EAT-Lancet Diet Pattern, Genetic Predisposition, Inflammatory Biomarkers, and Risk of Lung Cancer Incidence and Mortality.

SCOPE: The association between a planetary and sustainable EAT-Lancet diet and lung cancer remains inconclusive, with limited exploration of the role of genetic susceptibility and inflammation. METHODS AND RESULTS: The study includes 175 214 cancer-free participants in the UK Biobank. Fourteen food components are collected from a 24-h dietary recall questionnaire. A polygenic risk score is constructed through capturing the overall risk variants for lung cancer. Sixteen inflammatory biomarkers are assayed in blood samples. Participants with the highest EAT-Lancet diet scores (≥12) have a lower risk of lung cancer incidence (hazard ratio [HR] = 0.64, 95% confidence interval [CI]: 0.51-0.80) and mortality (HR = 0.65, 95% CI: 0.48-0.88), compared to those with the lowest EAT-Lancet diet scores (≤8). Interestingly, there is a significantly protective trend against both lung adenocarcinoma and lung squamous cell carcinoma with higher EAT-Lancet diet scores. Despite no significant interactions, a risk reduction trend for lung cancer is observed with increasing EAT-Lancet diet scores and decreasing genetic risk. Ten inflammatory biomarkers partially mediate the association between the EAT-Lancet diet and lung cancer risk. CONCLUSION: The study depicts a lower risk of lung cancer conferred by the EAT-Lancet diet associated with lower inflammation levels among individuals with diverse genetic predispositions.

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study.

INTRODUCTION: Metabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, laboratory, dietary, and genetic factors to predict the incidence of metabolic syndrome in Koreans are limited. In the present study, we propose a genome-wide polygenic risk score for the prediction of metabolic syndrome, along with other factors, to improve the prediction accuracy of metabolic syndrome. METHODS: We developed 7 machine learning-based models and used Cox multivariable regression, deep neural network (DNN), support vector machine (SVM), stochastic gradient descent (SGD), random forest (RAF), Naïve Bayes (NBA) classifier, and AdaBoost (ADB) to predict the incidence of metabolic syndrome at year 14 using the dataset from the Korean Genome and Epidemiology Study (KoGES) Ansan and Ansung. RESULTS: Of the 5440 patients, 2,120 were considered to have new-onset metabolic syndrome. The AUC values of model, which included sex, age, alcohol intake, energy intake, marital status, education status, income status, smoking status, dried laver intake, and genome-wide polygenic risk score (gPRS) Z-score based on 344,447 SNPs (p-value < 1.0), were the highest for RAF (0.994 [95% CI 0.985, 1.000]) and ADB (0.994 [95% CI 0.986, 1.000]). CONCLUSIONS: Incorporating both gPRS and demographic, clinical, laboratory, and seaweed data led to enhanced metabolic syndrome risk prediction by capturing the distinct etiologies of metabolic syndrome development. The RAF- and ADB-based models predicted metabolic syndrome more accurately than the NBA-based model for the Korean population.

Predictive value of polygenic risk score for prostate cancer incidence and prognosis in the Han Chinese.

Although prostate cancer is a common occurrence among males, the relationship between existing risk prediction models remains unclear. The objective of this hospital-based retrospective study is to investigate the impact of polygenic risk scores (PRSs) on the incidence and prognosis of prostate cancer in the Han Chinese population. A total of 24,778 male participants including 903 patients with prostate cancer at Taichung Veterans General Hospital were enrolled in the study. PRS was calculated using 269 single nucleotide polymorphisms and their corresponding effect sizes from the polygenic score catalog. The association between PRS and the risk prostate cancer was evaluated using Cox proportional hazards regression model. Among the 24,778 participants, 903 were diagnosed with prostate cancer. The risk of prostate cancer was significantly higher in the highest quartile of PRS distribution compared to the lowest (hazard ratio = 4.770, 95% CI = 3.999-5.689, p < 0.0001), with statistical significance across all age groups. Patients in the highest quartile were diagnosed with prostate cancer at a younger age (66.8 ± 8.3 vs. 69.5 ± 8.8, p = 0.002). Subgroup analysis of patients with localized or stage 4 prostate cancer showed no significant differences in biochemical failure or overall survival. This hospital-based cohort study observed that a higher PRS was associated with increased susceptibility to prostate cancer and younger age of diagnosis. However, PRS was not found to be a significant predictor of disease stage and prognosis. These findings suggest that PRS could serve as a useful tool in prostate cancer risk assessment.

Alzheimer's disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults.

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE-ɛ4, APOE-ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE-ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE-ɛ4 with atrophy were reduced among those with higher education (P < 0.04) and younger baseline ages (P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (P = 0.035) and the hippocampus (P = 0.014), independent of APOE-ɛ4 status. APOE-ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities (P = 0.014). Additionally, there was an APOE-ɛ4 × AD-PRS interaction in relation to white matter hyperintensities (P = 0.038), with greater increases in white matter hyperintensities among APOE-ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ɛ4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ɛ2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation.

Socioeconomic inequalities, genetic susceptibility, and risks of depression and anxiety: A large-observational study.

OBJECTIVES: This study aimed to investigate the interplay between genetic susceptibility and socioeconomic disparities on psychiatric disorders. METHODS: In this study, we utilized data from the UK Biobank to analyze the Generalized Anxiety Disorder (GAD)-7 scale (N = 74,425) and the Patient Health Questionnaire (PHQ)-9 (N = 74,101), along with the Index of Multiple Deprivation (IMD). The polygenic risk score (PRS) was calculated to assess the genetic risk associated with GAD-7/PHQ-9 scores, and the individuals were classified into low, medium, and high genetic risk groups according to tertiles of PRSs related to the GAD-7/PHQ-9. Linear regression models were used to explore the relationships between GAD-7/PHQ-9 scores and IMD scores in patients with different genetic susceptibilities. RESULTS: Disadvantaged socioeconomic status was associated with the risk of anxiety and depression across all strata of genetic risk, and stronger associations were shown for individuals with greater genetic susceptibility. From low to high genetic risk, the risk of psychiatric disorders increased for the GAD-7 (ß = 0.002 to 0.032) and PHQ-9 (ß = 0.003 to 0.045) scores. In addition, strong associations of high genetic risk with anxiety (ß = 0.875) and depression (ß = 1.152) were detected in the IMD quartile 4 group compared with the least deprivation quartile group. Furthermore, income and employment were estimated to contribute strongly to anxiety (ßemployment = 7.331, ßincome = 4.492) and depression (ßemployment = 9.951, ßincome = 6.453) in the high genetic risk group. CONCLUSION: The results suggest that we should pay more attention to psychiatric disorders with high genetic susceptibility and try to improve their socioeconomic status to prevent the development of psychiatric disorders.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits.

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Associations of dinitroaniline herbicide exposure, genetic susceptibility, and lifestyle with glucose dysregulation: A gene-environment interaction study from the Wuhan-Zhuhai cohort.

OBJECTIVE: To assess the association of dinitroaniline herbicides as well as their interactions with genetic susceptibility and lifestyle with glucose dysregulation. METHODS: A total of 4310 Chinese urban adults from the baseline of the Wuhan-Zhuhai Cohort were included in the cross-sectional study. A follow-up panel from the cohort was included in the longitudinal study, including 158 participants with 432 observations. Glucose dysregulation, including fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance (HOMA-IR), type 2 diabetes mellitus (T2DM), and impaired fasting glucose (IFG) were assessed. Serum dinitroaniline herbicides including benfluralin, trifluralin, and pendimethalin were measured. T2DM-related polygenic risk score (PRS) and healthy life scores were constructed. RESULTS: Cross-sectionally, each 2-fold increase in serum benfluralin was associated with a 1.12%, 2.03%, and 9% increase in FPG, HOMA-IR, and IFG risk, respectively. Each 2-fold increase in serum trifluralin was associated with a 0.70% increase in FPG. Each 2-fold increase in serum pendimethalin was associated with a 2.53% and 24% increase in FPG and IFG risk, respectively (all P < 0.05). Positive associations were found between the dinitroaniline herbicide mixture and glucose dysregulation. Longitudinally, serum benfluralin and pendimethalin were associated with the annual increases in FPG and HOMA-IR (P < 0.05). Joint and interaction effect analysis showed that compared with participants with high benfluralin/trifluralin/pendimethalin, high PRS, and unhealthy lifestyle, those with low benfluralin/trifluralin/pendimethalin, low PRS, and healthy lifestyle showed the greatest declines in FPG, i.e., -15.46%, -13.58%, and -10.51% changes, respectively; and the greatest reductions in IFG risks, i.e., 75%, 61%, and 73% reductions, respectively (all P < 0.05). CONCLUSIONS: This study highlighted the importance of controlling dinitroaniline herbicide exposure and following healthy lifestyles in glucose dysregulation prevention, especially among individuals with high genetic risk of T2DM.

Predictors of cardiovascular disease in individuals with dysbetalipoproteinemia: a prospective study in the UK Biobank.

BACKGROUND: Dysbetalipoproteinemia (DBL) is a disorder of remnant cholesterol metabolism associated with a severe risk of atherosclerotic cardiovascular disease (ASCVD). OBJECTIVE: The objective of this study was to investigate the univariate and multivariate predictors of ASCVD in individuals with DBL. METHODS: Data from 2,699 individuals with ε2/ε2 genotypes from the UK Biobank were included in this study. DBL was defined as having an ε2ε2 genotype with evidence of dyslipidemia, defined as total cholesterol ≥ 200 mg/dL [5.2 mmol/L] and TG ≥ 175 mg/dL [2.0 mmol/L]) or lipid-lowering therapy use (n=964). RESULTS: Age, hypertension, waist circumference and a polygenic risk score for coronary artery disease (PRSCAD) were independent predictors of ASCVD among individuals with DBL. Cumulative ASCVD-free survival was lower in the ε2/ε2 DBL group (84%) compared to the ε2/ε2 non-DBL group (94%) (p<0.0001), and for DBL individuals with a PRSCAD ≥ median (79%) compared to those with a PRSCAD < median (89%) (p=0.001). CONCLUSION: We show in a large prospective cohort that a PRSCAD predicts the ASCVD risk among individuals with DBL. The findings of the present study highlight the need for better risk stratification in ε2/ε2 carriers to identify high risk individuals that would need aggressive cardiovascular management despite their low apolipoprotein B value.

Colorectal Cancer Polygenic Risk Score Is Associated With Screening Colonoscopy Findings but Not Follow-Up Outcomes.

Background and Aims: Colorectal cancer (CRC) polygenic risk scores (PRS) may help personalize CRC prevention strategies. We investigated whether an existing PRS was associated with advanced neoplasia (AN) in a population undergoing screening and follow-up colonoscopy. Methods: We evaluated 10-year outcomes in the Cooperative Studies Program #380 screening colonoscopy cohort, which includes a biorepository of selected individuals with baseline AN (defined as CRC or adenoma ≥10 mm or villous histology, or high-grade dysplasia) and matched individuals without AN. A PRS was constructed from 136 prespecified CRC-risk single nucleotide polymorphisms. Multivariate logistic regression was used to evaluate the PRS for associations with AN prevalence at baseline screening colonoscopy or incident AN in participants with at least one follow-up colonoscopy. Results: The PRS was associated with AN risk at baseline screening colonoscopy (P = .004). Participants in the lowest PRS quintile had more than a 70% decreased risk of AN at baseline (odds ratio 0.29, 95% confidence interval 0.14-0.58; P < .001) compared to participants with a PRS in the middle quintile. Using a PRS cut-off of more than the first quintile to indicate need for colonoscopy as primary screening, the sensitivity for detecting AN at baseline is 91.8%. We did not observe a relationship between the PRS and incident AN during follow-up (P = .28). Conclusion: A PRS could identify individuals at low risk for prevalent AN. Ongoing work will determine whether this PRS can identify a subset of individuals at sufficiently low risk who could safely delay or be reassured about noninvasive screening. Otherwise, more research is needed to augment these genetic tools to predict incident AN during long-term follow-up.

Mapping associations of polygenic scores with autistic and ADHD traits in a single city region.

BACKGROUND: The genetic and environmental aetiology of autistic and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary spatially, but does this translate into variation in the association of specific common genetic variants? METHODS: We mapped associations between polygenic scores for autism and ADHD and their respective traits in the Avon Longitudinal Study of Parents and Children (N = 4,255-6,165) across the area surrounding Bristol, UK, and compared them to maps of environments associated with the prevalence of autism and ADHD. RESULTS: Our results suggest genetic associations vary spatially, with consistent patterns for autistic traits across polygenic scores constructed at different p-value thresholds. Patterns for ADHD traits were more variable across thresholds. We found that the spatial distributions often correlated with known environmental influences. CONCLUSIONS: These findings shed light on the factors that contribute to the complex interplay between the environment and genetic influences in autistic and ADHD traits.

The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models.

PURPOSE: The HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information? METHODS: A polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes (n = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study (n = 2663). RESULTS: The novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 (p < 0.001) and Tromsø (p < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p = 0.003 and 11 vs. 14, p = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p < 0.001 (HUNT2) and 0.013, p < 0.001 (Tromsø)). CONCLUSION: The HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.

Genetic Polymorphisms and Genetic Risk Scores Contribute to the Risk of Coronary Artery Disease (CAD) in a North Indian Population.

Coronary artery disease (CAD) is the leading cause of death in India. Many genetic polymorphisms play a role in regulating oxidative stress, blood pressure and lipid metabolism, contributing to the pathophysiology of CAD. This study examined the association between ten polymorphisms and CAD in the Jat Sikh population from Northern India, also considering polygenic risk scores. This study included 177 CAD cases and 175 healthy controls. The genetic information of GSTM1 (rs366631), GSTT1 (rs17856199), ACE (rs4646994), AGT M235T (rs699), AGT T174M (rs4762), AGTR1 A1166C (rs5186), APOA5 (rs3135506), APOC3 (rs5128), APOE (rs7412) and APOE (rs429358) and clinical information was collated. Statistical analyses were performed using SPSS version 27.0 and SNPstats. Significant independent associations were found for GST*M1, GST*T1, ACE, AGT M235T, AGT T174M, AGTR1 A1166C and APOA5 polymorphisms and CAD risk (all p < 0.05). The AGT CT haplotype was significantly associated with a higher CAD risk, even after controlling for covariates (adjusted OR = 3.93, 95% CI [2.39-6.48], p < 0.0001). The APOA5/C3 CC haplotype was also significantly associated with CAD (adjusted OR = 1.86, 95% CI [1.14-3.03], p < 0.05). A higher polygenic risk score was associated with increased CAD risk (adjusted OR = 1.98, 95% CI [1.68-2.34], p < 0.001). Seven polymorphisms were independently associated with an increase in the risk of CAD in this North Indian population. A considerable risk association of AGT, APOA5/C3 haplotypes and higher genetic risk scores is documented, which may have implications for clinical and public health applications.

Polygenic Risk Is Associated With Long-Term Coronary Plaque Progression and High-Risk Plaque.

BACKGROUND: The longitudinal relation between coronary artery disease (CAD) polygenic risk score (PRS) and long-term plaque progression and high-risk plaque (HRP) features is unknown. OBJECTIVES: The goal of this study was to investigate the impact of CAD PRS on long-term coronary plaque progression and HRP. METHODS: Patients underwent CAD PRS measurement and prospective serial coronary computed tomography angiography (CTA) imaging. Coronary CTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography imaging). The relationship between CAD PRS and change in percent atheroma volume (PAV), percent noncalcified plaque progression, and HRP prevalence was investigated in linear mixed-effect models adjusted for baseline plaque volume and conventional risk factors. RESULTS: A total of 288 subjects (mean age 58 ± 7 years; 60% male) were included in this study with a median scan interval of 10.2 years. At baseline, patients with a high CAD PRS had a more than 5-fold higher PAV than those with a low CAD PRS (10.4% vs 1.9%; P < 0.001). Per 10 years of follow-up, a 1 SD increase in CAD PRS was associated with a 0.69% increase in PAV progression in the multivariable adjusted model. CAD PRS provided additional discriminatory benefit for above-median noncalcified plaque progression during follow-up when added to a model with conventional risk factors (AUC: 0.73 vs 0.69; P = 0.039). Patients with high CAD PRS had an OR of 2.85 (95% CI: 1.14-7.14; P = 0.026) and 6.16 (95% CI: 2.55-14.91; P < 0.001) for having HRP at baseline and follow-up compared with those with low CAD PRS. CONCLUSIONS: Polygenic risk is strongly associated with future long-term plaque progression and HRP in patients suspected of having CAD.

Genome-wide association study and meta-analysis of phytosterols identifies a novel locus for serum levels of campesterol.

Sitosterolemia is a rare inherited disorder caused by mutations in the ABCG5/ABCG8 genes. These genes encode proteins involved in the transport of plant sterols. Mutations in these genes lead to decreased excretion of phytosterols, which can accumulate in the body and lead to a variety of health problems, including premature coronary artery disease. We conducted the first genome-wide association study (GWAS) in the Middle East/North Africa population to identify genetic determinants of plant sterol levels in Qatari people. GWAS was performed on serum levels of ß-sitosterol and campesterol using the Metabolon platform from Qatar Biobank (QBB) and genome sequence data provided by Qatar Genome Program. A trans-ancestry meta-analysis of data from our Qatari cohort with summary statistics from a previously published large cohort (9758 subjects) of European ancestry was conducted. Using conditional analysis, we identified two independent single nucleotide polymorphisms associated with ß-sitosterol (rs145164937 and rs4299376), and two others with campesterol (rs7598542 and rs75901165) in the Qatari population in addition to previously reported variants. All of them map to the ABCG5/8 locus except rs75901165 which is located within the Intraflagellar Transport 43 (IFT43) gene. The meta-analysis replicated most of the reported variants, and our study provided significant support for the association of variants in SCARB1 and ABO with sitosterolemia. Evaluation of a polygenic risk score devised from European GWAS data showed moderate performance when applied to QBB (adjusted-R2 = 0.082). These findings provide new insights into the genetic architecture of phytosterol metabolism while showing the importance including under-represented populations in future GWAS studies.

Impact of hyperuricemia on CKD risk beyond genetic predisposition in a population-based cohort study.

The bidirectional effect of hyperuricemia on chronic kidney disease (CKD) underscores the importance of hyperuricemia as a risk factor for CKD. We evaluated the effect of hyperuricemia on the presence and development of CKD after considering genetic background by calculating polygenic risk scores (PRSs). We employed genome-wide association study summary statistics-excluding the United Kingdom Biobank (UKB) datasets among published CKD Gen Consortium papers-to calculate the PRSs for CKD in white background subjects. To validate PRS performance, we divided the UKB into two datasets to validate and test the data. We used logistic regression analysis to evaluate the association between hyperuricemia and CKD, and performed Kaplan-Meier survival analysis exclusively for subjects with available follow-up data. In total, 438,253 clinical data and 4,307,940 single nucleotide polymorphisms from 459,155 samples were included. We observed a significant positive association between PRS and CKD and the presence and development of CKD. Hyperuricemia significantly increased CKD risk (adjusted odds ratio 1.55, 95% confidence interval 1.48-1.61). The impact of hyperuricemia on CKD was maintained irrespective of PRS range. In addition, negative interaction between hyperuricemia and PRS for CKD was found. Survival analysis indicates that the presence of hyperuricemia significantly increased the risk of CKD development. The PRS for CKD thoroughly reflects the risk of CKD development. Hyperuricemia is a significant indicator of CKD risk, even after incorporating the genetic risk score for CKD. Irrespective of genetic risk, patients with a prospective risk of developing CKD require uric acid monitoring and management.

Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults.

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk.

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.

Comparison of subjective cognitive decline and polygenic risk score in the prediction of all-cause dementia, Alzheimer's disease and vascular dementia.

BACKGROUND: Polygenic risk scores (PRS) and subjective cognitive decline (SCD) are associated with the risk of developing dementia. It remains to examine whether they can improve the established cardiovascular risk factors aging and dementia (CAIDE) model and how their predictive abilities compare. METHODS: The CAIDE model was applied to a sub-sample of a large, population-based cohort study (n = 5,360; aged 50-75) and evaluated for the outcomes of all-cause dementia, Alzheimer's disease (AD) and vascular dementia (VD) by calculating Akaike's information criterion (AIC) and the area under the curve (AUC). The improvement of the CAIDE model by PRS and SCD was further examined using the net reclassification improvement (NRI) method and integrated discrimination improvement (IDI). RESULTS: During 17 years of follow-up, 410 participants were diagnosed with dementia, including 139 AD and 152 VD diagnoses. Overall, the CAIDE model showed high discriminative ability for all outcomes, reaching AUCs of 0.785, 0.793, and 0.789 for all-cause dementia, AD, and VD, respectively. Adding information on SCD significantly increased NRI for all-cause dementia (4.4%, p = 0.04) and VD (7.7%, p = 0.01). In contrast, prediction models for AD further improved when PRS was added to the model (NRI, 8.4%, p = 0.03). When APOE ε4 carrier status was included (CAIDE Model 2), AUCs increased, but PRS and SCD did not further improve the prediction. CONCLUSIONS: Unlike PRS, information on SCD can be assessed more efficiently, and thus, the model including SCD can be more easily transferred to the clinical setting. Nevertheless, the two variables seem negligible if APOE ε4 carrier status is available.