Chronic kidney disease (CKD) is a heterogeneous disorder that affects the kidney structure and function. This study investigated the effect of the interaction between genetic factors and dietary pattern on kidney dysfunction in Korean adults.
Baseline data were obtained from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study involving 8230 participants aged 40-69 years. Kidney dysfunction was defined as an estimated glomerular filtration rate < 90 mL/minute/1.73 m2. Genomic DNAs genotyped on the Affymetrix® Genome-Wide Human SNP array 5.0 were isolated from peripheral blood. A genome-wide association study using a generalized linear model was performed on 1,590,162 single-nucleotide polymorphisms (SNPs). To select significant SNPs, the threshold criterion was set at P-value < 5 × 10-8. Linkage disequilibrium clumping was performed based on the R2 value, and 94 SNPs had a significant effect. Participants were divided into two groups based on their generic risk score (GRS): the low-GR group had GRS > 0, while the high-GR group had GRS ≤ 0.
Three distinct dietary patterns were extracted, namely, the \"prudent pattern,\" \"flour-based and animal food pattern,\" and \"white rice pattern,\" to analyze the effect of dietary pattern on kidney function. In the \"flour-based and animal food pattern,\" higher pattern scores were associated with a higher prevalence of kidney dysfunction in both the low and high GR groups (P for trend < 0.0001 in the low-, high-GR groups of model 1; 0.0050 and 0.0065 in the low-, high-GR groups of model 2, respectively).
The results highlight a significant association between the \'flour-based and animal food pattern\' and higher kidney dysfunction prevalence in individuals with both low and high GR. These findings suggest that personalized nutritional interventions based on GR profiles may become the basis for presenting GR-based individual dietary patterns for kidney dysfunction.

OBJECTIVE: We used a polygenic risk score (PRS) to identify high-risk groups for primary open-angle glaucoma (POAG) within population-based cohorts.
METHODS: Secondary analysis of four prospective population-based studies.
METHODS: We included four European-ancestry cohorts: the United States (US) based Nurses\' Health Study (NHS), NHS2, and the Health Professionals Follow-up Study, and the Rotterdam Study (RS) in the Netherlands. The US cohorts included female nurses and male health professionals aged 55+ years. The RS included residents aged 45 years or older living in Rotterdam, the Netherlands.
METHODS: PRS weights were estimated by applying the Lassosum method on imputed genotype and phenotype data from the UK-Biobank. This resulted in 144,020 variants, single nucleotide polymorphism (SNPs) and indels, with non-zero betas that we used to calculate a PRS in the target populations. Using multivariable Cox proportional hazard models, we estimated the relationship between the standardized PRS and relative risk for POAG. Additionally, POAG prediction was tested by calculating these models\' concordance (Harrell\'s C-statistic). Finally, we assessed the association between PRS tertiles and glaucoma-related traits.
METHODS: The relative risk for POAG and Harrell\'s C-statistic (the equivalent of an area-under-the-curve for longitudinal models).
RESULTS: Among 1,046 cases and 38,809‬ controls, the relative risk (95% confidence interval) for POAG for participants in the highest PRS quintile was 3.99 (3.08, 5.18) in the US cohorts, and 4.89 (2.93, 8.17) in the Rotterdam Study, compared with participants with median genetic risk (3rd quintile). In restricted cubic spline analyses, the relation between continuous PRS and POAG risk increased exponentially in the US and Rotterdam cohorts (Pspline<0.05). Combining age, sex, intraocular pressure >25 mmHg, and family history resulted in a meta-analyzed concordance of 0.75 (0.73, 0.75). Adding the PRS to this model improved the concordance to 0.82 (0.80, 0.84). In a meta-analysis of all cohorts, cases in the highest tertile had a larger cup-disc ratio at diagnosis, by 0.11 (0.07, 0.15), and a 2.07-fold increased risk of requiring glaucoma surgery (1.19, 3.60).
CONCLUSIONS: Incorporating a PRS into a POAG predictive model improves identification concordance from 0.75 up to 0.82, supporting its potential for guiding more cost-effective screening strategies.

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.

UNASSIGNED: Cardiovascular disease (CVD), which is an important global health challenge, is expanding. One of the main factors in the occurrence of CVD is a high genetic risk. The interaction between genetic risk in CVD and nutrition is debatable. Polyphenols are one of the important dietary components that may have a protective role in people who have a high genetic risk score (GRS) for cardiometabolic risk factors. This study, conducted in overweight and obese women, examines the interaction between polyphenol intake and specific genes (MC4r, Cav-1, and Cry1) related to maintaining body balance and their interaction with cardiometabolic risk factors.
UNASSIGNED: This cross-sectional study included 391 women who were overweight or obese, aged 18 to 48 years, with a body mass index (BMI) between 25 and 40 kg/m2. Body composition was measured using the InBody 770 scanner. Total dietary polyphenol intake (TDPI) was assessed with a validated 147-item food frequency questionnaire (FFQ), and polyphenol intakes were determined using the Phenol-Explorer database. Serum samples underwent biochemical tests. The Genetic Risk Score (GRS) was calculated based on the risk alleles of three genes: MC4r, Cav-1, and Cry1.
UNASSIGNED: The mean ± standard deviation (SD) age and BMI of women were 36.67 (9.1) years and 30.98 (3.9) kg/m2, respectively. The high GRS and high TDPI group had a significant negative interaction with fasting blood glucose (FBS) (p = 0.01). Individuals who had a high GRS and a high phenolic acid intake were found to have a significant negative interaction with Triglyceride (p = 0.04). Similarly, individuals with high GRS and a high intake of flavonoids had a significant negative interaction with TG (p < 0.01) and a significant positive interaction with High-density lipoprotein (HDL) (p = 0.01) in the adjusted model.
UNASSIGNED: According to our findings, those with a high GRS may have a protective effect on cardiometabolic risk factors by consuming high amounts of polyphenols. Further studies will be necessary in the future to validate this association.

Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.

UNASSIGNED: Using a genetic risk score (GRS) to predict coronary heart disease (CHD) may detect disease earlier. The current study aims to assess whether GRS is associated with CHD incidence and whether it is clinically useful for improving prediction using traditional risk factors (TRFs) as well as family history.
UNASSIGNED: Data from a total of 48,941 participants in the Korean Genome and Epidemiology Study were analyzed in the current study. The weighted GRS was constructed using 55 single-nucleotide polymorphisms based on published genome-wide association studies. The association of GRS with incident CHD was analyzed using Cox proportional hazard model. Discrimination and reclassification were assessed to demonstrate the clinical utility of GRS. The analyses were performed separately by sex.
UNASSIGNED: After adjusting for family history and TRFs, GRS was significantly associated with CHD incidence in men; compared to the low GRS group, men in the high GRS group had a 2.07-fold increased risk of CHD (95% confidence interval [CI]: 1.51-2.85). In men, the combination of TRFs, family history, and GRS had better performance than TRFs alone (C statistics for TRF-only model, 0.66, 95% CI, 0.64-0.69; C statistics for combination model, 0.68, 95% CI, 0.65-0.71; category-free reclassification index, 15%). In women, however, there was no significant association between GRS and CHD and no improvement between models.
UNASSIGNED: GRS was associated with CHD incidence and contributed to a small improvement of CHD prediction in men. The potential clinical use of GRS may not outweigh the value of family history.

UNASSIGNED: The MI-GENES clinical trial (NCT01936675), in which participants at intermediate risk of coronary heart disease (CHD) were randomized to receive a Framingham risk score (FRSg, n=103), or an integrated risk score (IRSg, n=104) that additionally included a polygenic risk score (PRS), demonstrated that after 6 months, participants randomized to IRSg had higher statin initiation and lower low-density lipoprotein cholesterol (LDL-C).
UNASSIGNED: In a post hoc 10-year follow-up analysis of the MI-GENES trial, we investigated whether disclosure of a PRS for CHD was associated with a reduction in adverse cardiovascular events.
UNASSIGNED: Participants were followed from randomization beginning in October 2013 until September 2023 to ascertain adverse cardiovascular events, testing for CHD, and changes in risk factors, by blinded review of electronic health records. The primary outcome was the time from randomization to the occurrence of the first major adverse cardiovascular event (MACE), defined as cardiovascular death, non-fatal myocardial infarction, coronary revascularization, and non-fatal stroke. Statistical analyses were conducted using Cox proportional hazards regression and linear mixed-effects models.
UNASSIGNED: We followed all 203 participants who completed the MI-GENES trial, 100 in FRSg and 103 in IRSg (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 FRSg and 2 in IRSg (hazard ratio (HR), 0.20; 95% confidence interval (CI), 0.04 to 0.94; P=0.042). In FRSg, 47 (47%) underwent at least one test for CHD, compared to 30 (29%) in IRSg (HR, 0.51; 95% CI, 0.32 to 0.81; P=0.004). IRSg participants had a longer duration of statin therapy during the first four years post-randomization and a greater reduction in LDL-C for up to 3 years post-randomization. No significant differences between the two groups were observed for hemoglobin A1C, systolic and diastolic blood pressures, weight, and smoking cessation rate during follow-up.
UNASSIGNED: The disclosure of an IRS that included a PRS to individuals at intermediate risk for CHD was associated with a lower incidence of MACE after a decade of follow-up, likely due to a higher rate of initiation and longer duration of statin therapy, leading to lower LDL-C levels.

A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 700k Finns and Estonians. We found that a high genetic generalized epilepsy PRS (PRSGGE) increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.73 per PRSGGE standard deviation [SD]) across lifetime and within 10 years after an unspecified seizure event. The effect of PRSGGE was significantly larger on idiopathic generalized epilepsies, in females and for earlier epilepsy onset. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). Here, we outline the potential of epilepsy specific PRSs to serve as biomarkers after a first seizure event.

OBJECTIVE: This study aimed to identify the genetic risk factors from donors or recipients that contribute to postliver transplantation (LT) steatotic liver disease (SLD), focusing on the genetic risk score (GRS) based on single nucleotide polymorphisms (SNPs) in SLD patients.
METHODS: This retrospective study included 55 Japanese SLD recipients and their respective donors. Genotyping of PNPLA3, TM6SF2, and HSD17B13 was undertaken, and the combined GRS was calculated. The relationship between the GRS and the incidence of posttransplant SLD was also evaluated.
RESULTS: The SLD recipients had a high prevalence of post-LT graft steatosis/steatohepatitis (76.4% and 58.2%, respectively). Although the recipients had a high frequency of risk alleles, there was no relationship between the number of risk alleles for each SNP and the incidence of posttransplant SLD. In contrast, an increased number of risk alleles for any SNP in the donor was correlated with high incidence rates of both post-LT steatosis and steatohepatitis. A multivariable analysis showed that a high donor GRS was an independent risk factor for graft steatosis (odds ratio 8.77; 95% CI, 1.94-52.94; p = 0.009). Similarly, a high donor GRS was an independent risk factor (odds ratio 6.76; 95% CI, 1.84-30.78; p = 0.007) for post-LT graft steatohepatitis.
CONCLUSIONS: Donor risk alleles of PNPLA3, TM6SF2, and HSD17B13, rather than recipient risk alleles, have been implicated in the development of posttransplant SLD. The combination of these donor risk alleles into a GRS could predict the development of posttransplant SLD.

BACKGROUND: Maternal genetic risk of type 2 diabetes (T2D) has been associated with fetal growth, but the influence of genetic ancestry is not yet fully understood. We aimed to investigate the influence of genetic distance (GD) and genetic ancestry proportion (GAP) on the association of maternal genetic risk score of T2D (GRST2D) with fetal weight and birthweight.
METHODS: Multi-ancestral pregnant women (n = 1,837) from the NICHD Fetal Growth Studies - Singletons cohort were included in the current analyses. Fetal weight (in grams, g) was estimated from ultrasound measurements of fetal biometry, and birthweight (g) was measured at delivery. GRST2D was calculated using T2D-associated variants identified in the latest trans-ancestral genome-wide association study and was categorized into quartiles. GD and GAP were estimated using genotype data of four reference populations. GD was categorized into closest, middle, and farthest tertiles, and GAP was categorized as highest, medium, and lowest. Linear regression analyses were performed to test the association of GRST2D with fetal weight and birthweight, adjusted for covariates, in each GD and GAP category.
RESULTS: Among women with the closest GD from African and Amerindigenous ancestries, the fourth and third GRST2D quartile was significantly associated with 5.18 to 7.48 g (weeks 17-20) and 6.83 to 25.44 g (weeks 19-27) larger fetal weight compared to the first quartile, respectively. Among women with middle GD from European ancestry, the fourth GRST2D quartile was significantly associated with 5.73 to 21.21 g (weeks 18-26) larger fetal weight. Furthermore, among women with middle GD from European and African ancestries, the fourth and second GRST2D quartiles were significantly associated with 117.04 g (95% CI = 23.88-210.20, p = 0.014) and 95.05 g (95% CI = 4.73-185.36, p = 0.039) larger birthweight compared to the first quartile, respectively. The absence of significant association among women with the closest GD from East Asian ancestry was complemented by a positive significant association among women with the highest East Asian GAP.
CONCLUSIONS: The association between maternal GRST2D and fetal growth began in early-second trimester and was influenced by GD and GAP. The results suggest the use of genetic GD and GAP could improve the generalizability of GRS.