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Body mass index-associated molecular characteristics involved in tumor immune and metabolic pathways.

ABSTRACT: Background:Overweight or obesity has been evidenced as an important risk factor involved in the incidence, mortality, and therapy response of multiple malignancies. However, the differences between healthy and obesity tumor patients at the molecular and multi-omics levels remain unclear. Methods:Our study performed a comprehensive and multidimensional analysis in fourteen tumor types of The Cancer Genome Atlas (TCGA) and found body mass index (BMI)-related genes in multiple tumor types. Furthermore, we compared composite expression between normal, overweight, and obese patients of each immune cell subpopulation and metabolism gene subset. Statistical significance was calculated via the Kruskal-Wallis rank-sum test. Results:Our analysis revealed that BMI-related genes are enriched in multiple tumor-related biological pathways involved in intracellular signaling, immune response, and metabolism. We also found the different relationships between BMI and different immune cell infiltration and metabolic pathway activity. Importantly, we found that many clinically actionable genes were BMI-affect genes. Conclusion:Overall, our data indicated that BMI-associated molecular characteristics involved in tumor immune and metabolic pathways, which may highlight the clinical importance of considering BMI-associated molecular signatures in cancer precision medicine.

SUBMITTER: Hu C

PROVIDER: S-EPMC7517824 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Body mass index-associated molecular characteristics involved in tumor immune and metabolic pathways.

Hu Chao C Chen Xiong X Yao Chengyun C Liu Yu Y Xu Haojun H Zhou Guoren G Xia Hongping H Xia Jinglin J

Cancer & metabolism 20200925

<h4>Background</h4>Overweight or obesity has been evidenced as an important risk factor involved in the incidence, mortality, and therapy response of multiple malignancies. However, the differences between healthy and obesity tumor patients at the molecular and multi-omics levels remain unclear.<h4>Methods</h4>Our study performed a comprehensive and multidimensional analysis in fourteen tumor types of The Cancer Genome Atlas (TCGA) and found body mass index (BMI)-related genes in multiple tumor ...[more]

PMID: 32999719

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Project description:BACKGROUND:Perceived neighborhood characteristics are linked to obesity, however, the mechanisms linking these two factors remain unknown. PURPOSE:This study aimed to examine associations between perceived neighborhood characteristics and body mass index (BMI), establish whether indirect pathways through psychological distress and inflammation are important, and determine whether these associations vary by race/ethnicity. METHODS:Participants were 1,112 adults enrolled in the Texas City Stress and Health Study. Perceived neighborhood characteristics were measured using the Perceived Neighborhood Scale. Psychological distress was measured with the Center for Epidemiological Studies Depression Scale, Perceived Stress Scale and mental health subscale of the Short Form Health Survey-36. Markers of inflammation included C-reactive protein, interleukin-6, and tumor necrosis factor receptor-1. Associations were examined with Structural Equation Modeling. RESULTS:A model linking neighborhood characteristics with BMI through direct and indirect (i.e., psychological distress and inflammation) paths demonstrated good fit with the data. Less favorable perceived neighborhood characteristics were associated with greater psychological distress (B = -0.87, ? = -0.31, p < .001) and inflammation (B = -0.02, ? = -0.10, p = .035). Psychological distress and inflammation were also significantly associated with BMI (Bdistress = 0.06, ? = 0.08, p = .006; Binflammation = 4.65, ? = 0.41, p < .001). Indirect paths from neighborhood characteristics to BMI via psychological distress (B = -0.05, ? = -0.03, p = .004) and inflammation (B = -0.08, ? = -0.04, p = .045) were significant. In multiple group analysis, a model with parameters constrained equal across race/ethnicity showed adequate fit suggesting associations were comparable across groups. CONCLUSION:Our study extends the literature by demonstrating the importance of neighborhood perceptions as correlates of BMI across race/ethnicity, and highlights the role of psychological and physiological pathways.

Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Project description:BackgroundNowadays, the burden of non-communicable diseases including obesity has been an increasing public health concern. This menace can be monitored using indexing method like body mass index. Studies indicate that socioeconomic indicators such as income, biology, behavior, and demographic factors associated with body mass index. In Ethiopia, few studies associate wealth index with body mass index in people of ages between 15 and 49. This study was aimed to assess the association of body mass index with wealth index, and behavioral and sociodemographic population characteristics.MethodsA cross-sectional population-based study was conducted using the 2016 Ethiopian Demographic and Health Survey population of ages 15-49. A total of 10,245 individuals were considered to detect the effect of socioeconomic, biological, behavioral, and demographic factors on body mass index using logistic regression.ResultsThe prevalence of underweight, overweight, and obesity among men are 23.8%, 6.6%, and 2.0%, respectively, which is lower than that of women (underweight 25.3%, overweight 9.1%, and obesity 2.9%). The poorest men had higher odds of being underweight (adjusted odds ratio = 2.395%; 95% confidence interval = 2.020-3.544) as compared to the richest men. Merchants and government employees have lower odds (adjusted odds ratio = 0.744; 95% confidence interval = 0.588-0.899) compared to men whose occupation is farming and labor, indicating that merchants and government employees are more likely to become overweight and obese compared to men who are farmers and laborers.ConclusionIt is concluded that wealth index is an important socioeconomic determinant of body mass index among men and women of age 15-49 in Ethiopia. A high prevalence of underweight, and overweight, and obesity is observed, which increases instances of non-communicable diseases. Effects of socioeconomic, biological, behavioral, and demographic indicators on body mass index differed according to sex.

Project description:ImportanceBody mass index (BMI) is used to diagnose obesity in adolescents worldwide, despite evidence that weight does not scale with height squared in adolescents. To account for this, health care providers diagnose obesity using BMI percentiles for each age (BMI z scores), but this does not ensure that BMI is accurate in adolescents.ObjectiveTo compare the accuracy of BMI vs other body fat indices of the form body mass divided by heightn in estimating body fat levels in adolescents.Design, setting, and participantsCross-sectional data from the 1999 to 2006 US National Health and Nutrition Examination Survey were analyzed between September 2015 and December 2016.Main outcomes and measuresDual-energy x-ray absorptiometry and anthropometric data were used to determine changes in body fat levels, body proportions, and the scaling relationships among body mass, height, and percent body fat. To assess the merits of each adiposity index, 3 criteria were used: stability with age, accuracy in estimating percent body fat, and accuracy in classifying adolescents as overweight vs normal weight.ResultsParticipants included 2285 non-Hispanic white participants aged 8 to 29 years. Percent body fat varied with both age and height during adolescence, invalidating the standard weight-to-height regression as the way of finding the optimal body fat index. Because the correct regression model (percent body fat is proportional to mass divided by heightn) suggested that percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-ponderal mass index (TMI; mass divided by height cubed) as an alternative to BMI z scores. For ages 8 to 17 years, TMI yielded greater stability with age and estimated percent body fat better than BMI (R2?=?0.64 vs 0.38 in boys and R2?=?0.72 vs 0.66 in girls). Moreover, TMI misclassified adolescents as overweight vs normal weight less often than BMI z scores (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 19.4%; 95% CI, 17.8%-20.0%; P?<?.001) and performed equally as well as updated BMI percentiles derived from the same data set (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 8.0%; 95% CI, 6.9%-9.1%; P?=?.62).Conclusions and relevanceThe tri-ponderal mass index estimates body fat levels more accurately than BMI in non-Hispanic white adolescents aged 8 to 17 years. Moreover, TMI diagnoses adolescents as overweight more accurately than BMI z scores and equally as well as updated BMI percentiles but is much simpler to use than either because it does not involve complicated percentiles. Taken together, it is worth considering replacing BMI z scores with TMI to estimate body fat levels in adolescents.

Dataset Information

Body mass index-associated molecular characteristics involved in tumor immune and metabolic pathways.

Publications

Body mass index-associated molecular characteristics involved in tumor immune and metabolic pathways.

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