European CAD PGS could have different risk estimates in peculiar populations, such as the Italian one, as well as in various geographical macro areas. Therefore, further evaluation is recommended for clinical applicability. Full article

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06. Full article

Coronary artery disease (CAD) is the leading cause of death in India. Many genetic polymorphisms play a role in regulating oxidative stress, blood pressure and lipid metabolism, contributing to the pathophysiology of CAD. This study examined the association between ten polymorphisms and CAD in the Jat Sikh population from Northern India, also considering polygenic risk scores. This study included 177 CAD cases and 175 healthy controls. The genetic information of GSTM1 (rs366631), GSTT1 (rs17856199), ACE (rs4646994), AGT M235T (rs699), AGT T174M (rs4762), AGTR1 A1166C (rs5186), APOA5 (rs3135506), APOC3 (rs5128), APOE (rs7412) and APOE (rs429358) and clinical information was collated. Statistical analyses were performed using SPSS version 27.0 and SNPstats. Significant independent associations were found for GST*M1, GST*T1, ACE, AGT M235T, AGT T174M, AGTR1 A1166C and APOA5 polymorphisms and CAD risk (all p < 0.05). The AGT CT haplotype was significantly associated with a higher CAD risk, even after controlling for covariates (adjusted OR = 3.93, 95% CI [2.39–6.48], p < 0.0001). The APOA5/C3 CC haplotype was also significantly associated with CAD (adjusted OR = 1.86, 95% CI [1.14–3.03], p < 0.05). A higher polygenic risk score was associated with increased CAD risk (adjusted OR = 1.98, 95% CI [1.68–2.34], p < 0.001). Seven polymorphisms were independently associated with an increase in the risk of CAD in this North Indian population. A considerable risk association of AGT, APOA5/C3 haplotypes and higher genetic risk scores is documented, which may have implications for clinical and public health applications. Full article

Coronary artery disease (CAD) stands as the most predominant type of cardiovascular disease (CVD). Polygenic risk scores (PRSs) have become essential tools for quantifying genetic susceptibility, and researchers endeavor to improve their predictive precision. The aim of the present work is to assess the performance and the relative contribution of PRSs developed for CVD or CAD within a Greek population. The sample under study comprised 924 Greek individuals (390 cases with CAD and 534 controls) from the THISEAS study. Nine PRSs drawn from the PGS catalog were replicated and tested for CAD risk prediction. PRSs computations were performed in the R language, and snpStats was used to process genotypic data. Descriptive characteristics of the study were analyzed using the statistical software IBM SPSS Statistics v21.0. The effectiveness of each PRS was assessed using the PRS R² metric provided by PRSice2. Among nine PRSs, PGS000747 greatly increased the predictive value of primary CAD risk factors by 21.6% (p-value = 2.63 × 10⁻²⁵). PGS000012 was associated with a modest increase in CAD risk by 2.2% (p-value = 9.58 × 10⁻⁴). The remarkable risk discrimination capability of PGS000747 stands out as the most noteworthy outcome of our study. Full article

Longevity is a complex phenotype influenced by both environmental and genetic factors. The genetic contribution is estimated at about 25%. Despite extensive research efforts, only a few longevity genes have been validated across populations. Long-lived individuals (LLI) reach extreme ages with a relative low prevalence of chronic disability and major age-related diseases (ARDs). We tested whether the protection from ARDs in LLI can partly be attributed to genetic factors by calculating polygenic risk scores (PRSs) for seven common late-life diseases (Alzheimer’s disease (AD), atrial fibrillation (AF), coronary artery disease (CAD), colorectal cancer (CRC), ischemic stroke (ISS), Parkinson’s disease (PD) and type 2 diabetes (T2D)). The examined sample comprised 1351 German LLI (≥94 years, including 643 centenarians) and 4680 German younger controls. For all ARD-PRSs tested, the LLI had significantly lower scores than the younger control individuals (areas under the curve (AUCs): ISS = 0.59, p = 2.84 × 10⁻³⁵; AD = 0.59, p = 3.16 × 10⁻²⁵; AF = 0.57, p = 1.07 × 10⁻¹⁶; CAD = 0.56, p = 1.88 × 10⁻¹²; CRC = 0.52, p = 5.85 × 10⁻³; PD = 0.52, p = 1.91 × 10⁻³; T2D = 0.51, p = 2.61 × 10⁻³). We combined the individual ARD-PRSs into a meta-PRS (AUC = 0.64, p = 6.45 × 10⁻¹⁵). We also generated two genome-wide polygenic scores for longevity, one with and one without the TOMM40/APOE/APOC1 gene region (AUC (incl. TOMM40/APOE/APOC1) = 0.56, p = 1.45 × 10⁻⁵, seven variants; AUC (excl. TOMM40/APOE/APOC1) = 0.55, p = 9.85 × 10⁻³, 10,361 variants). Furthermore, the inclusion of nine markers from the excluded region (not in LD with each other) plus the APOE haplotype into the model raised the AUC from 0.55 to 0.61. Thus, our results highlight the importance of TOMM40/APOE/APOC1 as a longevity hub. Full article

Pharmacogenetics—a branch of precision medicine—holds the promise of becoming a novel tool to reduce the social and healthcare burdens of cardiovascular disease (CVD) and coronary artery disease (CAD) in diabetes. The improvement in cardiovascular risk stratification resulting from adding genetic characteristics to clinical data has moved from the modest results obtained with genetic risk scores based on few genetic variants, to the progressively better performances of polygenic risk scores based on hundreds to millions of variants (CAD-PGRS). Similarly, over the past few years, the number of studies investigating the use of CAD-PGRS to identify different responses to cardio-preventive treatment has progressively increased, yielding striking results for lipid-lowering drugs such as proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and statins. The use of CAD-PGRS to stratify patients based on their likely response to diabetes-specific interventions has been less successful, but promising results have been obtained with regard to specific genetic variants modulating the effects of interventions such as intensive glycemic control and fenofibrate. The finding of diabetes-specific CAD-loci, such as GLUL, has also led to the identification of promising new targets that might hopefully result in the development of specific therapies to reduce CVD burden in patients with diabetes. As reported in consensus statements from international diabetes societies, some of these pharmacogenetic approaches are expected to be introduced in clinical practice over the next decade. For this to happen, in addition to continuing to improve and validate these tools, it will be necessary to educate physicians and patients about the opportunities and limits of pharmacogenetics, as summarized in this review. Full article

In the modern era, coronary artery disease (CAD) has become the most common form of heart disease and, due to the severity of its clinical manifestations and its acute complications, is a major cause of morbidity and mortality worldwide. The phenotypic variability of CAD is correlated with the complex etiology, multifactorial (caused by the interaction of genetic and environmental factors) but also monogenic. The purpose of this review is to present the genetic factors involved in the etiology of CAD and their relationship to the pathogenic mechanisms of the disease. Method: we analyzed data from the literature, starting with candidate gene-based association studies, then continuing with extensive association studies such as Genome-Wide Association Studies (GWAS) and Whole Exome Sequencing (WES). The results of these studies revealed that the number of genetic factors involved in CAD etiology is impressive. The identification of new genetic factors through GWASs offers new perspectives on understanding the complex pathophysiological mechanisms that determine CAD. In conclusion, deciphering the genetic architecture of CAD by extended genomic analysis (GWAS/WES) will establish new therapeutic targets and lead to the development of new treatments. The identification of individuals at high risk for CAD using polygenic risk scores (PRS) will allow early prophylactic measures and personalized therapy to improve their prognosis. Full article

Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated. Full article

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations. Full article