Search

Background: Prenatal alcohol exposure poses significant risks to offspring mental health. However, the interplay between genetic predispositions to mental health disorders and prenatal alcohol exposure remains incompletely understood, limiting our ability to develop effective interventions for these conditions. Methods: Data from the Adolescent Brain and Cognitive Development (ABCD) Study were analyzed to explore associations between polygenic risk scores (PRS) for mental disorders and maternal alcohol consumption during pregnancy. Logistic regression and structural equation modeling were utilized to assess these relationships. Results: Maternal alcohol consumption after pregnancy awareness was significantly associated with an increased genetic risk for specific mental health disorders, particularly bipolar disorder in offspring. The relationship between maternal alcohol consumption and mental health outcomes was influenced by polygenic risk scores, with both externalizing and internalizing problems being affected. Conclusions: Our findings highlight the specific interaction between increased genetic risk for bipolar disorder and prenatal alcohol exposure in shaping offspring mental health outcomes. The significant associations we observed underscore the importance of considering both polygenic risk scores and prenatal alcohol exposure when assessing mental health risks in children. These insights emphasize the need for targeted interventions that address both genetic predispositions and environmental exposures to better understand and mitigate the impact on offspring mental health. [ABSTRACT FROM AUTHOR]

Background: The majority of men referred with a raised PSA for suspected prostate cancer will receive unnecessary tertiary investigations including MRI and biopsy. Here, we compared different types of biomarkers to refine tertiary referrals and when different definitions of clinically significant cancer were used. Methods: Data and samples from 798 men referred for a raised PSA (≥ 3 ng/mL) and investigated through an MRI-guided biopsy pathway were accessed for this study. Bloods were acquired pre-biopsy for liquid biomarkers and germline DNA. Variables explored included PSA + Age (base model), free/total PSA (FTPSA), Prostate Health Index (phi), PSA density (PSAd), polygenic risk score (PRS) and MRI (≥ LIKERT 3). Different diagnostic endpoints for significant cancer (≥ grade group 2 [GG2], ≥ GG3, ≥ Cambridge Prognostic Group 2 [CPG2], ≥ CPG3) were tested. The added value of each biomarker to the base model was evaluated using logistic regression models, AUC and decision curve analysis (DCA) plots. Results: The median age and PSA was 65 years and 7.13 ng/mL respectively. Depending on definition of clinical significance, ≥ grade group 2 (GG2) was detected in 57.0% (455/798), ≥ GG3 in 27.5% (220/798), ≥ CPG2 in 61.6% (492/798) and ≥ CPG3 in 42.6% (340/798). In the pre-MRI context, the PSA + Age (base model) AUC for prediction of ≥ GG2, ≥ GG3, ≥ CPG2 and ≥ CPG3 was 0.66, 0.68, 0.70 and 0.75 respectively. Adding phi and PSAd to base model improved performance across all diagnostic endpoints but was notably better when the composite CPG prognostic score was used: AUC 0.82, 0.82, 0.83, 0.82 and AUC 0.74, 0.73, 0.79, 0.79 respectively. In contrast, neither FTPSA or PRS scores improved performance especially in detection of ≥ GG3 and ≥ CPG3 disease. Combining biomarkers did not alter results. Models using phi and PSAd post-MRI also improved performances but again benefit varied with diagnostic endpoint. In DCA analysis, models which incorporated PSAd and phi in particular were effective at reducing use of MRI and/or biopsies especially for ≥ CPG3 disease. Conclusion: Incorporating phi or PSAd can refine and tier who is referred for tertiary imaging and/or biopsy after a raised PSA test. Incremental value however varied depending on the definition of clinical significance and was particularly useful when composite prognostic endpoints are used. [ABSTRACT FROM AUTHOR]

Background and objectives: Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function, and a cure for this devastating disease remains elusive. This study aimed to identify pre-disposing genetic, phenotypic, and exposure-related factors for amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential. Methods: Utilizing data from the UK (United Kingdom) Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates. Results: Both PRSs modestly predicted ALS diagnosis but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved the prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a fourfold higher ALS risk (95% CI [2.04, 7.73]) versus those in the 40%–60% range. Discussion: By leveraging UK Biobank data, our study uncovers pre-disposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes. Methods: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose. Results: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone. Conclusions/interpretation: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models. [ABSTRACT FROM AUTHOR]

Background and Aims A cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown. Methods The Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40–74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012–2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of ≥10%), compared with QRISK2 alone. Results The distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score.32; controls, −.18, P = 8.28×10−9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%–68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%–75.2%), a relative increase of 11.7% (P = 1×10−4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03–5.64, P =.031) for cases compared with controls. In individuals aged 40–54 years, QRISK2 identified 26.0% (95% CI: 16.5%–37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%–50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P =.001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores. Conclusions In a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems. [ABSTRACT FROM AUTHOR]

BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling. [ABSTRACT FROM AUTHOR]

Our study aimed to identify sweetness preference-associated single-nucleotide polymorphisms (SNPs), characterize the related genetic loci, and develop SNP-based polygenic risk scores (PRS) to analyze their associations with obesity. For genotyping, we utilized a pooled genome-wide association study (GWAS) dataset of 18,499 females and 10,878 males. We conducted genome-wide association analyses, functional annotation, and employed the weighted method to calculate the levels of PRS from 677 sweetness preference-related SNPs. We used Cox proportional hazards modeling with time-varying covariates to estimate age-adjusted and multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for obesity incidence. We also tested the correlation between PRS and environmental factors, including smoking and dietary components, on obesity. Our results showed that in males, the TT genotype of rs4861982 significantly increased obesity risk compared to the GG genotype in the Health Professionals Follow-up Study (HPFS) cohort (HR = 1.565; 95% CI, 1.122–2.184; p = 0.008) and in the pooled analysis (HR = 1.259; 95% CI, 1.030–1.540; p = 0.025). Protein tyrosine phosphatase receptor type O (PTPRO) was identified as strongly associated with sweetness preference, indicating a positive correlation between sweetness preference and obesity risk. Moreover, each 10 pack-year increment in smoking was significantly associated with an increased risk of obesity in the HPFS cohort (HR = 1.024; 95% CI, 1.000–1.048) in males but not in females. In conclusion, significant associations between rs4861982, sweetness preference, and obesity were identified, particularly among males, where environmental factors like smoking are also correlated with obesity risk. [ABSTRACT FROM AUTHOR]

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

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis. [ABSTRACT FROM AUTHOR]

Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson's disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from a small cohort of post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes (ODCs) and oligodendrocyte precursor cells (OPCs) with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria. In an independent PD cohort with clinical measures (681 cases and 549 controls), polygenic risk scores derived from the dysregulated genes significantly predicted Montreal Cognitive Assessment (MoCA)-, and Beck Depression Inventory-II (BDI-II)-scores but not motor impairment (UPDRS-III). We extended our analysis of clinical outcome prediction by incorporating differentially expressed genes from three separate datasets that were previously published by different laboratories. In the first dataset from the anterior cingulate cortex, we identified an association between ODCs and BDI-II. In the second dataset obtained from the substantia nigra (SN), OPCs displayed an association with UPDRS-III. In the third dataset from the SN region, a distinct subtype of OPCs, labeled OPC_ADM, exhibited an association with UPDRS-III. Intriguingly, the OPC_ADM cluster also demonstrated a significant increase in PD samples. These results suggest that by expanding our focus to glial cells, we can uncover region-specific molecular pathways associated with PD symptoms. [ABSTRACT FROM AUTHOR]

Purpose: Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears. Methods: A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25–16 kHz) and DPOAEs (1–16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs. Results: The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears. Conclusion: The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages. [ABSTRACT FROM AUTHOR]

Background Attention-deficit hyperactivity disorder (ADHD) is often comorbid with other medical conditions in adult patients. However, ADHD is extremely underdiagnosed in adults and little is known about the medical comorbidities in undiagnosed adult individuals with high ADHD liability. In this study we investigated associations between ADHD genetic liability and electronic health record (EHR)-based ICD-10 diagnoses across all diagnostic categories, in individuals without ADHD diagnosis history. Methods We used data from the Estonian Biobank cohort (N = 111 261) and generated polygenic risk scores (PRS) for ADHD (PRS ADHD) based on the ADHD genome-wide association study. We performed a phenome-wide association study (PheWAS) to test for associations between standardized PRS ADHD and 1515 EHR-based ICD-10 diagnoses in the full and sex-stratified sample. We compared the observed significant ICD-10 associations to associations with (1) ADHD diagnosis and (2) questionnaire-based high ADHD risk analyses. Results After Bonferroni correction (p = 3.3 × 10 −5) we identified 80 medical conditions associated with PRS ADHD. The strongest evidence was seen with chronic obstructive pulmonary disease (OR 1.15, CI 1.11–1.18), obesity (OR 1.13, CI 1.11–1.15), and type 2 diabetes (OR 1.11, CI 1.09–1.14). Sex-stratified analysis generally showed similar associations in males and females. Out of all identified associations, 40% and 78% were also observed using ADHD diagnosis or questionnaire-based ADHD, respectively, as the predictor. Conclusions Overall our findings indicate that ADHD genetic liability is associated with an increased risk of a substantial number of medical conditions in undiagnosed individuals. These results highlight the need for timely detection and improved management of ADHD symptoms in adults. [ABSTRACT FROM AUTHOR]

Background: Associations between metabolic status and metabolic changes with the risk of cardiovascular outcomes have been reported. However, the role of genetic susceptibility underlying these associations remains unexplored. We aimed to examine how metabolic status, metabolic transitions, and genetic susceptibility collectively impact cardiovascular outcomes and all-cause mortality across diverse body mass index (BMI) categories. Methods: In our analysis of the UK Biobank, we included a total of 481,576 participants (mean age: 56.55; male: 45.9%) at baseline. Metabolically healthy (MH) status was defined by the presence of < 3 abnormal components (waist circumstance, blood pressure, blood glucose, triglycerides, and high-density lipoprotein cholesterol). Normal weight, overweight, and obesity were defined as 18.5 ≤ BMI < 25 kg/m2, 25 ≤ BMI < 30 kg/m2, and BMI ≥ 30 kg/m2, respectively. Genetic predisposition was estimated using the polygenic risk score (PRS). Cox regressions were performed to evaluate the associations of metabolic status, metabolic transitions, and PRS with cardiovascular outcomes and all-cause mortality across BMI categories. Results: During a median follow-up of 14.38 years, 31,883 (7.3%) all-cause deaths, 8133 (1.8%) cardiovascular disease (CVD) deaths, and 67,260 (14.8%) CVD cases were documented. Among those with a high PRS, individuals classified as metabolically healthy overweight had the lowest risk of all-cause mortality (hazard ratios [HR] 0.70; 95% confidence interval [CI] 0.65, 0.76) and CVD mortality (HR 0.57; 95% CI 0.50, 0.64) compared to those who were metabolically unhealthy obesity, with the beneficial associations appearing to be greater in the moderate and low PRS groups. Individuals who were metabolically healthy normal weight had the lowest risk of CVD morbidity (HR 0.54; 95% CI 0.51, 0.57). Furthermore, the inverse associations of metabolic status and PRS with cardiovascular outcomes and all-cause mortality across BMI categories were more pronounced among individuals younger than 65 years (Pinteraction < 0.05). Additionally, the combined protective effects of metabolic transitions and PRS on these outcomes among BMI categories were observed. Conclusions: MH status and a low PRS are associated with a lower risk of adverse cardiovascular outcomes and all-cause mortality across all BMI categories. This protective effect is particularly pronounced in individuals younger than 65 years. Further research is required to confirm these findings in diverse populations and to investigate the underlying mechanisms involved. [ABSTRACT FROM AUTHOR]

Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges. [ABSTRACT FROM AUTHOR]

Recently, the use of polygenic risk scores in embryo screening (PGT-P) has been introduced on the premise of reducing polygenic disease risk through embryo selection. However, it has been met with extensive critique: considered "technology-driven" rather than "evidence-based", concerns exist about its validity, utility, ethics, and societal effects. Its scientific foundations and criticisms thus need to be carefully considered. However, seeing as PGT-P is already offered in some settings, further questions need to be addressed, in order to give due diligence to various aspects of PGT-P. By examining the complexities of clinical introduction of PGT-P, we discuss whether PGT-P could be responsibly implemented in the first place, what elements need to be addressed if PGT-P is clinically implemented, and subsequently how counselling and decision-making of its users could be envisaged. By dissecting these elements, we provide an overview of important practical questions of PGT-P and emphasize elements of PGT-P that we think have yet to be given sufficient attention. These questions and elements are for example related to the potential target group, scope, and decision-making possibilities of PGT-P. The aspects we raise are crucial to consider by the scientific community and policy makers for the development of guidelines and/or an ethical framework for PGT-P. [ABSTRACT FROM AUTHOR]

Objective: Many but not all persons with bipolar disorder require hospital care because of severe mood episodes. Likewise, some but not all patients experience long-term occupational dysfunction that extends beyond acute mood episodes. It is not known whether these dissimilar outcomes of bipolar disorder are driven by different polygenic profiles. Here, polygenic scores (PGSs) for major psychiatric disorders and educational attainment were assessed for associations with occupational functioning and psychiatric hospital admissions in bipolar disorder. Methods: A total of 4,782 patients with bipolar disorder and 2,963 control subjects were genotyped and linked to Swedish national registers. Longitudinal measures from at least 10 years of registry data were used to derive percentage of years without employment, percentage of years with long-term sick leave, and mean number of psychiatric hospital admissions per year. Ordinal regression was used to test associations between outcomes and PGSs for bipolar disorder, schizophrenia, major depressive disorder, attention deficit hyperactivity disorder (ADHD), and educational attainment. Replication analyses of hospital admissions were conducted with data from the Bipolar Disorder Research Network cohort (N=4,219). Results: Long-term sick leave and unemployment in bipolar disorder were significantly associated with PGSs for schizophrenia, ADHD, major depressive disorder, and educational attainment, but not with the PGS for bipolar disorder. By contrast, the number of hospital admissions per year was associated with higher PGSs for bipolar disorder and schizophrenia, but not with the other PGSs. Conclusions: Bipolar disorder severity (indexed by hospital admissions) was associated with a different polygenic profile than long-term occupational dysfunction. These findings have clinical implications, suggesting that mitigating occupational dysfunction requires interventions other than those deployed to prevent mood episodes. [ABSTRACT FROM AUTHOR]

Aim: To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans‐diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full‐threshold disorders. Methods: Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans‐diagnostic clinical caseness (onset of major mental disorders) at follow‐up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on). Results: Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non‐cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings. Conclusions: The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth. [ABSTRACT FROM AUTHOR]

Background: Elucidation of the interaction of biological and psychosocial/environmental factors on opioid dependence (OD) risk can inform our understanding of the etiology of OD. We examined the role of psychosocial/environmental factors in moderating polygenic risk for opioid use disorder (OUD). Methods: Data from 1958 European ancestry adults who participated in the Yale-Penn 3 study were analyzed. Polygenic risk scores (PRS) were based on a large-scale multi-trait analysis of genome-wide association studies (MTAG) of OUD. Results: A total of 420 (21.1%) individuals had a lifetime diagnosis of OD. OUD PRS were positively associated with OD (odds ratio [OR] 1.42, 95% confidence interval [CI] 1.21–1.66). Household income and education were the strongest correlates of OD. Among individuals with higher OUD PRS, those with higher education level had lower odds of OD (OR 0.92, 95% CI 0.85–0.98); and those with posttraumatic stress disorder (PTSD) were more likely to have OD relative to those without PTSD (OR 1.56, 95% CI 1.04–2.35). Conclusions: Results suggest an interplay between genetics and psychosocial environment in contributing to OD risk. While PRS alone do not yet have useful clinical predictive utility, psychosocial factors may help enhance prediction. These findings could inform more targeted clinical and policy interventions to help address this public health crisis. [ABSTRACT FROM AUTHOR]

Purpose of Review: This review provides an overview of genetic and non-genetic causes of premature coronary artery disease (pCAD). Recent Findings: pCAD refers to coronary artery disease (CAD) occurring before the age of 65 years in women and 55 years in men. Both genetic and non-genetic risk factors may contribute to the onset of pCAD. Recent advances in the genetic epidemiology of pCAD have revealed the importance of both monogenic and polygenic contributions to pCAD. Familial hypercholesterolemia (FH) is the most common monogenic disorder associated with atherosclerotic pCAD. However, clinical overreliance on monogenic genes can result in overlooked genetic causes of pCAD, especially polygenic contributions. Non-genetic factors, notably smoking and drug use, are also important contributors to pCAD. Cigarette smoking has been observed in 25.5% of pCAD patients relative to 12.2% of non-pCAD patients. Finally, myocardial infarction (MI) associated with spontaneous coronary artery dissection (SCAD) may result in similar clinical presentations as atherosclerotic pCAD. Summary: Recognizing the genetic and non-genetic causes underlying pCAD is important for appropriate prevention and treatment. Despite recent progress, pCAD remains incompletely understood, highlighting the need for both awareness and research. [ABSTRACT FROM AUTHOR]

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches--linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results. [ABSTRACT FROM AUTHOR]

Copyright of Die Gynäkologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

STUDY QUESTION Would the different regulatory approaches for preimplantation genetic testing (PGT) in Europe permit the implementation of preimplantation genetic testing using polygenic risk scores (PGT-P)? SUMMARY ANSWER While the regulatory approaches for PGT differ between countries, the space provided for potential implementation of PGT-P seems limited in all three regulatory models. WHAT IS KNOWN ALREADY PGT is a reproductive genetic technology that allows the testing for hereditary genetic disorders and chromosome abnormalities in embryos before implantation. Throughout its history, PGT has largely been regarded as an ethically sensitive technology. For example, ethical questions have been raised regarding the use of PGT for adult-onset conditions, non-medical sex selection, and human leukocyte antigen typing for the benefit of existing siblings. Countries in which PGT is offered each have their own approach of regulating the clinical application of PGT, and a clear overview of legal and practical regulation of PGT in Europe is lacking. An emerging development within the field of PGT, namely PGT-P, is currently bringing new ethical tensions to the forefront. It is unclear whether PGT-P may be applied within the current regulatory frameworks in Europe. Therefore, it is important to investigate current regulatory frameworks in Europe and determine whether PGT-P fits within these frameworks. STUDY DESIGN, SIZE, DURATION The aim of this study was to provide an overview of the legal and practical regulation of the use of PGT in seven selected European countries (Belgium, France, Germany, Italy, the Netherlands, Spain, and the UK) and critically analyse the different approaches with regards to regulatory possibilities for PGT-P. Between July and September 2023, we performed a thorough and extensive search of websites of governments and governmental agencies, websites of scientific and professional organizations, and academic articles in which laws and regulations are described. PARTICIPANTS/MATERIALS, SETTING, METHODS We investigated the legal and regulatory aspects of PGT by analysing legal documents, regulatory frameworks, scientific articles, and guidelines from scientific organizations and regulatory bodies to gather relevant information about each included country. The main sources of information were national laws relating to PGT. MAIN RESULTS AND THE ROLE OF CHANCE We divided the PGT regulation approaches into three models. The regulation of PGT differs per country, with some countries requiring central approval of PGT for each new indication (the medical indication model: the UK, the Netherlands), other countries evaluating each individual PGT request at the local level (the individual requests model: France, Germany), and countries largely leaving decision-making about clinical application of PGT to healthcare professionals (the clinical assessment model: Belgium, Italy, Spain). In the countries surveyed that use the medical indication model and the individual requests model, current legal frameworks and PGT criteria seem to exclude PGT-P. In countries using the clinical assessment model, the fact that healthcare professionals and scientific organizations in Europe are generally negative about implementation of PGT-P due to scientific and socio-ethical concerns, implies that, even if it were legally possible, the chance that PGT-P would be offered in the near future might be low. LIMITATIONS, REASONS FOR CAUTION The results are based on our interpretation of publicly available written information and documents, therefore not all potential discrepancies between law and practice might have been identified. In addition, our analysis focuses on seven—and not all—European countries. However, since these countries are relevant players within PGT in Europe and since they have distinct PGT regulations, the insights gathered give relevant insights into diverse ways of PGT regulation. WIDER IMPLICATIONS OF THE FINDINGS To the best of our knowledge, this is the first paper that provides a thorough overview of the legal and practical regulation of PGT in Europe. Our analysis of how PGT-P fits within current regulation models provides guidance for healthcare professionals and policymakers in navigating the possible future implementation of PGT-P within Europe. STUDY FUNDING/COMPETING INTEREST(s) This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 813707. The authors declare no conflict of interest. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Genome‐wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large‐scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi‐omics approaches or searching for disease‐associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS‐derived PRSs have limited predictive performance in non‐European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta‐analyses for multi‐ethnic groups as base GWAS data and cross‐population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations. [ABSTRACT FROM AUTHOR]

Gambling Disorder (GD) is an impactful behavioural addiction for which there appear to be under-pinning genetic contributors. Twin studies show significant GD heritability results and intergenerational transmission show high rates of transmission. Recent developments in polygenic and multifactorial risk prediction modelling provide promising opportunities to enable early identification and intervention for at risk individuals. People with GD often have significant delays in diagnosis and subsequent help-seeking that can compromise their recovery. In this paper we advocate for more research into the utility of polygenic and multifactorial risk modelling in GD research and treatment programs and rigorous evaluation of its costs and benefits. [ABSTRACT FROM AUTHOR]

The clinical features and pathophysiology of neuropsychiatric symptoms (NPSs) in dementia have been extensively studied. However, the genetic architecture and underlying neurobiological mechanisms of NPSs at preclinical stages of cognitive decline and Alzheimer's disease (AD) remain largely unknown. Mild behavioral impairment (MBI) represents an at-risk state for incident cognitive impairment and is defined by the emergence of persistent NPSs among non-demented individuals in later life. These NPSs include affective dysregulation, decreased motivation, impulse dyscontrol, abnormal perception and thought content, and social inappropriateness. Accumulating evidence has recently begun to shed more light on the genetic background of MBI, focusing on its potential association with genetic factors related to AD. The Apolipoprotein E (APOE) genotype and the MS4A locus have been associated with affective dysregulation, ZCWPW1 with social inappropriateness and psychosis, BIN1 and EPHA1 with psychosis, and NME8 with apathy. The association between MBI and polygenic risk scores (PRSs) in terms of AD dementia has been also explored. Potential implicated mechanisms include neuroinflammation, synaptic dysfunction, epigenetic modifications, oxidative stress responses, proteosomal impairment, and abnormal immune responses. In this review, we summarize and critically discuss the available evidence on the genetic background of MBI with an emphasis on AD, aiming to gain insights into the potential underlying neurobiological mechanisms, which till now remain largely unexplored. In addition, we propose future areas of research in this emerging field, with the aim to better understand the molecular pathophysiology of MBI and its genetic links with cognitive decline. [ABSTRACT FROM AUTHOR]

Background Parkinson's disease (PD) is the second most common neurodegenerative disorder and is clinically characterized by the presence of motor (bradykinesia, rigidity, rest tremor and postural instability) and non-motor symptoms (cognitive impairment, autonomic dysfunction, sleep disorders, depression and hyposmia). The aetiology of PD is unknown except for a small but significant contribution of monogenic forms. Sources of data No new data were generated or analyzed in support of this review. Areas of agreement Up to 15% of PD patients carry pathogenic variants in PD-associated genes. Some of these genes are associated with mendelian inheritance, while others act as risk factors. Genetic background influences age of onset, disease course, prognosis and therapeutic response. Areas of controversy Genetic testing is not routinely offered in the clinical setting, but it may have relevant implications, especially in terms of prognosis, response to therapies and inclusion in clinical trials. Widely adopted clinical guidelines on genetic testing are still lacking and open to debate. Some new genetic associations are still awaiting confirmation, and selecting the appropriate genes to be included in diagnostic panels represents a difficult task. Finally, it is still under study whether (and to which degree) specific genetic forms may influence the outcome of PD therapies. Growing points Polygenic Risk Scores (PRS) may represent a useful tool to genetically stratify the population in terms of disease risk, prognosis and therapeutic outcomes. Areas timely for developing research The application of PRS and integrated multi-omics in PD promises to improve the personalized care of patients. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the association between localised vascular and retinal nerve fibre layer (RNFL) loss and genetic risk for glaucoma and cardiovascular disease using polygenic risk scores (PRS). Methods: 858 eyes were included from 455 individuals with suspect and early manifest primary open angle glaucoma. Eyes were characterised as having localised vascular and/or RNFL wedge‐shaped defects by scrutiny of optical coherence tomography angiography (OCTA) and OCT images, respectively. Investigations included associations with pre‐established scores for genetic risk of glaucoma and cardiovascular disease in the context of glaucoma risk factors and systemic vascular disease outcomes. Results: Higher genetic risk for glaucoma was associated with both vascular wedge defects and RNFL defects (p < 0.001 and p = 0.020, respectively). A greater genetic risk of glaucoma was associated with the presence of multiple vascular wedges per eye (p = 0.005). Glaucoma progression based on global RNFL loss was associated with vascular and RNFL wedge defects (p ≤ 0.001 and p = 0.008, respectively). The glaucoma PRS was significantly associated with vascular, but not RNFL, wedge defects after controlling for disc haemorrhage (p = 0.007 and p = 0.070, respectively). Vascular wedge defects were not related to the cardiovascular PRS. Conclusion: Individuals with a higher genetic risk of glaucoma based on the PRS were more likely to have retinal vascular defects, as well as structural glaucomatous loss, but this did not relate to systemic cardiovascular risk. This possibly implies a local pathophysiology for the vascular defects in some cases, which may have clinical relevance in the early stages of glaucoma and in individuals at high genetic risk. [ABSTRACT FROM AUTHOR]

Copyright of Herzschrittmachertherapie und Elektrophysiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the use of polygenic scores (PGSs) in understanding psychiatric outcomes. PGSs measure genetic risk based on common genetic variants identified through genome-wide association studies (GWASs). The study analyzed the relationship between PGSs for various psychiatric disorders and occupational disability and severity of illness in individuals with bipolar disorder. The findings showed that higher PGSs for major depressive disorder and ADHD were associated with negative occupational outcomes, and higher PGSs for schizophrenia and major depressive disorder were associated with longer periods of unemployment. The study highlights the importance of integrating genetic and phenotypic data to improve diagnosis and risk prediction in psychiatric disorders. [Extracted from the article]

Background: A heavy burden of cardiometabolic conditions on low- and middleincome countries like India that are rapidly undergoing urbanization remains unaddressed. Indians are known to have high levels of triglycerides and low levels of HDL-C along with moderately higher levels of LDL-C. The genomewide findings from Western populations need to be validated in an Indian context for a better understanding of the underlying etiology of dyslipidemia in India. Objective: We aim to validate 12 genetic variants associated with lipid levels among rural and urban Indian populations and derive unweighted and weighted genetic risk scores (uGRS and wGRS) for lipid levels among the Indian population. Methods: Assuming an additivemodel of inheritance, linear regressionmodels adjusted for all the possible covariates were run to examine the association between 12 genetic variants and total cholesterol, triglycerides, HDL-C, LDL-C, and VLDL-C among 2,117 rural and urban Indian participants. The combined effect of validated loci was estimated by allelic risk scores, unweighted and weighted by their effect sizes. Results: The wGRS for triglycerides and VLDL-C was derived based on five associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, and rs4420638 at APOC1), which was associated with 36.31mg/dL of elevated triglyceride and VLDL-C levels (β = 0.95, SE = 0.16, p < 0.001). Similarly, every unit of combined risk score (rs2293889 at TRPS1 and rs4147536 at ADH1B)was associatedwith 40.62mg/dL of higher total cholesterol (β=1.01, SE=0.23, p < 0.001) and 33.97 mg/dL of higher LDL-C (β=1.03, SE=0.19, p < 0.001) based on itswGRS (rs2293889 at TRPS1, rs4147536 at ADH1B, rs4420638 at APOC1, and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 at FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1, and rs7832643 at PLEC) was associated with 10.64mg/dL of lower HDL-C (β = -0.87, SE = 0.14, p < 0.001). Conclusion: We confirm the role of eight genome-wide association study (GWAS) loci related to different lipid levels in the Indian population and demonstrate the combined effect of variants for lipid traitsamong Indians by deriving the polygenic risk scores. Similar studies among different populations are required to validate the GWAS loci and effect modification of these loci by lifestyle and environmental factors related to urbanization. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting estimated 164 million people worldwide. Early detection and intervention strategies are essential to reduce the burden of COPD, but current screening approaches are limited in their ability to accurately predict risk. Machine learning (ML) models offer promise for improved accuracy of COPD risk prediction by combining genetic and electronic medical record data. In this study, we developed and evaluated eight ML models for primary screening of COPD utilizing routine screening data, polygenic risk scores (PRS), additional clinical data, or a combination of all three. To assess our models, we conducted a retrospective analysis of approximately 329,396 patients in the UK Biobank database. Incorporating personal information and blood biochemical test results significantly improved the model's accuracy for predicting COPD risk, achieving a best performance of 0.8505 AUC, a specificity of 0.8539 and a sensitivity of 0.7584. These results indicate that ML models can be effectively utilized for accurate prediction of COPD risk in individuals aged 20 to 50 years, providing a valuable tool for early detection and intervention. [ABSTRACT FROM AUTHOR]

Childhood acute lymphoblastic leukemia (cALL) survivors are at increased risk for bone comorbidities, but accurate screening tools for such comorbidities are limited. Polygenic scores (PGS) could stratify cALL survivors for risk of long–term adverse bone outcomes. We evaluated 214 (51% female) cALL survivors from the Prévenir les Effets TArdifs de la LEucémie study (median age 21 yr). Bone mineral density (BMD) measurements were obtained using dual X–ray absorptiometry at the lumbar spine (LS-BMD), femoral neck (FN-BMD), and total body (TB-BMD), and vertebral fractures (VF) were documented using the vertebral deformity criterion. We computed a PGS for adult heel quantitative ultrasound speed of sound (gSOS), known to be associated with the risk of osteoporotic fracture, using imputed genotype data of the participants, and tested it for association with BMD Z -scores and VF risk, adjusting for clinical risk factors, and in sex and prognostic risk-stratified analyses. We found that a gSOS below the mean was associated with lower BMD in all three sites in univariate and multivariate models. In univariate analyses, 1 SD increase in gSOS conferred a 0.16 SD increase in LS-BMD (95% CI 0.005-0.31), whereas a gSOS above the mean was associated with a 0.31 SD higher LS-BMD (95% CI 0.008-0.61), a 0.36 SD higher TB-BMD (95% CI 0.06-0.67), and a 0.43 SD higher FN-BMD (95% CI 0.13-0.72). Models combining gSOS with clinical risk factors explained up to 16% of the variance of BMD phenotypes and obtained an area under the receiver operating characteristic curve for VF of 0.77 in subgroup analyses. Cranial radiation, high cumulative glucocorticoid doses, high risk group, and male sex were significant risk factors for lower BMD Z-scores. In conclusion, a PGS, in combination with clinical risk factors, could be used as a tool to risk stratify cALL survivors for treatment–related bone morbidity. [ABSTRACT FROM AUTHOR]

Interactions between predictors play an important role in many applications. Popular and successful tree-based supervised learning methods such as random forests or logic regression can incorporate interactions associated with the considered outcome without specifying which variables might interact. Nonetheless, these algorithms suffer from certain drawbacks such as limited interpretability of model predictions and difficulties with negligible marginal effects in the case of random forests or not being able to incorporate interactions with continuous variables, being restricted to additive structures between Boolean terms, and not directly considering conjunctions that reveal the interactions in the case of logic regression. We, therefore, propose a novel method called logic decision trees (logicDT) that is specifically tailored to binary input data and helps to overcome the drawbacks of existing methods. The main idea consists of considering sets of Boolean conjunctions, using these terms as input variables for decision trees, and searching for the best performing model. logicDT is also accompanied by a framework for estimating the importance of identified terms, i.e., input variables and interactions between input variables. This new method is compared to other popular statistical learning algorithms in simulations and real data applications. As these evaluations show, logicDT is able to yield high prediction performances while maintaining interpretability. [ABSTRACT FROM AUTHOR]

Dysregulation of the urea cycle (UC) has been detected in colorectal cancer (CRC). However, the impact of the UC's end product, urea, on CRC development remains unclear. We investigated the association between serum urea and CRC risk based on the data of 348 872 participants cancer‐free at recruitment from the UK Biobank. Multivariable Cox proportional hazards models were fitted to conduct risk estimates. Stratification analyses based on sex, diet pattern, metabolic factors (including body mass index [BMI], the estimated glomerular filtration rate [eGFR] and type 2 diabetes [T2D]) and genetic profiles (the polygenic risk score [PRS] of CRC) were conducted to find potential modifiers. During an average of 9.0 years of follow‐up, we identified 3408 (1.0%) CRC incident cases. Serum urea showed a nonlinear relationship with CRC risk (P‐nonlinear:.035). Lower serum urea levels were associated with a higher CRC risk, with a fully‐adjusted hazard ratio (HR) of 1.26 (95% confidence interval [CI]: 1.13‐1.41) in the first quartile (Q1) of urea, compared to the Q4. This association was largely consistent across subgroups of sex, protein diet, BMI, eGFR and CRC‐PRSs (P‐interaction >.05); however, it was stronger in the T2D, with an interaction between urea and T2D on both additive (synergy index: 3.32, [95% CI: 1.24‐8.88]) and multiplicative scales (P‐interaction:.019). Lower serum urea concentrations were associated with an increased risk of CRC, with a more pronounced effect observed in individuals with T2D. Maintaining stable levels of serum urea has important implications for CRC prevention, particularly in individuals with T2D. [ABSTRACT FROM AUTHOR]

Mendelian randomisation and polygenic risk score analysis have become increasingly popular in the last decade due to the advent of large-scale genome-wide association studies. Each approach has valuable applications, some of which are overlapping, yet there are important differences which we describe here. [ABSTRACT FROM AUTHOR]

Polygenic scores (PGS) are increasingly being used for prediction of social and behavioral traits, but suffer from many methodological, theoretical, and ethical concerns that profoundly limit their value. Primarily, these scores are derived from statistical correlations, carrying no inherent biological meaning, and thus may capture indirect effects. Further, the performance of these scores depends upon the diversity of the reference populations and the genomic panels from which they were derived, which consistently underrepresent minoritized populations, leading to poor fit when applied to diverse groups. There is also inherent danger of eugenic applications for the information gained from these scores, and general risk of misunderstandings that could lead to stigmatization for underrepresented groups. We urge extreme caution in use of PGS particularly for social/behavioral outcomes fraught for misinterpretation, with potential harm for the minoritized groups least likely to benefit from their use. [ABSTRACT FROM AUTHOR]

This study examined the independent and interactive effects of alcohol use disorder genome-wide polygenic scores (AUD-PGS) and parenting and family conflict on early adolescent externalizing behaviors. Data were drawn from White (N = 6181, 46.9% female), Black/African American (N = 1784, 50.1% female), and Hispanic/Latinx (N = 2410, 48.0% female) youth from the adolescent brain cognitive development Study (ABCD). Parents reported on youth externalizing behaviors at baseline (T1, age 9/10), 1-year (T2, age 10/11) and 2-year (T3, age 11/12) assessments. Youth reported on parenting and family environment at T1 and provided saliva or blood samples for genotyping. Results from latent growth models indicated that in general externalizing behaviors decreased from T1 to T3. Across all groups, higher family conflict was associated with more externalizing behaviors at T1, and we did not find significant associations between parental monitoring and early adolescent externalizing behaviors. Parental acceptance was associated with lower externalizing behaviors among White and Hispanic youth, but not among Black youth. Results indicated no significant main effect of AUD-PGS nor interaction effect between AUD-PGS and family variables on early adolescent externalizing behaviors. Post hoc exploratory analysis uncovered an interaction between AUD-PGS and parental acceptance such that AUD-PGS was positively associated with externalizing rule-breaking behaviors among Hispanic youth, but only when parental acceptance was very low. Findings highlight the important role of family conflict and parental acceptance in externalizing behaviors among early adolescents, and emphasize the need to examine other developmental pathways underlying genetic risk for AUD across diverse populations. [ABSTRACT FROM AUTHOR]

Background: The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein–BMI trajectory associations in adolescents and adults and how these connect to other omics layers. Methods: Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23–27 years old) to 10 years (FinnTwin12: 12–22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. Results: We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions: Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels. [ABSTRACT FROM AUTHOR]

Copyright of Die Gynäkologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably. [ABSTRACT FROM AUTHOR]

With advances in genetic research, the understanding of the genetic structure of disease and the ability to predict disease risk have been enhanced. Polygenic risk scores (PRS) have been developed to assess a person's risk of developing any heritable disease. PRS has two primary utilities that make it particularly relevant for insurers: the ability to identify high-risk groups when using PRS independently or in combination with standard risk factors; and the ability to inform early interventions that may alter future morbidity and mortality. Using heart disease as a case study, a simulation-based model is designed that introduces polygenic risk scoring into the actuarial analysis framework and then quantifies the adverse selection due to information asymmetry introduced by PRS. Individual and parental disease liability as well as PRS were simulated under a liability threshold model. A series of validations were conducted to confirm the utility of our simulated data sets. We explored three scenarios describing how insurance applicants use their PRS results to guide their insurance purchasing decisions and calculated the increased premiums that insurers would need to change to counteract this. The accuracy of PRS has the most significant impact on premiums and the proportion of individuals who know their PRS also has a substantial impact. [ABSTRACT FROM AUTHOR]

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene–diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) and related dementia (ADRD) risk is affected by multiple dependent risk factors; however, there is no consensus about their relative impact in the development of these disorders. Objective: To rank the effects of potentially dependent risk factors and identify an optimal parsimonious set of measures for predicting AD/ADRD risk from a larger pool of potentially correlated predictors. Methods: We used diagnosis record, survey, and genetic data from the Health and Retirement Study to assess the relative predictive strength of AD/ADRD risk factors spanning several domains: comorbidities, demographics/socioeconomics, health-related behavior, genetics, and environmental exposure. A modified stepwise-AIC-best-subset blanket algorithm was then used to select an optimal set of predictors. Results: The final predictive model was reduced to 10 features for AD and 19 for ADRD; concordance statistics were about 0.85 for one-year and 0.70 for ten-year follow-up. Depression, arterial hypertension, traumatic brain injury, cerebrovascular diseases, and the APOE4 proxy SNP rs769449 had the strongest individual associations with AD/ADRD risk. AD/ADRD risk-related co-morbidities provide predictive power on par with key genetic vulnerabilities. Conclusion: Results confirm the consensus that circulatory diseases are the main comorbidities associated with AD/ADRD risk and show that clinical diagnosis records outperform comparable self-reported measures in predicting AD/ADRD risk. Model construction algorithms combined with modern data allows researchers to conserve power (especially in the study of disparities where disadvantaged groups are often grossly underrepresented) while accounting for a high proportion of AD/ADRD-risk-related population heterogeneity stemming from multiple domains. [ABSTRACT FROM AUTHOR]

Background: Genetics play an important role in risk for cardiometabolic diseases—including type 2 diabetes, cardiovascular disease and obesity. Existing research has explored the clinical utility of genetic risk tools such as polygenic risk scores—and whether interventions communicating genetic risk information using these tools can impact on individuals' cognitive appraisals of disease risk and/or preventative health behaviours. Previous systematic reviews suggest mixed results. To expand current understanding and address knowledge gaps, we undertook an interpretive, reflexive method of evidence synthesis—questioning the theoretical basis behind current interventions that communicate genetic risk information and exploring how the effects of genetic risk tools can be fully harnessed for cardiometabolic diseases. Methods: We obtained 189 records from a combination of database, website and grey literature searches—supplemented with reference chaining and expert subject knowledge within the review team. Using pre-defined critical interpretive synthesis methods, quantitative and qualitative evidence was synthesised and critiqued alongside theoretical understanding from surrounding fields of behavioural and social sciences. Findings: Existing interventions communicating genetic risk information focus predominantly on the "self", targeting individual-level cognitive appraisals, such as perceived risk and perceived behavioural control. This approach risks neglecting the role of contextual factors and upstream determinants that can reinforce individuals' interpretations of risk. It also assumes target populations to embody an "ascetic subject of compliance"—the idea of a patient who strives to comply diligently with professional medical advice, logically and rationally adopting any recommended lifestyle changes. We developed a synthesising argument—"beyond the ascetic subject of compliance"—grounded in three major limitations of this perspective: (1) difficulty applying existing theories/models to diverse populations, (2) the role of familial variables and (3) the need for a life course perspective. Conclusions: Interventions communicating genetic risk information should account for wider influences that can affect individuals' responses to risk at different levels—including through interactions with their family systems, socio-cultural environments and wider health provision. Protocol registration: PROSPERO CRD42021289269 [ABSTRACT FROM AUTHOR]

Bipolar disorder (BD) is a common and highly heritable psychiatric disorder, the study of BD genetic characteristics can help with early prevention and individualized treatment. At the same time, BD is a highly heterogeneous polygenic genetic disorder with significant genetic overlap with other psychiatric disorders. In recent years, polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) data have been widely used in genetic studies of various complex diseases and can be used to explore the genetic susceptibility of diseases. This review discusses phenotypic associations and genetic correlations with other conditions of BD based on PRS, and provides ideas for genetic studies and prevention of BD. [ABSTRACT FROM AUTHOR]

The outbreak of Covid-19 negatively affected mental health and increased loneliness. The subjective feeling of loneliness is influenced by genetic and social factors and has a negative impact on mental health. From March 2020 to June 2021 loneliness was investigated in N = 517 individuals using monthly acquired questionnaire data and Latent Growth Curve Analysis. Associations of social factors and polygenic risk scores (PRSs, n = 361) with class membership were investigated. Three classes ("average", 40%; "not lonely", 38%; "elevated loneliness", 22%) were identified, that differ significantly regarding loneliness, mental dysfunction, and response to the lockdown phases. Individuals with a high PRS for neuroticism are more likely to belong to the "elevated loneliness" class, living with another person is a protective factor. As the "elevated loneliness" class was at the highest risk of mental dysfunction, our findings underscore the importance of identifying those individuals to implement counteractive measures. [ABSTRACT FROM AUTHOR]

Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa. [ABSTRACT FROM AUTHOR]

Background: Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. Method: US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. Results: Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06–1.43) and 1.18 (1.02–1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01–1.25) and 1.16 (1.04–1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03–1.31)]. No other associations were statistically significant. Conclusions: Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs. [ABSTRACT FROM AUTHOR]