Numerous linear regression data was made use of, followed by a study of the possibility of a beneficial nonlinear dating established by the along with quadratic and you will cubic forms. Modified [R.sup.2] and you can standard mistake from estimate (SEE) opinions were utilized to compare the brand new abilities of the predictive model out of BF%.
Most of the records away from p have been several-sided and you may p lower than 0.05 try sensed mathematically significant. All analyses have been did using STATA application adaptation a dozen.0 (STATA Corp, University Station, Colorado, USA) and SPSS adaptation 17.0 (SPSS Inc., Chi town, USA).
3.1. Study Population and Baseline Characteristics. All 2,771 participants in this study were over 18 years and comprised 64% women with a median age of 52 years (IQR, 43-60) and 36% men with a median age of 60 years (IQR, 47-68). The BF% in men was statistically significantly lower than in women (27% and 34%, resp.; p < 0.001). The difference in HWDI between men and women was also statistically significant (101 [IQR, 95-107] for men and 98 [IQR, 92-104] for women; p < 0.001) (see Table 1).
Within our data, HWDI, a relatively the brand new obesity dimensions signal, is actually discover having a keen inverse reference to BF% in everyone
Figure 1 suggests the partnership ranging from average Body mass index and you may years. It had been unearthed that, regarding ages of 18 to help you 39 decades, the brand new suggest Body mass index expands since the years expands but, immediately after getting together with sixty yrs old, the new suggest Body mass index decrease because ages increases (pick Shape step one(a)). The opposite is available into relationship ranging from indicate HWDI and you may years.
3.2. Relationship between HWDI and BF%. Figure 2 shows the relationship between HWDI and BF%. Pearson’s correlation coefficient (r) = -0.200 (p < 0.001) in men and r = -0.473 (p < 0.001) in women. In contrast, the direct relationship was found between BMI and BF%, r = 0.144 (p < 0.001) in men and r = 0.421 (p < 0.001) in women. Furthermore, the relationship between HWDI and BF% was statistically significant even when analyzed with respect to age group and gender (p < 0.001) (see Figure 3).
Mathematically, an inverse matchmaking between HWDI and you can BF% try discover since HWDI increased when you find yourself BF% really worth rather decreased
3.3. The Effect of Age, Gender, and HWDI on BF%. The study of the effect of age, gender, and HWDI on BF% showed all three variables’ relationship with BF% to be statistically significant for building a prediction model (p < 0.001 for all variables). HWDI and BF% by age and gender resulted in r = -0.629/ - 0.518 (men/women) for the 18-39-year age group, r = -0.372/ - 0.560 for the 40-59-year age group, and r = -0.125/ - 0.369 for age group over 60 years (see Figure 3).
3.4. Predictive Modeling of BF% by Gender. In this study, several forms of relationship between HWDI and BF% were studied: linear, quadratic, and cubic. However, Figure 3 shows that the relationship tended to be in linear form more than the others, and so we elected to use a linear form in the construction of the BF% prediction model. The results of a multivariate linear regression analysis, which includes the HWDI 
and age variables, yielded a BF% for men of – 0.159 (HWDI) + 0.161 (age) [adjusted [R.sup.2] = 0.215, standard error of estimate (SEE) = 5.37%, p < 0.001], and, for women, - 0.265 (HWDI) + 0.132 (Age) [adjusted [R.sup.2] = 0.337, SEE = 4.39%, p < 0.001] (see Table 2).
not, Pearson’s relationship coefficients were discovered to be lower (r = -0.20 for men and you may roentgen = -0.47 for women) when comparing to past knowledge you to definitely put most other carrying excess fat assessment devices having BF% [16,18, 19]. When you look at the 1996, Gallagher et al. learned the connection anywhere between Bmi and you may BF% and said viewpoints out of roentgen = 0.58 for males and you can r = 0.72 for ladies. Ilman et al. enjoys stated r = 0.85 for males and you can r = 0.83 for females. For each studies described a definite BF% prediction design. It was previously reported that, in addition to years and gender, other factors such as nationality, ethnicity, and you will faith may also be helpful help the precision out-of an effective BF% anticipate model [sixteen,19-23].
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