Background: Metabolic syndrome (MetS) is a syndrome that comprises central obesity, increased serum triglyceride (TG) levels, decreased serum HDL cholesterol (HDL) levels, raised blood pressure (BP), and impaired glucose regulation, including prediabetic and diabetic glycaemic levels. Recently, the association with endometrial cancer (EC) has been described but it is unclear if the risk associated with MetS is higher than the individual effect of obesity alone. This study investigates the association between MetS components and differing MetS definitions on EC risk and compares the risk of MetS with the risk posed by obesity alone. It also analyses how MetS affects the risk of EC development in the pre- and post-menopausal subgroups. Methods: A prospective cohort study was undertaken using data from the UK biobank. Multivariable Cox proportional risk models with the time to diagnosis (years) were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of MetS and its components on the risk of EC. A subgroup analysis was also undertaken for pre- and post-menopausal participants. Kaplan–Meier (KM) was undertaken to assess the difference in the risk of EC development in differing BMI classes, and in pre- and post-menopausal subgroups. Results: A total of 177,005 females from the UK biobank were included in this study. Of those participants who developed EC (n = 1454), waist circumference > 80 cm, BMI > 30 kg/m², hypertension > 130/80 mmHg, hyperlipidaemia and diabetes (HbA1C > 48 mmol/L were significant predictors of EC development, with waist circumference being the strongest predictor (HR = 2.21; 95% CI: 1.98–2.47, p < 0.001). Comparing the pre- and post-menopausal subgroup, hypertriglyceridaemia and diabetes were the strongest predictors of EC in the pre-menopausal subgroup (HR = 1.53; 95% CI: 1.18–1.99 and HR = 1.51; 95% CI: 1.08–2.12, p < 0.05, respectively). Raised waist circumference was not a significant independent predictor in the pre-menopausal subgroup. A KM curve analysis showed a clear distinction between those with and without MetS in the pre-menopausal group, suggesting a benefit of testing for MetS components in pre-menopausal women with obesity. Conclusions: Components of MetS, both independently and in combination, significantly increase the risk of EC. Screening those with obesity for MetS in their pre-menopausal years may help to identify those at the highest risk.

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Background and Objective: Stroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally, and its increasing incidence has become a global public health concern. Accurate stroke prediction is highly valuable for early intervention and treatment. There is a scarcity of studies evaluating the prediction value of genetic liability in the prediction of the risk of stroke. Materials and Methods: Our study involved 243,339 participants of European ancestry from the UK Biobank. We created stroke genetic liability using data from MEGASTROKE genome-wide association studies (GWASs). In our study, we built four predictive models with and without stroke genetic liability in the training set, namely a Cox proportional hazard (Coxph) model, gradient boosting model (GBM), decision tree (DT), and random forest (RF), to estimate time-to-event risk for stroke. We then assessed their performances in the testing set. Results: Each unit (standard deviation) increase in genetic liability increases the risk of incident stroke by 7% (HR = 1.07, 95% CI = 1.02, 1.12, p-value = 0.0030). The risk of stroke was greater in the higher genetic liability group, demonstrated by a 14% increased risk (HR = 1.14, 95% CI = 1.02, 1.27, p-value = 0.02) compared with the low genetic liability group. The Coxph model including genetic liability was the best-performing model for stroke prediction achieving an AUC of 69.54 (95% CI = 67.40, 71.68), NRI of 0.202 (95% CI = 0.12, 0.28; p-value = 0.000) and IDI of 1.0 × 10⁻⁴ (95% CI = 0.000, 3.0 × 10⁻⁴; p-value = 0.13) compared with the Cox model without genetic liability. Conclusions: Incorporating genetic liability in prediction models slightly improved prediction models of stroke beyond conventional risk factors.

Background and objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension prediction model and investigate the potential influence of genetic liability for risk factors linked to CVD on hypertension risk using Random Forest (RF) and Neural Network (NN). Materials and methods: The study included 244,718 participants of European ancestry. Genetic liabilities were constructed using previously identified genetic variants associated with various cardiovascular risk factors through genome-wide association studies (GWAS). The sample was randomly split into training and testing sets at a 70:30 ratio. We used RF and NN techniques to develop prediction models in the training set with or without feature selection. We evaluated the models’ discrimination performance using the area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using RF and NN methods, respectively. Adding genetic liabilities resulted in a modest improvement in the AUC for RF but not for NN. The best prediction model was achieved using RF (AUC =0.71, Spiegelhalter z score= 0.10, P-value= 0.92, calibration slope=0.99) constructed in stage two. Conclusion: Incorporating genetic factors in the model may provide a modest incremental value for hypertension prediction beyond baseline characteristics. Our study highlighted the importance of genetic liabilities for both total cholesterol and LDL within the same prediction model adds value to the classification of hypertension.

Introduction: Stroke is one of the leading causes of death and long-term disability globally. Several studies have investigated the incidence and predictors of stroke in the healthy population; only a few have specifically focused on stroke risk prediction among individuals with hypertension. Given that hypertension is the most common modifiable risk factor for stroke, this represents an important research area to study. Improving our understanding of stroke risk among hypertensive individuals has the potential to significantly improve preventative efforts and decrease the worldwide stroke burden. Material and Methods: The current study involved 116,216 participants of European ancestry from the UK Biobank. We created stroke genetic liability using data from two predictive models using clinical, lifestyle, and genetic factors (stroke genetic liability) in the training set, namely a Cox proportional hazard (CoxPH) model, a penalized Cox proportional hazard model (CoxNet), a penalized logistic regression model (GLMnet), and a neural network (Nnet), to estimate time-to-event risk for stroke among hypertensive patients, older and younger hypertensive patients, and hypertensive males and females separately. We then assessed their performances in the testing set with the area under the curve (AUC) and Brier score (BS). Results: The CoxPH, CoxNet, and GLMnet achieved an equal AUC of 67.0% in hypertensive patients. All models demonstrated superior performance in men compared to women, with the CoxPH model achieving the best AUC of 68% in men. All models demonstrated superior performance in older patients compared to younger patients, with the GLMnet achieving the best AUC of 61% in this group. Conclusion: Including stroke genetic liability in prediction models slightly improves stroke prediction for hypertensive men and older patients. The Cox proportional hazards (CoxPH) model outperformed the machine learning models in predicting stroke risk among hypertensive men.

Introduction: Stroke is one of the leading causes of death and long-term disability globally. Several studies have investigated the incidence and predictors of stroke in the healthy population; only a few have specifically focused on stroke risk prediction among individuals with hypertension. Given that hypertension is the most common modifiable risk factor for stroke, this represents an important research area to study. Improving our understanding of stroke risk among hypertensive individuals has the potential to significantly improve preventative efforts and decrease the worldwide stroke burden. Material and Methods: The current study involved 116,216 participants of European ancestry from the UK Biobank. We created stroke genetic liability using data from two predictive models using clinical, lifestyle, and genetic factors (stroke genetic liability) in the training set, namely a Cox proportional hazard (CoxPH) model, a penalized Cox proportional hazard model (CoxNet), a penalized logistic regression model (GLMnet), and a neural network (Nnet), to estimate time-to-event risk for stroke among hypertensive patients, older and younger hypertensive patients, and hypertensive males and females separately. We then assessed their performances in the testing set with the area under the curve (AUC) and Brier score (BS). Results: The CoxPH, CoxNet, and GLMnet achieved an equal AUC of 67.0% in hypertensive patients. All models demonstrated superior performance in men compared to women, with the CoxPH model achieving the best AUC of 68% in men. All models demonstrated superior performance in older patients compared to younger patients, with the GLMnet achieving the best AUC of 61% in this group. Conclusion: Including stroke genetic liability in prediction models slightly improves stroke prediction for hypertensive men and older patients. The Cox proportional hazards (CoxPH) model outperformed the machine learning models in predicting stroke risk among hypertensive men.