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Original Article

Refined Carbohydrate Consumption and Facial Attractiveness

Claire Berticat 1

, Valérie Durand 1 , and Michel Raymond

1

Abstract Since the second half of the 20th century, a massive increase in the consumption of refined carbohydrates has occurred, gen- erating well-described detrimental health effects such as obesity, insulin resistance, type II diabetes, cardiovascular diseases and dental caries. Certain physiological mechanisms involved, particularly through chronic hyperglycaemia and hyperinsulinaemia, suggest that a non-medical trait such as facial attractiveness could also be affected. To explore this possibility, variation in facial attractiveness was evaluated relative to refined carbohydrate consumption. Attractiveness was assessed from facial pictures as judged by raters of the opposite sex. Estimates of refined carbohydrate consumption were based on the glycaemic load of three mealtimes at-higher glycaemic risk (breakfast, afternoon snack and between-meal snack). In the presence of several control variables, facial pictures of women and men with higher between-meal glycaemic loads were preferred by opposite-sex raters. Structural equation modeling suggests that this result is possibly mediated by an increase in apparent age for men and an increase in femininity for women. The different physiological ecologies of the three meals at-higher glycaemic risk are discussed as well as the interpretation of the results in terms of adaptation or maladaptation to the modern and unique dietary environment.

Keywords refined carbohydrates, sugars, evolutionary diet, dietary switch, facial attractiveness, social trait, glycaemic load

Date received: March 19, 2020. Revision Submitted: September 1, 2020; Accepted: September 1, 2020

Introduction

Each animal species is adapted to a specific diet (carnivorous,

herbivorous, etc.) through a specialized digestive process.

Therefore, any rapid change in this diet could result in health

problems, revealing that the change has driven the population

outside its adaptive peak (Fiennes, 1965). In humans, during

the 20th century, a drastic dietary switch occurred in Western

populations with the introduction of industrially processed food

such as refined products (carbohydrates, oil) and additives

(e.g., trans-fatty acids, gluten, sweeteners). In particular, a mas-

sive increase in the consumption of refined carbohydrates (pri-

marily sucrose, fiber-depleted gelatinous starches and high

sugar corn syrup) occurred in less than three generations (Cor-

dain et al., 2005). As a consequence, these refined carbohy-

drates currently represent a major element of the modern urban

diet (e.g., 36% of total energy in the typical US diet) compared with at most a few percentage points as recently as 200 years

ago (Cordain et al., 2003). Certain detrimental health effects of

this new refined carbohydrate-rich food consumption are now

well known, such as obesity, insulin resistance, type II diabetes,

cardiovascular diseases and dental caries (Hu et al., 2001; Jel-

linger, 2007; Johnson et al., 2007; Spencer et al., 2003; Touger-

Decker & Van Loveren, 2003). Many other direct or indirect

deleterious effects are also strongly suspected, e.g. Alzheimer’s

disease, hypertension, inflammatory diseases, cancer, myopia,

acne, etc. (Cordain et al., 2002; Cordain, Eaton, et al., 2002;

Craft, 2007; Gentreau et al., 2020; Johnson et al., 2007; Key

et al., 2004; Manzel et al., 2013).

Thus far, the increase in refined carbohydrates consumption

has been shown to affect body physiology and health. How-

ever, frequent refined carbohydrate-rich food consumption

generates chronic hyperglycaemia and thus chronic hyperinsu-

linaemia, the latter interfering with growth factors and sex

1 ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France

Corresponding Author:

Claire Berticat, ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier,

France.

Email: [email protected]

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hormones, which themselves modulate morphology and sec-

ondary sex characteristics (Cordain et al., 2003). Considering

that facial masculinity/femininity influences attractiveness (for

a review see: Puts et al., 2012), it is possible that an increase in

refined carbohydrate consumption has affected facial attrac-

tiveness. There are indirect cues that refined carbohydrate con-

sumption could affect attractiveness. For example, Zuniga et al.

(2017) showed that carbohydrate intake, mainly food item rich

in refined carbohydrate, reduces body odor attractiveness.

Also, refined carbohydrates are among the factors suspected

to accelerate skin aging and photo-aging (Cosgrove et al.,

2007; Purba et al., 2001), a trait affecting attractiveness (Buss,

1989). Attractiveness influences a diverse range of critical

social outcomes, from mate choice to decisions related to social

exchange. For example, physically attractive (relative to unat-

tractive) individuals are evaluated more favorably as romantic

partners (Eastwick et al., 2014), as students by teachers (Ritts

et al., 2016), and even as political candidates (Praino et al.,

2014).

In this study, we investigated whether refined carbohydrate

consumption affects facial attractiveness in healthy women and

men. General predictions on whether attractiveness is increased

or decreased as the result of refined carbohydrate consumption

are not straightforward. For example, hyperglycaemia could

have a possible aging effect (i.e. increase of apparent age), with

the consequence of increasing attractiveness in men and

decreasing attractiveness in women (Buss, 1989; Jencks & And

Others, 1979). However, hyperglycaemia generates hyperinsu-

linaemia. Hyperinsulinaemia with insulin resistance have been

linked to diseases associated with perturbation of sex hormones

(Cordain et al., 2003) and to testosterone levels increased in

women and decreased in men (Lutz et al., 2019), thus poten-

tially decreasing attractiveness for both sexes (Puts et al.,

2012). As a consequence, for women, it is expected that refined

carbohydrate consumption decreases attractiveness. For men,

no clear prediction could be unambiguously formulated. Eva-

luation of attractiveness was based on facial pictures evaluated

by raters of the opposite sex. Evaluation of refined carbohy-

drate consumption was based on total glycaemic load (a proxy

of glycaemic and insulinaemic responses) of three mealtimes

at-higher glycaemic risk (breakfast, afternoon snack and

between-meal snack). The choices of the raters were explained

by the diet variables in the presence of potentially confounding

variables including apparent age and a femininity/masculinity

index.

Methods

Individual Measures

Individuals between 18 and 26 years of age were recruited at

the University of Montpellier. Possible confounding variables

potentially affecting facial attractiveness were collected: sex,

year and month of birth, height and weight, socio-economic

status (scholarship level coded from 1: no scholarship to 4:

highest level), smoking (yes: 1, no: 0) and for women, use of

contraceptive pill (yes: 1, no: 0, Alvergne & Lummaa, 2010).

Facial photographs of all of the individuals were obtained from

a frontal perspective at a distance of approximately 1 m using

the same digital camera (Canon EOS 20D) with a 50-mm focal

length.

The subjects were asked to express a neutral face (without a

smile) and to remove any glasses or earrings. Photographs of

individuals declaring a European origin of their 4 grandparents

were further retained. All photographs were processed using

Adobe Photoshop CS3 to normalize size (photographs were

aligned on the eye position, with a fixed distance between the

eyes and chin). The backgrounds were replaced by a uniform

gray color.

Diet Variables

The various daily meals have different nutritional composition

and thus they do not bring the same glycaemic response.

Indeed, carbohydrates are rarely ingested alone, and their

degradation and absorption rates during digestion are modified

by the other macronutrients. The glycaemic response will be

higher with a meal rich in refined carbohydrates, poor in fat,

protein and fiber (Hätönen et al., 2011; Sun et al., 2014). The

order of food macronutrient intake also changes the glycaemic

and insulinaemic responses (Sun et al., 2020). As a conse-

quence, meals such as breakfast, afternoon snack and

between-meals snack, which are described as richer in refined

carbohydrates and displaying less food items, may be at-higher

glycaemic risk (Bellisle et al., 2003; Bellisle, 2014; Bellisle

et al., 2018). Thus the exhaustive list of the different foods and

drinks chronically consumed during these specific times in the

same day (breakfast, afternoon snack [“goûter” in French, cor-

responding to an after-school snack] and between-meals snack)

was collected.

For each food and drink item, the glycaemic load was eval-

uated according to the International Tables of Glycaemic Index

and Glycaemic Load Values and the corresponding serving size

(Atkinson et al., 2008). The glycaemic index refers to the rate

of glucose release by measuring the 2 h postprandial glycaemia

value after consumption of a food portion containing 50 g of

available carbohydrates relative to 50 g of glucose consump-

tion. The glycaemic load (GL) is calculated by multiplying the

glycaemic index by the amount of available carbohydrates (g)

per serving, divided by 100 (Monro & Shaw, 2008). Compared

with low-GL diets, high-GL diets elicit larger glycaemic and

insulinaemic responses (Foster-Powell et al., 2002). For each

subject, the glycaemic load for each item was summed leading

to an estimation of total glycaemic load for breakfast (GL1),

afternoon snack (GL2) and between-meal intake (GL3). Foods

with low carbohydrate content (e.g., meat, fat) were not

assigned any glycaemic load value (Bakel et al., 2009). Energy

intake (EI) and macronutrients (carbohydrates, fat, protein and

fiber) for each item were obtained from the Anses-Ciqual data-

base (www.anses.ciqual.fr) and were calculated for each parti-

cipant depending on its corresponding serving size (Atkinson

et al., 2008). For each subject, they were summed leading to an

2 Evolutionary Psychology

estimate of total energy intake for breakfast (EI1), afternoon

snack (EI2) and between-meal intake (EI3) and corresponding

macronutrient compositions.

Apparent Age and Attractiveness Estimation

Volunteer adult raters were recruited in public places in Mon-

tpellier, France. For each rater, the sex, age and geographic

origin (continent of birth for the rater, parents and grandpar-

ents) were recorded. A first set of raters estimated the age of the

subjects from their facial photographs. A Delphi-based com-

puter program was generated to present randomly drawn photo-

graphs to raters of the opposite sex. Each rater assessed

20 distinct photographs. If the rater knew one of the subjects,

the trial was removed. Three photographs randomly chosen

among those previously viewed were presented again at the

end to estimate judgment reliability. A second set of raters was

sampled to make decisions concerning the relative attractive-

ness of the facial photographs. A Delphi-based computer pro-

gram was generated to present randomly drawn pairs of

photographs to raters of the opposite sex (Figure 1). For each

pair, the raters were instructed to click on the photograph

depicting the face that they found the most attractive. The

position of the photograph on the screen (left or right) was

randomly ascribed. Each rater assessed 20 distinct pairs of

photographs, corresponding to 40 different randomly chosen

subjects. If the rater knew one of the subjects presented for

judgment, the trial was removed. Additionally, the first pair

of photographs viewed by each participant was not used in the

analyses because the task could require a certain amount of

habituation. Three pairs randomly chosen from among those

previously viewed were presented again at the end to estimate

judgment reliability.

Femininity/Masculinity Index

To generate the morphological facial femininity/masculinity

index (fem/masc index), a geometric morphometric analysis

of the faces was used following the methods described in

Dixson et al., 2017; Lee et al., 2014; Scott et al., 2010. First,

the coordinates of 142 landmarks (anatomical points present in

all individuals, e.g. corners of the lips) and semi-landmarks

(sliding points positioned along selected anatomical curves,

such as the bow of the eyebrow) were delineated for each male

and female face. The delineation of the landmarks and semi-

landmarks was performed using Psychomorph (Tiddeman

et al., 2005). The R package Geomorph (version 3.0.3) was

used to perform Procrustes superimposition of the landmark

and semi-land-mark data, which removes non-shape informa-

tion such as translation, size and rotational effects (Zelditch

et al., 2012). The coordinates were transformed into shape

variables via principal component analysis (PCA). The first

10 axes were retained (explaining 75.0% of variance) for fur- ther analyses. To compute a data-driven single measure of

facial masculinity, an LDA was conducted on the PCA coordi-

nates with sex as the grouping variable. The resulting discri-

minant function correctly classified 100% of individuals in the two categories. Each individual coordinate on the woman-man

axis was used as a facial femininity/masculinity index, with

high values indicating a more masculine facial morphology

(Dixson et al., 2017; Lee et al., 2014; Scott et al., 2010).

Statistical Analyses

For each individual, age estimates were averaged across raters,

and the resulting measure was used as the perceived age vari-

able. Logistic regression was used to analyze the rater prefer-

ences. The binary response variable corresponded to being

chosen or not for the focal subject (arbitrarily, the subject pre-

sented at the left position) during the presentation of each pair.

Subjects and raters occurred repeatedly (each subject was

viewed by several raters, and each rater evaluated several pairs

of subjects) and were thus random-effect variables. Therefore,

generalized linear mixed models with a binomial error structure

were applied. To force the models to fit away from singulari-

ties, the Bayesian bglmer function of the blme package for

R software was used (Chung et al., 2013). Maximum random

effects structure (intercept and slope) was tentatively included

according to Barr et al. (2013), but the random slope effect was

not included in the final models because it prevented conver-

gence. For each choice made by a judge, the difference (left

minus right) between the GL1 of the focal and the non-focal

subject was calculated, and the same procedure was performed

for the GL2 and GL3. These differences were integrated into

the model as the variables of interest. Because pairs of subjects

were rated by the opposite sex (men rated by women and

women rated by men), two models were performed, one for

each subject’s sex. For both, several control variables poten-

tially affecting facial attractiveness were added: differences

between the variables associated with the individuals of each

pair (left minus right) for age, age departure from actual age

(perceived age minus actual age), facial femininity/masculinity

index, BMI (calculated as weight divided by the squared

height), scholarship level [�3 to þ3], smoking [�1 to þ1], and for women, pill use [�1 to þ1]. All quantitative variables were centered. The significance of each term was assessed

Figure 1. Example of a pair of faces used during evaluation of women’s facial attractiveness by male raters. For each pair of women, the rater was instructed to click on the photograph of the woman that he found the most attractive. Photographs reproduced with permission.

Berticat et al. 3

from the model including all of the other variables. The var-

iance inflation factor was computed using the vif.mer function

adapted from the vif function of the R package rms (Harrell,

2015; Zuur et al., 2010). Differences between the EI variables

associated with the individuals of each pair were not included

in the model due to the high correlation of EI1, EI2 and EI3

with GL1, GL2 and GL3 respectively (Pearson’s correlation

coefficient r > 0.9 and P < 10 �4

for all). Rather, the same

models were conducted using these variables instead of GL.

Because the glycaemic load variables (GL1, GL2, and GL3)

could potentially affect certain control variables directly (e.g.,

age departure from actual age, femininity/masculinity index

and BMI), this could indirectly influence the effect of the GL

variables on the dependent variable. To evaluate this possibil-

ity, structural equation modeling was performed using the con-

trol variables from the model displaying P < 0.1. An

attractiveness index was constructed for each individual, com-

puted as the number of times this individual was chosen over

the number of occurrences. An hypothesized path model was

constructed for each sex, incorporating four linear regressions

with GL3 to explain attractiveness, age departure from actual

age, BMI, and femininity/masculinity index for women and

incorporating three linear regressions to explain attractiveness,

age departure from actual age, and BMI for men (Figure 2).

All statistical analyses were performed using R software

version 3.5.2 using the packages blme (v1.0-4, Chung et al.,

2013), rms (v5.1-4, Harrell, 2015), and lavaan v0.6-3, Rosseel,

2012). The SE values and P-values for standardized path coef-

ficients were obtained through the function standardisedSolu-

tion in the “lavaan” package.

Results

A total of 50 female and 49 male Caucasian subjects with fully

completed questionnaires were used. Descriptive statistics of

their physical characteristics are given in Table 1, and their

food consumption for each meal are detailed in Table 2. The

proportion of men and women taking a meal were 92% and 90% respectively for breakfast, 63% and 70% respectively for an afternoon snack, and 41% and 50% respectively for a between-meal snack. Mean glycaemic load (GL) and energy

attractiveness BMI

age departure

age

GL3

masc/fem index

Figure 2. Hypothesized path model to explain attractiveness with the variables of the generalized linear mixed model. Only independent variables displaying P < 0.1 are considered. For men, the variable fem/ masc index representing the femininity/masculinity index is not present.

Table 1. Descriptive Statistics of the Physical Characteristics of the Women and Men used as Stimuli.

Women (N ¼ 50) Men (N ¼ 49)

Range Mean SD Range Mean SD

Age (years) 18–26 20.6 2.0 18–26 21.1 2.0 Perceived age (years) 18–28 22.1 2.1 17–30 23.5 3.0 BMI (kg/m2) 16–30 20.8 3.1 18–31 22.6 3.0 Femininity/masculinity index �4.56 to �0.44 �2.63 1.02 0.56 to 4.46 2.63 0.97

Table 2. Number of Individuals Consuming the Different Food Groups for Each Meal. N Indicates the Number of Consumers Among the 50 Women, or Among the 49 Men Subjects.

Food group

Women Men

Breakfast (N ¼ 45)

Afternoon snack (N ¼ 35)

Between-meal snack (N ¼ 25)

Breakfast (N ¼ 45)

Afternoon snack (N ¼ 31)

Between-meal snack (N ¼ 20)

Cereals, bread 35 10 1 39 10 6 Biscuits, cakes, pastries 15 27 20 13 25 12 Sweets, chocolate 27 15 4 35 18 10 Sweetened-beverages 22 1 0 27 5 0 Dairy products 19 8 1 15 6 1 Fruits 26 8 2 30 7 6 Eggs, charcuterie 2 2 0 1 1 0 Nuts 0 0 0 0 0 2

4 Evolutionary Psychology

intake (EI) for each meal are computed considering only con-

sumers (Table 3). According to the general classification

(e.g. Eleazu, 2016), the means of GL obtained for each of the

three meals studied were high (>20) except for GL3 in women

which was medium (value between 11 to 19) (Table 3). GL and

EI values were in the same range of variation as those from

another study on a French population (Gentreau et al., 2020).

Raters were first recruited to assess the perceived age of the

subjects. Assessments from unreliable raters (i.e., with more

than 15 years for the sum of the absolute difference between

real ages and attributed ages during the three judgements of

reliability) were removed, resulting in a final sample of 222

raters (78 men and 144 women, age range: 18–76, mean age + s.d.: 36 + 14 years for men and 37 + 13 years for women). This process resulted in a total of 834 estimations of men toward

women and 1,435 estimations of women toward men. The mean

number of raters was 28.8 (range: 14–41) for each man and 16.6

(range: 12–25) for each woman. For each individual, the per-

ceived age variable was computed as the average estimated by

the raters. Overall, the perceived age was either younger (max-

imum 3.0 years) or older (maximum 7.8 years) than the chron-

ological age, with a mean + s.e.m. of 1.7 + 0.25 years older. Raters (N ¼ 169) were subsequently recruited to assess the

attractiveness of the subjects. The following conservative

selection of raters was applied. First, to reduce cultural hetero-

geneity, only raters from a European ancestry were considered.

Second, unreliable raters (i.e., with more than one incorrect

answer during the test of judgment reliability) or non-adult

raters (less than 18 years old) were removed. A total of 150

raters were retained in the final sample (61 men and 89 women,

age range: 18–68, mean age + s.d.: 35 + 10 years for men and 33 + 13 years for women). This process resulted in a total of

346 judgements of men toward women and 402 judgements of

women toward men. The mean number of raters was 17 (range:

8–24) for each man and 14 (range: 6–21) for each woman. The

probability that a subject was chosen as the most attractive was

significantly influenced by the variable GL3 (men: b ¼ 0.564, SE ¼ 0.171, P ¼ 0.001; women: b ¼ 0.468, SE ¼ 0.174, P ¼ 0.007, see Table 4). Women preferred men, and men preferred

women, with the highest between-meal glycaemic load. Some

control variables significantly influenced the choice of the

raters. For male and female subjects, BMI had a negative effect

on the probability of being chosen as the most attractive (men:

b ¼ �0.399, SE ¼ 0.172, P ¼ 0.021; women: b ¼ �0.792, SE ¼ 0.168, P < 10�5). A lower BMI was preferred for both sexes. For male subjects, age had a positive effect on the prob-

ability of being chosen (b ¼ 0.445, SE ¼ 0.159, P ¼ 0.005), and women preferred men with an older age. The difference in

age departure from actual age had a significant impact on

attractiveness (men: b ¼ 0.401, SE ¼ 0.167, P ¼ 0.015; women: b ¼�0.415, SE ¼ 0.191, P < 0.030). At equal chron- ological age, women preferred men with the oldest perceived

age. At equal chronological age, men preferred women with the

youngest perceived age. Scholarship level, smoking status,

femininity/masculinity index, and taking the contraceptive pill

(for women) did not have a significant effect on the probability

of being chosen (P > 0.25, for both sexes, except femininity/

masculinity index for women, P ¼ 0.07). The full model for men explained 9% of the total deviance and the variance infla- tion factors (VIF) were less than 1.50. The full model for

women explained 10% of the total deviance and the VIF were less than 2.50. VIF values for both models indicated that the

multicollinearity between covariables was weak and not of

concern (Zuur et al., 2010). Models using EI1, EI2 and EI3,

Table 3. Descriptive Statistics of Food Consumption for the Three Meals. Mean and Standard Deviation (SD) are Given for Consumers Only. GL1, GL2 and GL3 are the Three Variables of Glycaemic Load. EI1, EI2 and EI3 are the Three Variables of Energy Intake (Kcal).

Women (N ¼ 50) Men (N ¼ 49)

Range Mean SD Range Mean SD

GL1 0–60 28.6 13.2 0–55 30.1 11.3 GL2 0–47 24.3 12.1 0–59 28.6 15.0 GL3 0–45 17.4 9.0 0–48 23.0 12.1 EI1 0–924 344.4 175.7 0–719 353.5 155.3 Carbohydrates (g) 0–136 60.1 (75%) 28.2 0–110 65.3 (78%) 23.1 Fat (g) 0–39 10.5 (13%) 8.0 0–28 10.3 (12%) 7.3 Protein (g) 0–39 9.7 (12%) 7.3 0–29 9.0 (10%) 6.2 Fiber (g) 0–7 3.3 1.72 0–10 3.6 2 EI2 0–722 315.7 177.8 0–797 383.0 195.4 Carbohydrates (g) 0–89 39.4 (65%) 23.1 0–97 49.1 (66%) 25.9 Fat (g) 0–33 14.1 (23%) 8.5 0–39 17.7 (24%) 10.2 Protein (g) 0–21 7.4 (12%) 5.5 0–20 7.2 (10%) 4.8 Fiber (g) 0–7 3.3 1.5 0–6 3 1.4 EI3 0–639 223 128.7 0–989 308.3 231.8 Carbohydrates (g) 0–82 26.2 (65%) 16.3 0–88 37.5 (64%) 23.8 Fat (g) 0–30 10.6 (26%) 6.7 0–64 15.2 (26%) 14.9 Protein (g) 0–7 3.7 (9%) 1.8 0–15 5.3 (10%) 4.2 Fiber (g) 0–6 3.3 1.3 0–8 2.9 2.2

Berticat et al. 5

gave qualitatively similar results (Table S1). A hypothesized

path diagram was constructed for each sex to evaluate a pos-

sible direct effect of GL3 on the control variables BMI, age

departure from actual age, and femininity/masculinity index

(Figure 2). For men, GL3 had a significant effect on age depar-

ture from actual age (path coefficient ¼ 0.310, P ¼ 0.014). A higher between-meal glycaemic load increased appearance

toward an older age. For women, GL3 had a marginally sig-

nificant effect on the femininity/masculinity index (path coef-

ficient ¼ �0.249, P ¼ 0.056). A higher between-meal glycaemic load decreased masculinity.

Discussion

In this study, we investigated whether refined carbohydrate con-

sumption is related to facial attractiveness in healthy women and

men. We found that women and men with the highest between-

meal glycaemic loads were preferred by opposite-sex raters,

result in the opposite direction than the prediction based on

known physiological effects for women. This preference was

maintained when controlling for potential confounding effects

such as age, age departure from actual age, BMI, scholarship

level, smoking status, facial femininity/masculinity index, and

for women, whether they took the contraceptive pill.

Attractiveness is not independent of the refined carbohy-

drate content of the food eaten estimated through glycaemic

load, although this effect was evidenced from only one

(between-meal snack) of the three mealtimes at-higher glycae-

mic risk considered. This meal is not the one with the higher

mean GL, and is not particularly odd for its macronutrient

content as well as carbohydrates, protein, fat and fiber

(Table 3). A possible explanation is that these three types of

meals could correspond to different ecological food habits that

affect subjects differently with different physiological conse-

quences. For example, the usual mid-afternoon eating occasion

known in France as the “goûter” corresponds (for those who

usually have one) to a real dietary need. This meal is associated

with a pre-prandial decline in plasma glucose and insulin con-

centration and a high motivation to eat (Chapelot et al., 2004).

In contrast, between-meal snacks are often not associated with

physiological hunger and are rather motivated by social or

other external stimuli, with few resulting effects on satiety and

compensation mechanisms (Bellisle, 2014). Therefore, this

meal category could better reflect chronic and acute refined

carbohydrate consumption. Finally, it is worthy of note that

consumers of the between-meal snack were at a frequency

around 40%–50% (Table 2), allowing more statistical power to detect a difference between two groups, relative to the dis-

tribution of consumers for the two other meals (ca. 91% for breakfast, and 63%–70% for the afternoon snack).

How Chronic between-Meal Snacks Could Affect Attractiveness

Refined carbohydrate-rich food consumption generates hyperin-

sulinaemia as a consequence of hyperglycaemia, interfering with

growth factors and sex hormones, which themselves modulate

morphology and secondary sex characteristics (Cordain et al.,

2003). This result occurs because hyperinsulinaemia stimulates

androgen synthesis by the ovaries and testes, increasing the quan-

tity of free (and thus active) androgens in the blood. Androgens

are the precursors of male and female sex hormones such as

testosterone and estrogen. Hyperinsulinaemia has been linked

to diseases associated with significant perturbation of sex hor-

mone levels, such as polycystic ovary syndrome and premature

menarche (Cordain et al., 2003). If the influences of a refined

carbohydrate-rich diet on other downstream consequences have

been poorly studied to date, it is possible that these consequences

include the development of secondary sexual traits, typically mas-

culine or feminine facial features. In addition, it has been shown

that sex hormones modulate facial femininity/masculinity, which

in turn influences attractiveness, with men preferring more

Table 4. Effects of Different Variables on the Probability of Being Chosen During the Test of Attractiveness for Male or Female Faces. Raters Were Instructed to Choose the Individual Found to be the Most Attractive Between Two Facial Photographs. GL1, GL2 and GL3 are the Three Variables of Glycaemic Load. For Each Variable, the Difference Between the Two Individuals Presented was Integrated into the Model. The Estimate (b), Standard Error of the Mean (SE), w2 Statistic, and Corresponding P-Value are Given. Bold Characters Indicate Significant (P < 0.05) Effects.

Male faces evaluated by female raters Female faces evaluated by male raters

b SE w2 P(>w2) b SE w2 P(>w2)

Intercept 0.074 0.173 �0.208 0.165 GL1 0.132 0.148 0.799 0.371 �0.195 0.176 1.228 0.268 GL2 �0.001 0.154 0.001 0.992 0.106 0.170 0.392 0.531 GL3 0.564 0.171 10.79 0.001 0.468 0.174 7.226 0.007 Age 0.445 0.159 7.843 0.005 0.284 0.229 1.539 0.215 BMI �0.399 0.172 5.359 0.021 �0.792 0.168 22.35 < 10�5 Scholarship level �0.170 0.147 1.345 0.246 0.105 0.171 0.376 0.540 Smoker 0.273 0.251 0.893 0.345 �0.259 0.252 1.055 0.304 Age departure from actual age 0.401 0.167 5.883 0.015 �0.415 0.191 4.708 0.030 Femininity/masculinity index 0.069 0.162 0.183 0.669 �0.298 0.168 3.139 0.076 Contraceptive Pill — — — — �0.252 0.281 0.800 0.371

6 Evolutionary Psychology

feminine faces and women preferring more masculine ones (for a

review see Puts et al., 2012). To capture facial secondary sex

characteristics, a femininity/masculinity morphological index

was computed, for which the difference within each pair was used

as a control variable. Structural equation modeling showed that

for women, the effect of glycaemic load on attractiveness could be

indirectly mediated through a direct effect of the femininity/mas-

culinity index, leading to an increase of femininity. Sexual hor-

mones are possible candidates to explain this effect (Cordain

et al., 2003), although, as it is in the opposite direction than

expected, further work is required for a better understanding.

Another physiological effect of refined carbohydrate-rich food

consumption is hyperglycaemia, which has itself several physio-

logical consequences. For example, hyperglycaemia accelerates

glycation, a covalent bonding process that cross-links the amino

acids present in the collagen and elastin that support the dermis.

Cross-linked collagen fibers are incapable of repair through the

usual process of remodeling, directly impacting youthful skin

appearance, which relies on flexible and repairable collagen fibers

(Danby, 2010). Thus chronic hyperglycaemia generated by

chronic between-meal snacks could affect attractiveness because

skin aging directly impacts age appearance (Nkengne et al., 2008),

and age affects attractiveness (Samson et al., 2010). However, this

possible effect was controlled for because apparent age was inde-

pendently estimated, and the difference in age departure from the

chronological age within each pair was used as a control variable.

Structural equation modeling suggested that for men, the effect of

glycaemic load on attractiveness could be indirectly mediated

through a direct effect on age departure from actual age. Indeed,

an increase in the age difference (chronological or apparent)

within each pair increased attractiveness (Table 4), and thus any

skin aging effect, which increases apparent age, also increases

attractiveness. This increase in attractiveness with age is generally

described for relatively young men in the range 20–50 years old

and is classically explained by the correlation between age and

characteristics that advertise adequate parental investment in

terms of resource and social status (Buss, 1989; Jencks & And

Others, 1979). For older ages, the correlation between age and

attractiveness is reversed, perhaps due to somatic senescence com-

promising some type of paternal investment, fertility decline, or to

higher chances of passing on genetic defects to offspring (Hell-

strom et al., 2006; Kong et al., 2012). For women, the aging effect

is the opposite: an increase in the difference in age departure from

actual age within each pair decreased attractiveness, i.e. men pre-

ferred women that were perceived to be younger (or compared to

women evaluated older) (Table 4). Indeed, age supplies a powerful

cue related to female reproductive capacity, and men prefer

younger women (Buss, 1989).

Ultimately, Why is an Increase in Refined Carbohydrate Consumption Associated with an Increased Attractiveness?

Traditional foods with a high level of sugar contents are ener-

getically rewarding, although they are typically seasonal or

scarce, such as ripened fruit and honey. Because sugar excess

generated by hyperglycaemia is stored as fat, traditional foods

that generate hyperglycaemia are sometimes used to intention-

ally increase fat storage, e.g. the case of the food intake of

Japanese sumo wrestlers (Nishizawa et al., 1976) or the fatten-

ing sessions described in various ethnic groups before an

expected general shortage period (Garine & Koppert, 1990;

Garine & Koppert, 1991). Generally, traditional food, i.e.

pre-industrial or non-refined, does not generate hyperglycae-

mia. This is the case for fresh fruits, legumes, traditionally

prepared cereals, etc. although there are counter-examples such

as honey. Globally, traditional foods that generate hypergly-

caemia were not always readily available because they were

scare or expensive. Thus, it is possible that intake of foods that

generate hyperglycaemia represents an advantage in certain

traditional environments, particularly when food shortages are

not uncommon. In such an environment, detection and prefer-

ence of facial cues that display an ability to find refined carbo-

hydrates sources could constitute an evolutionary advantage for

the choice of mating partner. This hypothesis could explain

why an increased consumption of such food increases attrac-

tiveness. However, in the current industrial dietary environ-

ment, foods that generate hyperglycaemia are not limited,

and their consumption is not a signal of quality anymore.

Therefore, it is possible that the current increased attractiveness

associated with the increased consumption of such food is bet-

ter understood as a maladaptation.

How the Other Control Variables Could Affect Attractiveness

Several other variables potentially affecting attractiveness were

controlled for, although only BMI has a significant effect in

that a higher BMI decreased facial attractiveness for both men

and women (Table 4). Indeed, body weight can be accurately

judged from facial images alone (Coetzee et al., 2010), and

cues of adiposity affect social judgements of female and male

faces (Coetzee et al., 2009; Han et al., 2016; Re & Perrett,

2014).

Limitations

The effect of chronic between-meal snacks on attractiveness

could be confounded by a variable not considered in this work.

One possibility could be physical activity, which could have

the effect of both increasing attractiveness (Faurie et al., 2004;

Stephen et al., 2009) and increasing between-meal snacks (Ker-

ver et al., 2006; Ovaskainen et al., 2006). Other diet factors not

taken into account could affect facial attractiveness, such as

fruit and vegetable known to increase skin yellowness (Apple-

ton et al., 2018; Zuniga et al., 2017). Also, the main and more

complex meals, i.e. lunch and dinner, were not included, pre-

cluding the computation of an overall diet quality index, which

could have possibly captured other aspects of diet influencing

attractiveness. However, diet quality indexes are correlated

with low-GL food (higher values for an increase of low-GL

Berticat et al. 7

food, see e.g. Azadbakht et al., 2016; Jones et al., 2016), and

thus are partially described by GL measures. Finally, energy

intake of each meal was not controlled for in our model due to

the high correlation observed with glycaemic load, but gave

qualitatively similar results when integrated alone (Table S1).

As a consequence, it is unclear whether the associations

observed are due to energy intake or to glycaemic load.

Conclusion

The recent dietary change, and particularly the new refined

carbohydrate-rich diet, has well-known detrimental health con-

sequences. Non-medical traits also are apparently affected in

healthy women and men, as refined carbohydrate consumption

seems not independent of facial attractiveness, a trait with

important social consequences. Further studies are needed to

investigate whether other non-medical traits, but with non-

negligible social importance, could also be impacted.

Acknowledgments

The authors thank the City Hall of Montpellier, Luc Gomel and all

staff from the Serre Amazonienne for providing places for rater

recruitment and the women and men who participated in this study.

This is contribution ISEM 2020-229.

Declaration of Conflicting Interests

The author(s) have declared no potential conflicts of interest with

respect to the research, authorship, and/or publication of this article.

Ethical Statement

The protocol used to recruit participants and collect data was approved

by the French Committee of Information and Liberty (CNIL

#1783997V0). For each participant, the general purpose of the study

was explained (“a study on the determinants of mate choice”), and

written voluntary agreement was requested for statistical use of data

(private information and photographs). Data were analyzed

anonymously.

Funding

The author(s) have disclosed receipt of the following financial support

for the research, authorship, and/or publication of this article: Agence

Nationale pour la Recherche “HUMANWAY” project (ANR-12-

BSV7-0008-01). None of the funding organizations or sponsors

played a role in the design and conduct of the study; in the collection,

management, analysis, or interpretation of the data; or in the prepara-

tion, review, or approval of the manuscript.

ORCID iD

Claire Berticat https://orcid.org/0000-0003-1305-5337

Michel Raymond https://orcid.org/0000-0002-1714-6984

Supplemental Material

Supplemental material for this article is available online.

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