Development of gender- and age group-specific equations for estimating body weight from anthropometric measurement in Thai adults

Kaweesak Chittawatanarat1,2, Sakda Pruenglampoo3, Vibul Trakulhoon4, Winai Ungpinitpong5, Jayanton Patumanond21Department of Surgery, Faculty of Medicine, 2Clinical Epidemiology Unit, 3Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand; 4Department of Surgery, Bhumibol Adulyadej Hospital, Bangkok, Thailand; 5Surgical Unit, Surin Hospital, Surin, ThailandBackground: Many medical procedures routinely use body weight as a parameter for calculation. However, these measurements are not always available. In addition, the commonly used visual estimation has had high error rates. Therefore, the aim of this study was to develop a predictive equation for body weight using body circumferences.Methods: A prospective study was performed in healthy volunteers. Body weight, height, and eight circumferential level parameters including neck, arm, chest, waist, umbilical level, hip, thigh, and calf were recorded. Linear regression equations were developed in a modeling sample group divided by sex and age (younger <60 years and older ≥60 years). Original regression equations were modified to simple equations by coefficients and intercepts adjustment. These equations were tested in an independent validation sample.Results: A total of 2000 volunteers were included in this study. These were randomly separated into two groups (1000 in each modeling and validation group). Equations using height and one covariate circumference were developed. After the covariate selection processes, covariate circumference of chest, waist, umbilical level, and hip were selected for single covariate equations (Sco). To reduce the body somatotype difference, the combination covariate circumferences were created by summation between the chest and one torso circumference of waist, umbilical level, or hip and used in the equation development as a combination covariate equation (Cco). Of these equations, Cco had significantly higher 10% threshold error tolerance compared with Sco (mean percentage error tolerance of Cco versus Sco [95% confidence interval; 95% CI]: 76.9 [74.2–79.6] versus 70.3 [68.4–72.3]; P < 0.01, respectively). Although simple covariate equations had more evidence errors than the original covariate equations, there was comparable error tolerance between the types of equations (original versus simple: 74.5 [71.9–77.1] versus 71.7 [69.2–74.3]; P = 0.12, respectively). The chest containing covariate (C) equation had the most appropriate performance for Sco equations (chest versus nonchest: 73.4 [69.7–77.1] versus 69.3 [67.0–71.6]; P = 0.03, respectively). For Cco equations, although there were no differences between covariates using summation of chest and hip (C+Hp) and other Cco but C+Hp had a slightly higher performance validity (C+Hp versus other Cco [95% CI]: 77.8 [73.2–82.3] versus 76.5 [72.7–80.2]; P = 0.65, respectively).Conclusion: Body weight can be predicted by height and circumferential covariate equations. Cco had more Sco error tolerance. Original and simple equations had comparable validity. Chest- and C+Hp-containing covariate equations had more precision within the Sco and Cco equation types, respectively.Keywords: body weight, anthropometry, circumference, Thai, linear model

The variations of body mass index and body fat in adult Thai people across the age spectrum measured by bioelectrical impedance analysis

Kaweesak Chittawatanarat1,2, Sakda Pruenglampoo3, Siriphan Kongsawasdi4, Busaba Chuatrakoon4, Vibul Trakulhoon5, Winai Ungpinitpong6, Jayanton Patumanond21Department of Surgery, Faculty of Medicine, 2Clinical Epidemiology Unit, 3Research Institute for Health Sciences, 4Department of Physical Therapy, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 5Department of Surgery, Bhumibol Adulyadej Hospital, Bangkok, 6Surgical Unit, Surin Hospital, Surin, ThailandBackground: The measurements of body mass index (BMI) and percentage of body fat are used in many clinical situations. However, special tools are required to measure body fat. Many formulas are proposed for estimation but these use constant coefficients of age. Age spectrum might affect the predicted value of the body composition due to body component alterations, and the coefficient of age for body fat prediction might produce inconsistent results. The objective of this study was to identify variations of BMI and body fat across the age spectrum as well as compare results between BMI predicted body fat and bioelectrical impedance results on age.Methods: Healthy volunteers were recruited for this study. Body fat was measured by bioelectrical impedance. The age spectrum was divided into three groups (younger: 18–39.9; middle: 40–59.9; and older: ≥60 years). Comparison of body composition covariates including fat mass (FM), fat free mass (FFM), percentage FM (PFM), percentage FFM (PFFM), FM index (FMI) and FFM index (FFMI) in each weight status and age spectrum were analyzed. Multivariable linear regression coefficients were calculated. Coefficient alterations among age groups were tested to confirm the effect of the age spectrum on body composition covariates. Measured PFM and calculated PFM from previous formulas were compared in each quarter of the age spectrum.Results: A total of 2324 volunteers were included in this study. The overall body composition and weight status, average body weight, height, BMI, FM, FFM, and its derivatives were significantly different among age groups. The coefficient of age altered the PFM differently between younger, middle, and older groups (0.07; P = 0.02 vs 0.13; P < 0.01 vs 0.26; P <0.01; respectively). All coefficients of age alterations in all FM- and FFM-derived variables between each age spectrum were tested, demonstrating a significant difference between the younger (<60 years) and older (≥60 years) age groups, except the PFFM to BMI ratio (difference of PFM and FMI [95% confidence interval]: 17.8 [12.8–22.8], P < 0.01; and 4.58 [3.4–5.8], P < 0.01; respectively). The comparison between measured PFM and calculated PFM demonstrated a significant difference with increments of age.Conclusion: The relationship between body FM and BMI varies on the age spectrum. A calculated formula in older people might be distorted with the utilization of constant coefficients.Keywords: fat mass, fat free mass, age, body mass index, Thai&nbsp