Assessing the relationship between smoking and abdominal obesity in a National Survey of Adolescents in Brazil

Prevalence and related factors of abdominal obesity among urban adults aged 35 to 79 years in southwest China

Objectives

This study aimed to investigate the prevalence and related factors of abdominal obesity among urban adults aged 35 to 79 years in southwest China.

Methods

From September 2013 to March 2014, a multi-stage sampling was conducted, and a total of 10,981 people aged 35-79 years living in Chengdu and Chongqing were included. More than 30 investigators were trained in data collection, including questionnaire, anthropometric measurements and blood biomarkers testing. Abdominal obesity was defined as waist circumference ≥ 90 cm for men and ≥ 85 cm for women.

Results

The prevalence of abdominal obesity was 30.7%, 24.8% in males and 33.9% in females (p < 0.001). The prevalence of abdominal obesity increased with BMI. The prevalence of abdominal obesity was positively correlated with age, sex, marriage, alcohol consumption, hypertension and diabetes, and negatively correlated with high education level, smoking and Physical activity.

Conclusion

The prevalence of abdominal obesity among adults aged 35-79 in urban communities in southwest China is high, which is close to that of adults in urban communities in China. We should strengthen health education among the population, adopt healthy diet, maintain moderate physical activity and other measures to curb the prevalence of abdominal obesity in urban communities in southwest China.

[Estimated multimorbity among young Brazilians: results of the 2019 National Health Survey]

Multimorbidity, namely the presence of two or more chronic non-communicable diseases, is directly associated with behavioral factors. This study sought to estimate the prevalence of multimorbidity among young Brazilians by linking it to different social and lifestyle determinants. It involved a cross-sectional study of the data source, namely the 2019 National Health Survey. Data from individuals aged between 15 and 24 years (n = 10,460) were selected. Associated factors were investigated by calculating the Prevalence Ratio with robust variance, suitable for bivariate and multivariate analysis. The prevalence of multimorbidity in young people was estimated at 7.84% (95%CI: 7.01-8.75; N: 2,455,097). The most common conditions were mental illness, depression, asthma or bronchitis and chronic back problems. In the adjusted model, young females (PR: 1.84; 95%CI: 1.44-2.36), obese youths (PR: 1.97; 95%CI: 1.45-2.68) and former smokers (PR: 1.46; 95%CI: 1.12-1.90) showed a higher prevalence of multimorbidity. It was also revealed that the prevalence ratio for multimorbidity increased by 5% for each year of the individual's life. This study identified an association of multimorbidity with social determinants and lifestyle.

Determining the effective factors in predicting diet adherence using an intelligent model

Adhering to a healthy diet plays an essential role in preventing many nutrition-related diseases, such as obesity, diabetes, high blood pressure, and other cardiovascular diseases. This study aimed to predict adherence to the prescribed diets using a hybrid model of artificial neural networks (ANNs) and the genetic algorithm (GA). In this study, 26 factors affecting diet adherence were modeled using ANN and GA(ANGA). A dataset of 1528 patients, including 1116 females and 412 males, referred to a private clinic was applied. SPSS Ver.25 and MATLAB toolbox 2017 were employed to make the model and analyze the data. The results showed that the accuracy of the proposed ANN and ANGA models for predicting diet adherence was 93.22% and 93.51%, respectively. Also, the Pearson coefficient showed a significant relationship among the factors. The developed model showed the proper performance for predicting adherence to the diet. Moreover, the most effective factors were selected using GA. Some important factors that affect diet adherence include the duration of the marriage, the reason for referring to the clinic, weight, body mass index (BMI), weight satisfaction, lunch and dinner times, and sleep time. Therefore, applying the proposed model can help dietitians identify people who need more support to adhere to the diet.

Obesity and Endothelial Function

Obesity is a major public health problem and is related to increasing rates of cardiovascular morbidity and mortality. Over 1.9 billion adults are overweight or obese worldwide and the prevalence of obesity is increasing. Obesity influences endothelial function through obesity-related complications such as hypertension, dyslipidemia, diabetes, metabolic syndrome, and obstructive sleep apnea syndrome. The excess fat accumulation in obesity causes adipocyte dysfunction and induces oxidative stress, insulin resistance, and inflammation leading to endothelial dysfunction. Several anthropometric indices and imaging modalities that are used to evaluate obesity have demonstrated an association between obesity and endothelial function. In the past few decades, there has been great focus on the mechanisms underlying endothelial dysfunction caused by obesity for the prevention and treatment of cardiovascular events. This review focuses on pathophysiological mechanisms of obesity-induced endothelial dysfunction and therapeutic targets of obesity.

Adolescent Obesity Modeling: A Framework of Socio-Economic Analysis on Public Health

Bayesian Structural Equation Modeling (SEM-Bayesian) was applied across different research areas to model the correlation between manifest and latent variables. The primary purpose of this study is to introduce a new framework of complexity to adolescent obesity modeling based on adolescent lifestyle through the application of SEM-Bayesian. The introduced model was designed based on the relationships among several factors: household socioeconomic status, healthy food intake, unhealthy food intake, lifestyle, body mass index (BMI) and body fat. One of the main contributions of this study is from considering both BMI and body fat as dependent variables. To demonstrate the reliability of the model, especially in terms of its fitting and accuracy, real-time data were extracted and analyzed across 881 adolescents from secondary schools in Tehran, Iran. The output of this study may be helpful for researchers who are interested in adolescent obesity modeling based on the lifestyle and household socioeconomic status of adolescents.

The Role of Snack Choices, Body Weight Stereotypes and Smoking Behavior in Assessing Risk Factors for Adolescent Overweight and Obesity

This study investigated the relationship of behavioral factors, such as snack choices, obesity stereotypes and smoking with adolescents’ body weight. Individual-level data for 1254 Greek youths were selected via a formal questionnaire. Snack choices seem to be gender specific with girls showing a stronger preference for healthier snacks. Frequent consumption of high-calorie and more filling snacks was found to increase Body Mass Index (BMI) in both genders. Fruit/vegetable snacks were associated with lower body weight in females, whereas cereal/nut snacks had a negative influence in males’ BMI. The majority of participants expressed anti-fat attitudes and more boys than girls assigned positive attributes to lean peers. The endorsement of the thin-ideal was positively associated with the BMI of both adolescent boys and girls. This study also revealed that neglecting potential endogeneity issues can lead to biased estimates of smoking. Gender may be a crucial moderator of smoking–BMI relationships. Male smokers presented a higher obesity risk, whereas female smokers were more likely to be underweight. Nutrition professionals should pay attention to increase the acceptance of healthy snack options. Gender differences in the influence of weight stereotypes and smoking on BMI should be considered in order to enhance the efficacy of obesity prevention interventions.

The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

Cigarette Smoke During Breastfeeding in Rats Changes Glucocorticoid and Vitamin D Status in Obese Adult Offspring

Maternal smoking increases obesogenesis in the progeny. Obesity is associated with several hormonal dysfunctions. In a rat model of postnatal tobacco smoke exposure, we previously reported increased central fat depot and disruption of some hormonal systems in the adult offspring. As both glucocorticoids and vitamin D alter lipogenesis and adipogenesis, here we evaluated the metabolism of these two hormones in visceral adipose tissue (VAT) and liver by Western blotting, and possible associations with lipogenesis biomarkers in adult rats that were exposed to tobacco smoke during their suckling period. At postnatal day (PN) 3, dams and offspring of both sexes were exposed (S group) or not (C group) to tobacco smoke, 4 × 1 h/day. At PN180, corticosteronemia was lower in S male and higher in S female offspring, without alterations in peripheral glucocorticoid metabolism and receptor. Adrenal ACTH receptor (MC2R) was higher in both sexes of S group. Despite unchanged serum vitamin D, liver 25-hydroxylase was higher in both sexes of S group. Male S offspring had higher 1α-hydroxylase, acetyl-CoA carboxylase (ACC), and fatty acid synthase (FAS) in VAT. Both sexes showed increased ACC protein content and reduced sirtuin mRNA in liver. Male S offspring had lower liver peroxisome proliferator-activated receptor-α. Tobacco exposure during lactation induced abdominal obesity in both sexes via distinct mechanisms. Males and females seem to develop HPA-axis dysfunction instead of changes in glucocorticoid metabolism and action. Lipogenesis in VAT and liver, as well as vitamin D status, are more influenced by postnatal smoke exposure in male than in female adult rat offspring.

Associations of multiple unhealthy lifestyle behaviors with overweight/obesity and abdominal obesity among Brazilian adolescents: A country-wide survey

BACKGROUND AND AIMS

Physical inactivity, unhealthy diet, smoking and heavy drinking are four key unhealthy lifestyle behaviors (ULB) that may influence body weight and obesity development. More recently, sedentary time has been recognized as another potentially emerging ULB related to obesity. We therefore investigated the association of multiple ULB with overweight/obesity and abdominal obesity among Brazilian adolescents.

METHODS AND RESULTS

This cross-sectional study involved 62,063 students (12-17 years). Physical inactivity, high screen time, low fiber intake, binge drinking and smoking were self-reported and combined to a ULB risk score, ranging from zero to five. Participants were classified as overweight/obese or with abdominal obesity using sex and age-specific cut-off points for BMI and waist circumference, respectively. Poisson regression models were used to examine the associations between ULB with overweight/obesity and abdominal obesity, adjusted for socio-demographic variables. Overall, 2.3%, 18.9%, 43.9%, 32.3% and 2.6% of participants reported zero, one, two, three and four/five ULB, respectively. Higher ULB risk score was associated with overweight/obesity and abdominal obesity in a dose-response gradient. Among 32 possible combinations of ULB, the three most prevalent combinations (physical inactivity + low fiber intake; high screen time + low fiber intake; physical inactivity + high screen time + low fiber intake) were positively associated with general and abdominal obesity.

CONCLUSIONS

Our findings suggest a synergistic relationship between ULB and general and abdominal obesity. Preventive efforts targeting combined ULB should be sought to reduce the prevalence of general and abdominal obesity in Brazilian youth.