An open study of efficacy, safety and body weight changing in the Blendera-tube fed patients.

OBJECTIVE: To determine the efficacy and safety of Blendera in patients with tube feeding. MATERIAL AND METHOD: An open study with twenty-one subjects who required tube feeding in Bhumibol Adulyadej Hospital were fed with Blendera for seven days. Nutritional status, weight, laboratory values and complications were assessed on day 0 (baseline), day 3, and day 7. RESULTS: Tube-fed patients supported with commercial Blendera formula maintained nutritional status safely with the statistically significant improvement of potassium, calcium, magnesium, prealbumin, triglycerides, and HDL, p-value < 0.05. Especially patients with severe malnutrition status were getting better during the feeding period, p-value 0.028. In addition, there was no statistically significant difference in weight and nutritional status between genders. CONCLUSION: Subjects supported with Blendera are effective in preventing significant weight loss and improves nutritional status without complications. The formula provides all the nutrients when administered daily.

Height prediction from anthropometric length parameters in Thai people.

Height is an important clinical parameter. However, there were no specific measurements available for particular clinical situations. Although many anthropometric measurements were suggested, no formula was recommended in Thailand. The objective of this study was to develop a formula for height prediction with acceptable validity. Two thousand volunteers were included and were divided consecutively according to both age and gender. Model and validation groups were further separated independently. Linear regression was analyzed to create a predictive formula. Ten parameters were included and analyzed. Of these, demispan, sitting height and knee height were selected with a correlation coefficient of more than 0.5 and significant F test in all age groups and genders. All single parameters and the highest predictive value of double (sitting and knee height) and triple regression models (demispan, sitting and knee height) were proposed and these were modified into a simple formula. After validation of both formulas the correlation, quantitative error and relative error were comparable. The simple formula had more than 90% precision with an error of up to 10 cm in the validation group (89.7 to 99.0% in range). Of these, knee height had the least predictive error in all subgroups. The double and triple models had decreased error only in the younger group. In summary, anthropometric parameters with demispan, sitting height, knee height and combination could be applied to height prediction in the adult Thai with acceptable error. These formulas should be applied only in people who could not be directly measured.

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

BACKGROUND: 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.

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

BACKGROUND: 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.