Erratum: Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents

Steinberger, Julia; Jacobs, David R.; Raatz, Sarah; Moran, Alyssa J.; Hong, Ching Ping; Sinaiko, A. R.

doi:10.1038/sj.ijo.0803362

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of the influencing factors for CVD, Steinberger et al [8] collected the sample data set of seven cities in North Carolina from 1999 to 2001, fitted the three-stage Bayesian hierarchical model, and concluded that the higher the concentration of PM2.5, the greater the probability of residents suffering from CVD. Ferrandiz et al [9] used the Spanish Rapid Inquiry System (RIF) to conduct more precise and systematic exposure measurement and disease data collection.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established. Then, a two-stage model combining K-means cluster analysis and random forest (RF) is proposed to evaluate and predict the risk of CVD, and the predicted results are compared with the methods of Bayesian discrimination, K-means cluster analysis and RF. The results show that the prediction effect of the proposed two-stage model is better than that of the compared methods. Moreover, several suggestions for the government, the medical industry and the public are provided based on the research results.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

Relation of neural response to palatable food tastes and images to future weight gain: Using bootstrap sampling to examine replicability of neuroimaging findings

Stice

Yokum

2018

NeuroImage

View full text Add to dashboard Cite

Because understanding neural vulnerability factors that predict future weight gain may guide the design of more effective obesity prevention programs and treatments, we tested whether neural response to palatable food tastes and images predicted future weight gain. We recruited 135 initially healthy weight adolescents, to reduce the possibility that a history of overeating affected neural responsivity, had them complete fMRI paradigms examining neural response to tastes of milkshakes that varied in fat and sugar content and images of palatable foods, and assessed BMI annually over a 3-year follow-up. We used a novel bootstrapping analytic approach to investigate the replicability of the fMRI findings. Whole-brain analyses indicated that lower response in the pre-supplemental motor area to high-fat/low-sugar milkshake taste predicted future BMI gain in the full sample and in 5 out of the 10 bootstrap samples. Elevated response in the precentral gyrus/Rolandic operculum to images of appetizing foods predicted future BMI gain in the full sample and in 4 out of the 10 bootstrap samples. Other peaks that emerged in the full sample did not replicate in most of the bootstrap samples, suggesting they were not reliable. Region of interest analyses did not replicate the predictive effects of peaks reported in past papers that used similar paradigms, including the evidence that TaqIA polymorphism moderated the relation of striatal response to palatable food tastes to future weight gain. Results suggest that lower responsivity of a region implicated in motor processing in response to palatable taste was associated with greater BMI gain over time, and further that bootstrap sampling may be useful for estimating the replicability of findings that emerge from whole brain analyses or regions of interest analyses with the full sample.

show abstract

Intrauterine Exposure to Gestational Diabetes, Child Adiposity, and Blood Pressure

Wright

Rifas‐Shiman

Rich‐Edwards

et al. 2009

American Journal of Hypertension

200

181

View full text Add to dashboard Cite

Background Intrauterine exposure to gestational diabetes mellitus (GDM) may promote offspring obesity and higher systolic blood pressure (SBP) by adolescence. Few studies have examined adiposity or SBP in younger children exposed to GDM. This study’s objective was to examine associations of maternal glucose tolerance during pregnancy with offspring adiposity and SBP at age 3 years. Methods We studied 1,238 mother-child pairs in Project Viva, a prospective prebirth cohort study. Exposures were maternal blood glucose following oral glucose load, and GDM confirmed by 3-h glucose tolerance test. Main child outcomes were age 3-year body mass index (BMI) z-score, the sum (SS+TR) and ratio (SS/TR) of subscapular (SS) and tricep (TR) skinfold thicknesses, and SBP. We performed adjusted multivariable analyses. Results Fifty-one (4%) mothers had GDM. 9.3% of 3 year-old children were obese and mean (s.d.) SBP was 92 (11) mm Hg. Children exposed to GDM had higher SBP (3.2 mm Hg, 95% confidence interval (CI): 0.4, 5.9, P = 0.02) and greater adiposity when assessed by the sum of skinfolds (SS+TR 1.31 mm, 95% CI: 0.08, 2.55, P = 0.04) but not by BMI z-score (−0.08 units, 95% CI: −0.37, 0.22, P = 0.61). After additional adjustment for the sum of skinfold thicknesses (SS+TR), the relationship between GDM and SBP was attenuated and no longer significant (2.6 mm Hg, 95% CI: −0.2, 5.4, P = 0.07). Conclusions Children exposed to GDM have higher adiposity, which may mediate the higher SBP in these children. These findings extend to younger children the adverse effects of GDM previously found among adolescents and adults.

show abstract

Erratum: Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents

Abstract: The figure legend to Figure 1 of the above paper was published incorrectly.The correct legend is given below. The authors would like to apologise for this mistake.

Cited by 5 publications

References 0 publications

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Relation of neural response to palatable food tastes and images to future weight gain: Using bootstrap sampling to examine replicability of neuroimaging findings

Intrauterine Exposure to Gestational Diabetes, Child Adiposity, and Blood Pressure

Contact Info

Product

Resources

About