Facial Adiposity: A Cue to Health?

Abstract

Facial symmetry, averageness, sexual dimorphism, and skin colour/texture all serve as cues to attractiveness, but their role in the perception of health is less clear. This ambiguity could reflect the fact that these facial traits are not the only cues to health. We propose that adiposity is an important, but thus far disregarded, facial cue to health. Our results demonstrate two important prerequisites for any health cue. First, we show that facial adiposity, or the perception of weight in the face, significantly predicts perceived health and attractiveness. Second, we show that perceived facial adiposity is significantly associated with measures of cardiovascular health and reported infections. Perceived facial adiposity, or a correlate thereof, is therefore an important and valid cue to health that should be included in future studies.

Full text

1 Introduction
Healthy individuals confer direct and indirect benefits on their partners. Direct benefits
include reduced risk of infection and increased resources (Kirkpatrick and Ryan 1991),
while indirect benefits consist of the `good genes' passed on to the offspring. The
`good genes' hypothesis posits that individuals are attracted to partners that display
certain traits. These traits indicate a superior ability to survive, for instance a higher
resistance to pathogens (Hamilton and Zuk 1982). Attractive individuals are there-
fore expected to be healthier. Various studies support the relationship between facial
attractiveness and health, while some do not. Kalick et al (1998) found no significant
correlation between late adolescent facial attractiveness and health scores based on
detailed medical histories. A re-analysis of their data did show a significant relation-
ship between adult facial attractiveness and adult health in both sexes, but only when
using faces below the median for attractiveness (Zebrowitz and Rhodes 2004). Late
adolescent facial attractiveness is also linked to longevity (Henderson and Anglin 2003)
and facially attractive young adults have more heterozygous human leukocyte antigen
(HLA) genes (Roberts et al 2005). These genes play a crucial role in the immune
system and heterozygosity is thought to be associated with increased immune recogni-
tion of pathogens (Roberts et al 2005). Thornhill and Gangestad (2006) did not find
a significant association between facial attractiveness and the number and duration of
respiratory and stomach infections, or the use of antibiotics in the last three years.
Studies often find a difference between the sexes. Shackelford and Larsen (1999) showed
that facial attractiveness significantly correlates with cardiovascular recovery time after
exercise in men, but not women, and some common physical illness symptoms (eg runny
nose, nausea, backache, etc) in both sexes. Hume and Montgomerie (2001) also found
a difference between the sexes, with attractive women reporting less severe diseases during
their lifetime. No significant association was observed for the men. Facial attractiveness
is also positively linked to reproductive health. Facially attractive men have better sperm
quality (as assessed by morphology and motility) than less attractive men (Soler et al
2003, but see Peters et al 2008), while facially attractive women have higher late follic-
ular oestrogen levels than less attractive women (Law Smith et al 2006). On the whole,
most studies show a relationship between facial attractiveness and health, although the
relationship is far from consistent.
Facial adiposity: A cue to health?
Perception, 20 09, volume 38, pages 1700 ^ 1711
Vinet Coetzee, David I Perrett, Ian D Stephen
Perception Laboratory, School of Psychology, University of St Andrews, St Mary's Quad, South Street,
St Andrews, Fife KY16 9JP, Scotland, UK; e-mail: vc56@st-andrews.ac.uk, dp@st-andrews.ac.uk
Received 27 March 2009, in revised form 19 May 2009; published online 28 October 2009
Abstract. Facial symmetry, averageness, sexual dimorphism, and skin colour/texture all serve as
cues to attractiveness, but their role in the perception of health is less clear. This ambiguity could
reflect the fact that these facial traits are not the only cues to health. We propose that adiposity
is an important, but thus far disregarded, facial cue to health. Our results demonstrate two
important prerequisites for any health cue. First, we show that facial adiposity, or the perception
of weight in the face, significantly predicts perceived health and attractiveness. Second, we show
that perceived facial adiposity is significantly associated with measures of cardiovascular health
and reported infections. Perceived facial adiposity, or a correlate thereof, is therefore an impor-
tant and valid cue to health that should be included in future studies.
doi:10.1068/p6423

But which facial cues do people use to judge health and attractiveness? Past
research identified four cues to attractiveness: symmetry (Grammer and Thornhill 1994;
Penton-Voak et al 2001; Rhodes et al 2001), averageness (Rhodes et al 2007), sexual
dimorphism (Perrett et al 1998; Penton-Voak et al 2001) and, more recently, skin
colour/texture (Fink et al 2001; Jones et al 2004; Matts et al 2006; Stephen et al 2009).
The link between these traits and health has been mixed. Feminine female faces are
judged healthier (Rhodes et al 2003, 2007; Law Smith et al 2006), but femininity is not
consistently linked to actual health in women. Thornhill and Gangestad (2006) found
a significant association between femininity and number and duration of respiratory
infections, but an earlier study by Rhodes et al (2003) did not show a significant
association between femininity and health ratings calculated from detailed medical
histories.
As a rule, symmetrical faces are judged healthier (Grammer and Thornhill 1994;
Jones et al 2001; Penton-Voak et al 2001; Rhodes et al 2001, 2007; Fink et al 2006).
Paradoxically, actual health is only weakly associated with symmetry. Two studies
reported some evidence for a relationship between measured facial symmetry and
self-reported health measures (Shackelford and Larsen 1999; Thornhill and Gangestad
2006), while Rhodes et al (2001) did not find a significant association between symmetry
and medically assessed lifetime health data.
Averageness is fairly consistently linked to perceived health (Rhodes et al 2001,
2007). But, Grammer and Thornhill (1994) showed a significant relationship between
averageness and perceived health only in men. The relationship was not significant
in women's faces. Averageness is weakly associated with actual health. Rhodes et al
(2001) showed that rated averageness in young adults was linked to childhood health
in men, and adolescent and current health in women. Faces below median averageness
mainly drive this association (Zebrowitz and Rhodes 2004).
Masculinity shows a very different association with health than the other cues. Unlike
the other cues, masculinity in males is linked to actual health, but not reliably to perceived
health. An initial study by Rhodes et al (2003) found that male faces that are rated
more masculine are also rated healthier. This association seems to apply only to natural
faces and not to faces manipulated by computer graphics (Rhodes et al 2007); although
Boothroyd et al (2007) did not find any clear association between masculinity and
perceived health in a series of experiments. The relationship between masculinity
and actual health is less ambiguous. Male face masculinity is associated with better
health in puberty and adolescence (Rhodes et al 2003), use of less antibiotics, and fewer
and shorter respiratory infections (Thornhill and Gangestad 2006).
Colour and texture seem to be related to perceived health (Jones et al 2004; Fink
et al 2006; Matts et al 2006, 2007; Stephen et al 2009) but, to our knowledge,
their direct relationship to actual health has not yet been considered. Still, there is
some indirect evidence for the association between colour and texture cues, actual and
perceived health. Roberts et al (2005) found a significant association between HLA
heterozygosity and perceived health using skin patches, where the only information
available to the observer is presumably colour and texture cues.
This lack of congruence in the interrelation between facial cues and health might
be based on our inability to measure health, or even facial cues, accurately. Then
again, we might be overlooking other important cues to health. If multiple facial cues
to health and attractiveness exist, and these cues vary independently, variance in an
unacknowledged cue may disrupt the relationship between other cues and health.
We propose that facial adiposity, or the perception of weight in the face, could be
a valid cue to health. Body mass index (BMI), or weight scaled for height, plays an
important role in judgments of bodily attractiveness and health. In Western popula-
tions, obese, and to a lesser extent overweight, female bodies are judged less attractive
Facial adiposity 1701

(Thornhill and Grammer 1999; Tove
¨e et al 1998, 1999; Swami and Tove
¨e 2005a) and
less healthy than normal-weight bodies (BMI 18.5^ 25; Furnham et al 2006; Swami
et al 2008). Obese (and overweight) women are also judged facially less attractive
than lower-weight women (Hume and Montgomerie 2001). Preferences do change with
culture and, in some non-Western (especially African) cultures, high-weight female bodies
are considered attractive and fertile (Furnham and Baguma 1994; Yu and Shepard 1999;
Marlowe and Wetsman 2001; Furnham et al 2002); for example, in one rural South-
African Zulu population individuals with a BMI of 26.52 were considered optimally
attractive (Tove
¨e et al 2006). A BMI of 26.52 falls within the overweight category accord-
ing to the WHO classification (WHO 2000). Both Western and non-Western observers
judge underweight female bodies less attractive than normal-weight bodies (Furnham
and Baguma 1994; Tove
¨e et al 1998, 1999, 2006; Yu and Shepard 1999).
Obese and overweight individuals are at increased risk of developing coronary
artery disease, diabetes mellitus, stroke, gallbladder disease, gout, osteoarthritis, sleep
apnoea, respiratory problems, and a variety of cancers (Pi-Sunyer 1993; Manson et al
1995; Must et al 1999; Brown et al 2000; Wilson et al 2002; Mokdad et al 2003).
Underweight individuals, in turn, have decreased immunity (Ritz and Gardner 2006),
decreased vitality, poorer mental health, increased tiredness, and show increased use
of health services (Brown et al 2000) and increased mortality due to all causes com-
pared to normal-weight individuals (Flegal et al 2005). In this study, we particularly
wanted to focus on two main groups of health measures that have been related to
weight: infections and cardiovascular health.
Obesity is associated with impaired T and B cell function, an indication of immune
dysfunction (Tanaka et al 1993), which may explain why obese surgical patients develop
more post-surgical infections than non-obese patients (Choban et al 1995; Vilar-Compte
et al 2000). This proneness to infection is also observed in community settings, where
heavier adult women (Baik et al 2000) and children (Jedrychowski et al 1998) have a
higher susceptibility to respiratory infections. Underweight individuals often have an
energy deficiency due to malnutrition or under-nutrition. Subsequently, fewer resources
can be allocated to the immune function, causing underweight individuals to be more
prone to infection (Ritz and Gardner 2006). We therefore predict a curvilinear relation-
ship between weight and infection, with both the heavy and underweight individuals
more prone to infection than intermediate-weight individuals.
Various studies show a strong relationship between excess weight and cardio-
vascular health. Obese and overweight adults are at increased risk of hypertension
and cardiovascular disease (Hubert et al 1983; Manson et al 1995; Lusky et al 1996;
Wilson et al 2002). One might argue that overweight and obese children and adoles-
cents are not at risk of cardiovascular disease, but obese children are more likely to
become obese adults (Serdula et al 1993) and childhood obesity is associated with
increased adult mortality due to cardiovascular disease (Gunnell et al 1998). Underweight
individuals tend to have an even lower prevalence of hypertension than normal-
weight individuals (Lusky et al 1996). We therefore predict a linear relationship between
cardiovascular measures and weight, with heavier individuals more prone to high blood
pressure than intermediate-weight and low-weight individuals.
We propose that facial adiposity, or the perception of weight in the face is a valid
cue to health. In order to be a valid cue, perceived facial adiposity must: (i) be used
in the perception of health, and (ii) relate to actual health measures. Our aims were
to test these hypotheses. First, we tested whether perceived facial adiposity is signifi-
cantly associated with perceived facial health and attractiveness. Second, we tested
whether perceived facial adiposity is correlated with infections and cardiovascular health.
We also investigated how BMI relates to these groups of health measures in the study
population.
1702 V Coetzee, D I Perrett, I D Stephen

2 Methods
This work was approved by the University of St Andrews Ethics Committee (Approval
code: PS3137).
We recruited eighty-four Caucasian participants (forty-three female; forty-one male)
from the University of St Andrews (mean age 21.13 years, range 18 ^ 27 years; mean
BMI 22.95, range 17.82^ 33.38). All participants were photographed in front of a
uniform Munsell 5 background, in full colour and under standard lighting conditions.
Participants were seated a set distance from the camera, asked to maintain a neutral
expression, and had their hair pulled back. Each participant gave informed consent
to take part in this study, was asked to complete a questionnaire, and had his/her
blood pressure, body weight, and height measured. Questionnaires contained questions
on gender, parental income, respiratory diseases, and use of antibiotics (appendix A).
Systolic and diastolic blood pressures were measured twice with a Boots automatic
blood-pressure arm monitor (Boots, England) after a minimum initial rest period of
5 min. Weight and height measures were used to calculate BMI [(weight in kilograms)/
(height in metres)
2
] and BMI categories were assigned according to WHO criteria
(WHO 2000).
Skewness and kurtosis values were low for all measures (ÿ1:04skewness and
kurtosis 51:02), except BMI (kurtosis 1.43), cold and flu bouts per year (skewness 1.87,
kurtosis 4.25), average bout length (skewness 1.30) and use of antibiotics (skewness
1.94, kurtosis 6.23). BMI, cold and flu bouts per year, and average bout length were
log-transformed, successfully normalising the data (ÿ0:94 4skewness and kurtosis
50:47). Log-transformation did not sufficiently normalise the antibiotics-use data
(skewness 2:13, kurtosis 3:64), because very few individuals reported using more
than one course of antibiotics in the last year. We therefore grouped the antibiotics-
use data into three groups: high use (twice or more in the last year; N8), low use
(once in the last year; N34) and zero use (zero use in the last year; N13). Antibiotics
use was subsequently analysed with a Krustall ^ Wallis test followed by Mann ^ Whitney
tests to compare the three groups. All other relationships between health measures,
facial adiposity, and BMI were tested with Pearson's correlations. Data were missing
for BMI (one participant), parental income (two participants), use of antibiotics (twenty-
nine participants), and blood pressure (one participant).
Images were resized (female images: 3876478 pixels; male images: 3896518 pixels),
colour-corrected (DE2:44) with in-house software, and standardised for inter-pupillary
distance and position with PsychoMorph version 8.4.7.0. We recruited four groups of
participants to rate the facial images for health, attractiveness, and weight. First,
twenty-six Caucasian participants (twelve female, fourteen male; mean age 22.81 years,
range 18 ^ 28 years) rated each female image for health and attractiveness on a 7-point
Likert scale (0 very unhealthy/unattractive; 3 average; 6 very healthy/attractive).
Second, twenty-two Caucasian participants (twelve female, ten male; mean age 21.87
years, range 18^ 26 years) rated each male image for health and attractiveness on the
same scale. Third, twenty-six Caucasian participants (fourteen female, twelve male; mean
age 20.81 years, range 20^ 28 years) rated each female facial image for weight on a
7-point Likert scale (0very underweight; 3average weight; 6very overweight).
Last, twenty-nine Caucasian participants (seventeen female, twelve male; mean age21.1
years, range 19^ 26 years) rated each male facial image for weight on the same scale.
In all four studies, participants were shown all the images before rating commenced
to make them aware of the range and variability of the images. Images were presented
in a randomised order and participants were asked to indicate whether they knew the
person.
We recorded the time it took the participants to rate each image and excluded
all participants with an average time of less than 1.65 s per question, for two or more
Facial adiposity 1703

images (group 1: five participants; group 2: one participant; group 3: three participants;
group 4: four participants). The threshold value was defined by the maximum time
it took the experimenter to select random answers as quickly as possible and included
submission time. We also removed rating data if the participant knew the rated individual
(group 1: 9.6% of ratings; group 2: 5.9% of ratings; group 3: 5.8% of ratings; group 4:
4.8% of ratings). Facial health (p0:62 ), attractiveness ( p0:75 ) and weight ratings
(p0:22) did not differ significantly between male and female raters (independent-
samples t-test). Data from both sexes were therefore combined for analysis. Inter-rater
reliability was very high for facial health (Cronbach a0:87), attractiveness (a0:92),
and weight (a0:84). Given the consistency of ratings, the scores were averaged across
participants for each of the 84 images. Skewness and kurtosis values were low for all
three measures (ÿ0:86 4skewness and kurtosis 50.35).
3Results
3.1 Perceived facial adiposity and BMI
Despite the fact that a significant relationship between perceived facial adiposity and
BMI might seem evident, we wanted to examine the strength of this relationship.
To do so we fitted a general linear model (GLM) in SPSS version 16 to test if
participant's judgment of weight using facial images related to measured body weight.
BMI significantly and positively predicted perceived facial adiposity (perceived facial
adiposity ÿ10:72 10:08 BMI; F
181
61:217,p50:0005,R
2
0:430, figure 1).
3.2 Perceived facial adiposity, health, and attractiveness
We fitted a multiple polynomial GLM to test the relationship between judgments
of weight, health, and attractiveness. Second-order equations were included, since
both underweight and obese individuals should be rated less healthy or attractive.
The following polynomial model was fitted for both judgments:
yab
1
xb
2
x
2
e,
where yis the health or attractiveness rating, ais the intercept, b
1
and b
2
are coefficients,
xis perceived facial adiposity, and eis the random error. The quadratic model signifi-
cantly predicted perceived health [perceived health ÿ0:33 2:45 (perceived facial
adiposity) ÿ0:41 (perceived facial adiposity)
2
;F
281
14:457,p50:0005,R
2
0:263;
figure 2] and perceived attractiveness [perceived attractiveness ÿ0:06 2:06 (perceived
,
,
6
5
4
3
2
1
0
Facial adiposity
15 20 25 30 35
BMI
Figure 1. Interrelation between facial adiposity and body mass index (BMI) (perceived facial
adiposity ÿ10:72 10:08 BMI; p50:005,R
2
0:430). Heavier individuals are consistently
judged to have a higher facial adiposity throughout the BMI spectrum: underweight (solid
circles), normal weight (open circles), overweight (solid triangles), and obese (open triangles).
The continuous line gives the best-fit general linear model.
1704 V Coetzee, D I Perrett, I D Stephen

facial adiposity) ÿ0.37 (perceived facial adiposity)
2
;F
281
9:269,p50:0005,R
2
0:186;
figure 3]. Exploration of the relationship between perceived facial adiposity and both
perceived health and attractiveness showed that other regression models based on linear,
logarithmic, inverse, compound, power, S, growth, exponential, and logistic functions did
not fit the data significantly. Cubic models also fitted the perceived health (F
380
9:521,
p50:0005,R
2
0:263) and attractiveness data well (F
380
6:371,p0:001,R
2
0:193),
but we consider the quadratic models a more appropriate model for two reasons. First,
because R
2
increases with the number of regressors in the model, higher-order models
tend to have inflated R
2
values. Second, the cubic fit is very close to the quadratic fit
in both cases (figures 2 and 3).
3.3 Perceived facial adiposity and health measures
To test the relationship between perceived facial adiposity and actual health, we per-
formed separate zero-order and partial Pearson's correlations (all two-tailed). In the
partial correlations we partialled out age and parental income. Age is a determining
,
,
,
6
5
4
3
2
1
0
Perceived health
0123456
Perceived facial adiposity
Figure 2. Interrelation between facial adiposity and facial health judgments [perceived health
ÿ0:33 2:45 (perceived facial adiposity) ÿ0.41 (perceived facial adiposity)
2
;p50:0005,
R
2
0:263]. Individuals with intermediate perceived facial adiposity are judged healthier than
individuals with a perceived facial adiposity on either side of this optimum. The solid curve
is the best-fit second-order (quadratic) polynomial equation. The third-order (cubic) polynomial
equation is indicated as a dashed line (virtually indistinguishable from the quadratic curve).
6
5
4
3
2
1
0
Perceived attractiveness
0123456
Perceived facial adiposity
Figure 3. Interrelation between facial adiposity and facial attractiveness judgments [perceived attrac-
tiveness ÿ0:06 2:06 (perceived facial adiposity) ÿ0.37 (perceived facial adiposity)
2
;p50:0005,
R
2
0:186]. Individuals with intermediate perceived facial adiposity are judged more attractive
than individuals with a perceived facial adiposity on either side of this optimum. The curve
is the best-fit second-order polynomial equation. The third-order (cubic) polynomial equation is
indicated as a dashed line.
Facial adiposity 1705

factor of facial adiposity (Rohrich et al 2008). Socio-economic status is widely reported
as a covariate of health and we therefore controlled for parental income, as a limited
measure of socio-economic status. We report the partial correlations, although the
zero-order correlations were similar (see table 1). The frequency (r
77
0:235,p0:037;
table 1) and duration of cold and flu bouts (r
77
0:269,p0:016; table 1) signifi-
cantly and positively correlated with how heavy the individual was perceived to be. Use
of antibiotics was significantly associated with perceived facial adiposity (Krustall^Wallis
x
2
2
11:706,p0:003; table 1). Facial images of individuals who reported high use
of antibiotics were judged significantly heavier than those who reported low use of
antibiotics (Mann ^ Whitney U50:00,p0:006) and those that reported zero use
(U10:50,p0:001), while there was only a tendency for low-use individuals to have
a higher perceived facial adiposity than zero-use individuals (U150:50,p0:094).
Cold and flu bouts and bout length are similar measures; we therefore included
them in a principal component analysis (PCA). Use of antibiotics was not included in
the PCA because of the low sample number and skewed distribution. We identified
one component with an eigenvalue 41, which explained 60% of the variance. This
respiratory-illness component correlated significantly with perceived facial adiposity
(r
77
0:327,p0:003; table 1).
We also observed a significant quadratic relationship between facial adiposity and
the respiratory-illness component [respiratory-illness component ÿ0:90 0:25 (per-
ceived facial adiposity) 0.01 (perceived facial adiposity)
2
;F
280
3:752,p0:028,
R
2
0:086], but the quadratic relationship was almost identical to the linear relation-
ship and did not support our prediction of increased infection in the underweight
group (figure 4). The linear model fitted the data better than the quadratic relationship
[respiratory illness component ÿ1:02 0:34 (perceived facial adiposity); F
281
7:580,
p0:007,R
2
0:086].
Both cardiovascular measures significantly correlated with perceived facial adipos-
ity. Individuals who were perceived as heavier had significantly higher systolic blood
pressure (r
77
0:264,p0:019; table 1) and diastolic blood pressure (r
77
0:452,
p50:0005; table 1). PCA of the cardiovascular measures revealed one component
with an eigenvalue 41, which explained 74% of the variance. The cardiovascular-
illness component correlated significantly with the perception of weight (r
77
0:416,
p50:0005; table 1).
,
,
Table 1. Pearson's correlations showing the relationship between perceived facial adiposity, BMI,
and actual health measures. Partial correlations controlled for parental income and age. All
correlations were two-tailed. Associations with antibiotics use were tested with a Krustall ^ Wallis
test. Principal component analysis components are underscored.
Facial adiposity BMI
zero-order, r(df) partial, r(df) zero-order, r(df) partial, r(df)
Infections
Cold and flu number 0.209
À
(81) 0.235 * (77) 0.198
À
(80) 0.216
À
(77)
Bout length 0.244* (81) 0.269* (77) 0.177 (80) 0.190
À
(77)
Antibiotics x
2
11:706** (2) x
2
9:050* (2)
Respiratory component 0.293** (81) 0.327 ** (77) 0.242 * (80) 0.263 * (77)
Cardiovascular health
Systolic BP 0.278* (81) 0.264 * (77) 0.441*** (80) 0.431 *** (77)
Diastolic BP 0.432*** (81) 0.452*** (77) 0.399*** (80) 0.405 *** (77)
Cardiovascular component 0.412*** (81) 0.416 *** (77) 0.487 *** (80) 0.487*** (77)
Note: *** p40:001, ** p40:01, * p40:05, Àp40:10; BP blood pressure.
1706 V Coetzee, D I Perrett, I D Stephen

3.4 BMI and health measures
If perceived facial adiposity is accurately displaying relative body weight, one might
expect perceived facial adiposity and BMI to correlate with health measures in a
similar way. To test this we performed zero-order and partial Pearson's correlations
controlling for parental income and age. Results for BMI were fairly similar to those
obtained for perceived facial adiposity (table 1). Cold and flu bouts (r
77
0:216,
p0:056; table 1) and bout length (r
77
0:180,p0:094; table 1) showed a positive,
but non-significant, correlation with BMI; the respiratory-illness component corre-
lated significantly with BMI (r
77
0:263,p0:019; table 1). Use of antibiotics was
significantly associated with BMI (Krustall ^ Wallis x
2
2
9:050,p0:011; table 1).
Individuals who reported high use of antibiotics were significantly heavier than
those who reported low use of antibiotics (Mann ^ Whitney U110:00,p0:014)
and those that reported zero use (U24:00,p0:003), while there was no significant
difference in BMI between low-use and zero-use individuals. All the cardiovascular
measures significantly correlated with BMI. Individuals with higher BMIs had signifi-
cantly higher systolic (r
77
0:431,p50:0005; table 1) and diastolic (r
77
0:405,
p50:0005; table 1) blood pressure. The cardiovascular-illness component also signifi-
cantly correlated with BMI (r
77
0:487,p50:0005;table1).
4 Discussion
In this study we set out to test whether facial adiposity, or apparent weight judged from
the face, is a cue to health. As expected, people are quite accurate at judging weight
on the basis of facial cues alone, enabling facial adiposity to serve as a potential cue
to health and attractiveness. In order to be a valid cue to health, any cue must fulfil
two prerequisites. First, people must use this cue in their judgments of health. Second,
this cue must be associated with actual health measurements. Both criteria were met
in the results.
We present evidence that people use perceived facial adiposity as a cue when judging
health. Adiposity produces a fairly salient shape cue in the face, so it is parsimonious to
assume that facial adiposity, rather than a correlate thereof, provides the basis for esti-
mating health. In further investigations it should be possible to isolate salient shape
cues. Individuals with intermediate facial adiposity are judged healthier than individuals
3
2
1
0
ÿ1
ÿ2
ÿ3
ÿ4
Respiratory-illness component
0123456
Perceived facial adiposity
Figure 4. Interrelation between perceived facial adiposity and the respiratory-illness component.
The quadratic relationship between perceived facial adiposity and the respiratory-illness com-
ponent [respiratory-illness component ÿ0:90 0:25 (perceived facial adiposity) 0.01 (perceived
facial adiposity)
2
;p0:028,R
2
0:086] was almost identical to the linear relationship (respiratory-
illness component ÿ1:02 0:34 (perceived facial adiposity); p0:007,R
2
0:086; not indicated
on the graph, because of its similarity to the quadratic relationship].
Facial adiposity 1707

with a facial adiposity on either side of this optimum. For the body, weight is an
extremely important cue to perceived health. Swami et al (2008) showed that BMI
explains more than 70% of the variance in health judgments of female bodies. We show
that this is also true for the face, although perceived facial adiposity explains only
26% of the variance in health judgments (both sexes combined). This discrepancy in
the amount of variance explained is not surprising given that there is a whole range
of putative facial cues such as symmetry, demeanour, colour, and texture that also
signal health.
Not only is perceived facial adiposity (or a correlate thereof) used as a cue to
health, it is also a powerful correlate of attractiveness. Individuals with intermediate
perceived facial adiposity are judged more attractive than individuals with high and,
to some extent, low perceived facial adiposity.
Furthermore, we show that perceived facial adiposity provides information about
past health and probable future health. In general, individuals with high perceived
facial adiposity report more infections than those with low perceived facial adiposity.
These apparently-high-weight individuals report a significantly higher frequency of use
of antibiotics and longer and more frequent respiratory infections. When we combined
cold and flu frequency and duration in a single component, the respiratory-illness
component significantly and positively correlated with perceived facial adiposity, indi-
cating that individuals with higher perceived facial adiposity are more likely to have a
respiratory infection. A similar relationship is also seen for the association between
infections and actual BMI, further strengthening the observed effect between perceived
facial adiposity and reported infections.
We did not find the expected curvilinear relationship between perceived facial
adiposity and the respiratory-illness component, indicating that individuals judged
to be underweight do not suffer from significantly more respiratory infections than
normal-weight individuals. The association with respiratory infections therefore does not
explain why underweight individuals are judged less healthy and/or attractive. Under-
weight individuals could be judged less healthy and attractive because of other health
and/or fertility factors not tested here. For instance, underweight women (BMI 518:5)
often have amenorrhoea or anovulatory menstrual cycles (Frish 1987), both of which
reduce reproductive potential. Underweight individuals also report lower iron levels
(Brown et al 2000) and could be more prone than normal-weight individuals to a
variety of infections that fall outside the scope of this study.
Lastly, we show a strong association between perceived facial adiposity and cardio-
vascular health. Individuals with higher perceived facial adiposity have significantly
higher blood pressure, a condition that increases their risk of coronary heart disease
and stroke (MacMahon et al 1990). This association between blood pressure and
weight was replicated for BMI. One might argue that cardiovascular disease did not
play an important role in shaping the mate preferences of our ancestors. Yet, cardio-
vascular disease is currently an enormous health burden in the developed world
(WHO 2003). It is therefore plausible that current mate-choice preferences are shaped
by the environment the individual finds himself/herself in. People change their prefer-
ences according to their local environment (Yu and Shepard 1999; Furnham et al
2002; Sugiyama 2004; Swami and Tove
¨e 2005a, 2005b). For instance, people in rural
areas, where food can be scarce, find heavier women more attractive than people in
urban areas (Swami and Tove
¨e 2005a, 2007). Preferences can also change rapidly
(Silverstein et al 1986), so it is probable that modern people are associating excess
weight and obesity with negative health outcomes. In future, we will test the association
between weight and health in cross-cultural settings.
In summary, we set out to test the hypothesis that facial adiposity can act as a
cue to health. We showed that perceived facial adiposity significantly predicts health
1708 V Coetzee, D I Perrett, I D Stephen

and attractiveness in a Western population and is associated with actual health risk in
the same population. Perceived facial adiposity explains a substantial amount of the
variance of perceived health and attractiveness; thus studies focusing on other facial
cues (eg symmetry, sexual dimorphism, averageness) could benefit from controlling
for facial adiposity. For example, the association between facial symmetry and health
may be strengthened by controlling for facial adiposity. Our study provides one route
through which facial characteristics can provide an accurate reflection of health, and
thereby influence mate choice.
Acknowledgments. We thank Johannes Schindelin and Michael Stirrat for their help with soft-
ware development; Lesley Ferrier, Janek Lobmaier, Lindsey Macdougall, Rachel McCarey, and
Stephanie Sharples for their help with data collection, and Anne Perrett for proofreading
the manuscript.
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Facial adiposity 1711

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