scholarly journals The Eating Inventory, body adiposity and prevalence of diseases in a quota sample of Czech adults

2006 ◽  
Vol 30 (5) ◽  
pp. 830-836 ◽  
Author(s):  
V Hainer ◽  
M Kunesova ◽  
F Bellisle ◽  
J Parizkova ◽  
R Braunerova ◽  
...  
Keyword(s):  
Body Adiposity ◽  
Eating Inventory

scholarly journals The Eating Inventory and Body Adiposity from Leanness to Massive Obesity: a Study of 2509 Adults

2004 ◽  
Vol 12 (12) ◽  
pp. 2023-2030 ◽  
Author(s):  
France Bellisle ◽  
Karine Clément ◽  
Michelle Le Barzic ◽  
Annie Le Gall ◽  
Bernard Guy-Grand ◽  
...  
Keyword(s):  
Massive Obesity ◽  
Body Adiposity ◽  
Eating Inventory

Obesity and adiposity: the culprit of dietary protein efficacy

2020 ◽  
Vol 134 (4) ◽  
pp. 389-401
Author(s):  
Carla El-Mallah ◽  
Omar Obeid
Keyword(s):  
Food Intake ◽  
Behavioural Change ◽  
Physiological Mechanism ◽  
Short Term ◽  
The Past ◽  
Body Adiposity ◽  
Reduce Sugar ◽  
Eye Opening ◽  
Potential Strategy ◽  
Limitations And Exceptions

Abstract Obesity and increased body adiposity have been alarmingly increasing over the past decades and have been linked to a rise in food intake. Many dietary restrictive approaches aiming at reducing weight have resulted in contradictory results. Additionally, some policies to reduce sugar or fat intake were not able to decrease the surge of obesity. This suggests that food intake is controlled by a physiological mechanism and that any behavioural change only leads to a short-term success. Several hypotheses have been postulated, and many of them have been rejected due to some limitations and exceptions. The present review aims at presenting a new theory behind the regulation of energy intake, therefore providing an eye-opening field for energy balance and a potential strategy for obesity management.

scholarly journals Discordance between Body-Mass Index and Body Adiposity Index in the Classification of Weight Status of Elderly Patients with Stable Coronary Artery Disease

2021 ◽  
Vol 10 (5) ◽  
pp. 943
Author(s):  
Bartosz Hudzik ◽  
Justyna Nowak ◽  
Janusz Szkodzinski ◽  
Aleksander Danikiewicz ◽  
Ilona Korzonek-Szlacheta ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Body Mass Index ◽  
Coronary Artery ◽  
Weight Status ◽  
Stable Coronary Artery Disease ◽  
Reference Method ◽  
Normal Weight ◽  
Body Adiposity ◽  
Artery Disease

Background and Aims: Body-mass index (BMI) is a popular method implemented to define weight status. However, describing obesity by BMI may result in inaccurate assessment of adiposity. The Body Adiposity Index (BAI) is intended to be a directly validated method of estimating body fat percentage. We set out to compare body weight status assessment by BMI and BAI in a cohort of elderly patients with stable coronary artery disease (CAD). Methods: A total of 169 patients with stable CAD were enrolled in an out-patient cardiology clinic. The National Research Council (US) Committee on Diet and Health classification was used for individuals older than 65 years as underweight BMI < 24 kg/m2, normal weight BMI 24–29 kg/m2, overweight BMI 29–35 kg/m2, and obesity BMI > 35 kg/m2. In case of BAI, we used sex- and age-specific classification of weight status. In addition, body fat was estimated by bioelectrical impedance analysis (BImpA). Results: Only 72 out of 169 patients (42.6%) had concordant classification of weight status by both BMI and BAI. The majority of the patients had their weight status either underestimated or overestimated. There were strong positive correlations between BMI and BImpA (FAT%) (R = 0.78 p < 0.001); BAI and BImpA (FAT%) (R = 0.79 p < 0.001); and BMI and BAI (R = 0.67 p < 0.001). BMI tended to overestimate the rate of underweight, normal weight or overweight, meanwhile underestimating the rate of obesity. Third, BMI exhibited an average positive bias of 14.4% compared to the reference method (BImpA), whereas BAI exhibited an average negative bias of −8.3% compared to the reference method (BImpA). Multivariate logistic regression identified independent predictors of discordance in assessing weight status by BMI and BAI: BImpA (FAT%) odds ratio (OR) 1.29, total body water (%) OR 1.61, fat mass index OR 2.62, and Controlling Nutritional Status (CONUT) score OR 1.25. Conclusions: There is substantial rate of misclassification of weight status between BMI and BAI. These findings have significant implications for clinical practice as the boundary between health and disease in malnutrition is crucial to accurately define criteria for intervention. Perhaps BMI cut-offs for classifying weight status in the elderly should be revisited.

scholarly journals Cardiorespiratory Fitness: Reference on the Six-Minute Walk Test and Oxygen Consumption in Adolescents from South-Central Chile

2021 ◽  
Vol 18 (5) ◽  
pp. 2474
Author(s):  
Jaime Vásquez-Gómez ◽  
Nelson Gatica Salas ◽  
Pedro Jiménez Villarroel ◽  
Luis Rojas-Araya ◽  
Cesar Faundez-Casanova ◽  
...  
Keyword(s):  
Cardiorespiratory Fitness ◽  
Reference Values ◽  
Estimation Error ◽  
Walk Test ◽  
Central Chile ◽  
Six Minute Walk Test ◽  
Body Adiposity ◽  
South Central ◽  
Males And Females ◽  
Minute Walk

Cardiorespiratory fitness (CRF) provides oxygen to the exercising muscles and is related to body adiposity, with cardiometabolic variables. The aim was to develop reference values and a predictive model of CRF in Chilean adolescents. A total of 741 adolescents of both genders (15.7 years old) participated in a basic anthropometry, performance in the six-minute walk test (SMWT), and in Course Navette was measured. Percentiles were determined for the SMWT, for the V̇O2max, and an equation was developed to estimate it. The validity of the equation was checked using distribution assumptions and the Bland–Altman diagram. The STATA v.14 program was used (p < 0.05). The 50th percentile values for males and females in the SMWT and in the V̇O2max of Course Navette were, respectively, from 607 to 690 and from 630 to 641 m, and from 43.9 to 45 and from 37.5 to 31.5 mlO2·kg·min−1, for the range of 13 to 17 years. For its part, the model to predict V̇O2max incorporated gender, heart rate, height, waist-to-height ratio (WHR), and distance in the SMWT (R2 = 0.62; estimation error = 0.38 LO2·min−1; p <0.001). Reference values can guide physical fitness in Chilean adolescents, and V̇O2max was possible to predict from morphofunctional variables.

scholarly journals Associations of Heavy Metals with Metabolic Syndrome and Anthropometric Indices

Nutrients ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2666 ◽  
Author(s):  
Wei-Lun Wen ◽  
Chih-Wen Wang ◽  
Da-Wei Wu ◽  
Szu-Chia Chen ◽  
Chih-Hsing Hung ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Metabolic Syndrome ◽  
Multivariable Analysis ◽  
Significant Trend ◽  
Volume Index ◽  
Visceral Adiposity Index ◽  
Anthropometric Indices ◽  
Adiposity Index ◽  
Body Adiposity ◽  
Southern Taiwan

Previous studies have revealed associations between heavy metals and extensive health problems. However, the association between heavy metals and metabolic problems remains poorly defined. This study aims to investigate relationships between heavy metals and metabolic syndrome (MetS), lipid accumulation product (LAP), visceral adiposity index (VAI), and anthropometric indices, including body roundness index (BRI), conicity index (CI), body adiposity index (BAI), and abdominal volume index (AVI). We conducted a health survey of people living in southern Taiwan. Six heavy metals were measured: lead (Pb) in blood and nickel (Ni), chromium (Cr), manganese (Mn), arsenic (As), and copper (Cu) in urine. A total of 2444 participants (976 men and 1468 women) were enrolled. MetS was defined according to the Adult Treatment Panel III for Asians. Multivariable analysis showed that participants with high urine Ni (log per 1 μg/L; odds ratio (OR): 1.193; 95% confidence interval (CI): 1.019 to 1.397; p = 0.028) and high urine Cu (log per 1 μg/dL; OR: 3.317; 95% CI: 2.254 to 4.883; p < 0.001) concentrations were significantly associated with MetS. There was a significant trend of a stepwise increase in blood Pb and urine Ni, As, and Cu according to the number of MetS components (from 0 to 5, a linear p ≤ 0.002 for trend). For the determinants of indices, urine Cu was positively correlated with LAP, BRI, CI, and VAI; blood Pb was positively correlated with BRI, BAI, and AVI; urine Ni was positively correlated with LAP. High urine Cu and urine Ni levels were significantly associated with MetS, and there was a significant trend for stepwise increases in blood Pb and urine Ni, As, and Cu, accompanied by an increasing number of MetS components. Furthermore, several indices were positively correlated with urine Cu, urine Ni, and blood Pb.

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