scholarly journals Cardiovascular risk factors in a child population: The Health Oriented Pedagogical Project (HOPP)

2018 ◽  
Vol 46 (21_suppl) ◽  
pp. 28-37
Author(s):  
Wei Hai Deng ◽  
Morten Lindberg ◽  
Ole Petter Hjelle ◽  
Asgeir Mamen ◽  
Per Morten Fredriksen
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Aerobic Fitness ◽  
Cardiometabolic Risk ◽  
Small Sample Size ◽  
Density Lipoprotein ◽  
Small Sample ◽  
Haemoglobin A1c ◽  
Child Population ◽  
Clustering Of Risk Factors

Aims: The main aim of the present study was to investigate the clustering of risk factors for cardiovascular diseases and metabolic syndrome in a large, healthy representative Norwegian child population. Methods: From a population of 2817, parents of 2297 children agreed to participate. Values of waist circumference (WC), total cholesterol (TC), high-density lipoprotein (HDL), systolic blood pressure (sysBP), haemoglobin-A1c (HbA1c) and Andersen aerobic fitness test were used to test clustering of cardiometabolic risk factors in this sample. Expected distributions of probability for zero to five risk factors are, respectively, 23.7%, 39.6%, 26.4%, 8.8%, 1.5% and 0.1%. A cardiometabolic risk score from zero to five for each individual was derived by adding the number of variables in the least desirable quartile (highest for WC, sysBP, TC and HbA1c; lowest for aerobic fitness and HDL). Results: A risk ratio of 5.8 (95% confidence interval 0.7–46.9) was found for five risk factors, though the small sample size rendered the results non-significant. An explorative analysis combining children with four and five risk factors did not reveal any significant clustering either. Conclusions: No clustering of risk factors was found among Norwegian children aged 6–12 years.

scholarly journals Relationship Between Meeting 24-Hour Movement Guidelines and Cardiometabolic Risk Factors in Children

2017 ◽  
Vol 14 (10) ◽  
pp. 779-784 ◽  
Author(s):  
Peter T. Katzmarzyk ◽  
Amanda E. Staiano
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Blood Pressure ◽  
Adipose Tissue ◽  
Subcutaneous Adipose Tissue ◽  
Cardiometabolic Risk ◽  
Vigorous Physical Activity ◽  
Density Lipoprotein ◽  
Cardiometabolic Risk Factors ◽  
Subcutaneous Adipose

Background:The purpose of this study was to evaluate the relationship between adherence to pediatric 24-hour movement guidelines (moderate to vigorous physical activity, sedentary behavior, and sleep) and cardiometabolic risk factors.Methods:The sample included 357 white and African American children aged 5–18 years. Physical activity, television viewing, and sleep duration were measured using questionnaires, and the 24-hour movement guidelines were defined as ≥60 minutes per day of moderate to vigorous physical activity on ≥5 days per week, ≤ 2 hours per day of television, and sleeping 9–11 hours per night (ages 5–13 y) or 8–10 hours per night (ages 14–18 y). Waist circumference, body fat, abdominal visceral and subcutaneous adipose tissue, blood pressure, fasting triglycerides, high-density lipoprotein cholesterol, and glucose were measured in a clinical setting.Results:A total of 26.9% of the sample met none of the guidelines, whereas 36.4%, 28.3%, and 8.4% of the sample met 1, 2, or all 3 guidelines, respectively. There were significant associations between the number of guidelines met and body mass index, visceral and subcutaneous adipose tissue, triglycerides, and glucose. There were no associations with blood pressure or high-density lipoprotein cholesterol.Conclusions:Meeting more components of the 24-hour movement guidelines was associated with lower levels of obesity and several cardiometabolic risk factors. Future efforts should consider novel strategies to simultaneously improve physical activity, sedentary time, and sleep in children.

scholarly journals Adverse differences in cardiometabolic risk factor levels between individuals with pre-diabetes and normal glucose metabolism are more pronounced in women than in men: the Maastricht Study

2019 ◽  
Vol 7 (1) ◽  
pp. e000787 ◽  
Author(s):  
Rianneke de Ritter ◽  
Simone J S Sep ◽  
Carla J H van der Kallen ◽  
Miranda T Schram ◽  
Annemarie Koster ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Pressure Difference ◽  
Cardiometabolic Risk ◽  
Density Lipoprotein ◽  
Hdl Cholesterol ◽  
Cardiometabolic Risk Factors ◽  
Blood Pressure Difference

ObjectiveTo investigate whether adverse differences in levels of cardiovascular risk factors in women than men, already established when comparing individuals with and without diabetes, are also present before type 2 diabetes onset.Research design and methodsIn a population-based cohort study of individuals aged 40-75 years (n=3410; 49% women, 29% type 2 diabetes (oversampled by design)), we estimated associations with cardiometabolic and lifestyle risk factors of (1) pre-diabetes and type 2 diabetes (reference category: normal glucose metabolism) and (2) among non-diabetic individuals, of continuous levels of hemoglobin A1c (HbA1c). Age-adjusted sex differences were analyzed using linear and logistic regression models with sex interaction terms.ResultsIn pre-diabetes, adverse differences in cardiometabolic risk factors were greater in women than men for systolic blood pressure (difference, 3.02 mm Hg; 95% CI:−0.26 to 6.30), high-density lipoprotein (HDL) cholesterol (difference, −0.10 mmol/L; 95% CI: −0.18 to −0.02), total-to-HDL cholesterol ratio (difference, 0.22; 95% CI: −0.01 to 0.44), triglycerides (ratio: 1.11; 95% CI: 1.01 to 1.22), and inflammation markers Z-score (ratio: 1.18; 95% CI: 0.98 to 1.41). In type 2 diabetes, these sex differences were similar in direction, and of greater magnitude. Additionally, HbA1c among non-diabetic individuals was more strongly associated with several cardiometabolic risk factors in women than men: per one per cent point increase, systolic blood pressure (difference, 3.58 mm Hg; 95% CI: −0.03 to 7.19), diastolic blood pressure (difference, 2.10 mm Hg; 95% CI: −0.02 to 4.23), HDL cholesterol (difference, −0.09 mmol/L; 95% CI: −0.19 to 0.00), and low-density lipoprotein cholesterol (difference, 0.26 mmol/L; 95% CI: 0.05 to 0.47). With regard to lifestyle risk factors, no consistent pattern was observed.ConclusionOur results are consistent with the concept that the more adverse changes in cardiometabolic risk factors in women (than men) arise as a continuous process before the onset of type 2 diabetes.

scholarly journals Cardiometabolic risk factors are associated with immune cell mitochondrial respiration in humans

2020 ◽  
Vol 319 (2) ◽  
pp. H481-H487
Author(s):  
Theodore M. DeConne ◽  
Eric R. Muñoz ◽  
Faria Sanjana ◽  
Joshua C. Hobson ◽  
Christopher R. Martens
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Mitochondrial Respiration ◽  
Cardiometabolic Risk ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol

Independent of other cardiometabolic risk factors, low-density lipoprotein cholesterol, and systolic blood pressure were found to be negatively associated with several parameters of mitochondrial respiration in peripheral blood mononuclear cells of healthy adults. These data suggest that low-density lipoprotein cholesterol and systolic blood pressure may induce metabolic reprogramming of immune cells, contributing to increased cardiovascular disease risk and impaired immune health.

scholarly journals Association between access to social service resources and cardiometabolic risk factors: a machine learning and multilevel modeling analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e025281 ◽  
Author(s):  
Seth A Berkowitz ◽  
Sanjay Basu ◽  
Atheendar Venkataramani ◽  
Gally Reznor ◽  
Eric W Fleegler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cardiometabolic Risk ◽  
Learning Algorithm ◽  
Density Lipoprotein ◽  
Social Needs ◽  
Cardiometabolic Risk Factors ◽  
Secondary Outcome ◽  
Haemoglobin A1c ◽  
Cross Sectional

ObjectivesInterest in linking patients with unmet social needs to area-level resources, such as food pantries and employment centres in one’s ZIP code, is growing. However, whether the presence of these resources is associated with better health outcomes is unclear. We sought to determine if area-level resources, defined as organisations that assist individuals with meeting health-related social needs, are associated with lower levels of cardiometabolic risk factors.DesignCross-sectional.SettingData were collected in a primary care network in eastern Massachusetts in 2015.Participants and primary and secondary outcome measures123 355 participants were included. The primary outcome was body mass index (BMI). The secondary outcomes were systolic blood pressure (SBP), low-density lipoprotein (LDL) cholesterol and haemoglobin A1c (HbA1c). All participants were included in BMI analyses. Participants with hypertension were included in SBP analyses. Participants with an indication for cholesterol lowering were included in LDL analyses and participants with diabetes mellitus were included in HbA1c analyses. We used a random forest-based machine-learning algorithm to identify types of resources associated with study outcomes. We then tested the association of ZIP-level selected resource types (three for BMI, two each for SBP and HbA1c analyses and one for LDL analyses) with these outcomes, using multilevel models to account for individual-level, clinic-level and other area-level factors.ResultsResources associated with lower BMI included more food resources (−0.08 kg/m2per additional resource, 95% CI −0.13 to −0.03 kg/m2), employment resources (−0.05 kg/m2, 95% CI −0.11 to −0.002 kg/m2) and nutrition resources (−0.07 kg/m2, 95% CI −0.13 to −0.01 kg/m2). No area resources were associated with differences in SBP, LDL or HbA1c.ConclusionsAccess to specific local resources is associated with better BMI. Efforts to link patients to area resources, and to improve the resources landscape within communities, may help reduce BMI and improve population health.

scholarly journals Cardiovascular disease risk factors and blood pressure response during exercise in healthy children and adolescents: The European Youth Heart Study

2010 ◽  
Vol 109 (4) ◽  
pp. 1125-1132 ◽  
Author(s):  
Niels C. Møller ◽  
Anders Grøntved ◽  
Niels Wedderkopp ◽  
Mathias Ried-Larsen ◽  
Peter L. Kristensen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Children And Adolescents ◽  
Aerobic Fitness ◽  
Random Effect Model ◽  
Density Lipoprotein ◽  
Healthy Children ◽  
Cvd Risk Factors ◽  
Cvd Risk

Raised blood pressure (BP) response during exercise independently predicts future hypertension. Subjects with higher BP in childhood also have elevated BP later in life. Therefore, the factors related to the regulation of exercise BP in children needs to be well understood. We hypothesized that physiological cardiovascular disease (CVD) risk factors would influence BP response during exercise in children and adolescents. This is a cross-sectional study of 439 Danish third-grade children and 364 ninth-grade adolescents. Systolic blood pressure (SBP) was measured with sphygmomanometer during a maximal aerobic fitness test. Examined CVD risk factors were high-density lipoprotein (HDL)- and low-density lipoprotein (LDL)-cholesterol, triglyceride, homeostasis model of assessment of insulin resistance (HOMA-IR) score, body mass index (BMI), waist circumference, and aerobic fitness. A random effect model was used to test the hypotheses. In boys, HOMA-IR score and BMI were positively related to SBP response during exercise (β = 1.03, P = 0.001, and β = 0.58, P = 0.017, respectively). The effects sizes of HOMA-IR score and BMI and the significance levels only changed slightly (β = 0.91, P = 0.004, and β = 0.43, P = 0.08, respectively) when the two variables were added in the same model. A significant positive association was observed between aerobic fitness and SBP response in girls (β = 3.13 and P = 0.002). HOMA-IR score and BMI were found to be positively related to the SBP response in male children and youth. At least partly, adiposity and insulin sensitivity seem to influence exercise SBP through different mechanisms. The positive relationship observed between aerobic fitness and SBP response in girls remains unexplainable for us, although post hoc analyses revealed that it was the case in the ninth graders only.

scholarly journals Association between eating habits, body mass index, cardiorespiratory fitness, and cardiometabolic risk factors in children

2021 ◽  
Vol 34 ◽  
Author(s):  
Miguel Angelo dos Santos DUARTE JUNIOR ◽  
Adroaldo Cezar Araujo GAYA ◽  
Vanilson Batista LEMES ◽  
Camila Felin FOCHESATTO ◽  
Caroline BRAND ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Body Mass Index ◽  
Body Mass ◽  
Cardiorespiratory Fitness ◽  
Cardiometabolic Risk ◽  
Eating Habits ◽  
Density Lipoprotein ◽  
The Body ◽  
Cardiometabolic Risk Factors

ABSTRACT Objective To verify the multivariate relationships between eating habits, cardiorespiratory fitness, body mass index, and cardiometabolic risk factors in children. Methods This is a cross-sectional study developed in a public elementary school with 60 first- to sixth-graders. Their eating habits were assessed using the Food Frequency Survey, weight, height, and cardiorespiratory fitness, assessed according to the Projeto Esporte Brasil protocol. Moreover, the variables, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, adiponectin, leptin, diastolic and systolic blood pressure were evaluated. Descriptive statistics were used for data analysis and generalized estimation equations were used for the analysis of direct and indirect relations, in a multivariate analysis model with several simultaneous outcomes. Results It appears that the eating habits and cardiorespiratory fitness explain 20% of the body mass index. Cardiometabolic risk factors are explained by the relationship between eating habits, cardiorespiratory fitness, and body mass index, according to the following percentages: 29% (systolic blood pressure), 18% (diastolic blood pressure), 63% (leptin), 4% (adiponectin), 14% (C-reactive protein), 17% (insulin), 10% (high-density lipoprotein), 1% (low-density lipoprotein), 4% (glucose). It is also observed that the effects of the eating habits on cardiometabolic risk factors are indirect, that is, they are dependent on changes in the body mass index and cardiorespiratory fitness levels. Conclusions The relationship between eating habits and cardiometabolic risk factors in children is dependent on cardiorespiratory fitness and body mass index. Thus, our findings suggest a multivariate relationship between these factors.

scholarly journals Cardiometabolic risk factors predict executive function scores in high-risk individuals

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 651-651
Author(s):  
Joshua Gills ◽  
Megan Jones ◽  
Anthony Campitelli ◽  
Sally Paulson ◽  
Erica Madero ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Blood Pressure ◽  
Alzheimer's Disease ◽  
Executive Function ◽  
High Risk ◽  
Cardiometabolic Risk ◽  
Density Lipoprotein ◽  
Cardiometabolic Risk Factors ◽  
Walking Test

Abstract Alzheimer’s disease (AD) is expected to triple by 2050, affecting 16 million Americans. As a result, it is essential to combat this alarming increase in cognitive impairment through early detection. Cardiometabolic risk factors have shown to be associated with higher risk of AD. The purpose of this study was to determine if cardiometabolic risk factors could predict executive function scores in a high-risk population. Fifty (60.9±8.8 years) high-risk adults (classified by the Australian National University Alzheimer’s Disease Risk Index) were enrolled in this study. Participants completed a 6-minute walking test, venous blood draw, blood pressure measurement, and the digit coding symbol test (DCS). Results were examined through a multiple linear regression with DCS as the dependent variable and age, sex, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), glucose, 6-minute walking test, systolic blood pressure (SBP), and diastolic blood pressure (DBP) as predictor variables. The model explained 42% of the variance of DCS (p = .04) with SBP (45%; p = .003) as a significant predictor. LDL (p = .087) and DBP (p = .123) accounted for 24% and 22% of the variance for this model, respectively. These results suggest cardiometabolic risk factors predict executive function values in high-risk individuals. Higher SBP was significantly associated with lower DCS scores indicating SBP as a valuable tool for practitioners when evaluating cognitive decline. Further research should expand sample size and track values longitudinally to substantiate these claims.

scholarly journals The Clustering of Low Diet Quality, Low Physical Fitness, and Unhealthy Sleep Pattern and Its Association with Changes in Cardiometabolic Risk Factors in Children

Nutrients ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 591
Author(s):  
Xianwen Shang ◽  
Yanping Li ◽  
Haiquan Xu ◽  
Qian Zhang ◽  
Ailing Liu ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Blood Pressure ◽  
Diet Quality ◽  
Cardiometabolic Risk ◽  
Fasting Glucose ◽  
Density Lipoprotein ◽  
Sleep Pattern ◽  
Cardiometabolic Health ◽  
Z Scores

The clustering of diet quality, physical activity, and sleep and its association with cardiometabolic risk (CMR) factors remains to be explored. We included 5315 children aged 6–13 years in the analysis. CMR score (CMRS) was computed by summing Z-scores of waist circumference, an average of systolic and diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol (multiplying by −1), and triglycerides. Low diet quality and low cardiorespiratory fitness (CRF) were more likely to be seen in a pair, but low diet quality was less likely to be clustered with unhealthy sleep patterns. Low diet quality, low CRF, and unhealthy sleep pattern was associated with a 0.63, 0.53, and 0.25 standard deviation (SD) higher increase in CMRS, respectively. Compared to children with no unhealthy factor (−0.79 SD), those with ≥1 unhealthy factor had a higher increase (−0.20 to 0.59 SD) in CMRS. A low diet quality-unhealthy sleep pattern resulted in the highest increase in CMRS, blood pressure, and triglycerides. A low diet quality–low CRF-unhealthy sleep pattern resulted in the highest increase in fatness and fasting glucose. Unhealthy factor cluster patterns are complex; however, their positive associations with changes in CMR factors are consistently significant in children. Some specific patterns are more harmful than others for cardiometabolic health.

Speed of Movement, Fatness, and the Change in Cardiometabolic Risk Factors in Children

2021 ◽  
Author(s):  
Xianwen Shang ◽  
Yanping Li ◽  
Haiquan Xu ◽  
Qian Zhang ◽  
Ailing Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
High Density Lipoprotein ◽  
Body Fat ◽  
Risk Score ◽  
High Density Lipoprotein Cholesterol ◽  
Cardiometabolic Risk ◽  
Density Lipoprotein ◽  
Cardiometabolic Risk Factors ◽  
Percent Body Fat

AbstractWe aimed to examine speed of movement and its interactive association with fatness to changes in cardiometabolic risk factors over one year in children. The analysis included 8345 children aged 6–13 years. Cardiometabolic risk score was computed by summing Z-scores of waist circumference, the average of systolic and diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol (multiplied by −1), and triglycerides. Both high baseline and improvement in speed of movement were associated with favourable changes in percent body fat, lipids, and cardiometabolic risk score. Percentages of the association between baseline speed of movement and changes in cardiometabolic risk score, triglycerides, and high-density lipoprotein cholesterol explained by baseline BMI were 24.6% (19.6–29.1%), 26.2% (19.7–31.1%), and 12.5% (9.6–15.4%), respectively. The corresponding number for percent body fat was 47.0% (40.4–54.1%), 43.3% (36.7–51.7%), and 29.8% (25.0–34.6%), respectively. Speed of movement mediated the association between fatness and cardiometabolic risk factors. Improved speed of movement was associated with a lower increase in blood pressure in obese children only. Speed of movement is a strong predictor of changes in cardiometabolic risk factors. Fatness and speed of movement are interactively associated with cardiometabolic risk factors. Speed of movement may attenuate the positive association between fatness and blood pressure.

scholarly journals Cycling but not walking to work or study is associated with physical fitness, body composition and clustered cardiometabolic risk in young men

2020 ◽  
Vol 6 (1) ◽  
pp. e000668 ◽  
Author(s):  
Jani P Vaara ◽  
Tommi Vasankari ◽  
Mikael Fogelholm ◽  
Harri Koski ◽  
Heikki Kyröläinen
Keyword(s):  
Risk Factors ◽  
Body Composition ◽  
Waist Circumference ◽  
Physical Fitness ◽  
Aerobic Fitness ◽  
Cardiometabolic Risk ◽  
Healthy Adult ◽  
Density Lipoprotein ◽  
Active Commuting ◽  
Adult Men

IntroductionActive commuting is an inexpensive and accessible form of physical activity and may be beneficial to health. The aim of this study was to investigate the association of active commuting and its subcomponents, cycling and walking, with cardiometabolic risk factors, physical fitness and body composition in young men.MethodsParticipants were 776 Finnish young (26±7 years), healthy adult men. Active commuting was measured with self-report. Waist circumference was measured and body mass index (BMI) calculated. Aerobic fitness was measured with bicycle ergometer and muscular fitness with maximal leg and bench press, sit-ups, push-ups and standing long jump. Cardiometabolic risk factors were analysed from blood samples and selected variables (glucose, insulin, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, as well as systolic and diastolic blood pressure) were further converted to z-score to form clustered cardiometabolic risk.ResultsA total of 24% used active commuting consisting of 10% of walkers and 14% of cyclists. After adjustments for age, smoking, time of year, leisure-time and occupational physical activities, cycling was inversely associated with the clustered cardiometabolic risk (β=−0.11, 95% CI −0.22 to −0.01), while walking was not (β=−0.04, 95% CI −0.16 to 0.08). However, further adjustment for waist circumference attenuated the associations to non-significant. Moreover, cycling but not walking was inversely associated with BMI, waist circumference and maximal strength, while a positive association was observed with aerobic fitness (p<0.05).ConclusionThis study shows that cycling to work or study has beneficial associations to clustered cardiometabolic risk, body composition and aerobic fitness in young, healthy adult men.

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