A study was conducted to assess gender differences of body fat distribution (i. e., total body fat mass, subcutaneous fat mass, and internal fat mass) in a homogeneous group of Japanese children. Body composition was estimated in 141 boys and 139 girls (aged 3-6 yr) using bioelectrical impedance analysis (BIA) . All subjects were apparently healthy. Determinations of impedance were made using a four-terminal impedance analyzer (TP-95 K, Toyo Physical, Inc., Fukuoka) . The lean body mass (LBM) was calculated using the equation of Kushuner et al. (1992) and Goran et al. (1993) . Total body fat mass (TFM) was calculated as the difference between body weight and LBM. The subcutaneous fat mass (SFM) was calculated using a modification of the equation derived by Skerjl et al. (1953) . The internal fat mass (IFM) was calculated as the difference between TFM and SFM. From ages 3 through 6 years, the mean LBM increased with age in boys and girls, but showed no significant gender differences. There were also no obvious gender differences in TFM and IFM within the same age range. Percentage body fat decreased in both sexes until the age of approximately 5-yr, and then increased again slightly at 6 yr, although it showed no significant differences between the sexes. The gender-specific pattern of fat accumulation during childhood was characterized by an almost steady increase of SFM in girls. These differences were independent of gender differences in physical characteristics.

In 176 Japanese women aged 20-79, participating in a health assessment programme, we studied the associations between several metabolic features and the type of obesity based on body mass index and waist to hip circumference ratio. We propose the existence of three types of obesity based on BMI and WHR : Type-I, obese women who have a high BMI and a low WHR, Type-II, obese women who have a low BMI and a high WHR, and Type-III, obese women who have both a high BMI and a high WHR. Age was categorized into three levels for all analyses (≤49, 50 - 59, and ≥60 yr) . There was no significant association of WHR with any metabolic variable after adjustment for the effects of the BMI in any group. The results of the present study show that the amount of body fat is in itself more important than the WHR. Where p values were significant, levels of metabolic risk factors for disease increased across the tertiles composed from a combination of the BMI and WHR, except for HDL-ch and HDL-ch/T-ch, where levels fell. Although the means of these metabolic parameters in each type of obesity were all within the normal range, significant differences were observed between the group with Type-III obesity and the nonobese group for these parameters.
In conclusion, the most“dangerous”subgroup of obesity appeared to be Type-III obesity in which high levels of BMI and WHR coexist.

This study was conducted to assess body composition and somatotype in elite female distance runners and to investigate the relationship between body composition and somatotype. Nine elite female distance runners (21.6±2.7 yrs) and 10 female college students (21.4±0.5 yrs) as a control group participated in this study. Body composition was estimated in terms of total body water based on the analysis of dilution of orally ingested deuterium oxide (D₂O) in urine. Somatotype was estimated by Heath-Carter method.
The skinfold thickness in distance runners were significantly thinner at 14 sites than those of the control group. The mean subcutaneous fat in distance runners was 4.1±1.6 kg (8.6±2.8%), which was significantly lower than that of the control group (9.4±3.0 kg & 17.2±3.8%) . The mean internal fat in distance runners was 7.2±2.0 kg (15.5±3.5%), which tended to be lower than that of the control group (8.2±1.5 kg & 15.4±2.2%) . The mean fat mass and percentage of body fat in distance runners were 11.3±3.1 kg and 24.1±4.7%, respectively, which were siginificantly lower than those of the control group (17.6±4.2 kg & 32.6±5.2%) . On the other hand, somatotype scores in distance runners were 2.35-2.32-3.82, suggesting more ectomorphic. In addition, fat mass positively correlated with the first component (r=0.890-0.916, p<0.05), and negatively correlated with the third component (r=-0.635--0.818, p<0.05) . The subjects who were more ectomorphic had less fat mass. However, the second component that was mesomorphic had no significant correlation with lean body mass. And the mean lean body mass in distance runners was almost the same as that of the control group (35.1±2.5 kg & 36.1±3.5 kg) .
The results of this study suggest that although endurance training done by elite female distance runners does not increase their lean body mass, it has a great influence on their subcutaneous fat.

In the present study, we investigated the associations of the body mass index (BMI ; kg/m²) and the waist to hip circumference ratio (WHR) with metabolic features in 134 Japanese women ranging in age from 24 to 79 years. We found that the BMI was reasonably well correlated with the WHR (r=0.693, p<0.001), which may be related to the health risk profile. The BMI and WHR both showed significant negative correlation with high-density lipoprotein cholesterol (HDL) level, and positive correlation with triglyceride (TG) and plasma glucose (GL) levels and systolic blood pressure (SBP) and diastolic blood pressure (DBP) . Following adjustment for the effect of the BMI, the partial correlation coefficients of WHR with HDL, TG, and GL were lower than those obtained before adjustment, and were not significant. However, the partial correlation coefficient of WHR with HDL/total cholesterol ratio (HDL/TC), SBP, and DBP were significant. These results suggest that the levels of HDL, TG and GL are associated with the BMI among subjects with similar WHR. We therefore propose that a combination of the BMI and WHR would be a superior indicator in obesity screening than the BMI alone.

A study was conducted to investigate the validity of bioelectrical impedance measurement (BIM) for determining changes in body composition during treatment of obesity with an exercise and diet regimen. Eleven obese women, aged 38-57 yr (44±6.0 yr), participated in a weight reduction study. Before and after the weight reduction period, body composition was measured by the deuterium oxide (D₂O) dilution and BIM methods. For both methods, the correlation coefficients were of the same order of magnitude under both pre- and postregimen conditions. Mean weight reduction was 3.59±1.518 kg and loss of total body fat was estimated to be 4.8±1.72 kg by the D₂O method and 2.5±1.14 kg by the BIM method. Thus the BIM method underestimated the change in body fat compared with the D₂O method. Changes in resistance (R) and height squared divided by R were not significant at the p>0, 05 level. However, the mean change in fat-free mass (FFM) found by the D₂O method was significantly greater than that found by the BIM method. Furthermore, there was no significant correlation between the changes in FFM estimated by the D₂O and BIM methods. These results show that after weight reduction the BIM method overestimates body composition. It is concluded that the BIM method is not a valid approach for measuring the small changes in body composition that occur during treatment of obesity.

A new method for estimation of total body water in human subjects by measurement of bioelectrical impedance is described. Determinations of impedance (Z) were made in 24 healthy women aged 35.9±15.39 yr using an electrical impedance analyzer (T-1988 K, Toyo Physical Inc.) with a four-electrode arrangement that delivers a painless signal (500 μA at 50 kHz) into the body. The mean coefficient of variation for ten impedance measurements in 4 male subsamples was 0.8% (range, 0.2-1.2%) . Total body water determined by deuteriumdilution was 26.1±2.44l. The estimation equation was developed by regression analysis of data from 24 women. Height²/resistive impedance was the most significant variable for prediction of deuterium-dilution space (TBW) and yielded r=0.804 (p<0.001) with a SE of estimate= 1.41l. The regression equation generated was Total Body Water=0.5294 (Ht²/Z) + 2.5139. These data indicate that bioelectrical impedance measurement is a reliable and valid approach for the estimation of total body water in human subjects. This noninvasive method, which is safe, simple, rapid and convenient, should prove useful in a clinical setting, and also for epidemiological and exercise physiology studies.

In this paper, a predictive equation for TBW (Total Body Water) from various anthropometric measurements was established. Fifty-seven healthy adult males, aged 19 to 54 years old, volunteered as subjects in this experiment.
Ten anthropometric measurements were taken for each subject such as standing height (HT), body weight (WT), breadth of humerus and femurs (B1, B2), girth of upper arm and calf (G1, G2), and skinfold thickness of triceps, subscaplar, suprailliac and abdomen (S1, S2, S3, S4) along with the amount of ingested deutrium oxide (D₂O) .
Total body water was quantitied by the analysis of dilution of orally ingested D₂O in urine. The method of forward stepwise regression analysis was adopted to establish the predictive equation. The stopping rule to select the variables was F statistics (F=2.0) . Furthermore, some criteria such as AIC (Akaike's an information criterion), Mallows' Cp, Schwarz's criterion, R* (adjusted for degree of freedom R) were derived as the regression equation was constructed at each step. These criteria contributed to selecting the best and most valid equation of all equations possible. The results obatained was summarized as follows.
1) Firstly, descriptive statistics were derived for all subjects. Mean (±S. D, ) of TBW was 34.85 (±5.38) l. Skewness and kurtosis were not significant. Multico-linearlity was suggested by correlation matrix (10×10) of all independent variables.
2) Six variables were entered into the equation such as sequences WT, S4, HT, G2, S1, B1. The multiple correlation coefficient (R) and standard error of estimates (SEE) of this equation were 0.961 and 1.568, respectively. It was derived as follows:
Y=-34.56+0.170×HT+0.231×WT+0.567×G2+1.37×B1-0.167×S1-0.086×S4
3) Since the analysis of residuals suggested that abnormal values were contained in this sample, the next regression analysis was adopted after deleted the results of 7 subjects whose standardized residuals were over 1.5. Consequently, the regression equation composited from WT, S4, S2, HT, S1 was evaluated as the best equation according to Cp and Schwarz criterion. AIC selected the equation which added S3 as the 6 th variables. The multiple regression equation established at this stage was described as follows, and R and SEE were 0.9857, 0.940, respectively.
Y=8.10+0.4573×WT-0.0839×S4-0.0951×S2+0.1089×HT-0.1368×S1
4) The specific problem was not obtained from statistics of residuals. However, the co-ordinates of eis (standardized residuals) and predicted value suggested a specific changing pattern. The low possibility of existing multico-linearlity was determined from the eigen value of correlation matrix between independent variables.

The validity to estimate changes in body fat and lean body mass (LBM) through total body water and prediction equation based on triceps and subscapular skinfolds was evaluated after a jogging-type training program. Eight obese boys and 11 obese girls had their total body water determined by D₂O dilution method and underwent a complete series of anthropometric measurements at begining and end of a 2-year program. There was no significant reduction in body weight but a significant change in body water as determined by D₂O dilution method. When compared to the body water method, the prediction equation was found to be highly inconsistent. The change values of the prediction equation was significantly higher than the body water method. The actual changes in LBM and % Fat were found to have low correlations with the predicted changes of boys. In addition, the prediction equation gave significantly higher water content of LBM than criterion value.
This suggests that the practice of using prediction equation to estimate actual changes in body composition parameters after physical training program is basically unsound when used for research purposes.