Banded Sleeve Gastrectomy Improves Weight Loss Compared to Nonbanded Sleeve: Midterm Results from a Prospective Randomized StudyRead the full article
Journal of Obesity focuses on topics such as obesity, lipid metabolism, metabolic syndrome, diabetes, paediatric obesity, genetics, nutrition & eating disorders, exercise & human physiology, weight control and risks associated with obesity.
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Explaining the Inverse Association between Altitude and Obesity
Purpose. To better understand the inverse association between altitude and adult obesity. Methods. An ecological study design was used, involving 3,108 counties in the contiguous United States. Data were from several national sources, and assessment involved various statistical techniques, including multiple regression analysis. Results. Living in counties at higher altitude is associated with lower adult obesity. Compared with counties <500 meters, the percent of adult obesity decreases by 5.18% at 500–999 meters, 9.69% at 1,000–1,499 meters, 16.77% at 1,500–1,999 meters, 24.14% at 2,000–2,499 meters, and 35.28% at ≥2,500 meters. After adjusting for physical inactivity, smoking, and other variables, corresponding decreases in adult obesity with higher altitude groupings are 3.87%, 5.64%, 8.03%, 11.41%, and 17.54%, respectively. Various mechanisms are presented as possible explanations for the association between higher altitude and lower obesity. In addition, altitude may indirectly influence adult obesity, primarily through its relationship with physical inactivity and smoking. In an adjusted regression model, adult obesity was most strongly associated with physical inactivity followed by adult smoking and then altitude. Together they explain 39.04% of the variation in adult obesity. After accounting for these variables, sunlight, precipitation, ambient air temperature, education, income, food insecurity, limited access to healthy foods, race, sex, and rural living explain an additional 4.68% of the variation in adult obesity. Conclusions. The inverse association between altitude and adult obesity remains significant after adjustment for several variables.
Epidemiology, Predisposing Factors, Biomarkers, and Prevention Mechanism of Obesity
Background. Globally, obesity is becoming a public health problem in the general population. Various determinants were reported by different scholars even though there are inconsistencies. Different biomarkers of obesity were identified for the prediction of obesity. Even though researchers speculate the factors, biomarkers, consequences, and prevention mechanisms, there is a lack of aggregate and purified data in the area of obesity. Summary. In this review, the epidemiology, predisposing factors, biomarkers, consequences, and prevention approaches of obesity were reviewed. Key Messages. The epidemiology of obesity increased in low-, middle-, and high-income countries. Even if the factors vary across regions and socioeconomic levels, sociodemographic, behavioral, and genetic factors were prominent for the development of obesity. There are a lot of biomarkers for obesity, of which microRNA, adipocytes, oxidative stress, blood cell profile, nutrients, and microbiota were promising biomarkers for determination of occurrence of obesity. Since the consequences of obesity are vast and interrelated, multidimensional prevention strategy is mandatory in all nations.
Predictors of One-Year Change in How Youth Perceive Their Weight
Overall, perceptions of being at “about the right weight” appear advantageous for youth physical and mental health, regardless of BMI classification, whereas perceptions at either extreme (overweight or underweight) may negatively impact health behaviours and mental health. Instead of considering weight misperceptions as problematic, some researchers have proposed that underestimations of weight status may offer resiliency among individuals with overweight or obesity. Promoting “about right” WPs and preventing change to overweight or underweight perceptions may offer an effective public health strategy for supporting youth health over time. However, limited prospective evidence exists on factors that shape perceptions of weight status over time. The current study examined modifiable predictors of one-year change in weight perception among youths. We used 2-year linked data of 18,112 grade 9–12 students from Year 3 (Y3:2014–2015) and Year 4 (Y4:2015–2016) of the COMPASS study. Generalized Estimating Equation models tested screen use, physical activity, and bullying victimization as predictors of change from perceptions of “about the right weight” to “overweight” or “underweight” perceptions, adjusting for Y3 covariates (body mass index, ethnicity, and grade) and school cluster. Results support the value of team sports among females and resistance exercise among males as protective against changes to overweight or underweight perceptions over one year. Also, various forms of bullying victimization predicted overweight perceptions in males and females. Watching TV/movies or messaging/texting for over 2 hours/day was associated with overweight and underweight perceptions, respectively, in females only. Playing video/computer games for over 2 hours/day was associated with overweight perceptions in males and underweight perceptions in females. Findings support the potential of bullying prevention, limiting certain screen use, and supporting engagement in team sports for females and resistance exercise for males as strategies to maintain perceptions of being at “about the right weight.”
Weight Change and Its Association with Cardiometabolic Risk Markers in Overweight and Obese Women
Introduction. The effect of weight loss magnitude on cardiometabolic risk markers has been sparsely studied, particularly among overweight and obese women from low socioeconomic areas. Objectives. To examine the association of weight loss magnitude with changes in cardiometabolic risk markers in overweight and obese women from low socioeconomic areas engaged in a lifestyle intervention. Methods. Analyses were performed on 243 women (mean body mass index 31.27 ± 4.14 kg/m2) who completed a 12-month lifestyle intervention in low socioeconomic communities in Klang Valley, Malaysia. Analysis of covariance (ANCOVA) was used to compare changes of cardiometabolic risk factors across weight change categories (2% gain, ±2% maintain, >2 to <5% loss, and 5 to 20% loss) within intervention and control group. Results. A graded association for changes in waist circumference, fasting insulin, and total cholesterol (, for all variables) across the weight change categories were observed within the intervention group at six months postintervention. Participants who lost 5 to 20% of weight had the greatest improvements in those risk markers (−5.67 cm CI: −7.98 to −3.36, −4.27 μU/mL CI: −7.35, −1.19, and −0.59 mmol/L CI: −.99, −0.19, respectively) compared to those who did not. Those who lost >2% to <5% weight reduced more waist circumference (−4.24 cm CI: −5.44 to −3.04) and fasting insulin (−0.36 μU/mL CI: −1.95 to 1.24) than those who maintained or gained weight. No significant association was detected in changes of risk markers across the weight change categories within the control group except for waist circumference and adiponectin. Conclusion. Weight loss of >2 to <5% obtained through lifestyle intervention may represent a reasonable initial weight loss target for women in the low socioeconomic community as it led to improvements in selected risk markers, particularly of diabetes risk.
Metabolomic Links between Sugar-Sweetened Beverage Intake and Obesity
Background. Sugar-sweetened beverage (SSB) consumption is highly associated with obesity, but the metabolic mechanism underlying this correlation is not understood. Objective. Our objective was to examine metabolomic links between SSB intake and obesity to understand metabolic mechanisms. Design. We examined the association of plasma metabolomic profiles with SSB intake and obesity risk in 781 participants, aged 45–75 y, in the Boston Puerto Rican Health Study (BPRHS) using generalized linear models, controlling for potential confounding factors. Based on identified metabolites, we conducted pathway enrichment analysis to identify potential metabolic pathways that link SSB intake and obesity risk. Variants in genes encoding enzymes known to function in identified metabolic pathways were examined for their interactions with SSB intake on obesity. Results. SSB intake was correlated with BMI (β = 0.607, ). Among 526 measured metabolites, 86 showed a significant correlation with SSB intake and 148 with BMI (); 28 were correlated with both SSB intake and BMI (). Pathway enrichment analysis identified the phosphatidylcholine and lysophospholipid pathways as linking SSB intake to obesity, after correction for multiple testing. Furthermore, 8 of 10 genes functioning in these two pathways showed strong interaction with SSB intake on BMI. Our results further identified participants who may exhibit an increased risk of obesity when consuming SSB. Conclusions. We identified two key metabolic pathways that link SSB intake to obesity, revealing the potential of phosphatidylcholine and lysophospholipid to modulate how SSB intake can increase obesity risk. The interaction between genetic variants related to these pathways and SSB intake on obesity further supports the mechanism.
Physical Activity and Insulin Resistance in 6,500 NHANES Adults: The Role of Abdominal Obesity
This cross-sectional investigation studied differences in insulin resistance across levels of physical activity in 6,500 US adults who were randomly selected as part of the National Health and Nutrition Examination Survey (NHANES). Another important objective was to determine the influence of abdominal obesity on the physical activity and insulin resistance relationship. MET-minutes were utilized to quantify total activity based on participation in 48 different physical activities. Two strategies were employed to categorize levels of physical activity: one was based on relative MET-minutes (quartiles), and the other approach was based on the US physical activity guidelines. Insulin resistance was indexed using the homeostatic model assessment (HOMA). Abdominal obesity was indexed using waist circumference. Effect modification was tested by dividing waist circumferences into sex-specific quartiles and then evaluating the relationship between physical activity and HOMA-IR within each quartile separately. Results showed that relative physical activity level was associated with HOMA-IR after controlling for demographic and demographic and lifestyle covariates (F = 11.5, and F = 6.0, , respectively). Adjusting for demographic and demographic and lifestyle covariates also resulted in significant relationships between guideline-based activity and HOMA-IR (F = 8.0, and F = 4.9, , respectively). However, statistically controlling for differences in waist circumference with the other covariates nullified the relationship between total physical activity and HOMA-IR. Effect modification testing showed that when the sample was delimited to adults with abdominal obesity (Quartile 4), relative (F = 5.6, ) and guideline-based physical activity (F = 3.7, ) and HOMA-IR were significantly associated. Physical activity and HOMA-IR were not related within the other three quartiles. In conclusion, it appears that differences in physical activity may play a meaningful role in insulin resistance in those with abdominal obesity, but total activity does not seem to account for differences in insulin resistance among US adults with smaller waists.