scholarly journals Body composition and osteoporotic fracture using anthropometric prediction equations to assess muscle and fat masses

2021 ◽  
Author(s):  
Changbin Hong ◽  
Seulggie Choi ◽  
Minseon Park ◽  
Sang Min Park ◽  
Gyeongsil Lee
Keyword(s):  
Body Composition ◽  
Osteoporotic Fracture ◽  
Prediction Equations

scholarly journals Length-weight relationship and prediction equations of body composition for growing-finishing cage-farmed Nile tilapia

2015 ◽  
Vol 44 (4) ◽  
pp. 133-137 ◽  
Author(s):  
Tarcila Souza de Castro Silva ◽  
Lilian Dena dos Santos ◽  
Lilian Carolina Rosa da Silva ◽  
Mariana Michelato ◽  
Valéria Rossetto Barriviera Furuya ◽  
...  
Keyword(s):  
Body Composition ◽  
Nile Tilapia ◽  
Prediction Equations ◽  
Weight Relationship

scholarly journals Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

2020 ◽  
Author(s):  
Claudia Kasper ◽  
Patrick Schlegel ◽  
Isabel Ruiz-Ascacibar ◽  
Peter Stoll ◽  
Giuseppe Bee
Keyword(s):  
Body Composition ◽  
Crude Protein ◽  
Imaging Techniques ◽  
Dual Energy ◽  
Growing Pigs ◽  
Regression Equations ◽  
Chemical Analyses ◽  
Prediction Equations ◽  
Animal Science ◽  
X Ray

AbstractStudies in animal science assessing nutrient and energy efficiency or determining nutrient requirements necessitate gathering exact measurements of body composition or body nutrient contents. Wet chemical analysis methods or standardized dissection are commonly applied, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (body weight range 20-100 kg) as determined by both DXA and chemical analyses, with R2 ranging between 0.89 for ash and 0.99 for water and crude protein. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error rCV ranging between 4.3% for crude protein and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of body weights with the equations derived from our DXA measurements and evaluate their fit with our chemical analyses data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.

Accuracy of predicting chemical body composition of gilts and sows

2018 ◽  
Vol 98 (3) ◽  
pp. 597-602
Author(s):  
E.G. Miller ◽  
L. Huber ◽  
C.L. Levesque ◽  
C.F.M. de Lange
Keyword(s):  
Body Composition ◽  
Body Weight ◽  
Prediction Equation ◽  
Longissimus Dorsi ◽  
Prediction Equations ◽  
Longissimus Dorsi Muscle ◽  
Dorsi Muscle ◽  
Physical And Chemical

Physical and chemical body composition of gilts and parity 3 sows were used to determine current prediction equation accuracy and propose alternative prediction equations that incorporate additional variables. Longissimus dorsi muscle depth and parity can be combined with body weight and backfat to improve gilt and sow body composition prediction.

scholarly journals Prediction equations for the estimation of body composition in the elderly using anthropometric data

1994 ◽  
Vol 71 (6) ◽  
pp. 823-833 ◽  
Author(s):  
Marjolein Visser ◽  
Ellen Van Den Heuvel ◽  
Paul Deurenberg
Keyword(s):  
Body Composition ◽  
Body Fat ◽  
The Elderly ◽  
The Body ◽  
Elderly Subjects ◽  
Body Density ◽  
Prediction Equations ◽  
Anthropometric Data ◽  
Percentage Body Fat ◽  
The Relationship

To study the relationship between health and nutritional status in elderly populations, information about body composition is essential. To collect this information in large epidemiological studies, practical methods based on anthropometric data must be available. In the present study the relationship between body composition, determined by densitometry, and anthropometric data in 204 elderly men and women, aged 60–87 years, was analysed. Existing prediction equations described in the literature, and mainly based on young and middle-aged subjects, generally underestimated percentage body fat in the elderly study population. Therefore, new prediction equations were developed, based on sex and the sum of two (biceps and triceps) or four (biceps, triceps, suprailiaca and subscapula) skinfolds or the body mass index (BMI). Addition of age or body circumferences to the models did not improve the prediction of body density. Internal cross validation and external validation revealed that the formulas are valid for the estimation of body density in elderly subjects. The standard errors of estimate of the three models, expressed as percentage body fat, were 5.6, 5.4 and 4.8% respectively.

DEVELOPMENT OF PREDICTION EQUATIONS FOR DETERMINING BODY COMPOSITION FROM BIORESISTANCE IN OBESE WOMEN

1980 ◽  
Vol 21 (Supplement) ◽  
pp. S39
Author(s):  
M. McCaughey ◽  
J. Graves ◽  
M. Pollock ◽  
R. Boileau ◽  
T. Lohman ◽  
...  
Keyword(s):  
Body Composition ◽  
Obese Women ◽  
Prediction Equations

Cross-Validation of Field-Based Assessments of Body Composition for Individuals with Down Syndrome

2005 ◽  
Vol 22 (2) ◽  
pp. 198-206 ◽  
Author(s):  
Phillip C. Usera ◽  
John T. Foley ◽  
Joonkoo Yun
Keyword(s):  
Body Composition ◽  
Down Syndrome ◽  
Body Fat ◽  
Cross Validation ◽  
Mean Squared Error ◽  
The Body ◽  
Anthropometric Measurements ◽  
Prediction Equations ◽  
Root Mean Squared Error ◽  
Squared Error

The purpose of this study was to cross-validate skinfold and anthropometric measurements for individuals with Down syndrome (DS). Estimated body fat of 14 individuals with DS and 13 individuals without DS was compared between criterion measurement (BOP POD®) and three prediction equations. Correlations between criterion and field-based tests for non-DS group and DS groups ranged from .81 – .94 and .11 – .54, respectively. Root-Mean-Squared-Error was employed to examine the amount of error on the field-based measurements. A MANOVA indicated significant differences in accuracy between groups for Jackson’s equation and Lohman’s equation. Based on the results, efforts should now be directed toward developing new equations that can assess the body composition of individuals with DS in a clinically feasible way.

scholarly journals Length-weight relationships and prediction equations of body composition of farm raised Astyanax aff. Fasciatus (Actinopterygii: Characiformes: Characidae)

2014 ◽  
Vol 31 (6) ◽  
pp. 521-524 ◽  
Author(s):  
Valéria R.B. Furuya ◽  
Mariana Michelato ◽  
Thais P. da Cruz ◽  
Wilson M. Furuya
Keyword(s):  
Body Composition ◽  
Prediction Equations
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