Search Results (390)

Background/Objectives: Gestational diabetes mellitus (GDM) is a state of hyperglycemia during pregnancy, increasing the risk of birth complications, and subsequent type 2 diabetes mellitus in the mother and offspring. Risk factors such as diet, obesity, and family history have demonstrated strong association with GDM, but no clear pathophysiology has been ascertained. Methods: An analysis was conducted on 38 women with and 296 without GDM, within a case/control study of pre-eclampsia. The genetic variants examined were selected from among a published polygenic risk score of 10 variants (PRS-10). Genetic models were evaluated for each variant by multivariate logistic regression methods adjusted for age, body mass index, and pre-eclampsia. Since the genotypes for three of the PRS-10 were not available, a risk score comprising the total risk alleles among seven of the variants (PRS-7) was evaluated among those with all genotypes available. Results: Multivariate logistic regression showed significant, independent, positive associations between body mass index (BMI) and age. The posited PRS-7 showed a trend (OR 1.56, 95% CI 0.92–2.56, p = 0.070), and sensitivity analysis comprising three variants (PRS-3) was significantly associated with GDM (OR 2.43, 95% CI 1.17–5.06, p = 0.017). In univariate analysis, rs1421085 was associated with GDM (OR 0.50, 95% CI 0.26–0.95, p = 0.034), but not after adjustment for covariates, and paradoxically not for the expected risk allele. None of the other six variants showed an individual association with GDM. The previously published meta-analysis of PRS-10 showed a degree of heterogeneity (p_Q= 0.03) among the three cohorts analyzed, suggesting that variant effects may differ according to the genetic background, which points to the importance of examining the generalizability of any posited polygenic risk scores. Conclusions: In conclusion, we provide additional support for and further refine the results of a previously published polygenic risk score for GDM in an ethically unrelated population. Full article

Background: Malignant hyperthermia (MH) is a pharmacogenetic disorder of skeletal muscle triggered by certain anesthetic agents. While Ryanodine Receptor 1 (RYR1) and Calcium Voltage-Gated Channel Subunit Alpha1 S (CACNA1S) are well-established susceptibility genes, the complete genetic basis of MH remains unclear, particularly in Asian populations. Methods: We conducted gene panel testing targeting 24 calcium-related genes in 338 individuals from 247 Japanese families with suspected or confirmed MH. Variants were analyzed on a gene-by-gene basis, and their pathogenicity was assessed using in silico prediction tools. Additionally, patients were classified into subgroups based on the results of the calcium-induced calcium release (CICR) assay and the Clinical Grading Scale (CGS) score. Results: Candidate pathogenic variants were identified in 118 families (48.2%), including 73 (29.8%) in RYR1, 16 (6.5%) in CACNA1S, and 62 (25.3%) in other genes. Among CICR-positive families, RYR1 and CACNA1S variants were detected in 42.0% and 5.3% of cases, respectively. In individuals with high CGS scores (Ranks 5–6), RYR1 and CACNA1S variants were observed in 56.0% and 12.0%, respectively. Variants in other genes such as STAC3, CASQ1, ATP2A1, ASPH, HRC and TRPV1 were also detected. Conclusions: Our findings confirm the predominant role of RYR1 and CACNA1S in MH susceptibility in the Japanese population and highlight additional candidate genes that may contribute to the condition. Broader genetic screening and functional validation studies are warranted to further elucidate the polygenic nature of MH. Full article

Background: Breast cancer remains a major public health challenge in Latin America, where access to personalized risk assessment tools is still limited. This study aimed to evaluate the implementation of a polygenic risk score (PRS)-based stratification model combined with remote genomic counseling in Colombian women with sporadic breast cancer and healthy women. Methods: In 2023, an embedded mixed-methods observational study was conducted in Medellín involving 1997 women aged 40–75 years who underwent clinical PRS testing. The intervention integrated PRS-based risk categorization with individualized risk factor assessment and lifestyle recommendations delivered through a remote counseling platform. Results: PRS analysis classified 9.7% of women as high risk and 46% as low risk. Healthier lifestyle patterns were significantly associated with lower PRS categories (p = 0.034). Physical activity showed a protective effect (OR = 0.60, 95% CI: 0.5–0.8), while prior smoking, elevated BMI, and sedentary behavior were associated with higher risk. The counseling model achieved high delivery (93%) and satisfaction (85%) rates. Qualitative insights revealed improved understanding of genomic risk and greater engagement in preventive behaviors. Only one new case of breast cancer was detected among intermediate-risk participants, with a diagnostic lead time of 12 months. Conclusions: These findings support the feasibility, acceptability, and potential impact of integrating PRS and genomic counseling in cancer prevention strategies in middle-income settings. Full article

Background: Preimplantation genetic testing for polygenic diseases (PGT-P) is a reproductive technology that has made it possible to assign risk scores to embryos for various complex polygenic conditions such as diabetes, hypertension, breast cancer, and schizophrenia. Whether there is public interest in utilizing PGT-P and what public opinions are regarding this technology is unknown. Therefore, the objective of our study was to evaluate the opinion of the general United States (US) public regarding PGT-P. Methods: A web-based questionnaire consisting of 25 questions was administered to a nationally representative sample of adult US residents according to age and sex. The survey contained a description of PGT-P, followed by questions with Likert-scale responses ranging from strongly agree to strongly disagree. Results: Of the 715 respondents recruited, 673 (94%) completed the survey. Most respondents agreed that use of PGT-P is ethical (54%), and another 37% were neutral; however, approximately 9% of respondents disagreed and were opposed to the use of PGT-P. Those that opposed PGT-P cited that it was “unethical” (46%) or “not natural” (39%), believed children could be negatively affected (31%), or stated that it went against their religion (15%). The majority of respondents did not know whether PGT-P was safe for embryos (68%) or children (67%) and felt that anyone should be able to utilize it (53%). Conclusions: Participants who were younger, were Atheist, or were Democrats were more likely to agree that “PGT-P is ethical”. This study identified that more than half of respondents supported the use of PGT-P. However, concerns regarding its safety and ethical implications persist. Full article

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims to examine the relationship of ACEs with incident osteoporosis and investigate interactions with polygenic risk score (PRS). Materials and Methods: This study analyzed 124,789 UK Biobank participants initially free of osteoporosis. Cumulative ACE burden (emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse) was ascertained through validated questionnaires. Multivariable-adjusted Cox proportional hazards models assessed osteoporosis risk during a median follow-up of 12.8 years. Moderation analysis examined genetic susceptibility interactions using a standardized PRS incorporating osteoporosis-related SNPs. Results: Among 2474 incident osteoporosis cases, cumulative ACEs showed dose–response associations with osteoporosis risk (adjusted hazard ratio [HR]_{per one-unit increase} = 1.07, 95% confidence interval [CI] 1.04–1.11; high ACEs [≥3 types] vs. none: HR = 1.26, 1.10–1.43). Specifically, emotional neglect (HR = 1.14, 1.04–1.25), emotional abuse (HR = 1.14, 1.03–1.27), physical abuse (HR = 1.17, 1.05–1.30), and sexual abuse (HR = 1.15, 1.01–1.31) demonstrated comparable effect sizes. Sex-stratified analysis revealed stronger associations in women. Joint exposure to high ACEs/high PRS tripled osteoporosis risk (HR = 3.04, 2.46–3.76 vs. low ACEs/low PRS) although G × E interaction was nonsignificant (P-interaction = 0.10). Conclusions: These results suggest that ACEs conferred incremental osteoporosis risk independent of genetic predisposition. These findings support the inclusion of ACE screening in osteoporosis prevention strategies and highlight the need for targeted bone health interventions for youth exposed to ACEs. Full article

Familial dysbetalipoproteinemia (FD) is a prevalent and highly atherogenic hyperlipoproteinemia associated with the ε2/ε2 APOE genotype or rare APOE variants. The contributions of additional genetic and clinical factors to the FD phenotype remain unclear. We investigated these factors in both autosomal recessive and autosomal dominant forms of FD. Targeted (n = 4666) and exome (n = 194) sequencing were used to identify the ε2/ε2 APOE genotype or rare FD-causative APOE variants. Twenty-four lipid-related genes and forty variants included in a polygenic risk score for hypertriglyceridemia (HTG) were analyzed. FD was defined by the presence of FD variants and triglycerides (TG) ≥ 1.5 mmol/L (main study group). The comparison group consisted of patients with FD variants but TG < 1.5 mmol/L. Univariable and multivariable regression analyses were performed. A total of 71 unrelated subjects were identified (45.1% male, median age 50 years). FD was diagnosed in 52 patients, while 19 had FD variants only. Age (p = 0.019), elevated polygenic risk for HTG (p = 0.001), and the presence of metabolic syndrome components (p = 0.014) were independently associated with the FD phenotype. TG levels were significantly associated with polygenic burden (0.05 mmol/L per percentile), the presence of additional rare lipid-related variants (7.0 mmol/L), and glucose metabolism disorders (3.62 mmol/L), together explaining 30% of TG variance in cross-validated model. These results highlight the interplay of genetic and metabolic factors in FD development and support the integration of HTG genetic risk scores and metabolic control into personalized FD management. Full article

Metabolic and bariatric surgery (MBS) is an effective treatment for severe obesity, though individual responses vary widely, partly due to genetic predisposition. This study investigates the association of a body mass index (BMI) polygenic score (PGS) with weight loss and metabolic outcomes following surgery. A cohort of 225 patients undergoing MBS was analyzed at baseline (T₀), six (T₆), and twelve (T₁₂) months, with anthropometric and biochemical parameters recorded at each time point. Total weight loss (TWL) and excess weight loss (EWL) percentages were calculated. PGS was computed using the LDpred-grid Bayesian method. The mean age was 45.9 ± 9.4 years. Males had a higher baseline prevalence of type 2 diabetes (T2D) and comorbidities (p < 0.001). Linear regression analysis confirmed an association between PGS and baseline BMI (p = 0.012). Moreover, mediation analysis revealed that baseline BMI mediated the effect of the PGS on %TWL at T₁₂, with an indirect effect (p-value = 0.018). In contrast, high-density lipoprotein-cholesterol (HDL-C) at T₆ and triglycerides (TG) at T₁₂ showed direct associations with the PGS (p-value = 0.004 and p-value = 0.08, respectively), with no significant mediation by BMI. This study showed a BMI-mediated association of PGS with %TWL and a direct association with lipid changes, suggesting its potential integration into personalized obesity treatment. Full article

Background: Polygenic risk scores (PRS) have emerged as promising tools for disease risk stratification. However, their validity across different populations remains unclear, particularly for autoimmune diseases, where environmental factors may play crucial roles. Methods: We calculated the population-level PRS for Sjögren’s syndrome using seven validated genetic variants (PGS001308) and allele frequency data from the 1000 Genomes Project Phase 3 for five European populations (CEU, TSI, FIN, GBR, and IBS). PRS values were correlated with published prevalence estimates from a systematic literature review. Statistical analyses included Pearson’s correlation and sensitivity analyses. Results: PRS values varied across European populations, ranging from 0.317 in the Spanish population to 0.370 in the Northern European population. A non-significant negative trend was observed between population PRS and Sjögren’s syndrome prevalence (r = −0.407, R² = 0.166). Italy showed the lowest genetic risk score (TSI: 0.349) but the highest disease prevalence (58.2 per 100,000), while Northern European populations demonstrated a higher PRS but lower prevalence. Conclusions: No significant correlation was found between genetic risk scores and disease prevalence in this limited sample of five European populations. Larger studies are needed to clarify the relationship between polygenic risk and disease prevalence. Full article

Background/Objectives: The implementation of polygenic scores (PGSs) and multifactorial risk assessments (MFRAs) has the potential to enhance breast cancer risk stratification, particularly in carriers of moderate-penetrance pathogenic variants (PVs), whose risk profiles often remain unclear if testing is limited to monogenic risk factors. Methods: To enhance breast cancer risk stratification, we included the BCAC313 polygenic score, together with MFRA, for carriers of moderate-penetrance pathogenic variants (PVs) during routine diagnostics and assessed its effect on the classification of patients’ risk categories in a real-world cohort at our center in its first year of implementation. Seventeen carriers with PVs in moderate-risk breast cancer genes were included in this study. Thirteen of them qualified for analysis for a full MFRA, including PGS, according to ancestry estimation and clinical criteria. The MFRA was performed using the CanRisk tool, which incorporates clinical, lifestyle, familial, and genetic data, including the BCAC313 score. Results: PGS z-scores were significantly higher in breast cancer patients compared to the unaffected control cohort (p = 0.016). The MFRA, including PGS, increased risk estimates for contralateral breast cancer in seven of eight patients with breast cancer and for primary breast cancer in three of five healthy carriers, compared to the risk conferred by the MFRA and moderate-penetrance pathogenic variant alone. Risk estimates varied widely, demonstrating the value of MFRA in personalized care. In five cases, one with a CHEK2-PV and four with an ATM-PV, the modified risk assessment contributed to the surgical decision for a prophylactic mastectomy. Conclusions: The MFRA, including PGS, provides the clinically meaningful refinement of breast cancer risk estimates in individuals with moderate-risk PVs. Personalized risk predictions can inform clinical management and support decision-making, which highlights the utility of this approach in clinical practice. Full article

Ischemic stroke is a complex, multifactorial disorder with a significant heritable component. Recent developments in genome-wide association studies (GWASs) have identified several common variants associated with clinical outcomes, stroke subtypes, and overall risk. Key loci implicated in biological pathways related to vascular integrity, lipid metabolism, inflammation, and atherogenesis include 9p21 (ANRIL), HDAC9, SORT1, and PITX2. Although polygenic risk scores (PRSs) hold promise for early risk prediction and stratification, their clinical utility remains limited by Eurocentric bias and missing heritability. Integrating multiomics approaches, such as functional genomics, transcriptomics, and epigenomics, enhances our understanding of stroke pathophysiology and paves the way for precision medicine. This review summarizes the current genetic landscape of ischemic stroke, emphasizing how evolving methodologies are shaping its prevention, diagnosis, and treatment. Full article

The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. EEG data were recorded during three cognitive task conditions, and samples were labeled based on APOE genotype and polygenic risk scores. A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was used to evaluate model performance and generalizability. The model achieved an ROC AUC of 60.84% across the tasks and subjects, with a Kappa value of 0.22, indicating fair agreement. Interpretation revealed a consistent focus on theta and alpha activity in the parietal and temporal regions—areas commonly associated with AD pathology. Task-related EEG combined with interpretable deep learning can reveal early AD risk signatures in healthy individuals. InterpretableCNN enhances transparency in feature identification, offering a valuable tool for preclinical screening. Full article

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality, prompting significant interest in individualized prevention and treatment strategies. This review synthesizes recent advances in genomic and precision medicine approaches relevant to ASCVD, with a focus on genetic risk scores, lipid metabolism genes, and emerging gene editing techniques. A structured literature search was conducted across PubMed, Scopus, and Web of Science databases to identify key publications from the last decade addressing genomic mechanisms, therapeutic targets, and computational tools in ASCVD. Notable findings include the identification of causal genetic variants such as PCSK9 and LDLR, the development of polygenic risk scores for early prediction, and the use of deep learning algorithms for integrative multi-omics analysis. In addition, we highlight current and future therapeutic applications including PCSK9 inhibitors, RNA-based therapies, and CRISPR-based genome editing. Collectively, these advances underscore the promise of precision medicine in tailoring ASCVD prevention and treatment to individual genetic and molecular profiles. Full article

Genomic score testing is increasingly being integrated into the management of prostate cancer (PCa) to overcome the limitations of traditional clinical and pathological parameters. Genomic tools will represent essential components of precision medicine, supporting risk stratification, therapeutic decision-making, and personalized screening strategies. Genomic score tests can be broadly classified into two main categories: polygenic risk scores (PRSs) and tumor-derived genomic classifiers (GCs). While not yet standard in routine practice, several international guidelines recommend their selective use when results are likely to impact clinical management. PRSs estimate an individual’s susceptibility to PCa based on the cumulative effect of multiple low-penetrance germline genetic variants. These scores show promise in enhancing early detection strategies and identifying men at higher genetic risk who may benefit from tailored screening protocols. Tumor-based GCs assays provide prognostic information that complements conventional clinical and pathological parameters, and are used to guide treatment decisions, including eligibility for active surveillance (AS) or adjuvant therapy after treatment of the primary tumor. This review summarizes and analyzes the current evidence on genomic testing in PCa, with a focus on the available assays, their clinical applications, and their predictive and prognostic value across the disease spectrum. When integrated with clinical and pathological parameters, these tools have the potential to significantly enhance personalized care and should be increasingly considered in routine clinical practice. Full article

Background/Objective: Dyslipidemia significantly contributes to cardiovascular disease (CVD), with triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) as key components. While genetics play a key role in lipid levels, the interplay between genetic predisposition and modifiable lifestyle factors remains unexplored in population-based studies. We aimed to study the associations between weighted polygenic risk scores (wPRS) for TG and HDL-C, lifestyle, and metabolic factors with lipid traits and CVD. Methods: In this cross-sectional study, genotype, metabolic and lifestyle data from an Israeli cohort (n = 5584 adults) were analyzed. Individual wPRSs were constructed for TG and HDL-C based on SNPs associated with each trait. Gene-environment (lifestyle and metabolic factors) associations were evaluated by stratifying participants into high wPRS (≥90th percentile) vs. lower wPRS (<90th percentile). Results: High wPRSs were significantly associated with unfavorable lipid profiles (higher TG and lower HDL-C) and elevated TG/HDL-C ratios. Males and females in the high wPRS_HDL had 97- and 10-fold higher odds of CVD, respectively (p < 0.0001). Individuals with a combined high wPRS_HDL and wPRS_TG showed a 44-fold increase in CVD odds (p < 0.0001). Obesity (BMI > 30) and HbA1c ≥5.7% were significantly associated with elevated TG and reduced HDL-C levels, particularly in high wPRS_HDL and WPRS_TG individuals, while moderate wine (1–3 drinks/week) consumption and coffee intake (≥1 cup/day) mitigated these effects, particularly among individuals with high wPRS. Conclusions: Risk stratification based on genetic, lifestyle and metabolic profiles may inform personalized prevention strategies for dyslipidemia. Full article

The coronavirus disease 2019 (COVID-19) pandemic has led to substantial health and financial burdens worldwide, and vaccines provide hope for reducing the burden of this pandemic. However, vaccinated people remain at risk for SARS-CoV-2 infection. Genome-wide association studies (GWASs) may identify potential genetic factors involved in the development of COVID-19 breakthrough infections (BIs); however, very few or no GWASs have been conducted for COVID-19 BI thus far. We conducted a GWAS and detailed bioinformatics analysis on COVID-19 BIs in a European population via the UK Biobank (UKBB). We conducted a series of analyses at different levels, including SNP-based, gene-based, pathway, and transcriptome-wide association analyses, to investigate genetic factors associated with COVID-19 BIs and hospitalized infections. The polygenic risk score (PRS) and Hoeffding’s test were performed to reveal the genetic relationships between BIs and other medical conditions. Two independent loci (LD-clumped at r² = 0.01) reached genome-wide significance (p < 5 × 10⁻⁸), including rs36170929, which mapped to LOC102725191/VWDE, and rs28645263, which mapped to RETREG1. A pathway enrichment analysis highlighted pathways such as viral myocarditis, Rho-selective guanine exchange factor AKAP13 signaling, and lipid metabolism. The PRS analyses revealed significant genetic overlap between COVID-19 BIs and heart failure and between HbA1c and type 1 diabetes. Genetic dependence was also observed between COVID-19 BIs and asthma, lung abnormalities, schizophrenia, and type 1 diabetes on the basis of Hoeffding’s test. This GWAS revealed two significant loci that may be associated with COVID-19 BIs and a number of genes and pathways that may be involved in BIs. Genetic overlap with other diseases was identified. Further studies are warranted to replicate these findings and elucidate the mechanisms involved. Full article