scholarly journals Application of Urinary Polyphenol Biomarkers Measured by Liquid Chromatography Tandem Mass Spectrometry to Assess Polyphenol Intake and Their Association with Overweight and Obesity in Free-Living Healthy Subjects

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ruiyue Yang ◽  
Helu Xiu ◽  
Qi Zhou ◽  
Liang Sun ◽  
Hongna Mu ◽  
...  
Keyword(s):  
Regression Models ◽  
Large Scale ◽  
Epidemiologic Studies ◽  
Overweight And Obesity ◽  
Targeted Metabolomics ◽  
Free Living ◽  
Logistic Regression Models ◽  
Liquid Chromatography Tandem Mass ◽  
Polyphenol Intake ◽  
Chinese Subjects

Although some polyphenol biomarkers in serum or urine have been identified by untargeted metabolomics and proved to reflect dietary polyphenol intake, only a few of them have been validated in different studies and populations with simple and reliable targeted methods. In the present study, a targeted metabolomics method by LC/MS/MS for the measurement of twenty-two polyphenol biomarkers in urine samples was established and validated to effectively assess the habitual polyphenol intake in free-living healthy Chinese subjects. Multivariate logistic regression models were used to assess relationships of biomarkers with overweight and obesity after adjusting for potential confounders. The levels of urinary polyphenol biomarkers, especially gut microbial metabolites of polyphenols, were inversely associated with overweight and obesity, and this association was more pronounced in the inflammatory groups, suggesting that it is of great importance to maintain polyphenol biomarkers at high levels or intake-sufficient polyphenols in obesity with chronic inflammation than others. The measurement of these biomarkers may offer a valid alternative or complementary addition to self-reported survey for the evaluation of polyphenol intake and investigation into their relationships with chronic disease-related endpoints in large-scale clinical and epidemiologic studies.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

scholarly journals A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values

2020 ◽  
Author(s):  
Pål Vegard Johnsen ◽  
Signe Riemer-Sørensen ◽  
Andrew Thomas DeWan ◽  
Megan E. Cahill ◽  
Mette Langaas
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Large Scale ◽  
Association Studies ◽  
New Method ◽  
Theoretical Ground ◽  
Genome Wide Association Studies ◽  
Ensemble Models ◽  
Logistic Regression Models ◽  
Gene Environment

AbstractBackgroundThe identification of gene-gene and gene-environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Classical logistic regression models are suitable to look for pre-defined interactions while more complex models, such as tree ensemble models, with the ability to detect any interactions have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models with a strong theoretical ground and efficiently.ResultsWe propose a tree ensemble- and SHAP-based method for identifying as well as interpreting both gene-gene and gene-environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interactions. The analysis suggests that the new method finds interactions between features that logistic regression models have difficulties in detecting.ConclusionsThe new method robustly detects interesting interactions, and can be applied to large-scale biobanks with high-dimensional data.

scholarly journals Defensible inferences from a nested sequence of logistic regressions: a guide for the perplexed

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Gulsah Gurkan ◽  
Yoav Benjamini ◽  
Henry Braun
Keyword(s):  
Logistic Regression ◽  
Cognitive Skills ◽  
Regression Models ◽  
Large Scale ◽  
Family Background ◽  
National Income ◽  
Logistic Models ◽  
Logistic Regression Models ◽  
Logistic Regressions ◽  
The Impact

AbstractEmploying nested sequences of models is a common practice when exploring the extent to which one set of variables mediates the impact of another set. Such an analysis in the context of logistic regression models confronts two challenges: (i) direct comparisons of coefficients across models are generally biased due to the changes in scale that accompany the changes in the set of explanatory variables, (ii) conducting a large number of tests induces a problem of multiplicity that can lead to spurious findings of significance if not heeded. This article aims to illustrate a practical strategy for conducting analyses in the face of these challenges. The challenges—and how to address them—are illustrated using a subset of the findings reported by Braun (Large-scale Assess Educ 6(4):1–52, 2018. 10.1186/s40536-018-0058-x), drawn from the Programme for the International Assessment of Adult Competencies (PIAAC), an international, large-scale assessment of adults. For each country in the dataset, a nested pair of logistic regression models was fit in order to investigate the role of Educational Attainment and Cognitive Skills in mediating the impact of family background and demographic characteristics on the location of an individual’s annual income in the national income distribution. A modified version of the Karlson–Holm–Breen (KHB) method was employed to obtain an unbiased estimate of the true differences in the coefficients between nested logistic models. In order to address the issue of multiplicity, a recent generalization of the Benjamini–Hochberg (BH) False Discovery Rate (FDR)-controlling procedure to hierarchically structured hypotheses was employed and compared to two conventional methods. The differences between the changes in coefficients calculated conventionally and with the KHB adjustment varied from negligible to very substantial. When combined with the actual magnitudes of the coefficients, we concluded that the more proximal factors indeed act as strong mediators for the background factors, but less so for Age, and hardly at all for Gender. With respect to multiplicity, applying the FDR-controlling procedure yielded results very similar to those obtained by applying a standard per-comparison procedure, but quite a few more discoveries in comparison to the Bonferroni procedure. The KHB methodology illustrated here can be applied wherever there is interest in comparing nested logistic regressions. Modifications to account for probability sampling are practicable. The categorization of variables and the order of entry should be determined by substantive considerations. On the other hand, the BH procedure is perfectly general and can be implemented to address multiplicity issues in a broad range of settings.

scholarly journals The association between Internet Usage and Overweight/Obesity modified by Gender: Evidence from a nationally representative survey in Nepal

2020 ◽  
Author(s):  
Juwel Rana ◽  
Md Momin Islam ◽  
John Oldroyd ◽  
Nandeeta Samad ◽  
Rakibul M Islam
Keyword(s):  
Regression Models ◽  
Internet Use ◽  
Wald Test ◽  
Overweight And Obesity ◽  
The Internet ◽  
Logistic Regression Models ◽  
Interaction Terms ◽  
Obesity Interventions ◽  
Nationally Representative ◽  
The Individual

Objective: Using a nationally representative data, we examined the associations between internet use and overweight/obesity in people aged 15-49 years in Nepal, and the extent to which these associations vary by gender. Materials and methods: The study analyzed the nationally representative Nepal Demographic and Health Survey (NDHS) 2016 data, collected between June 2016 and January 2017. The outcome was overweight/obesity. Exposures were internet use (IU) in the last twelve months and internet use frequency (FIU) in the last month. Multivariable ordinal logistic regression models were fitted to estimate the total effects of IU and FIU on overweight/obesity adjusted for minimal sufficient adjustment set of potential confounders. P-difference was extracted using a Wald test for the models with interaction terms. Results: Of the 10,380 participants, 33.9% used internet in the last 12 months, and 13.1 % used less than/at least once in a week, and 17.5% used internet almost every day. The prevalence of overweight/obesity by IU was 38% (95% CI: 35.9%, 40.1%) for male and 44.1% (95% CI: 41.6%, 46.6) for female. The risk of overweight and obesity was significantly 1.55 times higher (aOR: 1.55; 95% CI: 1.40, 1.73; p < 0.001) among those participants who used the internet compared to the individual who did not use the internet in the last 12 months or earlier of the interview. Similar associations were observed when using the augmented measure of exposure-FIU. We observed modification effect of gender in the associations of IU (p-difference<0.001) and FIU (p-difference<0.002) with overweight and obesity in Nepal. Conclusions: Our findings suggest that it is imperative for future overweight/obesity interventions in LMICs, including Nepal, to discourage unnecessary internet use, particularly among males.

scholarly journals Influence of the request and purchase of television advertised foods on dietary intake and obesity among children in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinli Xian ◽  
Mao Zeng ◽  
Zhengjie Cai ◽  
Changxiao Xie ◽  
Yuqian Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Dietary Intake ◽  
Odds Ratio ◽  
Regression Models ◽  
Overweight And Obesity ◽  
Confounding Factors ◽  
Nutrition Survey ◽  
Logistic Regression Models ◽  
Health And Nutrition ◽  
Children Health

Abstract Background This study aims to examine the effects of the request and purchase of Television (TV) advertised foods on children’s dietary intake, overweight and obesity in China. Methods Data from 1417 children (aged 6–17 years) in the 2011 China Health and Nutrition Survey were analysed. The request and purchase of TV advertised foods were assessed through the frequency of children’s requests to purchase TV advertised foods and the frequency of parents’ purchases of these advertised foods, as well as the frequency of children’s purchases of TV advertised foods. The height and weight of children were measured. Logistic regression models were used to identify the associations between the request and purchase of TV advertised foods and overweight/obesity of children. Results The request and purchase of TV advertised foods were positively associated with children’s dietary intake of energy, protein, fat and carbohydrates. After adjusting for potential confounding factors, children’s request and purchase of TV advertised foods and parent’s purchase of TV advertised foods were positively associated with children’s overweight/obesity: odds ratio (OR) and 95% confidence interval (CI) for overweight/obesity were: 1.46 (1.01–2.11) for children purchasing advertised foods ≥1 time/week, 1.59 (1.15–2.18) for parents purchasing advertised foods for their children ≥1 time/week and 1.39 (1.00–1.95) for children requesting advertised foods ≥1 time/week. Conclusions The request and purchase of TV advertised foods are associated with children’s dietary intake. Moreover, the request and purchase of TV advertised foods can increase the risk of overweight and obesity of children. Health education involving children’s request and purchase of TV advertised foods and parents’ purchase of TV advertised foods should be considered in China.

scholarly journals Influence of Television Food Advertisements Related Purchasing Behaviours on Dietary Intake and Obesity Among Children in China

2020 ◽  
Author(s):  
Jinli Xian ◽  
Mao Zeng ◽  
Zhengjie Cai ◽  
Changxiao Xie ◽  
Yuqian Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Dietary Intake ◽  
Regression Models ◽  
Overweight And Obesity ◽  
Confounding Factors ◽  
Nutrition Survey ◽  
Logistic Regression Models ◽  
Health And Nutrition ◽  
Food Advertisements

Abstract Background: This study aims to examine the effects of Television (TV) food advertisements (ads) related purchasing behaviours on children’s dietary intake, overweight and obesity in China. Methods: Data from 1,417 children (aged 6–17.99 years) in the 2011 China Health and Nutrition Survey were analysed. TV food ads related purchasing behaviours were assessed through the frequency of children’s requests to purchase advertised foods and the frequency of parents’ purchases of these advertised foods, as well as the frequency of children’s purchases of advertised foods. The height and weight of children were measured. Logistic regression models were used to identify the associations between TV food ads related purchasing behaviours and overweight/obesity of children.Results: TV food ads related purchasing behaviours were positively associated with children’s dietary intake of energy, protein, fat and carbohydrates. After adjusting for potential confounding factors, TV food ads related purchasing behaviours were positively associated with children’s overweight/obesity: OR (95% CI) for overweight/obesity were: 1.46 (1.01–2.11) for children purchasing advertised foods, 1.59 (1.15–2.18) for parents purchasing advertised foods for their children and 1.39 (0.99–1.95) for children requesting advertised foods.Conclusions: TV food ads related purchasing behaviours are associated with children’s dietary intake. Moreover, TV food ads related purchasing behaviours can increase the risk of overweight and obesity of children. Regulations on TV food ads should be considered in China.

scholarly journals Overweight in the Dutch National Cohort of Long-Term Survivors of Childhood Cancer

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3054-3054
Author(s):  
Vincent G. Pluimakers ◽  
Jenneke E. Van Atteveld ◽  
Demi T.C. de Winter ◽  
Marta Fiocco ◽  
Rutger A.J. Nievelstein ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Childhood Cancer ◽  
Cancer Diagnosis ◽  
Regression Models ◽  
Overweight And Obesity ◽  
High Dose ◽  
Logistic Regression Models ◽  
Overweight Prevalence

Abstract BACKGROUND Overweight is a common problem in the general population, but occurs more frequently among childhood cancer survivors (CCS) and is regarded as a late adverse effect. However, risk factors are not fully elucidated and it is often disguised in CCS because they can have normal weight but high fat percentage (fat%) on dual-energy X-ray absorptiometry (DXA, gold standard). We aimed to assess overweight prevalence in a nationwide survivor cohort, to clarify risk factors and to identify which measurement method captures overweight best. METHODS The prevalence of overweight and obesity (body mass index (BMI) ≥25 and ≥30 kg/m 2) was assessed in the Dutch nationwide cohort of adult CCS treated between 1963 and 2002. Risk factors for overweight were analyzed using multivariable logistic regression models. In addition, overweight prevalence was calculated according to fat%, waist circumference (WC), waist/hip ratio (WHR) and waist/height ratio (WHtR). The validity of BMI, WC, WHR and WHtR for characterizing obesity, compared to fat% (expressed as false-negative percentage and in logistic regression models to identify treatment-related risk factors for disguised overweight) was studied. RESULTS A total of 2,338 (51.2% male) survivors (54.7% hematologic malignancies) participated, with mean age 35.5 (±9.3) years and 28.3 (±8.4) years follow-up. In men and women respectively, overweight prevalence was 45.9% and 43.8%, for obesity this was 11.2% and 15.5%. Risk factors for overweight included overweight at cancer diagnosis (adjusted odds ratio (aOR) 3.43, p&lt;0.001), cranial radiotherapy (CRT, aOR 3.27, p&lt;0.001) and growth hormone deficiency (GHD) (unadjusted OR 2.28, p&lt;0.001, after adjustment the effect partially disappeared, aOR 1.60, p=0.072). Previous treatment with corticosteroids was not associated with overweight. Using BMI, WC, WHR and WHtR, similar overweight prevalence was observed. However, this was 58.4% in men and even 83.7% in women when measured with DXA. Disguised overweight was more frequent after treatment with abdominal radiotherapy, high dose anthracyclines and stem cell transplantation (SCT) (aOR up to 3.37). CONCLUSIONS Overweight occurs in almost half of all long-term CCS, and risk factors include overweight at cancer diagnosis, CRT and potentially GHD. DXA identified overweight in an additional 25% of survivors. In CCS treated with abdominal irradiation, anthracyclines and SCT, overweight is more often missed with conventional methods. Hence, in these risk groups DXA needs serious consideration in surveillance, to enable early intervention and prevent complications of overweight including diabetes and atherosclerotic disease. Disclosures No relevant conflicts of interest to declare.

scholarly journals Body Mass Index and Pregnancy Outcome after Assisted Reproduction Treatment

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Khaled Kasim ◽  
Ahmed Roshdy
Keyword(s):  
Body Mass Index ◽  
Pregnancy Outcome ◽  
Body Mass ◽  
Regression Models ◽  
Overweight And Obesity ◽  
Obese Women ◽  
Logistic Regression Models ◽  
Overweight Women ◽  
The Impact ◽  
Assisted Reproduction Treatment

The present study aimed to evaluate the impact of body mass index (BMI) on pregnancy outcome after intracytoplasmic sperm injection (ICSI). The study analyzed pregnancy outcome of 349 women who underwent ICSI by their BMI: <25, 25–<30, and ≥30 kg/m2. The associations were generated by applying logistic regression models. A significant reduction in positive pregnancy outcome was observed among overweight and obese women (odds ratio (OR) = 0.50; 95% confidence interval (CI) = 0.25–0.99 for overweight women and OR = 0.45; 95% CI = 0.20–0.89 for obese women). These estimates show that the pregnancy rates are reduced with increasing BMI. The effect of obesity on pregnancy outcome was absent when three and more embryos were transferred. Our study contributes to the reports linking overweight and obesity with decreased positive pregnancy outcome after ICSI and suggests women’s age, infertility type, and number of embryos transferred to modify this reducing effect.

Flourishing and academic engagement among adolescents with overweight and obesity

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Kristie Rupp ◽  
Stephanie M. McCoy
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Academic Engagement ◽  
Regression Models ◽  
Weight Status ◽  
Health Indicators ◽  
Well Being ◽  
Overweight And Obesity ◽  
Healthy Weight ◽  
Logistic Regression Models

Abstract Background Overweight and obesity in adolescence are associated with several negative health indicators; the association with flourishing, an indicator of overall well-being, is less clear. Objectives To examine associations between weight status and indicators of flourishing and academic engagement in adolescents. Subjects Analyses included 22,078 adolescents (10–17 years) from the 2016 National Survey of Children’s Health. Methods Adolescents were grouped according to body mass index (BMI) classification; outcomes included indicators of flourishing and academic engagement. Logistic regression models assessed the odds of each outcome comparing adolescents with overweight and adolescents with obesity to healthy weight adolescents. Results For flourishing, adolescents with overweight and adolescents with obesity were less likely to stay calm during a challenge (17% and 30%, respectively; p < 0.01); adolescents with obesity were 30% less likely to finish a task they started (p < 0.001), and 34% less likely to show interest in new things (p < 0.001) in comparison to healthy weight peers. Adolescents with obesity were 26% less likely to care about doing well in school (p < 0.001), and adolescents with overweight and adolescents with obesity were significantly less likely to complete all required homework (19% and 34%, respectively) (p < 0.001), in comparison to healthy weight peers. Conclusions A comprehensive approach to addressing overweight and obesity in adolescence should target improving academic engagement and flourishing to promote overall well-being.

scholarly journals Association of Obesity, Multiple Chronic Conditions, and Frailty: the National Health and Aging Trends Survey

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 613-613
Author(s):  
John Batsis ◽  
Christian Haudenschild ◽  
Anna Kahkoska ◽  
Rebecca Crow ◽  
David Lynch ◽  
...  
Keyword(s):  
National Health ◽  
Chronic Conditions ◽  
Regression Models ◽  
Overweight And Obesity ◽  
Multiple Chronic Conditions ◽  
Obesity Epidemic ◽  
Logistic Regression Models ◽  
Adjusted Logistic Regression ◽  
Using Data ◽  
The Relationship

Abstract As life expectancy increases, so does the risk of developing multiple chronic conditions (MCC). This is concerning as there is a growing obesity epidemic in older adults which is also associated with developing chronic diseases. Both obesity and MCC also increase the risk of frailty, yet the intersection of the three is not well understood. We evaluated the relationship between obesity, multimorbidity, and frailty using data from adults ≥65 years from the National Health and Aging Trends Survey. Obesity was classified using standard body mass index categories (e.g., ≥30kg/m2) and waist circumference (WC; females≥88cm; males≥102cm). MCC was classified as having ≥2 chronic conditions. Adjusted logistic regression models evaluated the association of BMI or WC categories on MCC (yes/no). An analysis limited to persons with obesity evaluated the relationship between frailty phenotypes (e.g, robust, pre-frail, frail) and MCC. Of the 4,967 participants (59.7% female), 79% resided in a private residence. The 70-79 age category was most prevalent. In those with MCC, there were 1,511 (30.4%) classified as having obesity using BMI, and 3,358 (67.6%) using WC. In those without MCC, there were 287 (17.6%) and 744 (51.7%). Compared to normal BMI, the odds of MCC was 0.71 [0.46,1.09], 1.25 [1.08,1.45] and 2.59 [2.15,3.11] in underweight, overweight and obesity. In pre-frailty and frailty, the odds of MCC were 2.52 [1.77,3.59] and 8.35 [3.7,18.85] in BMI-defined obesity. Using WC, the odds were 2.38 [1.94,2.91], and 5.89 [3.83,9.06]. Obesity using both BMI and WC are both strongly associated with multimorbidity and frailty.

Sign in / Sign up

Export Citation Format

Share Document