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Novel Anthropometry-Based Calculation of the Body Heat Capacity in the Korean Population.

Pham DD, Lee JH, Lee YB, Park ES, Kim KY, Song JY, Kim JE, Leem CH - PLoS ONE (2015)

Bottom Line: Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4).In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC.For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Ulsan College of Medicine, 88 OlympicRo 43-gil Songpa-gu, Seoul, Republic of Korea.

ABSTRACT
Heat capacity (HC) has an important role in the temperature regulation process, particularly in dealing with the heat load. The actual measurement of the body HC is complicated and is generally estimated by body-composition-specific data. This study compared the previously known HC estimating equations and sought how to define HC using simple anthropometric indices such as weight and body surface area (BSA) in the Korean population. Six hundred participants were randomly selected from a pool of 902 healthy volunteers aged 20 to 70 years for the training set. The remaining 302 participants were used for the test set. Body composition analysis using multi-frequency bioelectrical impedance analysis was used to access body components including body fat, water, protein, and mineral mass. Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4). HC_Eq1 generally produced a larger HC than the other HC equations and had a poorer correlation with the other HC equations. HC equations using body composition data were well-correlated to each other. If HC estimated with HC_Eq4 was regarded as a standard, interestingly, the BSA and weight independently contributed to the variation of HC. The model composed of weight, BSA, and gender was able to predict more than a 99% variation of HC_Eq4. Validation analysis on the test set showed a very high satisfactory level of the predictive model. In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC. For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

No MeSH data available.


Related in: MedlinePlus

Bland Altman plot with marginal histograms for the agreement between the predictive model and HC_Eq4 in the test set.Bias, mean of differences between the two HC equations, the predictive model and HC_Eq4; Upper LoA, upper level of limits of agreement = mean of differences + 1.96 standard deviation; Lower LoA, lower level of limits of agreement = mean of differences—1.96 standard deviation; r, Pearson correlation coefficients; ICC, Intraclass correlation coefficient; SEM, standard error of measurement.
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pone.0141498.g003: Bland Altman plot with marginal histograms for the agreement between the predictive model and HC_Eq4 in the test set.Bias, mean of differences between the two HC equations, the predictive model and HC_Eq4; Upper LoA, upper level of limits of agreement = mean of differences + 1.96 standard deviation; Lower LoA, lower level of limits of agreement = mean of differences—1.96 standard deviation; r, Pearson correlation coefficients; ICC, Intraclass correlation coefficient; SEM, standard error of measurement.

Mentions: As for the analysis of concordance on the test set, the predictive model for HC with anthropometry data showed extremely highly satisfactory values (r = 0.996; ICC = 0.996). The predictive model also had a low standard error of measurement (SEM = 0.77 kcal·°C-1) with the limits of agreement varying approximately±1.5 kcal·°C-1 (Fig 3). The final predictive model for HC with anthropometry data from all of the study patients was HC (kcal ⋅ °C−1) = 14.482 × BSA (m2) + 0.456 × BW (kg) − 1.996(if women) − 6.064 (r = 0.996, S1 Table.)


Novel Anthropometry-Based Calculation of the Body Heat Capacity in the Korean Population.

Pham DD, Lee JH, Lee YB, Park ES, Kim KY, Song JY, Kim JE, Leem CH - PLoS ONE (2015)

Bland Altman plot with marginal histograms for the agreement between the predictive model and HC_Eq4 in the test set.Bias, mean of differences between the two HC equations, the predictive model and HC_Eq4; Upper LoA, upper level of limits of agreement = mean of differences + 1.96 standard deviation; Lower LoA, lower level of limits of agreement = mean of differences—1.96 standard deviation; r, Pearson correlation coefficients; ICC, Intraclass correlation coefficient; SEM, standard error of measurement.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631517&req=5

pone.0141498.g003: Bland Altman plot with marginal histograms for the agreement between the predictive model and HC_Eq4 in the test set.Bias, mean of differences between the two HC equations, the predictive model and HC_Eq4; Upper LoA, upper level of limits of agreement = mean of differences + 1.96 standard deviation; Lower LoA, lower level of limits of agreement = mean of differences—1.96 standard deviation; r, Pearson correlation coefficients; ICC, Intraclass correlation coefficient; SEM, standard error of measurement.
Mentions: As for the analysis of concordance on the test set, the predictive model for HC with anthropometry data showed extremely highly satisfactory values (r = 0.996; ICC = 0.996). The predictive model also had a low standard error of measurement (SEM = 0.77 kcal·°C-1) with the limits of agreement varying approximately±1.5 kcal·°C-1 (Fig 3). The final predictive model for HC with anthropometry data from all of the study patients was HC (kcal ⋅ °C−1) = 14.482 × BSA (m2) + 0.456 × BW (kg) − 1.996(if women) − 6.064 (r = 0.996, S1 Table.)

Bottom Line: Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4).In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC.For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Ulsan College of Medicine, 88 OlympicRo 43-gil Songpa-gu, Seoul, Republic of Korea.

ABSTRACT
Heat capacity (HC) has an important role in the temperature regulation process, particularly in dealing with the heat load. The actual measurement of the body HC is complicated and is generally estimated by body-composition-specific data. This study compared the previously known HC estimating equations and sought how to define HC using simple anthropometric indices such as weight and body surface area (BSA) in the Korean population. Six hundred participants were randomly selected from a pool of 902 healthy volunteers aged 20 to 70 years for the training set. The remaining 302 participants were used for the test set. Body composition analysis using multi-frequency bioelectrical impedance analysis was used to access body components including body fat, water, protein, and mineral mass. Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4). HC_Eq1 generally produced a larger HC than the other HC equations and had a poorer correlation with the other HC equations. HC equations using body composition data were well-correlated to each other. If HC estimated with HC_Eq4 was regarded as a standard, interestingly, the BSA and weight independently contributed to the variation of HC. The model composed of weight, BSA, and gender was able to predict more than a 99% variation of HC_Eq4. Validation analysis on the test set showed a very high satisfactory level of the predictive model. In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC. For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

No MeSH data available.


Related in: MedlinePlus