Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension

Oort, Sabine van; Beulens, Joline W.J.; Ballegooijen, Adriana J. van; Grobbee, Diederick E.; Larsson, Susanna C.

doi:10.1161/hypertensionaha.120.15761

Cited by 90 publications

(69 citation statements)

References 63 publications

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“…The results showed that NAFLD, FPG, Age, TG, LDL-c, UA and Cr were positively correlated with overweight and obese hypertension, and GFR was negatively correlated with overweight and obese hypertension. Then the Decision tree correlation statistical analysis was carried out by CRT method, and it was concluded that the risk factors of hypertension in people who were Overweight and obesity include ve factors such as Age, FPG, UA, TG and LDL-c, which are similar to previous studies [17,18] . At the same time, the Decision tree model also showed that the prevalence of hypertension in people who were Overweight and obesity with Age ≥ 46 years old and FPG ≥ 6.1mmol/l was 47.1%,.At the same time, it is consistent with Age ≥ 46 years, FPG ≥ 6.1mmol/l and UA ≥ 420µmol/L.…”

Section: Discussionsupporting

confidence: 70%

Risk factors and Interaction analysis of Hypertension in People who were Overweight and obesity:Based on Machine Learning and Data Mining

Wei

2022

Preprint

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Objective To screen the independent risk factors of hypertension in People who were Overweight and obesity by Logistic regression and Decision tree model, and to analyze the two possible interactive risk factors by multiplying interaction scale and adding interaction scale.Methods 5098 People who were Overweight and obesity were selected as the research object. Firstly, Logistic regression and Decision tree model were used to screen the independent risk factors and possible interactive risk factors of hypertension in People who were Overweight and obesity. Finally, the multiplicative scale analysis and additive scale analysis were used to analyze the two possible interactive risk factors from the perspective of statistics and biology.Results Logistic regression showed that NAFLD, FPG, Age, TG, LDL-c, UA and Cr were positively correlated with overweight and obese hypertension, and GFR was negatively correlated with overweight and obese hypertension (P<0.05). The decision tree model suggests that Age, FPG and UA may interact. Multiplication scale analysis showed that FPG+UA, Age UA and Age+FPG had positive multiplication interaction (P<0.05, B≠0, OR > 1). Additive scale analysis shows that Age+UA single factor analysis and multi factor analysis have additive interaction, FPG+UA single factor analysis has additive interaction, multi factor analysis does not have additive interaction, and Age+FPG single factor analysis and multi factor analysis do not have additive interaction.Conclusion The independent risk factors of hypertension in People who were Overweight and obesity include NAFLD, FPG, Age, TG, LDL-c, UA and Cr. Age+UA has statistical and biological synergistic interaction, which can provide a reference for the study of the etiological interaction of hypertension in People who were Overweight and obesity.

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Section: Discussionsupporting

confidence: 70%

Risk factors and Interaction analysis of Hypertension in People who were Overweight and obesity:Based on Machine Learning and Data Mining

Wei

2022

Preprint

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“…24 Sedentary time was estimated based on major screen exposure (watching television, video compact disc, video games, surfing internet, joining chat rooms, and playing computer games) and other sedentary activities. The following covariates were included, which have been demonstrated in published literature to be associated with hypertension: 25 family income per capita (tertiles), smoke (yes vs no), drank alcohol (yes vs no), PA (tertiles), sedentary time (tertiles). Models for each outcome accounted for the same covariates to maintain consistency.…”

Section: Covariatesmentioning

confidence: 99%

The Associations of Trajectory of Sleep Duration and Inflammation with Hypertension: A Longitudinal Study in China

Huang¹,

Long²,

Lyu³

et al. 2021

NSS

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Background: Existing evidence suggested that sleep duration may be involved in hypertension; however, the conclusions were still controversial. This study aimed to examine the association of longitudinal trajectory of sleep duration with hypertension and to explore the role of the inflammation in such associations. Methods: A total of 3178 subjects over 30 years of age without hypertension were enrolled in 2004, and they were followed until 2009. Self-reported sleep duration was recorded, and inflammation was measured by highly sensitive C reactive protein (hs-CRP). Log-binomial regression models were applied to examine the association of sleep duration trajectory and inflammation with the risk of hypertension. The mediating effect of elevated hs-CRP was examined by the bootstrap and the process software. Results: The prevalence of persistent short (≤7 hours/day), normal (8-9 hours/day), and long (>9 hours/day) sleep duration over 5 years were 9.1%, 37.7%, and 2.3%, respectively. The incidence of hypertension was 26.6% during the follow-up period. Compared with those who persistently slept 8-9 hours/day from baseline to follow-up, those who persistently slept ≤7 hours/day, persistently slept ≥10 hours/day, and those whose sleep duration changed have higher risks of hypertension by 1.375-fold (95% CI: 1.121, 1.686), 1.557-fold (95% CI: 1.171, 2.069) and 1.299-fold (95% CI: 1.135, 1.487), respectively. In addition, persistently slept ≤7 hours/day was found to be associated with higher risk of inflammation (RR: 1.285, 95% CI: 1.008, 1.638). The mediation analysis did not find significant mediating effect of elevated CRP on the association between sleep duration trajectory and hypertension. Conclusion: Experiencing both a short or long sleep duration, especially for a long time, could lead to higher risk of hypertension. Persistent exposure to short sleep duration was also associated with inflammation. However, the higher risk of hypertension caused by persistent short sleep duration does not seem to be directly mediated through inflammation.

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“…The two-sample MR method means genetic variants for exposure and outcome are extracted from the different dataset which makes it more robust in statistical power, but alternative sources of bias may be caused if the two samples used in a study overlap ( 11 ). Previous MR studies have proved the causal effect of T2DM on stroke, coronary heart disease (CHD) ( 12 , 13 ), the causal effect of T2DM on blood pressure ( 14 ), and also causal effects of metabolic factors on CVD ( 15 , 16 ). Results from these studies indicate that these cardiometabolic factors might partly explain the causal effect of T2DM on CVD, but none of them have quantified the mediation effect.…”

Section: Introductionmentioning

confidence: 99%

Roles of Cardiometabolic Factors in Mediating the Causal Effect of Type 2 Diabetes on Cardiovascular Diseases: A Two-Step, Two-Sample Multivariable Mendelian Randomization Study

Chen

Zhuang

Shao

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveThe objective of this study is to investigate the roles of cardiometabolic factors (including blood pressure, blood lipids, thyroid function, body mass, and insulin sensitivity) in mediating the causal effect of type 2 diabetes (T2DM) on cardiovascular disease (CVD) outcomes.DesignTwo-step, two-sample multivariable Mendelian randomization (MVMR) study.SettingInternational genome-wide association study (GWAS) consortia data.ExposureType 2 diabetes, blood pressure: systolic blood pressure (SBP), diastolic blood pressure (DBP); blood lipids: low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), triglycerides (TG); thyroid function: hyperthyroidism, hypothyroidism; body mass index (BMI), waist-hip-ratio (WHR), and insulin sensitivity.Main OutcomesCardiovascular disease includes coronary heart disease (CHD), myocardial infarction (MI), and stroke.MethodsSummary-level data for exposures and main outcomes were extracted from GWAS consortia. We used two-sample MR to illustrate the causal effect of T2DM on CVD subtypes and regression-based MVMR to quantify the possible mediation effects of cardiometabolic factors on CVD.ResultsEach additional unit of log odds of T2DM increased 16% risk of CHD [odds ratio (OR): 1.16, 95% CI: 1.12–1.21], 15% risk of myocardial infarction (MI) (OR: 1.15, 95% CI: 1.10–1.20), and 10% risk of stroke (OR: 1.10, 95% CI: 1.06–1.13). In mediation analysis, SBP, DBP, and TG were found as main mediators, while the mediation effects of other cardiometabolic factors were not significant. The proportion of total effect of T2DM on CHD mediated by SBP, DBP, and TG was 16% (95% CI: 8–24%), 7% (95% CI: 1–13%) and 10% (95% CI: 2–18%), respectively. Mediation effect of SBP and DBP on MI and stroke, TG on MI was also prominent, while mediation effect of TG on stroke was not significant. The combined mediation effect of all the three mediators accounted for 29%, 26%, and 13% of the total effect of T2DM on CHD, MI, and stroke, respectively.ConclusionSystolic blood pressure, DBP, and TG mediate a substantial proportion of the causal effect of T2DM on CVD and thus interventions on these factors might reduce the considerable excess risk of CVD among patients with T2DM.

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Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension

Cited by 90 publications

References 63 publications

Risk factors and Interaction analysis of Hypertension in People who were Overweight and obesity:Based on Machine Learning and Data Mining

Risk factors and Interaction analysis of Hypertension in People who were Overweight and obesity:Based on Machine Learning and Data Mining

The Associations of Trajectory of Sleep Duration and Inflammation with Hypertension: A Longitudinal Study in China

Roles of Cardiometabolic Factors in Mediating the Causal Effect of Type 2 Diabetes on Cardiovascular Diseases: A Two-Step, Two-Sample Multivariable Mendelian Randomization Study

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