Brain response to food odors is not associated with body mass index and obesity-related metabolic health measures

Appetite ◽  
2021 ◽  
pp. 105774
Author(s):  
Maria Poessel ◽  
Filip Morys ◽  
Nora Breuer ◽  
Arno Villringer ◽  
Thomas Hummel ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Metabolic Health ◽  
Brain Response ◽  
Health Measures ◽  
Food Odors

Body mass index delineates ALS from FTD: implications for metabolic health

2014 ◽  
Vol 261 (9) ◽  
pp. 1774-1780 ◽  
Author(s):  
R. M. Ahmed ◽  
E. Mioshi ◽  
J. Caga ◽  
M. Shibata ◽  
M. Zoing ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Metabolic Health

scholarly journals Association Between Maternal Prepregnancy Body Mass Index and Plasma Folate Concentrations With Child Metabolic Health

2016 ◽  
Vol 170 (8) ◽  
pp. e160845 ◽  
Author(s):  
Guoying Wang ◽  
Frank B. Hu ◽  
Kamila B. Mistry ◽  
Cuilin Zhang ◽  
Fazheng Ren ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Metabolic Health ◽  
Prepregnancy Body Mass Index ◽  
Plasma Folate

scholarly journals Impact of Body Mass Index and Metabolic Health Status on All‐Cause Mortality Risk among Older Adults

2016 ◽  
Vol 30 (S1) ◽  
Author(s):  
Feon W. Cheng ◽  
Xiang Gao ◽  
Diane C. Mitchell ◽  
Craig Wood ◽  
Christopher Still ◽  
...  
Keyword(s):  
Older Adults ◽  
Body Mass Index ◽  
Health Status ◽  
Body Mass ◽  
Mortality Risk ◽  
Metabolic Health ◽  
All Cause Mortality

scholarly journals Perceptual and Brain Response to Odors Is Associated with Body Mass Index and Postprandial Total Ghrelin Reactivity to a Meal

2016 ◽  
Vol 41 (3) ◽  
pp. 233-248 ◽  
Author(s):  
Xue Sun ◽  
Maria G. Veldhuizen ◽  
Amanda E. Babbs ◽  
Rajita Sinha ◽  
Dana M. Small
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Brain Response ◽  
Total Ghrelin

scholarly journals Natural course of metabolically healthy phenotype and risk of developing Cardiometabolic diseases: a three years follow-up study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel Elías-López ◽  
◽  
Arsenio Vargas-Vázquez ◽  
Roopa Mehta ◽  
Ivette Cruz Bautista ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Body Mass ◽  
Continuous Process ◽  
Incidence Rates ◽  
Metabolic Health ◽  
History Of ◽  
Metabolically Healthy ◽  
Over Time

Abstract Background Whether the metabolically healthy obese (MHO) phenotype is a single, stable or a transitional, fluctuating state is currently unknown. The Mexican-Mestizo population has a genetic predisposition for the development of type 2 diabetes (T2D) and other cardiometabolic complications. Little is known about the natural history of metabolic health in this population. The aim of this study was to analyze the transitions over time among individuals with different degrees of metabolic health and body mass index, and evaluate the incidence of cardiometabolic outcomes according to phenotype. Methods The study population consisted of a metabolic syndrome cohort with at least 3 years of follow up. Participants were apparently-healthy urban Mexican adults ≥20 years with a body mass index (BMI) ≥20 kg/m2. Metabolically healthy phenotype was defined using the criteria of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) metabolic syndrome criteria and the subjects were stratified into 4 groups according to their BMI and metabolic health. For cardiometabolic outcomes we estimated the incidence of cardiometabolic outcomes and standardized them per 1, 000 person-years of follow-up. Finally, to evaluate the risk for transition and development of cardiometabolic outcomes, we fitted Cox Proportional Hazard regression models. Results Amongst the 5541 subjects, 54.2% were classified as metabolically healthy and 45.8% as unhealthy. The MHO prevalence was 39.3%. Up to a third of the population changed from their initial category to another and the higher transition rate was observed in MHO (42.9%). We also found several novel factors associated to transition to metabolically unhealthy phenotype; socioeconomic status, number of pregnancies, a high carbohydrate intake, history of obesity and consumption of sweetened beverages. Similarly, visceral adipose tissue (VAT) was a main predictor of transition; loss of VAT ≥5% was associated with reversion from metabolically unhealthy to metabolically healthy phenotype (hazard ratio (HR) 1.545, 95%CI 1.266–1.886). Finally, we observed higher incidence rates and risk of incident T2D and hypertension in the metabolically unhealthy obesity (MUHO) and metabolically unhealthy lean (MUHL) phenotypes compared to MHO. Conclusions Metabolic health is a dynamic and continuous process, at high risk of transition to metabolically unhealthy phenotypes over time. It is imperative to establish effective processes in primary care to prevent such transitions.

scholarly journals Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations

BMC Obesity ◽  
2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Stephanie K. Tanamas ◽  
Viandini Permatahati ◽  
Winda L. Ng ◽  
Kathryn Backholer ◽  
Rory Wolfe ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Waist Circumference ◽  
Health Outcomes ◽  
Body Mass ◽  
Metabolic Health ◽  
Cross Sectional

Body Mass Index versus Other Adiposity Traits: Best Predictor of Cardiometabolic Risk

2020 ◽  
Author(s):  
Muhammad SAQLAIN ◽  
Zainab AKHTAR ◽  
Raheela KARAMAT ◽  
Samra MUNAWAR ◽  
Maria IQBAL ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Strong Predictor ◽  
Large Population ◽  
Population Group ◽  
Metabolic Health ◽  
Size Analysis ◽  
Visceral Adiposity Index ◽  
Class Iii ◽  
Adiposity Index

Background: A number of anthropometric indices have been used in different world populations as markers to estimate obesity and its related health risks. The present study is large population based study dealing with five anthropometric obesity scales; Body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR), basal adiposity index (BAI), and Visceral adiposity index (VAI) to identify common adiposity trait(s) that best predict obesity and associated health complication(s). Methods: A total of 4000 subjects including 1000 in each category of BMI from four provinces (Punjab, Sindh, Kahyber pakhtoonkha and Balochistan) of Pakistan from 2012-2017 were collected. Complete anthropometric measurementswere obtained and blood samples were collected and Biochemical profiling was performed. Descriptive statistics, linear regression, binary and multiple regression analysis was done. Results: Our data analysis explored the relationships of obesity five indices; BMI, WC, WHR, BAI, and VAI with common metabolic health complications. Effect size analysis clearly indicates that a unit increase in BMI significant raised all anthropometric and clinical parameters. General and sex specific association analysis of adiposity traits with risk phenotypes (hypertension, hyperglycemia and dyslipidemia) indicated significant associations of WC with all three metabolic risks. Varying degrees of correlations of other adiposity traits with metabolic risks were observed. Frequency of different obesity classes among obese population group were as follows; 55.7% class I, 28.50% Class II and 15.80% Class III. Conclusion: WC is the strong predictor of obesity associated metabolic health issues in Pakistani populations. While BMI has significant increasing effect on other obesity indices like WHR, VAI and BAI.

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