Raha Pazoki

Background and purpose: Excessive sitting and genetic liability to obesity are associated with risk of obesity and hypertension, two significant risk factors for cardiovascular disease. This study aimed to investigate the interactive effects of genetic liability to obesity and excessive sitting on prevalence of hypertension. Methods: Obesity genetic liability was estimated in unrelated individuals of European ancestry (n=208,594) using previously identified genetic variants and their effect sizes for adiposity related traits. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the use of anti-hypertensive medications. Logistic regression was used to examine the association between obesity genetic liability and across different levels of self-reported sitting time. Results: excessive sitting and increased genetic liability were independently associated with higher odds of hypertension. The greatest odds of hypertension was observed in participants with high sitting time combined with increased genetic liability to obesity (OR=1.29; 95% CI = 1.25, 1.33, P −16 ) compared to individuals with low genetic liability and low sitting time. Interaction analysis identified that in individuals with excessive sitting, the effect of genetic liability of waist circumference on hypertension was greater compared to individuals with low sitting time ( P _interaction =0.03). Conclusion: Combined excessive sitting and high genetic susceptibility to obesity is associated with greatest odds of hypertension. These findings highlight the importance of lifestyle in offsetting risk imposed by genetic factors.

Background: The relationship between COVID-19 hospitalisation and stroke occurrence remains incompletely understood, with reported incidence rates varying significantly across studies. This systematic review and meta-analysis aimed to comprehensively evaluate the association between COVID-19 hospitalisation and stroke occurrence, while examining key subgroup variations and potential risk factors. Methods: A systematic search was conducted across PubMed, Scopus, and Web of Science databases from December 2019 to 2023. Studies reporting stroke occurrence in hospitalised COVID-19 patients were included. The Newcastle-Ottawa Scale was used for quality assessment. Random-effects meta-analysis was performed to calculate pooled odds ratios (OR) and risk ratios (RR) with 95% confidence intervals (CI). Results: Nineteen studies met the inclusion criteria. The primary meta-analysis of 17 studies, comprising 98,297 patients, revealed a pooled stroke occurrence of 1% (95% CI: 1%,2%). A comparative analysis of three studies showed no significant difference in stroke risk between hospitalised COVID-19 patients and non-COVID-19 hospitalised controls (OR: 1.13, 95% CI: 0.49,2.65). However, within the COVID-19 hospitalised population, stroke risk was strongly associated with disease severity, including the need for mechanical ventilation (OR: 3.59, 95% CI: 2.21,5.83) and ICU admission (OR: 6.33, 95% CI: 4.95, 8.09). Significant pre-existing comorbidities included hypertension (OR: 2.35, 95% CI: 1.12,4.92) and atrial fibrillation (OR: 1.98, 95% CI: 1.18,3.31). Ischemic stroke was the predominant subtype, accounting for 81% of cases (95% CI: 73%,90%). Quality assessment of the 19 studies identified 9 as high quality and 10 as moderate quality. Conclusions: This meta-analysis indicates that while the overall occurrence of stroke in hospitalised COVID-19 patients is approximately 1%, the risk is not significantly elevated compared to non-COVID-19 hospitalised patients. Instead, stroke occurrence is powerfully driven by the severity of the illness and the presence of traditional vascular risk factors. These findings underscore the critical need for vigilant neurological monitoring and targeted stroke prevention strategies, particularly for COVID-19 patients who require intensive care or mechanical ventilation.

Background: Hypertension is a global health issue, and the risk factors include genetics, physical inactivity, and excess body fat (adiposity). Genetic predisposition to adiposity generally increases risk of hypertension. Several genetic variants have been identified to increase adiposity but unexpectedly reduce hypertension (favorable adiposity genes). Here, we tested the effect of these genetic variants on risk of hypertension in European ancestry participants under various scenarios of physical activity and body mass index. Methods: Favorable adiposity genetic liability was estimated using previously identified genetic variants and their effect sizes. The study analyzed data from 210 290 unrelated participants in the UK Biobank. Logistic regression was used to examine the association between this genetic liability and hypertension (defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or the use of antihypertensive medications). The analyses were conducted separately based on physical activity status (physically active and inactive) within low and high body mass groups. Results: Individuals with high body mass, could reduce their risk of hypertension by up to 13% depending on the favorable adiposity genetic liability and physical activity status ( P _adjusted =5.44×10 ⁻⁷ ). In high body mass individuals, physical activity alone contributes to 5% to 7% reduction in risk of hypertension. Conclusions: The study provides evidence that the protective effect of favorable adiposity on hypertension risk varies according to body mass composition and physical activity status.

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.

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: 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. Previous studies have utilized statistical and machine learning techniques to develop stroke prediction models. Only a few have included genome-wide stroke genetic liability and evaluated its predictive values. This study aimed to assess the added predictive value of genetic liability in the prediction of the risk of stroke. Materials and methods: The study included 243,339 participants of European ancestry. Stroke genetic liability was constructed using previously identified genetic variants associated with stroke by the MEGASTROKE project through genome-wide association studies (GWAS). In our study, we built four predictive models with and without stroke genetic liability in the training set: Cox proportional hazard (Coxph), 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^-04 (95% CI = 0.000, 3.0×10^-04; P-value = 0.13) compared with the Cox model without genetic liability. Conclusion: Incorporating genetic factors in the model may provide a slight incremental value for stroke prediction beyond conventional risk factors.

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. Previous studies have utilized statistical and machine learning techniques to develop stroke prediction models. Only a few have included genome-wide stroke genetic liability and evaluated its predictive values. This study aimed to assess the added predictive value of genetic liability in the prediction of the risk of stroke. Materials and methods: The study included 243,339 participants of European ancestry. Stroke genetic liability was constructed using previously identified genetic variants associated with stroke by the MEGASTROKE project through genome-wide association studies (GWAS). In our study, we built four predictive models with and without stroke genetic liability in the training set: Cox proportional hazard (Coxph), 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 ^-04 (95% CI = 0.000, 3.0×10 ^-04 ; P-value = 0.13) compared with the Cox model without genetic liability. Conclusion: Incorporating genetic factors in the model may provide a slight incremental value for stroke prediction beyond conventional risk factors.

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.