Association between family history of diabetes and cardiovascular disease and lifestyle risk factors in the United States population: The 2009–2012 National Health and Nutrition Examination Survey

Akhuemonkhan, Eboselume; Lazo, Mariana

doi:10.1016/j.ypmed.2016.12.015

Cited by 25 publications

(23 citation statements)

References 24 publications

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“…Physical activity. In the Physical Activity Questionnaire, women were asked, “In a typical week, on how many days do you do vigorous-intensity sports, fitness or recreational activities?,” “How much time (minutes) is spent doing vigorous recreational activities?,” “In a typical week, on how many days do you do moderate-intensity sports, fitness or recreational activities?,” and “How much time (minutes) is spent doing moderate recreational activities?” To calculate the total minutes of physical activity, the number of minutes spent in vigorous-intensity physical activity was doubled (assuming that 1 minute of vigorous activity equals 2 minutes of moderate activity [ 10 ]) and added to the minutes of moderate-intensity physical activity ( 11 ). This variable was coded as binary: either meeting the guideline for physical activity (if ≥150 minutes per week) or not meeting the guideline (if <150 minutes per week).…”

Section: Methodsmentioning

confidence: 99%

“…In the Dietary Recall Questionnaire, women were asked about their daily food consumption, and the amount of sodium, fiber, and cholesterol intake was estimated. According to the American Heart Association and the American Diabetes Association, a daily cholesterol intake of less than 300 mg, a daily fiber intake of more than 25 g, and a daily sodium intake of less than 1,500 mg meet the daily dietary guidelines ( 11 ). Daily intakes that met these guidelines were classified as meeting guidelines, and daily intakes that exceeded the guidelines (cholesterol or sodium) or did not reach the guideline (fiber) were classified as not meeting the guidelines.…”

Section: Methodsmentioning

confidence: 99%

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Gestational Diabetes and Health Behaviors Among Women: National Health and Nutrition Examination Survey, 2007–2014

et al. 2018

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IntroductionWomen with gestational diabetes are at 7 times greater risk of developing type 2 diabetes than are women without gestational diabetes. The objectives of this study were to examine recent changes in the prevalence of gestational diabetes mellitus among women of reproductive age in the United States and assess the prevalence of factors associated with participating in healthy lifestyle behaviors.MethodsData were from 4 waves of the National Health and Nutrition Examination Survey (2007–2014). Gestational diabetes was identified by participants’ response to whether they were ever told by a health care professional that they had diabetes during pregnancy. The health behaviors were participation in physical activity, healthy dietary patterns (intake of cholesterol, sodium, and fiber within recommended guidelines), and smoking. The analytical sample included 3,034 women aged 20 to 44 years. Multivariate logistic regression was used to assess the association between gestational diabetes and health behaviors.ResultsThe overall prevalence of gestational diabetes was 8.9% (95% confidence interval [CI], 7.6%–10.4%) during 2007–2014. The prevalence increased from 8.4% in 2007–2008 to 10.4% in 2013–2014, an increase of 24%, but the change was not significant (P = .28). The proportions of women meeting recommended guidelines for the health behaviors did not change significantly. We found no significant difference in practicing healthy behaviors between women with gestational diabetes and women without gestational diabetes.ConclusionThe prevalence of gestational diabetes increased slightly in recent years, and women with the condition were generally not meeting guidelines for healthy behaviors. Coordinated interventions are needed to promote healthy lifestyle behaviors among women with gestational diabetes because they are at increased risk for diabetes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Gestational Diabetes and Health Behaviors Among Women: National Health and Nutrition Examination Survey, 2007–2014

et al. 2018

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“…Although the indirect effects of family history of CVD, age, lipid profiles, anthropometric indices, healthy lifestyle, quality of life, high-risk behaviours, having hypertension and high blood sugar on CVDs were not covered in the literature, many studies discussed the direct effect of such factors in which these results are in line with [9,11,12,15,[38][39][40][41][42][43][44][45][46][47][48][49][50]. Causal networks evaluate causal relationships among variables beyond partial correlations and thus play a fundamental step in risk prediction.…”

Section: Discussionmentioning

confidence: 99%

Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

et al. 2020

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Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socioeconomics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs.

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“…Although the indirect effects of family history of CVD, age, lipid pro les, anthropometric indices, healthy lifestyle, quality of life, high-risk behaviours, having hypertension and high blood sugar on CVDs were not covered in the literature, many studies discussed the direct effect of such factors in which these results are in line with (9,11,12,15,(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Causal networks evaluate causal relationships among variables beyond partial correlations and thus play a fundamental step in risk prediction.…”

Section: Discussionmentioning

confidence: 99%

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

Shakibaei

Hassannejad

Mohammadifard

et al. 2020

Preprint

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Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs.

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Association between family history of diabetes and cardiovascular disease and lifestyle risk factors in the United States population: The 2009–2012 National Health and Nutrition Examination Survey

Cited by 25 publications

References 24 publications

Gestational Diabetes and Health Behaviors Among Women: National Health and Nutrition Examination Survey, 2007–2014

Gestational Diabetes and Health Behaviors Among Women: National Health and Nutrition Examination Survey, 2007–2014

Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

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