Anthropometric Markers as a Paradigm for Obesity Risk Assessment

Abstract

Background: Quantification of obesity/adiposity is feasible with different anthropometric characteristics along with the bioelectrical impedance analysis techniques. Recent advancements are now witnessing development of further computations derived from previously established measures to gauge obesity. Objective: Main aim of our study was to evaluate the association of anthropometric determinants of obesity with body compositional adiposity variables, and thus identifying the best marker among them emerging out as the probable predictor for compositional adiposity. Participants and Setting: 550 female participants within the age of 18 to 23 years were enrolled under this study attending graduation course at University of Delhi. Ethical clearance was received from the institutional head. Informed written consent was taken from every participant. Design: All the body measurements were recorded by trained staff using standard techniques. Derived measurements were calculated further. Analysis: Data, hence, gathered was undertaken for descriptive and inferential statistical analysis with SPSS 20.0. Variables Measured and Results: WHR overestimated the count for those at risk compared to waist circumference and WHtR. Skeletal muscle fat associated negatively with all anthropometric adiposity indicators. BMI, BAI, WHtR and waist circumference related closely with all body composition cum obesity markers compared to WHR, CI and ABSI. BAI overrated the risk for fat determining body composition parameters the most followed by BMI. ABSI revealed an underestimated risk for augmenting fat content in body, compared to other markers. Conclusion and Implications: It is difficult to establish with compliance as to which of the measures used in the study could better predict the perils of obesity but it could be ascertained that some of the newly verified anthropometric adiposity indicators could be administered for determining clinical situations after further validation.

The Influence of Body Fat and Lean Mass on HbA1c and Lipid Profile in Children and Adolescents with Type 1 Diabetes Mellitus

“…Circumference, body weight, and height measurements were used to calculate the conicity index to analyze visceral fat [ 37 ]. For the estimation of lean mass and body fat through bioimpedance, specific equations for age and gender were used [ 38 , 39 ].…”

A Multivariate Analysis of “Metabolic Phenotype” Patterns in Children and Adolescents with Obesity for the Early Stratification of Patients at Risk of Metabolic Syndrome

“…Adiposity indices, including ABSI; TMI; VAI; ConI; and eFM, were considered and calculated as follows: ABSI = 1000 × WC × Wt −2/3 × Ht 5/6 [ 37 ]; TMI = weight (kg)/height (m) 3 [ 26 ]; ConI = WC/(0.109 × (Wt/Ht) 0.5 ) [ 38 ]; VAI [ 39 ] Male = [WC/(39.68 + (1.88 × BMI))] × (TG/1.03) × (1.31/HDL-C); Female = [WC/(36.58 + (1.89 × BMI))] × (TG/0.81) × (1.52/HDL-C); Fat Mass (eFM) = weight − exp(0.3073 × height 2 −10.0155 × d-growth-standards/standards/body-mass-index-for-age-bmi-for-age weight − 1 + 0.004571 × weight − 0.9180 × ln(age) + 0.6488 × age 0.5 + 0.04723 × male + 2.8055) [ 40 ] (exp = exponential function, ln = natural logarithmic transformation, male = 1, female = 0). …”

Anthropometric Indices With Insulin Resistance in Obese Patients: A Literature Review

“…Due to a growing sedentary lifestyle of many occupations, high-calorie, high-fat diets, shifting methods of travel, and growing urbanization, obesity is brought on [ 5 ]. Hence, it is very important to know the cause of obesity and diagnose it as soon as possible by using appropriate anthropometric assessment tools, which among the measurements used in the investigation could most accurately foresee the dangers of obesity is hard to determine with compliance, but it can be determined that some of the recently validated anthropometric adiposity markers could be used to determine clinical circumstances after additional verification [ 28 ].…”