The relationship of female physical attractiveness to body fatness.
Bottom Line:
WHR was a significant independent but less important factor, which was more important (greater r (2)) in African populations.Raters appeared to use body fat percentage (BF%) and BMI as markers of age.The covariance of BF% and BMI with age indicates that the role of body fatness alone, as a marker of attractiveness, has been overestimated.
Affiliation: State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences , Beijing , China.
ABSTRACT
Aspects of the female body may be attractive because they signal evolutionary fitness. Greater body fatness might reflect greater potential to survive famines, but individuals carrying larger fat stores may have poor health and lower fertility in non-famine conditions. A mathematical statistical model using epidemiological data linking fatness to fitness traits, predicted a peaked relationship between fatness and attractiveness (maximum at body mass index (BMI) = 22.8 to 24.8 depending on ethnicity and assumptions). Participants from three Caucasian populations (Austria, Lithuania and the UK), three Asian populations (China, Iran and Mauritius) and four African populations (Kenya, Morocco, Nigeria and Senegal) rated attractiveness of a series of female images varying in fatness (BMI) and waist to hip ratio (WHR). There was an inverse linear relationship between physical attractiveness and body fatness or BMI in all populations. Lower body fat was more attractive, down to at least BMI = 19. There was no peak in the relationship over the range we studied in any population. WHR was a significant independent but less important factor, which was more important (greater r (2)) in African populations. Predictions based on the fitness model were not supported. Raters appeared to use body fat percentage (BF%) and BMI as markers of age. The covariance of BF% and BMI with age indicates that the role of body fatness alone, as a marker of attractiveness, has been overestimated. No MeSH data available. Related in: MedlinePlus |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC4556148&req=5
Mentions: Many studies have associated the risk of developing various diseases with different levels of body fatness (Borugian et al., 2003; Chan et al., 1994; Despres, 2012; Terry, Page & Haskell, 1992). We found three reviews which compiled data for different ethnic groups to establish ethnic specific patterns of mortality in relation to fatness. These included reviews involving >900 k Caucasians (Whitlock et al., 2009) and >1.1 million Asians (Zheng et al., 2011). We could not locate any summary of the same relationship pertaining to Africans living in Africa, but found reviews including >360 k African Americans (Cohen et al., 2014; Cohen et al., 2012; Flegal et al., 2013). In the studies involving Caucasians and African Americans the data was subdivided by gender so we could extract female specific curves, but for the Asians this was not possible from the data in the original paper. However, the patterns for males and females in the Caucasians and African Americans were almost identical so this is unlikely to be a serious source of error. The pattern of all cause mortality (total mortality irrespective of cause) from these three studies in relation to BMI is shown in Fig. 1A. We expressed the mortality in each BMI class as the excess mortality above that of the lowest BMI class, since this reflects the negative impact of differences in body fatness, and then fitted a polynomial to the data for each ethnic group using ordinary least squares regression. The resultant best fit (least squares) equations were a series of third order polynomials which explained respectively 97.1% for Caucasians (Eq. (1a)), 99.8% for Asians (Eq. (1b)) and 98.4% for African Americans (Eq. (1c)), of the variance in excess annual mortality (1a)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*} {y}_{1 c}=-0.002359{x}^{3}+0.24392{x}^{2}-7.6714 x+76.089 \end{eqnarray*}\end{document}y1c=−0.002359x3+0.24392x2−7.6714x+76.089(1b)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*} {y}_{1 a}=-0.0034{x}^{3}+0.3286{x}^{2}-10.004 x+97.859 \end{eqnarray*}\end{document}y1a=−0.0034x3+0.3286x2−10.004x+97.859(1c)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*} {y}_{1 a a}=-0.0005{x}^{3}+0.0649{x}^{2}-2.2071 x+23.272 \end{eqnarray*}\end{document}y1aa=−0.0005x3+0.0649x2−2.2071x+23.272 where y1c, y1a, y1aa are the excess annual mortalities per thousand population due to all causes and x is the BMI for Caucasian, Asian and African American populations respectively. Many studies have also studied aspects of reproductive biology in relation to body fatness (or BMI). However, we could not find any summaries for Asian or African/African American populations. Among the most comprehensive studies of Caucasians was the Adventist Health study (Jacobsen et al., 2013) which included lifetime fertility records for 33,159 females along with their BMI at age 20. The relationship between the probability of having no children during a reproductive life of 20 years, and BMI class at age 20 is shown in Fig. 1B. The 20 year excess probability of not having children, compared to the BMI class with the lowest rate of iparity, contributes to the negative effect of BMI on fertility. To obtain the annualized rate of excess ‘missing births’ we divided this lifetime rate by 20 and then fitted a polynomial to these data using ordinary least squares regression. In this case the best fit was a second order polynomial which explained 97.5% of the variation (2)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*} {y}_{2}=0.1065{x}^{2}-4.6346 x+50.145 \end{eqnarray*}\end{document}y2=0.1065x2−4.6346x+50.145 The same study also showed that the probability of having a second child was also impacted by obesity status at age 20. The data are also shown in Fig. 1B and in this case the excess missing births relative to the BMI class with the lowest rate of not having a second child were best described by a second order polynomial (3)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*} {y}_{3}=0.0478{x}^{2}-2.2438 x+26.282 \end{eqnarray*}\end{document}y3=0.0478x2−2.2438x+26.282 which explained 95.9% of the variation. |
View Article: PubMed Central - HTML - PubMed
Affiliation: State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences , Beijing , China.
No MeSH data available.