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.

UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.

BACKGROUND: There is limited information on the mixture effect and weights of light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) on dementia risk.
METHODS: A prospective cohort study was conducted based on the UK Biobank dataset. We included participants aged at least 45 years old without dementia at baseline between 2006-2010. The weighted quantile sum regression was used to explore the mixture effect and weights of three types of physical activity on dementia risk.
RESULTS: This study includes 354,123 participants, with a mean baseline age of 58.0-year-old and 52.4 % of female participants. During a median follow-up time of 12.5 years, 5,136 cases of dementia were observed. The mixture effect of LPA, MPA, and VPA on dementia was statistically significant (β: -0.0924, 95 % Confidence Interval (CI): (-0.1402, -0.0446), P < 0.001), with VPA (weight: 0.7922) contributing most to a lower dementia risk, followed by MPA (0.1939). For Alzheimer\'s disease, MPA contributed the most (0.8555); for vascular dementia, VPA contributed the most (0.6271).
CONCLUSIONS: For Alzheimer\'s disease, MPA was identified as the most influential factor, while VPA stood out as the most impactful for vascular dementia.

UNASSIGNED: Coronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare.
UNASSIGNED: We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression.
UNASSIGNED: We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.

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.

Advances in gene sequencing technology and decreasing costs have resulted in a proliferation of genomic data as an integral component of big data. The availability of vast amounts of genomic data and more sophisticated genomic analysis techniques has facilitated the transition of genomics from the laboratory to clinical settings. More comprehensive and precise DNA sequencing empowers patients to address health issues at the molecular level, facilitating early diagnosis, timely intervention, and personalized healthcare management strategies. Further exploration of disease mechanisms through identification of associated genes may facilitate the discovery of therapeutic targets. The prediction of an individual\'s disease risk allows for improved stratification and personalized prevention measures. Given the vast amount of genomic data, artificial intelligence, as a burgeoning technology for data analysis, is poised to make a significant impact in genomics.

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.

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.

Despite the deployment of specific breast cancer screening strategies, breast cancer incidence rates have escalated significantly over recent decades. In a bid to reverse this trend, scientists have engaged in extensive epidemiological research into breast cancer prevalence, identifying numerous individual risk factors and promoting population-wide health education. Coupled with advances in genetic testing, risk prediction models based on breast cancer genes have been developed, albeit with inherent limitations. In the new millennium, the emergence of artificial intelligence (AI) as a dominant technological force suggests that breast cancer prediction models developed with AI may represent the next frontier in research.

BACKGROUND: Polygenic risk scores for Alzheimer\'s disease (AD-PRSs) have been associated with cognition. However, few studies have examined the effect of AD-PRS beyond the APOE gene, and the influence of genetic variants related to level of cognitive ability (COG-PRS) on cognitive performance over time in the general older population.
METHODS: A population-based sample of 965 individuals born in 1930, with genetic and standardized cognitive data on six psychometric tests (Thurstone\'s picture memory, immediate recall of 10 words, Block design, word fluency, figure identification, delayed recall of 12 items), were examined at age 70, 75, 79, and 85 years. Non-APOE AD-PRSs and COG-PRSs (P < 5e-8, P < 1e-5, P < 1e-3, P < 1e-1) were generated from recent genome-wide association studies. Linear mixed effect models with random intercepts and slope were used to analyze the effect of APOE ε4 allele, AD-PRSs, and COG-PRSs, on cognitive performance and rate of change. Analyses were repeated in samples excluding dementia.
RESULTS: APOE ε4 and AD-PRS predicted change in cognitive performance (APOE ε4*age: β = -0.03, P < 0.0001 and AD-PRS *age: β = -0.01, P = 0.02). The results remained similar in the sample excluding those with dementia. COG-PRS predicted level of cognitive performance, while APOE ε4 and AD-PRS did not. COG-PRSs did not predict change in cognitive performance.
CONCLUSIONS: We found that genetic predisposition of AD predicted cognitive decline among 70-year-olds followed over 16 years, regardless of dementia status, while polygenic risk for general cognitive performance did not.