Method

Participants

The study involved 163 young, non-pregnant, non-lactating, Caucasian women (Table 1). These women were students at several schools in Poznań (western Poland), a city being nearly homogenous ethnically. Women were recruited opportunistically and examined at their dwelling place, usually a dormitory.

Table 1 Descriptive statistics for women participating in Study 1 and Pearson correlations with breast size Full size table

Measures

Assessment of each participant involved the taking of anthropometric measurements, filling out of questionnaires, photography of the face and hands for estimation of body asymmetry, and collection of saliva for hormonal assessment. Participants’ stature was measured with a stadiometer, body mass with scales, and circumference of hips, waist, and the trunk at the level of maximally protruding breasts and just below the breasts, with a measuring tape. We refer to the last two measurements as breast and chest circumference, respectively. Susceptibility to infectious diseases was estimated with a questionnaire that inquired into the number and duration of infectious diseases of the respiratory system (e.g., cold, influenza) and digestive system (e.g., stomach or intestinal flu) in the past 3 years, and the frequency of antibiotic use for both types of infection (Thornhill & Gangestad, 2006).

Openness to casual sex, or sociosexual orientation, was determined with the 9-item Revised Sociosexual Orientation Inventory developed by Penke and Asendorpf (2008, http://www.larspenke.eu/research/soi-r.html). In addition to a general measure of sociosexual orientation, the inventory also assesses its three facets: behavior, attitude, and desire (3 items per facet). We applied the questionnaire version with 5-point response scales; hence, the result for each facet can assume values from 3 to 15. Participants’ answers were summed up across items with the reverse-coding of Item 6 (Penke & Asendorpf, 2008). Several women declined to provide these data (Table 1).

Procedure

Anthropometric measurements were taken by a trained researcher. The examined women were lightly dressed, the type of clothes worn noted in writing, and each woman provided information on the kind of bra she currently wore, if any. Two other women were measured in various clothing and bra type in order to establish their impact on the value of measured circumferences. Then, the appropriate corrections were applied to the values measured. Specifically, we established that, in comparison with a no brassiere state, a bra with soft cups increased the breast circumference, on average, by 1 cm, a bra with rigid cups by 1.5 cm, and a push-up brassiere by 2.5 cm. Breast size was calculated as the difference between breast circumference and chest circumference. We contend that the difference between these circumferences is a more accurate estimation of the breast size than their quotient, which has been used for some previous studies (see the Supplementary Material for details).

To assess the level of asymmetry for each woman, we took color photographs of her face and hands with a digital camera (Panasonic DMC-FZ200, 12.1 MPx). Frontal facial was taken from a distance of 2 m. Subjects were asked to remove their glasses and jewelry, sweep their hair from their face, and display a neutral expression with a direct gaze and lips held gently together. The ventral side of both hands was photographed from a distance of 0.5 m. Participants placed their hands on a white sheet attached to the wall with the dorsal side of the hand flush with the wall, fingers and wrist straightened and in natural arrangement. Subjects were photographed in a standing position and illuminated with the room fluorescent lighting and the camera flash.

To assess estradiol and testosterone levels, saliva samples of 102 women were collected in the mid-luteal phase of their menstrual cycles, which was estimated from the length of the menstrual cycle and the day of last menses as provided by the participant. The mid-luteal phase was chosen because the estradiol concentration is relatively stable during this period, even though for some women there is an estradiol peak at that time (Stricker et al., 2006). We decided not to collect saliva in the fertile (periovulatory) phase because the estradiol level then changes rapidly and determination of the ovulation day-by-day counting method has low accuracy (Blake, Dixson, O’Dean, & Denson, 2016). The date for saliva collection was assigned by the formula: the day of last menses plus the menstrual cycle length minus 7. Participants obtained glass vials one day earlier and were instructed to provide saliva samples by spitting into them the next day shortly after waking up and rinsing the mouth with still water and then to store the vial in a refrigerator. The vial was taken from the participant within several hours and frozen (− 18 °C). Then vials were sent to the laboratory at the Department of Internal Medicine and Endocrinology in Warsaw for assaying.

Analysis

To determine repeatability of the breast size assessment, we measured 19 women several months after the main examination. Test–retest correlation was 0.93 for breast circumference, 0.91 for chest girth, and 0.78 for calculated breast size. Differences between the first and second measurements included both measurement error and the actual change in breast size ensuing, for example, from body mass change (Schautz, Later, Heller, Muller, & Bosy-Westphal, 2011).

The number of disease episodes (respiratory or digestive) and their average duration were log-transformed to achieve normal-like distributions. Variables related to respiratory and digestive infections underwent two factorial analyses to obtain indices of respiratory infections and digestive infections, respectively. Factor loadings for the number of diseases, average duration of diseases, and frequency of antibiotic use (the number of disease episodes with antibiotic use divided by the total number of disease episodes) were, respectively, 0.70, 0.77, and 0.64 for respiratory infections and 0.83, 0.92, and 0.63 for digestive infections.

Participants’ photographs were digitally processed. Using Adobe Photoshop software, facial images were rotated to eliminate any head tilting, and a white mask was digitally applied to each photograph so as to hide all extraneous elements around the face (DeBruine, Jones, Smith, & Little, 2010; Little, Jones, & DeBruine, 2011). Photographs were evened out for facial size, which was calculated as the average distance of several landmarks from their centroid: trichion, zygions, gonions, gnathion, pupils, and stomion (Farkas, 1994). We then measured (in pixels) differences between left and right facial side for: (1) eye height, (2) horizontal distance between the mouth corner and face contour, (3) pupil’s y-coordinate, and (4) mouth corner y-coordinate. In addition, the distance of the point lying midway between nasal alae from the line crossing points lying midway between inner eye corners and mouth corners was measured. We thus endeavored to involve measurements that (1) were based on landmarks that can be precisely located on a frontal facial photograph and (2) captured various forms of facial asymmetry, horizontal and vertical, related to shape and to location of facial elements. Values of the five measurements were z-scored, which, apart from normalizing the variables, removed possible directional asymmetry from the asymmetry measures, thus preserving only fluctuating asymmetry which is supposedly the only asymmetry type related to biological quality and facial attractiveness (Gangestad & Thornhill, 1999). We then calculated the absolute values for the z-scores because we were interested in the magnitude of the fluctuating asymmetry rather than its direction. Finally, the five resultant values for the face were summed to obtain the Index of Facial Asymmetry.

We then digitally rotated the hand images so as to make them visually vertical and measured hand width and length of all digits but thumbs. The length of each finger was taken from its tip to the midpoint of the proximal crease at the digit base (Fink, Manning, Neave, & Grammer, 2004; Manning, 2002). The hand width was taken as the distance between the points where proximal and distal transverse flexure creases reach the hand contour (Kościński, 2012). The original values of the digital measurements were in pixels but were converted into millimeters using a line of length 10 cm that was drawn on the sheet and photographed along with each hand; this ensured comparability between values for the left and right hand. Next, for each of the four digits and for hand width we calculated the relative fluctuating asymmetry: we took the difference between values for the left and right hand, subtracted the mean difference (across individuals) from it (to discard directional asymmetry), calculated the absolute value for the outcome, and divided it by the mean value from the two hands of the individual. In cases of fractures or dislocations of the measured digits (as uncovered in the interview), the calculated asymmetry was substituted with the average relative asymmetry for the given trait in the sample (Hume & Montgomerie, 2001; Thornhill, Gangestad, & Comer, 1995). The resultant values for the five traits were averaged for each subject to obtain the Index of Hand Asymmetry.

Indices of facial and hand asymmetry were log-transformed to achieve normal-like distributions, z-scored to even out their variations, and averaged to obtain the Index of Total Asymmetry. To assess the reliability of the asymmetry measurements, we placed all landmarks on photographs of 10 women and calculated the asymmetry indices a second time. The test–retest correlation was 0.91 for Index of Facial Asymmetry and 0.95 for Index of Hand Asymmetry.

Hormone concentrations were estimated with enzyme immunoassay kits from DRG Instruments GmbH (Marburg, Germany), no. SLV-4188 for estradiol and no. SLV-3013 for testosterone. The analysis was conducted for 83 women who declared no use of hormonal contraceptives. Estradiol and testosterone assessments succeeded for 74 and 78 women, respectively (Table 1). The assessments were obtained in duplicate, and the intraassay coefficient of variation was 9.0% for estradiol and 5.9% for testosterone. Distribution of testosterone level was not normal, and we applied log transformation to make it normal-like.

All statistical analyses were conducted using Statistica StatSoft 8.0, and reported p values are two-tailed.

Results

Table 1 shows descriptive statistics for participants’ age, height, weight, body mass index (i.e., the weight in kilograms divided by the square of the height in meters, BMI), breast and chest circumference, breast size, characteristics of respiratory and digestive infections (count, average duration, frequency of antibiotic use), sociosexual orientation and its facets, and estradiol and testosterone levels. It also presents the Pearson correlation between these traits and breast size. It can be seen that breast size was not related to age and stature, but positively correlated with body mass, BMI, and breast circumference. Breast size was unrelated to chest circumference, which is compatible with our previous claim that breast size calculated as the difference between breast and chest girth is not confounded by the chest girth. Conversely, the quotient of breast circumference to chest circumference was negatively correlated with chest girth (n = 163, r = − .34, p < .001) and not related to breast circumference (n = 163, r = .08, p = .298), indicating that the quotient is confounded by the chest girth.

Breast size was negatively associated with Total Asymmetry (Table 1, Fig. 1), indicating that women with larger breasts were in general more symmetric. The correlation coefficient was also negative, though not statistically significant, for each component of Total Asymmetry (Table 1). In addition, breast size was positively related to respiratory infections and two of its components, average duration of illness and frequency of antibiotic use (Table 1, Fig. 2), indicating that women with larger breasts experienced longer episodes of respiratory diseases and took antibiotics more frequently. Breast size was unrelated to estradiol and testosterone levels, digestive infections and each of its components, as well as sociosexual orientation and its components (Table 1). In additional regression analyses, sex hormone levels were also independent of waist-to-hip ratio and interaction between waist-to-hip ratio and breast size (all ps > .20).

Fig. 1 Association between breast size and Total Asymmetry Index (n = 163, r = − 0.23, p = .003). Regression line (solid) and 95% confidence band (intermittent lines) attached to the empirical data (disks) Full size image

Fig. 2 Association between breast size and Respiratory Infections Index (n = 163, r = 0.21, p = .008). Regression line (solid) and 95% confidence band (intermittent lines) attached to the empirical data (disks) Full size image

To exclude the possibility that the relationship between breast size and respiratory infections was produced by the six women who declared no respiratory illness in the past three years and seem somewhat outlying (see Fig. 2), we carried out the respective Pearson correlations for the winsorized respiratory infections where the lowest values were replaced with the minimum value for the remaining participants. Results changed only slightly: n = 163, r = .20, p = .012.

We also wanted to exclude the possibility that relationships of breast size with asymmetry and health history were confounded by body mass. We therefore conducted a series of multiple regression analyses with asymmetry, health or sociosexuality index as the dependent variable and breast size and body mass as two independent variables. These made virtually no impact on the results previously obtained in bivariate analysis: breast size significantly predicted Total Asymmetry (standardized β = − 0.16, p = .041), respiratory infections (standardized β = 0.20, p = .011), their duration (standardized β = 0.18, p = .027), and antibiotic use (standardized β = − 0.26, p = .001), with still no significant influence on Facial Asymmetry (standardized β = − 0.12, p = .147), Hand Asymmetry (standardized β = − 0.12, p = .126), number of respiratory infections (standardized β = 0.03, p = .738), digestive infections, sociosexual orientation and their components (all ps > .3). Very similar results were obtained when the series of regressions was conducted with the possible confounder being body height or an indicator of body shape (body mass index or waist-to-height ratio) instead of body mass.

To test possible curvilinear relationships between breast size and other variables, we conducted a series of multiple regression analyses with Total Asymmetry, respiratory or digestive infections, or sociosexual orientation as the dependent variable and breast size and its square as two independent variables. The squared term was nonsignificant for each case (all ps > .07) except for Total Asymmetry (standardized β = − 1.31, p = .007). However, the last effect lost significance when the outlying point for the woman with breast size of 4 cm (see Fig. 1) was excluded from the analysis (standardized β = − 0.73, p = .18).

Discussion

We found that breast size was negatively correlated with body asymmetry and positively with respiratory infections, but unrelated to infections of the digestive system, openness to casual sex, and testosterone and estradiol level. These two significant effects were quite weak, precluding a reliable inference on the health, biological quality, or mate value of an individual woman on the basis of her breast size. However, such findings extend our knowledge on women at the population level and it is known that even weak effects can result in substantial evolutionary changes (e.g., in breast morphology) if operative in the population over many generations (Futuyma, 2009).

We also observed that breast size is positively correlated with body mass. Such relationship has been previously reported in the literature (Brown et al., 2012) and is a result of the association between the amount of adipose tissue in the breast and the body as a whole (Schautz et al., 2011; Wade et al., 2010). In the present study, we conducted analyses that excluded the possibility that relationships between breast size and asymmetry or health history were confounded by body mass.

That bigger breasts are associated with a more symmetric body is compatible with previous studies that found a negative correlation between breast size and relative breast asymmetry (Manning et al., 1997; Møller et al., 1995). It is also compatible with the theory of evolution of biological signals (and the so-called handicap principle), which proposes that traits which are costly to produce and/or maintain should be honest cues to high biological quality of the individual, because only individuals of sufficiently high quality can afford development and maintenance of costly traits (Gangestad & Scheyd, 2005; Zahavi, 1975). A low level of body asymmetry is commonly regarded as a cue to high developmental stability, and thereby high genetic or biological quality (Gangestad & Thornhill, 1999; Van Dongen & Gangestad, 2011), and large breasts can be a costly structure for several reasons. Breasts are composed mainly of fibroglandular tissue (related to milk production and excretion) and adipose tissue (an energy store), while the relative proportion of these tissues varies enormously among women (Lejour, 1997; Vandeweyer & Hertens, 2002). Adipose tissue is energetically dense and its metabolism rate is low; hence, it requires relatively more energy for development and little energy for maintenance; the opposite is true for fibroglandular tissue (Wang et al., 2010; Waterlow, 1981). However, because the total mass of the two breasts is usually about 1 kg (Cox, Kent, Casey, Owens, & Hartmann, 1999; Jansen et al., 2014), the above-mentioned energetic costs, although not negligible, cannot be burdensome for an organism. Breasts are also mechanically costly due to their weight and unsteadiness, which raise various forms of pain, especially during body movements (Kerrigan et al., 2001; Spector & Karp, 2007). This must have been particularly important in the evolutionary past when women wore no bras. Another form of cost is related to breasts being an element of inter-female rivalry (Fink, Klappauf, Brewer, & Shackelford, 2014). Intrasexual rivalry can be costly, particularly for individuals who unreliably signal their high quality (Berglund, Bisazza, & Pilastro, 1996; Mueller & Mazur, 1997). One more cost is related to breast sagging, which is unattractive to men (Groyecka, Zelaźniewicz, Misiak, Karwowski, & Sorokowski, 2017; Havlíček et al., 2017), and larger breasts are more likely to sag (Rinker, Veneracion, & Walsh, 2008; Soltanian, Liu, Cash, & Iglesias, 2012). Although the fitness consequences of the above-mentioned costs and problems related to development and maintenance of big breasts have not been measured, they are arguably not negligible. It is possible that these costs are lower for women characterized by relatively high biological quality.

The observed positive correlation between breast size and respiratory infections may seem to contradict the claim that size of costly traits (e.g., breasts) should be positively associated with the individual’s quality, including health. However, evolutionary biology theorists have argued that it sometimes pays individuals of higher genetic quality to invest in mating at the expense of health to the extent that they are more attractive and have higher reproductive success but are less healthy than conspecifics of a lower quality (Getty, 2002; Kokko, 2001). Indeed, studies on nonhuman animals revealed both positive and negative correlations between putative cues to genetic quality and survival rate (Jennions, Møller, & Petrie, 2001). It is therefore possible that women of high biological quality have more symmetric bodies (because of higher developmental stability), larger breasts (because they can afford it), and worse infectious health (because of a particular life strategy being adopted).

The negative relationships of breast size with infectious health obtained here are also compatible with literature findings that large breasts are associated with higher risk of type 2 diabetes (Ray et al., 2008) and breast cancer (Jansen et al., 2014). The risk of breast cancer is supposedly increased both by amount of fibroglandular tissue, via the number of epithelial cells which can become cancerous, and adipose tissue, due to its carcinogenous properties (Boyd et al., 2007; Jansen et al., 2014). Hormonal activity of adipose tissue in breasts has been suggested to be responsible for the influence of breast size on the risk of diabetes (Ray et al., 2008). Since excessive adipose tissue in body is associated with decreased function of the immune system (Rantala et al., 2013; Samartín & Chandra, 2001), it is possible that the correlation between breast size and respiratory infections observed here was mediated by the amount of adipose tissue in the breasts.

We found that breast size was associated with length of respiratory infections and frequency of antibiotic use but not with number of respiratory infections and any parameter describing digestive infections. Correlations with respiratory but not digestive infections have been already reported for facial appearance (Gray & Boothroyd, 2012; Thornhill & Gangestad, 2006). The individual’s immunocompetence is arguably better reflected in the length of illness episodes (the time the organism needs to eliminate pathogens) and frequency of antibiotic use (which is associated with severity of diseases) than the number of episodes, which depends, to a higher degree, on exposure to pathogens. The reason for lack of significant correlations for digestive infections may be that they are much less frequent than respiratory ones.

It should be also noted that we relied on health data as declared by the participants. Such data are commonly used in epidemiological studies because they are easy to collect at low cost and in a short time. Although some researchers pointed to limited accuracy of the method (Savilahti, Uitti, & Husman, 2005), others defended its usability (Stevenson, Case, & Oaten, 2009). If the error in providing health data was associated with breast size, a spurious correlation between infectious health and breast size could be revealed. Our results require replication with an objective measure of infectious health, e.g., the response of the immune system by antibody production (Rantala et al., 2013).

We found no significant relationship between breast size and sex hormone levels. This corresponds with results reported by Garver-Apgar et al. (2011) and Grillot et al. (2014) but not with Jasieńska et al. (2004), who found that women with larger breasts have a higher level of estradiol. It is possible that our sample was too small to demonstrate a significant association for such a labile trait as concentration of estradiol or testosterone (Shultz, Wideman, Montgomery, & Levine, 2011; Stricker et al., 2006), all the more so that we had only one saliva sample per woman. Alternatively, a biologically meaningful correlation between breast size and sex hormone levels may not exist. Jasieńska et al. estimated breast size with the breast-to-chest circumference ratio, which is severely confounded with chest circumference (see the Supplementary Material). Their results may therefore pertain to chest size rather than breast size. Further research is needed to clarify this.

Breast size proved unrelated to a woman’s sociosexual orientation and each of its facets. Therefore, no support was found for Smith’s (1984) hypothesis that ancestral women evolved permanent breasts in order to facilitate cheating of the partner. This hypothesis has previously been criticized on theoretical grounds (Caro, 1987; Marlowe, 1998), and our study failed to provide any empirical support for it.