Addition Of Genetic Risk Score Research Articles

Comparison between a genetic risk score and the European SCORE in cardiovascular events prediction in a primary prevention population

Abstract Background The risk for Coronary Artery Disease (CAD) can be estimated using different scores, such as the European SCORE (Systematic Coronary Risk Evaluation) scale or genetic risk score (GRS). The addition of GRS to the European SCORE may increase the precision of predicting MACE (Major Adverse Cardiovascular Events). Purpose This study aims to compare the European SCORE and the multiplicative genetic risk score (mGRS) in predicting MACE. Methods and results The study included 1110 asymptomatic individuals without known CAD from GENEMACOR prospective registry. We defined the primary endpoint of all-cause cardiovascular events. The study population had mean age of 51.6 years, 74.1% male and had risk factors of diabetes (11.6%), dyslipidemia (67.5%), hypertension (48.1%) and smoking (22.9%). Using C-index methodology, mGRS score was superior to SCORE in predicting MACE (mGRS = 0.832 Vs SCORE = 0.615; p=0.014). Conclusions The mGRS score was superior to SCORE in predicting MACE in an asymptomatic and free of CAD population. Genetic information may improve cardiovascular risk stratification in primary prevention. Funding Acknowledgement Type of funding sources: None.

Predicting clinical outcome to specialist multimodal inpatient treatment in patients with treatment resistant depression

BackgroundTreatment resistant depression (TRD) poses a significant clinical challenge, despite a range of efficacious specialist treatments. Accurately predicting response a priori may help to alleviate the burden of TRD. This study sought to determine whether outcome prediction can be achieved in a specialist inpatient setting. MethodsPatients at the Affective Disorders Unit of the Bethlam Royal Hospital, with current depression and established TRD were included (N = 174). Patients were treated with an individualised combination of pharmacotherapy and specialist psychological therapies. Predictors included clinical and sociodemographic characteristics, and polygenic risk scores for depression and related traits. Logistic regression models examined associations with outcome, and predictive potential was assessed using elastic net regularised logistic regressions with 10-fold nested cross-validation. Results47% of patients responded (50% reduction in HAMD-21 score at discharge). Age at onset and number of depressive episodes were positively associated with response, while degree of resistance was negatively associated. All elastic net models had poor performance (AUC<0.6). Illness history characteristics were commonly retained, and the addition of genetic risk scores did not improve performance. LimitationsThe patient sample was heterogeneous and received a variety of treatments. Some variable associations may be non-linear and therefore not captured. ConclusionsThis treatment may be most effective for recurrent patients and those with a later age of onset, while patients more severely treatment resistant at admission remain amongst the most difficult to treat. Individual level prediction remains elusive for this complex group. The assessment of homogenous subgroups should be one focus of future investigations.

Mobile phone use and body weight: evidence from the UK COSMOS and UK Biobank cohort studies

Background/Aim: Television viewing and computer use has been associated with obesity due to the sedentary time associated with these activities. Mobile phone technology would not necessarily predict sedentary behaviour and thus obesity in the same way. However, literature on mobile phone use and weight is limited to a few studies in adolescents with inconsistent findings.Methods: This analysis included 81,113 and 330,285 adults from the UK COSMOS and UK Biobank cohorts respectively. Multiple linear and logistic regression were used to explore the association between weight outcomes (BMI, weight, obesity) and mobile phone use frequency (calls/day), duration (min/week) and lifetime mobile phone use (years), with adjustment for potential confounders. Analyses of UK Biobank were enhanced with the addition of genetic risk scores for BMI, sleep and personality traits, to investigate potential effect modification. Longitudinal analyses of UK COSMOS are ongoing.Results: Cross-sectional analyses revealed statistically significant positive dose-response relationships between increasing mobile phone use and increasing body weight, BMI, and odds of obesity in both populations independently. For example, after adjustment, spending ≥7 hrs per week on mobile phone calls was associated with mean difference in body weight of 2.1 kg (95% CI 1.7, 2.5) and 2.4 kg (95% CI 2.1, 2.6), for UK COSMOS and UK Biobank participants respectively, compared to those who spend 5-29 mins per week making calls. Longitudinal analyses from the UK COSMOS study will be presented.Conclusion: We found a novel association between mobile phone use and body weight. Further research with longitudinal data is essential to explore the direction of these relationships. However, given the widespread use of mobile phones by adults and children, and public health concerns regarding obesity, mobile phone use might be an important lifestyle factor that can be modified to help reduce obesity on a population level.

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany

BackgroundRisk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation.MethodsWe conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50–75 years with 14-year follow-up. Lifetime smoking history was obtained in detail at recruitment. We built a GRS based on 31 previously identified LC-associated single-nucleotide polymorphisms (SNPs) and a DNA methylation score (MRS) based on methylation of 151 previously identified smoking-associated cytosine-phosphate-guanine (CpG) loci. We evaluated associations of GRS and MRS with LC incidence by logistic regression models, controlling for age, sex, smoking status, and pack-years. We compared the predictive performance of models based on pack-years alone with models additionally including GRS and/or MRS using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsGRS and MRS showed moderate and strong associations with LC risk even after comprehensive adjustment for smoking history (adjusted odds ratio [95% CI] comparing highest with lowest quartile 1.93 [1.05–3.71] and 5.64 [2.13–17.03], respectively). Similar associations were also observed within the risk groups of ever and heavy smokers. Addition of GRS and MRS furthermore strongly enhanced LC prediction beyond prediction by pack-years (increase of optimism-corrected AUC among heavy smokers from 0.605 to 0.654, NRI 26.7%, p = 0.0106, IDI 3.35%, p = 0.0036), the increase being mostly attributable to the inclusion of MRS.ConclusionsConsideration of MRS, by itself or in combination with GRS, may strongly enhance LC risk stratification.

Abstract MP055: Early and Long-Term Average Lipid Levels, As Compared With Contemporary Lipid Levels, Are More Strongly Associated with Coronary Artery Calcium in the Framingham Heart Study

Introduction We hypothesized that early and long-term average lipid levels, as compared with contemporary lipid measures, would be more strongly associated with coronary artery calcium (CAC), a marker of atherosclerosis burden, in the Framingham Heart Study (FHS). We sought to further examine the cumulative impact of genetic variants known to alter lipid levels on these associations. Methods FHS Offspring Cohort participants (n=1156, 44%M, 63±9 yrs) underwent serial fasting lipid profiles [low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG)] from Exam 1 (1971-1975) to Exam 7 (1998-2001). CAC was measured by multi-detector computed tomography (2002-2005). Lipid genetic risk scores for each lipid subtype were computed from significant single nucleotide polymorphism associations reported by the Global Lipids Genetic Logistic Consortium. Logistic regression assessed the association between early, long term average (∼30 yrs), and contemporary lipid measurements and the 75 th percentile of CAC. C statistics assessed the impact of addition of lipid genetic risk score to models. Results For each lipid subtype, Exam 1 and average lipid measurements were associated with elevated CAC, with the associations stronger than that for Exam 7 measurements ( TABLE ). When we examined association of genetic risk scores with CAC, only the LDL genetic risk score was associated with CAC (age- and sex-adjusted OR 1.14, 95% CI 1.00-1.29, p=0.04). However, addition of the genetic risk score to the lipid models did not result in a statistically significant increase in the OR ( TABLE ) or C-statistic (not shown) for any of the lipid measures. Conclusion Early and long-term average lipid levels are more strongly associated with elevated CAC when compared to contemporary lipid measures, with lipid genetic risk scores adding only marginal information. Our findings suggest that early and long-term average lipid levels should be considered as important information when assessing patient risk for CAD and CV events. TABLE Model OR (95% CI) P value LDL, Exam 1 MV 1.42 (1.24-1.63) &lt;0.0001 MV + LDL genetic risk score 1.41 (1.23-1.62) &lt;0.0001 LDL, Exam 7 MV + lipid treatment (Exam 7) 1.06 (0.93-1.20) 0.41 MV + lipid treatment + LDL genetic risk score 1.04 (0.91-1.18) 0.56 LDL, Average Exams 1-7 MV + lipid treatment (any Exam) 1.24 (1.09-1.42) 0.002 MV + lipid treatment + LDL genetic risk score 1.23 (1.07-1.42) 0.004 HDL, Exam 1 MV 0.84 (0.74-0.95) 0.007 MV + HDL genetic risk score 0.85 (0.75-0.97) 0.01 HDL, Exam 7 MV + lipid treatment (Exam 7) 0.91 (0.80-1.03) 0.13 MV + lipid treatment + HDL genetic risk score 0.92 (0.80-1.05) 0.19 HDL, Average Exams 1-7 MV + lipid treatment (any Exam) 0.84 (0.74-0.95) 0.007 MV + lipid treatment + HDL genetic risk score 0.84 (0.74-0.96) 0.01 TG, Exam 1 MV 1.36 (1.19-1.56) &lt;0.0001 MV + TG genetic risk score 1.36 (1.19-1.56) &lt;0.0001 TG, Exam 7 MV + lipid treatment (Exam 7) 1.19 (1.05-1.35) 0.008 MV + lipid treatment + TG genetic risk score 1.18 (1.03-1.34) 0.01 TG, Average Exams 1-7 MV + lipid treatment (any Exam) 1.36 (1.18-1.56) &lt;0.0001 MV + lipid treatment + TG genetic risk score 1.36 (1.18-1.57) &lt;0.0001 MV= multivariable: adjusted for age and sex.

Analysis of 32 common susceptibility genetic variants and their combined effect in predicting risk of Type 2 diabetes and related traits in Indians

Recent genome-wide association studies have identified several Type 2 diabetes-related loci. We investigated the effect of susceptibility genetic variants, individually, together and in combination with conventional risk factors, on Type 2 diabetes and diabetes-related traits in Indians. We genotyped 33 variants in 1808 Indian patients and 1549 control subjects and performed association analyses with Type 2 diabetes and related traits using an additive model for individual variant and for genetic risk score based on 32 polymorphisms. The discriminatory value of genetic risk over conventional risk factors was analysed using receiver-operating characteristics curve analysis. The allelic odds ratio ranged from 1.01 (95% CI 0.85-1.19) to 1.66 (95% CI 1.32-2.01) for single-variant analyses. Although, only 16 variants had significant odds ratios, the direction of association for others was similar to earlier reports. The odds ratio for Type 2 diabetes at each genetic risk score point was 1.11 (95% CI 1.09-1.14; P = 5.6 × 10(-17)) and individuals with extremes of genetic risk score (≥ 29.0 and ≤ 17.0) had a 7.5-fold difference in risk of Type 2 diabetes. The discrimination rate between control subjects and patients improved marginally on addition of genetic risk score to conventional risk factors (area under curve = 0.959 and 0.963, respectively; P = 0.001). Of all the quantitative traits analysed, MC4R variants showed strong association with BMI (P = 4.1 × 10(-4)), fat mass per cent (P = 2.4 × 10(-4)) and other obesity-related traits, including waist circumference and hip circumference (P = 2.0 × 10(-3) for both), as well as insulin resistance (P =0.02). We replicated the association of well-established common variants with Type 2 diabetes in Indians and observed a similar association as reported in Western populations. Combined analysis of 32 variants aids identification of subgroups at increased risk of Type 2 diabetes, but adds only a minor advantage over conventional risk factors.