Polygenic Scores Research Articles

Which is better for predicting and discriminating cardiovascular events: a traditional risk score, a calcium score or a genetic risk score?

Abstract Background The most effective approach to prevent coronary artery disease (CAD) in the general population remains to quantify the individual risk of CAD followed by controlling risk factors. There has been a lot of curiosity to investigate several scores individually for improving risk prediction. However, whether one would be better than the other in the same population is still unclear. Aim To evaluate the performance of the three individual scores, European SCORE2, Coronary artery calcium score (CACS) and a Genetic risk score (GRS), in predicting and discriminating cardiovascular (CV) events in an asymptomatic Portuguese population. Methods Prospective, observational population-based study, including 1002 asymptomatic subjects (mean age 53.1±6.8 years, 73.8% male) selected from a normal Portuguese population, without apparent CAD and diabetes at baseline. Data were recorded at the end of an extended follow-up (average 6.5±4.9 years). The European SCORE2, a computed tomography CACS, and a GRS were created to evaluate CV events’ predictive and discriminative ability through Receiver Operating Characteristics (ROC) analysis and Harrell’s C-statistics. Results ROC curve analysis showed 0.723 for SCORE2, 0.815 for CACS and 0.649 for GRS. Harrell’s C-statistics were 0.731 in the SCORE2, 0.785 for CACS and 0.684 for GRS. Conclusion The CAC score was the best individual score to predict and discriminate CV events in our asymptomatic population. Its capacity to identify high-risk individuals and those asymptomatic with subclinical atherosclerosis may improve CV risk management strategies to prevent future cardiovascular events. This result can be explained because CAC score, more than a risk marker, represents the existence of disease in the vessel wall.

Long-term effects of arsenic on glucose-insulin homeostasis: A gene-environment interaction study

Abstract Background Arsenic exposure and genetic factors are associated with an increased risk of type 2 diabetes, yet study of environment-gene interaction on glucose-insulin homeostasis remains scarce. We aimed to investigate the associations and interactions of urinary total arsenic (UTAs) and genetic susceptibility with glucose-insulin homeostasis through a longitudinal epidemiological study. Methods A total of 6136 observations (3063 participants) from the baseline, 3-year follow-up, and 6-year follow-up of the Wuhan-Zhuhai cohort were included in this study. In each study period, we repeatedly measured UTAs, fasting plasma glucose (FPG), fasting plasma insulin (FPI), and homeostasis model-assessed insulin resistance (HOMA-IR) and β-cell function (HOMA-β). Polygenic risk scores (PRSs) specific for all traits were constructed by corresponding genome-wide association summary statistics. Linear mixed models were used to assess associations and interactions of UTAs and PRSs with four indicators of glucose-insulin homeostasis. Results After adjustment for all covariates, the βs (95% CIs) for the annual growth rates of FPG, FPI, and HOMA-IR were 0.020 (0.008, 0.032), 0.007 (0.002, 0.013), and 0.011 (0.004, 0.017), respectively, associated with each ln-unit increase in UTAs. Compared to subjects with the first-quartile PRS and low UTAs, participants with the fourth-quartile PRS and high UTAs had a 0.029 (0.012, 0.047) or 0.032 (0.012, 0.051) ln-unit increase in the annual growth rate of FPI or HOMA-IR, respectively. UTAs and PRS had significant interactions on the longitudinal progressive increases of FPI and HOMA-IR (P interaction < 0.05). Conclusions Arsenic exposure and genetic variants are significantly synergistic risk factors for dysregulation and progressive deterioration of glucose-insulin homeostasis. Our findings emphasize the importance of reducing arsenic exposure to maintain normal glucose-insulin metabolism, especially for those at higher genetic risk. Key messages • Arsenic exposure was associated with annual increased rates of FPG, FPI, and HOMA-IR. • Arsenic exposure and trait-specific PRSs are synergistic risk factors for glucose-insulin homeostasis impairment.

Coronary artery disease polygenic risk score in patients undergoing coronary angiography and risk of future revascularization

Abstract Background Coronary artery disease (CAD) polygenic risk score (PRS) is a strong predictor of incident CAD. The role of CAD PRS in patients with known or suspected CAD already presenting for invasive coronary angiography remains poorly understood. Purpose We aimed to (i) study the association between CAD PRS and coronary angiography traits, and (ii) determine whether CAD PRS can predict risk of future revascularization after an initial coronary angiogram. Methods Patients who underwent coronary angiography at a single hospital and were part of its biobank were included in the study. Participants were classified into 3 risk groups based on CAD PRS distribution–low (0-20%), intermediate (21-80%), and high (81-100%)–computed from a recently reported PRS comprised of 1,296,172 variants (GPSMult, PGS003725). Revascularization was defined as a composite of coronary artery bypass graft and percutaneous coronary intervention with a 3-month blocking window after the initial angiogram. Associations between coronary stenosis severity and burden, and CAD PRS were assessed using multinomial logistic regression adjusted for age, sex, and genetic ancestry. The cumulative incidence of revascularization was tested using Fine-Gray regression accounting for competing risk of all-cause mortality and compared across CAD PRS strata by log-rank test. Results Among 3,518 participants (29.90% female, mean age 63.29±12.14 years), 2,568 (73%) had angiographic evidence of CAD – 340 (13.24%) mild, 346 (13.47%) moderate, and 1,882 (73.29%) severe. Out of those with CAD, 844 (32.87%) presented with acute coronary syndromes (ACS) and 1,724 (67.13%) with stable CAD. CAD PRS was strongly associated with angiographic presence of CAD (OR 2.62, 95%CI 2.08-3.29) and this did not differ among patients presenting with ACS vs. stable CAD (Fig1A). The association was weakest for mild CAD (OR 1.14, 95%CI 0.77-1.68) and strongest for severe CAD (OR 3.04, 95%CI 2.40-3.84). Besides severity, CAD PRS was also strongly associated with angiographic burden of CAD with the strongest association observed for presence of left main coronary stenosis (OR 2.63, 95%CI 1.86-3.73). Each standard deviation increase in CAD PRS was associated with a 10-point increase in the Gensini score, a continuous measure of angiographic burden of CAD (p<2e-16) (Fig1B). Over a mean follow-up period of 9.26±5.81 years, 659 (18.73%) patients required revascularization at a mean period of 5.31±4.69 years. Risk of revascularization varied dramatically by CAD PRS; HR was 1.64 (95%CI 1.28-2.10, p=8.7e-5) for high vs. low CAD PRS (Fig2). CAD PRS remained predictive of future revascularization even after adjusting for the angiographic burden of CAD at baseline (HR 1.41, 95%CI 1.09-1.82, p=7.9e-3). Conclusion CAD PRS is strongly associated with severity and burden of CAD assessed on coronary angiography and is predictive of future risk of revascularization even after accounting for baseline disease burden.Figure 1.CAD PRS and angiographic traitsFigure 2.CAD PRS and revascularization

Personality changes during adolescence predict young adult psychosis proneness and mediate gene-environment interplays of schizophrenia risk.

Psychotic symptoms in adolescence are associated with social adversity and genetic risk for schizophrenia. This gene-environment interplay may be mediated by personality, which also develops during adolescence. We hypothesized that (i) personality development predicts later Psychosis Proneness Signs (PPS), and (ii) personality traits mediate the association between genetic risk for schizophrenia, social adversities, and psychosis. A total of 784 individuals were selected within the IMAGEN cohort (Discovery Sample-DS: 526; Validation Sample-VS: 258); personality was assessed at baseline (13-15 years), follow-up-1 (FU1, 16-17 years), and FU2 (18-20 years). Latent growth curve models served to compute coefficients of individual change across 14 personality variables. A support vector machine algorithm employed these coefficients to predict PPS at FU3 (21-24 years). We computed mediation analyses, including personality-based predictions and self-reported bullying victimization as serial mediators along the pathway between polygenic risk score (PRS) for schizophrenia and FU3 PPS. We replicated the main findings also on 1132 adolescents recruited within the TRAILS cohort. Growth scores in neuroticism and openness predicted PPS with 65.6% balanced accuracy in the DS, and 69.5% in the VS Mediations revealed a significant positive direct effect of PRS on PPS (confidence interval [CI] 0.01-0.15), and an indirect effect, serially mediated by personality-based predictions and victimization (CI 0.006-0.01), replicated in the TRAILS cohort (CI 0.0004-0.004). Adolescent personality changes may predate future experiences associated with psychosis susceptibility. PPS personality-based predictions mediate the relationship between PRS and victimization toward adult PPS, suggesting that gene-environment correlations proposed for psychosis are partly mediated by personality.

Genetic and lifestyle risks for coronary artery disease and risk of incident dementia

Abstract Background Lifestyle risk factors for coronary artery disease (CAD) have been associated with neurodegeneration and dementia. Whether an underlying genetic risk for the development of CAD further increases this risk, however, remains unclear. Purpose To examine whether an increased genetic risk for CAD is associated with an increased risk of dementia, and to investigate whether lifestyle factors modify these associations. Methods A prospective cohort study of 457,353 participants of European ancestry without dementia at baseline (mean [SD], age 57 [8] years; 55% were female) was conducted within the UK Biobank study. Exposures were a genome-wide polygenic risk score (PRS) for CAD and a modified version of the American Heart Association’s Life’s Essential 8 (LE8) cardiovascular health score comprising four biological (BMI, BP, lipids, glucose) and four lifestyle (smoking status, diet, physical activity, and sleep duration) risk factors previously linked to both CAD and dementia. Dementia diagnoses were obtained from death register and inpatient records. Results 9,337 cases of all-cause dementia were observed over a median 13.9-year follow-up. There were no significant interactions between genetic risk for CAD and lifestyle factors for all-cause dementia, or any other dementia subtype. After adjusting for potential demographic confounders, a higher genetic risk for CAD was associated with elevated risk of developing all-cause dementia (hazard ratio (HR) per 1-SD increase in PRS = 1.08 [95% CI, 1.05-1.10]), Alzheimer’s disease (HR 1.08 [1.03-1.12]), and vascular dementia (HR 1.13 [1.06-1.19]). An unfavourable LE8 cardiovascular health score was also associated with an increased risk of developing all-cause dementia (HR 1.05 [1.02-1.08]), predominantly through its association with vascular dementia (HR 1.14 [1.06-1.22]), as we found no evidence for any associations between LE8 and Alzheimer’s disease (HR 1.00 [0.97-1.02]). Conclusion These findings indicate that a high genetic risk for CAD is associated with an increased risk of developing major dementia subtypes. Regardless of this underlying genetic risk, however, our results suggest that lifestyle interventions designed to reduce the risk of CAD may also reduce one’s risk of developing dementia, particularly in those with underlying vascular pathology.

3.Y.1. Genomics in EU Health Systems: Navigating the Opportunities and Challenges for Personalized Health

Abstract The rapid advancements in genomics research offer significant promise for revolutionizing healthcare, potentially reshaping how we approach disease prevention, diagnosis, and treatment. However, unlocking the full potential of genomics requires more than just scientific breakthroughs-it necessitates translating these discoveries into clinical practice. This involves bridging the gap between research and healthcare implementation, addressing challenges such as technical infrastructure, ethical and legal frameworks, healthcare professional competencies, and citizen engagement. As we navigate the evolving landscape of individualized patient care, it becomes increasingly crucial to implement effective risk stratification methodologies and personalized preventive measures. Among these methodologies, polygenic risk scores are gaining prominence. However, the adoption of these innovations necessitates careful consideration of their clinical utility, including their feasibility, cost-effectiveness, and acceptability in diverse healthcare settings. Validating these approaches from multiple perspectives is essential to ensure their effectiveness in real-world clinical practice. Integrating genomic sequencing data with health records presents an invaluable resource that can enhance patient outcomes and drive further research. The creation of infrastructures and initiatives such as those carried out by projects like the European Health Data Space and Genomic Data Infrastructure are fundamental to unleash the enormous potential of data-driven innovation. To date, the implementation of genomics in medical practice varies across European countries, with discrepancies in adoption levels across different aspects of healthcare, including prevention, diagnosis, and personalized treatment. Addressing these disparities and promoting standardized approaches to genomic medicine implementation are crucial steps toward ensuring equitable access and maximizing the benefits of genomics for all individuals across Europe. This workshop aims to provide decision-makers, healthcare professionals and researchers with a comprehensive overview of the state-of-the-art for the adoption of personalized approaches, and of the maturity of genomic medicine practices within different healthcare systems as well outlining the benefits of data accessibility for research. The speakers are coordinators and partners of EU projects as B1MG (Beyond One Million Genomes), Prophet (A PeRsOnalized Prevention roadmap for the future Healthcare), EHDS (European Health Data Space), GDI (European Genomic Data Infrastructure), GoE (Genome of Europe) and CanHeal. Key messages • Genomics has the potential to improve health outcomes. • Equal access to genomic medicine critical for personalized health. Ensuring equity, while considering feasibility.

Longitudinal associations between accelerometry-assessed sleep clusters and obesity in adolescence and young adulthood: the German GINIplus and LISA birth cohorts study

Abstract Background Some studies have identified diverse sleep patterns in adolescents and adults by considering multidimensional objective sleep parameters. However, whether sleep patterns are consistent from adolescence to young adulthood and their long-term associations with obesity remain unknown. Purpose This study aimed to investigate the longitudinal associations of objectively measured and clustering-identified sleep patterns with body mass index (BMI) and overweight/obesity in adolescence and young adulthood, and to test whether these associations change over time or are modified by genetic risk. Methods A total of 1973 participants wearing 7-day accelerometers in either 15-year (n=1347, aged 14.3-16.4 years) and/or 20-year (n=1262, aged 19.5-22.4 years) follow-ups of German GINIplus and LISA birth cohorts were included. Sleep clusters were identified by K-means cluster analysis on 12 accelerometry-assessed sleep characteristics at each follow-up. BMI and overweight/obesity were measured by physical exams or self-/parent-reported questionnaires. Multivariable linear and logistic regression models with generalized estimating equations were tested. The interaction effects with time of follow-ups and polygenic risk scores (PRS, n=1087) for BMI were further examined. Results Although sleep characteristics changed from adolescence to young adulthood, such as shorter total sleep time (mean 7.2 to 6.6 hours), the consistent five sleep clusters were classified in both periods: good sleep, delayed sleep phase, sleep irregularity and variability, fragmented sleep, and prolonged sleep latency. Compared to the "good sleep" cluster, the "prolonged sleep latency" cluster showed significant associations with higher BMI (β=0.56, 95% confidence interval (CI) = [0.06, 1.05]) and increased odds of overweight/obesity (odds ratio (OR)=1.55, 95%CI= [1.02, 2.34]). PRS was associated with higher BMI (β=0.65, 95%CI= [0.39, 0.91]) and overweight/obesity (OR=1.52, 95%CI= [1.15, 2.00]), with no significant gene-sleep cluster interaction effect. Only among males, the interaction effects with time of follow-ups on overweight/obesity were observed in the "delayed sleep phase", "sleep irregularity and variability", and "fragmented sleep" clusters (p-interaction <0.05), showing more harmful effects with increasing age. Conclusions Five sleep patterns were identified consistently across adolescence and young adulthood, and the "prolonged sleep latency" pattern was associated with higher BMI and overweight/obesity, independent of genetic risk. For young adult males, "delayed sleep phase", "sleep irregularity and variability" and "fragmented sleep" patterns seemed to have more unfavorable impacts on overweight/obesity.

Role of common variants and new genes associated with arrhythmogenic cardiomyopathy

Abstract Introduction Arrhythmogenic Cardiomyopathy (ACM) is an inherited cardiac disease, predominantly affecting males, characterized by fibro-fatty myocardial replacement mainly in the right ventricle. It leads to 5% sudden cardiac death in affected young adults (<25 yo) without an implantable cardioverter defibrillator. Early diagnosis is crucial for appropriate clinical management to reduce mortality and prevent disease progression. Rare variants in desmosomal genes (PKP2,DSC2,DSG2,DSP and JUP) are found in 50% of cases. However, challenges persist since we observe a low penetrance in relatives (28-58%) and a high variability in expressivity and severity annihilating any preventive strategy. Objective We aim to decipher the genetic architecture of ACM by investigating the role of common variants in disease susceptibility/severity and to idendify new genes associated to ACM. Method A Genome Wide Association Study (GWAS) was performed on 1079 ACM index patients and 7305 controls, followed by conditional analysis. New candidate genes were knocked out (KO) in Zebrafish and/or hiPSC using CRISPR/Cas9. Impedance and external field potential measurements were performed on hiPSC derived cardiomyocytes (CM-hiPSC). Results The GWAS identified 5 significant loci (Figure 1, P<5.10-8), including 2 desmosomal genes (PKP2 a n d DSG2) and 3 new candidates (KLF12, CTNNA3, and ARL17B/WNT3). Conditional analysis revealed 3 additional loci with suggestive p-values (P<1.10-6), highlighting 3 new susceptible loci. Among them, we uncovered key non-coding regulatory regions near desmosomal genes (DSC2 and DSG2). Initial findings in PKP2-KO zebrafish present hearts with structural defects, while CM-hiPS KLF12-KO(-/-) exhibited altered interbeat interval and relaxation time compared to isogenic controls. Conclusion Our findings reveal ACM's complex genetic architecture, implicating common variants in disease susceptibility. A polygenic risk score will be implemented to improve patient risk stratification. Three novel loci/genes for ACM as well as specific regulatory regions of desmosomal genes have been highlighted. Their functional studies in isogenic hiPSC and Zebrafish models will offer new insights in ACM underlying molecular mechanism and potential therapeutic targets.

Polygenic risk score for prediction of atrial fibrillation according to presence of the hypertrophic cardiomyopathy

Abstract Background The significance of genetic predisposition in predicting atrial fibrillation (AF) is well recognized, yet its role in hypertrophic cardiomyopathy (HCM) patients remains less clear. Objective In this study, we aimed to elucidate the impact of genetic risk on the development of AF in both HCM and non-HCM patients. Methods This retrospective analysis examined 1,180 HCM patients (mean age 61.1±7.1, 63.0% male) and 476,238 non-HCM patients (mean age 57.0±8.1, 45.3% male) from the UK Biobank. Participants were stratified based on their validated polygenic risk score (PRS) for AF: the bottom 10% constituted the low genetic risk group, the top 10% comprised the high genetic risk group, and the remainder fell into the intermediate genetic risk group. We assessed the incidence of AF and major cardiovascular events and analyzed predictors, including genetic risk. Results Among HCM patients, no significant differences in baseline characteristics were observed based on genetic risk, except for the rate of CIED implantation (P=0.036). Conversely, among non-HCM patients, variations in age, gender, and comorbidities were noted according to genetic risk. The HCM patients were categorized into three groups based on risk, with respective AF incidence rates of 2.4, 3.6, and 5.4 events per 100 person-years, respectively. In non-HCM patients, the incidence of AF per 100 person-years was 0.2, 0.5, and 1.0, respectively. Genetic risk emerged as a significant predictor of AF in both HCM and non-HCM patients, although with a higher hazard ratio observed in non-HCM patient (HR, 2.51; 95% CI, 1.59–3.98 vs. HR, 4.77; 95% CI, 4.47–5.09) (P=0.003 for interaction). In non-HCM patients, predictors of AF included age, male sex, body mass index, smoking history, and a history of hypertension, diabetes, heart failure, stroke, and myocardial infarction, in addition to PRS. However, in HCM patients, only age, diabetes, and a history of stroke were identified as independent predictors of AF. Moreover, high genetic risk was significantly associated with the risk of major adverse cardiovascular events. Conclusions In conclusion, our study indicates associations between genetic predisposition and AF occurrence in both HCM and non-HCM patients.

Socioeconomic deprivation and atrial fibrillation risk: an observational and gene-environment interaction analysis

Abstract Background The association between socioeconomic deprivation and incident atrial fibrillation (AF) is debated, along with limited evidence on how genetic predisposition to AF interacts with socioeconomic deprivation and AF risk. Purpose To evaluate the association and gene-environmental interaction between socioeconomic deprivation and the risk of incident AF. Methods From the UK Biobank prospective cohort, we selected participants who were free from previous AF. Participants with missing values and those who did not pass the genetic quality control were excluded. As an indicator of socioeconomic deprivation, we utilised the indices of multiple deprivation (IMD), which is calculated from scores of multiple socioeconomic factors, including crime, education, employment, health, housing, income, and living environment. Participants were divided into 3 groups by IMD; the top 20% as high IMD (most deprived), the bottom 20% as low IMD, and 20-80% as mid IMD. Using a polygenic risk score (PRS) for AF generated using a Bayesian approach applied to external genome-wide association study data, the interaction between genetic risk and socioeconomic deprivation was assessed. The primary endpoint was incident AF during a median of 13.7 years of follow-up. Results A total of 377,386 (aged 56.5±8.1 years, 45.2% male) were analysed. The high IMD group (n=85,108) was younger and had a higher proportion of comorbidities than the mid or low-IMD groups. An increase in IMD as a continuous variable was significantly associated with an increased risk of incident AF. The high IMD group showed a significantly increased risk of AF (multivariable-adjusted hazard ratio [aHR]=1.12, 95% confidence interval=1.07-1.17, P<0.001) compared with the low IMD group (incidence rate 7.7 vs. 4.8/1,000 person-year). All components of IMD except housing score significantly increased the risk of incident AF (aHR ranged from 1.07 to 1.15). Within each IMD group, higher AF PRS groups were significantly associated with an increased risk of incident AF with the low AF PRS group as a reference, showing aHRs of 1.50 for mid-PRS and 2.57 for high-PRS in the low IMD group, 1.60 and 2.65 in the mid-IMD group, and 1.55 and 2.33 in the high IMD group, respectively (all P<0.001). The impact of socioeconomic deprivation was attenuated in participants with high AF PRS (P for interaction=0.042), mainly driven by the interaction effect of crime (P for interaction [Pi]=0.005), employment deprivation (Pi=0.005), and health deprivation scores (Pi=0.043). Conclusion Socioeconomically deprived individuals had a significantly higher risk of AF, and the effect was partially modified by genetic predisposition to AF. A personalised approach to applying AF screening for high-risk individuals may be more efficacious. Legal measures and policies to enhance public health by alleviating socioeconomic inequalities are needed to mitigate AF risk and improve cardiovascular health outcomes.

Metabolomic profiling of the entire UK Biobank enables interpretation of familial hypercholesterolemia mutations and prediction of severe cardiovascular outcomes

Abstract Background Familial hypercholesterolemia (FH) is an inherited disorder resulting in high levels of LDL-C from birth. Premeature coronary heart disease occurs in half of affected men by age 50 years(1). While many genetic variants that cause FH are well understood, there are also many variants in FH genes that are of uncertain clinical significance. Furthermore, the variability in residual cardiovascular risk of FH carriers after statin treatment is not fully understood. We here investigate whether metabolomic and genomic data from the 500,000 person UK Biobank cohort can aid in interpretation of candidate pathogenic variants for FH, and characterise future risk of severe cardiac events in the carriers of such variants. Methods We have quantified 249 metabolomic parameters in 490,830 blood samples from UK Biobank cohort. Among 186,483 participants with exome sequence data we identified 896 who carried known pathogenic FH variants and 405 who carried variants of unknown significance in an established FH gene. We fitted a logistic predictive model to identify pathogenic mutations using LDL levels, statin use and 17 principal components of the metabolomic parameters. We further tested whether metabolomic and genomic scores for predicting future risk of myocardial infarction (MI), that were trained in the general population (i.e. individuals without FH), could predict risk in FH carriers. Results A model including all metabolites had a higher accuracy at identifying known pathogenic FH variants (36/56 at FDR < 0.05), compared to LDL and statin use alone (30/56). Our model also identified 8 FH variants marked as uncertain significance in ClinVar with significant (FDR < 0.05) evidence of predicted pathogenicity. We found that our metabolomic risk score predicted 10-year risk of MI among FH carriers (HR=1.96 per SD, p = 0.0011). This was indeed stronger than the effect in the general population (HR = 1.64 per SD, p = 2.5e-168). A combined metabolomic and polygenic risk score had an even larger effect at predicting MI in FH carriers (HR = 3.06 per SD, p = 2.0e-6), and this score was significantly better at predicting MI in FH carriers than in non-carriers (HR = 3.06 vs 1.88 per SD, interaction p = 0.042). Conclusion Familial hypercholesterolemia is an important source of risk for early cardiovascular disease. Population-scale metabolomics can help distinguish pathogenic from benign variants that have not yet been characterised. Metabolomic and genomic scores can also identify individuals at most elevated risk, and provide even more information to FH individuals than in the general population.

Innovative Approaches for Personalized Cardiovascular Prevention: the INNOPREV multicentric trial

Abstract Background Cardiovascular diseases (CVD) represent a major global health concern, requiring innovative primary prevention strategies. Personalized prevention, through the use of digital technologies and genetic risk scores (PRS), holds promise but requires further investigation to assess clinical efficacy. Consequently, the INNOPREV trial aims to investigate the efficacy of PRS and/or digital technologies in the uptake of CV preventive behaviors uptakes. Methods The INNOPREV trial is a multicenter randomized controlled trial conducted in Italy across three hospital centers in Rome, Catania, and Palermo. It aims to recruit 1020 participants aged 40-69 with a high 10-year CVD risk. Participants are allocated to traditional CVD risk assessment, or genetic testing, or digital interventions, or a combination of both. The study involves comprehensive CV risk assessments at baseline and follow-up visits at one, five, and twelve months. Data analysis will utilize mixed-effects models to analyze longitudinally collected data and explore potential moderators and mediators of behavior change. Results Preliminary results indicate good progress in recruitment, which currently stands at 250 participants (25% of expected). Recruited females have a mean age of 59 ± 6 years, while males have a mean age of 58 ± 6 years. Currently, PRS analyses indicate that 77% of participants have intermediate scores, and 23% have low scores. Follow-up will be essential to evaluate the efficacy of the proposed interventions. Conclusions We expect that the INNOPREV trial will provide evidence towards integration of innovative approaches based on PRS and digital devices, in the traditional preventive approaches for CVDs. Expectations are high based on the limited effectiveness of the current approaches in CV risk reduction based on lifestyle advice. Key messages • INNOPREV trial examines PRS and digital technology for CVD prevention, aiming to enhance preventive efficacy. Initial findings suggest promising potential for innovative CVD prevention strategies. • The INNOPREV trial aims to investigate the efficacy of personalized prevention strategies, including PRS and digital technologies, in improving cardiovascular preventive behaviors.

The association between periodontal disease and coronary artery disease: insights from imaging, observational and genome-wide data

Abstract Background Periodontal disease is the sixth most common disease worldwide. It has been suggested that it may be a contributory risk factor for coronary artery disease (CAD). From a public health perspective, prevention of periodontal disease is relatively straightforward and cost-effective: daily brushing, flossing and, where applicable, smoking cessation. Purpose This study utilises imaging, longitudinal and genomic data to characterise the association between periodontal disease and CAD. Methods 481,915 individuals from the UK Biobank were included in this study, 18.9% (n = 91,022) of whom had self-reported periodontal disease. For the imaging analysis, a maximum of 59,019 had paired cardiovascular magnetic resonance (CMR) data. Multivariable linear regression models were constructed to examine the association of periodontal disease on CMR outcomes with LV end-diastolic volume, LV mass, LV mass:volume ratio (MVR), LV ejection fraction, LV global longitudinal strain and native T1 values considered as prognostically important phenotypes. The relationship between periodontal disease and incident CAD was assessed using Cox proportional hazards regression models. To examine the relationship of genetically-determined periodontal disease on CAD, a de novo GWAS for periodontal disease was performed and a genome-wide polygenic risk score (PRS) was constructed. Results Periodontal disease was associated with a significantly higher LV MVR (effect size = 0.00233, 95% confidence interval [CI] = 0.0006, 0.004) and significantly lower LV native global T1 value (effect size = -0.86 ms, 95% CI = -1.63, -0.09). The presence of periodontal disease was independently associated with a significantly increased hazard of incident CAD (HR = 1.09, 95% CI = 1.07, 1.13) at a median follow-up time of 13.8 years (Figure 1). Each standard deviation increase in the periodontal disease PRS was associated with increased odds of CAD (OR = 1.03, 95% CI = 1.02, 1.05) (Figure 2). Conclusion Using an integrated approach across imaging, observational and genomic data, periodontal disease is independently associated with biomarkers of subclinical remodelling as well as incident CAD. These findings highlight the potential importance of periodontal disease in the broader context of cardiovascular disease prevention.

Can variants of genes associated with familial heart block predict conduction disease in the general population? A UK Biobank study.

Abstract Background Progressive familial heart block is a genetic disease that can present with worsening cardiac conduction disease (CCD) and risk of life-threatening bradycardia. The prevalence, penetrance, and expressivity of known rare pathogenic/likely pathogenic variants (P/LP) in the general population is unclear. In addition, the role of common genetic variation in this phenotypic expression is unknown. Understanding this is essential for patient management in clinic. Purpose 1). To investigate the prevalence of variants linked with early-onset CCD and phenotypic expression in a large biobank. 2). To investigate whether a PR interval polygenic risk score (PRS) improves risk prediction. Methods Early-onset conduction disease P/LP or variants of uncertain significance (VUS) were identified from ClinVar, genetic testing reports at a teaching hospital (6 variants), and the literature, mapping to 22 genes. From the full UK Biobank prospective cohort, 469,511 participants passed genetic quality control and were assigned to P/LP, VUS, or genotype negative (G-) groups, using whole exome sequencing analysed with bcftools. A PR-PRS was constructed using weights from a published meta-analysis excluding UKB data. The primary outcome was CCD (atrioventricular (AV) block, bundle branch block, pacemaker or ICD implantation) and secondary outcomes were high-grade AV block or pacemaker insertion, compared with lifetime risk Cox proportional hazards controlled for sex and age as a timescale. Results 55% of participants were female, 95% had European ancestry, and average age at censor was 71 years. There were 25 P/LP carriers, and 8,862 with a VUS, in 6 genes (MYH7, SCN5A, TNNI3K, TRPM4, TTN, and LMNA). Major demographic factors were the same between groups. CCD was significantly more prevalent in P/LP individuals (28% vs 5.8% in VUS vs 5.5% in G-; p<0.001) with a hazard ratio of HR 6.47 [95% CI=3.09-13.6] vs G-. This was driven by AV block and pacemaker implantation (HR 23.2 [95% CI=8.69-61.8] and 13.4 [95% CI=6.01-29.8], respectively). Results were similar in an unrelated subset and controlling for ischaemic heart disease and heart failure. The 7 P/LP individuals with conduction disease were mostly linked to a single LMNA variant (4 out of 6 carriers with penetrant disease). In the full cohort, the PR-PRS was predictive of CCD (HR=1.07 [95% CI=1.06-1.08]), AV block (HR=1.09 [95% CI=1.06-1.13]), and pacemaker implantation (HR=1.04 [95% CI=1.02-1.06]). When combined with P/LP status, both PR-PRS (HR=1.07 [95% CI=1.06-1.08]) and P/LP (HR=6.42 [95% CI=3.06-13.5]) were independent predictors of CCD. Conclusion(s) Rare variant carrier status predicts a 6.5-fold increased lifetime risk of CCD, a 23-fold increased risk of AV block and 13-fold risk for pacemaker requirement. However, phenotypic expression for some variants is highly variable. A PR-PRS captures risk independent of P/LP variants and warrants further investigation with updated PRS construction methods.

Prediction of depressive symptoms in young adults by polygenic score and childhood maltreatment: Results from a population-based birth cohort.

Childhood maltreatment is linked with later depressive symptoms, but not every maltreated child will experience symptoms later in life. Therefore, we investigate whether genetic predisposition for depression (i.e., polygenic score for depression, PGSDEP) modifies the association between maltreatment and depressive symptoms, while accounting for different types of maltreatment and whether it was evaluated through prospective and retrospective reports. The sample included 541-617 participants from the Quebec Longitudinal Study of Child Development with information on maltreatment, including threat, deprivation, assessed prospectively (5 months-17 years) and retrospectively (reported at 23 years), PGSDEP and self-reported depressive symptoms (20-23 years). Using hierarchical linear regressions, we found that retrospective, but not prospective indicators of maltreatment (threat/deprivation/cumulative) were associated with later depressive symptoms, above and beyond the PGSDEP. Our findings also show the presence of gene-environment interactions, whereby the association between maltreatment (retrospective cumulative maltreatment/threat, prospective deprivation) and depression was strengthened among youth with higher PGSDEP scores. Consistent with the Diathesis-Stress hypothesis, our findings suggest that a genetic predisposition for depression may exacerbate the putative impact of maltreatment on later depressive symptoms, especially when maltreatment is retrospective. Understanding the gene-environment interplay emerging in the context of maltreatment has the potential to guide prevention efforts.

Measurement invariance of the Center for Epidemiological Studies-Depression scale and associations with genetic risk in older adults.

As populations are aging, it needs to be ensured that valid depression rating scales are available across old adulthood. Center for Epidemiological Studies-Depression scale (CES-D) is a common depression rating scale, however, few studies have assessed its validity in individuals with age over 90 and/or cognitive impairment. We examined the factor structures of 20-, 15-, and 8-item CES-D scales, their measurement invariance for age and cognition, and associations with genetic risk of depression. Participants were from a population-based older Finnish Twin Cohort study including 71-79-year-olds from the MEMTWIN II (n = 1034 for exploratory and n = 664 for confirmatory factor analyses) and 90+ year-olds from the NONAGINTA (n = 134, confirmatory factor analyses) sub-studies. Associations of polygenic risk score of major depressive disorder (MDD-PRS) with CES-D scales were examined in MEMTWIN II. Exploratory factor analyses (n = 1034) suggested four- (CES-D 20) and three-factor (CES-D 8) structures and these models fit well in confirmatory analyses (n = 664). Unidimensional models had good (CES-D 15 & 20) or fair fit (CES-D 8). Results supported scalar invariance of all CES-D versions for age and cognitive status. Higher MDD-PRS was associated with more depressive symptoms in different CES-D versions. Different CES-D versions are adequate for measuring depressive symptoms across age groups and cognitive spectrum in old age. Genetic risk of depression predicts depressive symptoms even in old age.

Subphenotypes of adult-onset diabetes: Data-driven clustering in the population-based KORA cohort.

A data-driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above-mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications. We used data from n = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing k = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open k-means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity. Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using k = 3 clusters and including high sensitivity C-reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters (n = 96, n = 172, n = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age-related diabetes and cluster 3 showed highest risk of genetic and obesity-related diabetes. T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity.

Circulating ketone bodies, genetic predisposition, and incident atrial fibrillation

Abstract Background Ketone bodies (KB) are essential alternative fuel sources for the myocardium. However, the evidence on the level of circulating KB and heart disorders is inconsistent. Whether the total and individual KB are associated with risk of onset atrial fibrillation (AF) in the general population and in individuals with different genetic susceptibilities to AF are still unclear. Purpose To examine the association between circulating KB and AF risk and to further test whether genetic risk could modify the above association. Methods In the UK Biobank, 247,304 participants (mean age 56.5, 45.3% men) with available data on KB and without baseline AF were included. The concentrations of KB, including β-hydroxybutyrate (β-OHB), acetoacetate, and acetone, were measured using nuclear magnetic resonance, and the values of KB were increased by one and then log10-transformed for analyses. The genetic risk of AF was based on the standard polygenic risk score. The definitions of AF were based on the ICD-10 code I48, which was linked to primary care, hospital admission electronic health records and death records. Cox proportional hazard model was used to estimate the hazard ratio (HR) with 95% confidence interval (CI). Results During a median follow-up of 13.8 years, 16,936 cases of onset of AF were recorded. After adjusting for age, sex, race, Townsend index, education, alcohol intake frequency, smoking status, healthy diet, metabolic equivalent, body mass index, systolic blood pressure (SBP), total cholesterol, estimated glomerular filtration rate, cardiovascular diseases, and diabetes, elevated total KB, β-OHB, acetoacetate, and acetone were all with the risk of AF (HR, 95% CI per 10-fold increase: 1.20, 1.13-1.28; 1.13, 1.07-1.19; 1.12, 1.06-1.18; and 1.78, 1.57-2.01, respectively). The highest risk of AF was observed among individuals at high genetic risk to AF and had the highest quartile of total KB (HR 2.91, 95% CI 2.66-3.19). Participants who had low genetic risk were more susceptible to high total KB for incident AF (p for interaction = 0.036). The associations of total KB, β-OHB, and acetone with AF risk were more prominent for men (HR, 95% CI per 10-fold increase: 1.32, 1.22-1.44; 1.22, 1.14-1.31; 2.01, 1.72-2.36) than for women (p for interaction <0.05). The association between total KB and AF risk was strengthened in individuals <60 years, current smokers, and those with SBP<130 mmHg (p for interaction = 0.033, 0.021, and 0.017, respectively). Conclusions Circulating KB concentrations were associated with AF risk, with acetone showing the strongest relationship with AF. Individuals at low genetic risk were more susceptible to the level of total KB for incident AF. Our findings suggest that KB may be a biomarker, particularly for identifying individuals without AF at baseline who are at low genetic risk. The additive value of plasma KB to a risk prediction score warrants further investigation.

Association between genetic predisposition of AF and the risk of ischemic stroke and thromboembolism

Abstract Background Ischemic stroke and thromboembolism are the major complications of atrial fibrillation (AF), however, its association with genetic background of AF remains unclear. To investigate the association between genetic predisposition of AF underlying common variants and the risk of ischemic stroke and thromboembolism. Methods We included unrelated individuals with non-valvular AF from UK biobank. The polygenic risk scores of AF (PRSAF) was calculated for each individual using the effect weight from a previous study which has been validated in both European and mixed-ancestry population[1, 2]. The SNPs from the original score were lifted over to GRCh38 and 6514708 of 6730541 were available in the UK biobank genotype data. The clinical data was defined by ICD-10 code. The primary endpoint is ischemic stroke or thromboembolism resulting in hospitalization after first AF diagnosis. Results A total of 28810 unrelated patients were included in the study. Among them, 4064 had PRSAF over 90th percentile of the general population, and were considered having high genetic risk. The rest 18746 with per SD increase in PRSAF lower than 90th percentile of the general population were considered having lower genetic risk. Generally, age and sex adjusted model demonstrated that per standard deviation change in PRSAF is associated with 9% increase in the risk of stroke or thromboembolism (HR=1.09, 95%CI: 1.03-1.14), and patients with high genetic risk was associated with 15% increase in the risk of stroke or thromboembolism (HR=1.15, 95%CI: 1.02-1.31).The result remained stable when further adjusted for clinical risk factors (1.09, 95%CI: 1.04-1.15 per SD increase in PRSAF) and competing risk of death (1.10, 95%CI: 1.05-1.16 per SD increase in PRSAF). There was no interaction between CHA2DS2-VASc score risk stratification and the per SD increase in PRSAF (P interaction = 0.751, Figure 1A). However, the association between per SD increase in PRSAF and the event tended to be affected by age at AF onset (HR = 1.02 95%CI: 0.94, 1.11 for early onset AF; HR = 1.12 95%CI: 1.05, 1.19 for non-early onset AF, P for interaction = 0.062, Figure 1B). Multi-variate COX regression including PRSAF, components of CHA2DS2-VASc score, and other AF risk factors revealed that history of stroke/thromboembolism, age over 65 years, hypertension, diabetes and high AF genetic risk were significantly associated with the outcome (Figure 1C). To evaluate the importance of these risk factors, average population attributable fraction for 10-year risk of stroke/thromboembolism in the study population was calculated, indicating that genetic risk of AF is responsible for 3.26% (0.89%-5.63%) of the events (Figure 1D). Conclusion Genetic predisposition of AF underlying common variants is associated with ischemic stroke and thromboembolism independent of clinical risk factors. But its role in different AF populations, like early onset AF, remains further investigation.Figure 1

Is genetic information helpful in predicting recurrent events in young patients after acute coronary syndrome?

Abstract Coronary Artery Disease (CAD) shares environmental and genetic factors. Young patients with acute coronary syndrome (ACS) carry a poor long-term prognosis. The clinical utility of genetic information in predicting CAD events remains unknown. Aim Evaluate the clinical utility of a multiplicative Genetic Risk Score (mGRS) to predict lifelong residual risk in CAD patients below 55 years with few risk factors. Methods 475 non-diabetic patients with LDL cholesterol levels below 100mg/dL at the first admission who suffered a prior MI at age ≤55 years were followed prospectively for a mean of 5.6±5.3 years. A mGRS was performed with nine variants associated with CAD but not with traditional risk factors (TRF): CDKN2B-AS1 (rs1333049 and rs4977574), TCF21 (rs12190287), PHACTR1 (rs1332844), MIA3 (rs17465637), ADAMTS7 (rs3825807), ZC3HC1 (rs11556924), SMAD3 (rs17228212) and GJA4 (rs618675). The mGRS was subdivided into terciles. Using Cox regression analysis, the predictive and discriminative score ability for events was evaluated. C-statistic discriminated cardiovascular events occurrence. Whether mGRS was included in the TRF model, Net Reclassification Improvement (NRI), or Integrated Discrimination Improvement (IDI) reclassified patients. Kaplan-Meier method estimated survival curves. Results There were 153 patients with at least one cardiovascular event. The bivariate analysis showed the strongest correlation with second and third mGRS tercile, multivessel disease, left ventricular dysfunction, and inflammation. Multivariate Cox regression HR adjusted for events was 1.44 (p=0.103) for the intermediate and 2.00 (p=0.001) for the high-risk tercile. The mGRS inclusion improved C-statistic (∆C-index=0.010; p=0.004), NRI (35.4%; p<0.0001) and IDI (2.7%; p=0.0005). Conclusions Our 9-SNP mGRS better identified and reclassified younger CAD patients with a high probability of cardiovascular events, improving clinical decision-making and reducing costs for therapeutic strategies.