PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

Body height is a life-history component. It involves important costs for its expression and maintenance, which may originate trade-offs on other costly components such as reproduction or immunity. Although previous evidence has supported the idea that human height could be a sexually selected trait, the explanatory mechanisms that underlie this selection are poorly understood. Despite extensive studies on the association between height and attractiveness, the role of immunity in linking this relation is scarcely studied, particularly in non-Western populations. Here, we tested whether human height is related to health measured by self-perception, and relevant nutritional and health anthropometric indicators in three Latin-American populations that widely differ in socioeconomic and ecological conditions: two urbanised populations from Bogota (Colombia) and Mexico City (Mexico), and one isolated indigenous population (Me’Phaa, Mexico). Results of linear mixed models showed that self-rated health is best predicted by an interaction between height and waist circumference, and the costs associated with large waist circumference are height-dependent, affecting taller people more than shorter individuals. If health and genetic quality cues play an important role in human mate-choice, and height and waist interact to signal health, its evolutionary consequences, including cognitive and behavioural effects, should be addressed in future research.
Content may be subject to copyright.
1
Self-reported Health is Related to Body Height and Waist Circumference in
1
Rural Indigenous and Urbanized Latin-American Populations
2
3
Juan David Leongoméz1*, Oscar R. Sánchez1, Milena Vásquez-Amézquita2, Eugenio Valderrama1,#a,
4
Andrés Castellanos-Chacón1, Lina Morales-Sánchez 1,#b, Javier Nieto3, Isaac González-Santoyo4*
5
6
1Human Behavior Lab, Faculty of Psychology, El Bosque University. Bogota, Colombia.
7
2Experimental Psychology Lab, Faculty of Psychology, El Bosque University, Bogota, Colombia.
8
3Laboratory of Learning and Adaptation, Faculty of Psychology, National Autonomous University of
9
Mexico, Mexico City, Mexico.
10
4Neuroecology Lab, Faculty of Psychology, National Autonomous University of Mexico, Mexico City,
11
Mexico.
12
#aCurrent Address: LH Bailey Hortorium, Plant Biology Section, School of Integrative Plant Science,
13
Cornell University, Ithaca, NY, United States of America.
14
#bCurrent Address: Department of Psychology, Faculty of Social Sciences, Los Andes University,
15
Bogota, Colombia.
16
17
*Corresponding authors
18
E-mail address: jleongomez@unbosque.edu.co (JDL), isantoyo.unam@gmail.com (IG-S)
19
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
2
Abstract
20
Body height growth is a life history component. It involves important costs for its expression and
21
maintenance, which may originate trade-offs on other costly components such as reproduction or
22
immunity. Although previous evidence has supported the idea that human height could be a sexually
23
selected trait, the explanatory mechanisms that underlie this selection is poorly understood. Moreover,
24
despite the association between height and attractiveness being extensively tested, whether immunity
25
might be linking this relation is scarcely studied, particularly in non-Western samples. Here, we tested
26
whether human height is related to health measured by both, self-perception, and relevant nutritional and
27
health anthropometric indicators in three Latin-American populations that widely differ in
28
socioeconomic and ecological conditions: two urbanized samples from Bogota (Colombia) and Mexico
29
City (Mexico), and one isolated indigenous population (Me´Phaa, Mexico). Using Linear Mixed
30
Models, our results show that, for both men and women, self-rated health is best predicted by an
31
interaction between height and waist, and that the costs associated to a large waist circumference are
32
differential for people depending on height, affecting taller people more than shorter individuals in all
33
population evaluated. The present study contributes with information that could be important in the
34
framework of human sexual selection. If health and genetic quality cues play an important role in human
35
mate choice, and height and waist interact to signal health, its evolutionary consequences, including its
36
cognitive and behavioral effects, should be addressed in future research.
37
38
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
3
Introduction
39
In modern Western societies, it has been seen that while women usually show a marked
40
preference for men significantly taller, over significantly shorter, than average [1,2], men are more
41
tolerant in choosing women who are taller or shorter than average [3]. This is consistent with the idea
42
that male height can be adaptive [4] and that sexual selection favors taller men, possibly because it
43
provides hereditary advantages, such as genetic quality for the offspring [5,6], or direct benefits,
44
provisioning resources and protection for women and their children [7]. This because height has been
45
proposed as an indicator of resource holding potential (RHP), in terms of social dominance and
46
deference [8,9], and socioeconomic status [5,10].
47
Supporting this idea, it has been found a direct linear relationship between male height and
48
reproductive success, which would not apply to women, and suggest unrestricted directional selection,
49
that would work to favor even very tall men, but not to very tall women [11]. In fact, it has been
50
reported that taller men (but not extremely tall men) are more likely to find a long-term partner and have
51
several different long-term partners [12], while the maximum reproductive success of women is below
52
female average height [13]. Furthermore, heterosexual men and women tend to adjust the preferred
53
height of hypothetical partners depending on their own stature [14]. In general, heterosexual men and
54
women prefer couples in which the man is taller than the woman, and women show a preference for
55
facial cues that denote a taller man [15].
56
Although previous evidence has supported the idea that human height could be a sexually
57
selected trait, the explanatory mechanisms that underlie this selection is poorly understood.
58
One possibility can be addressed in the framework of the Life-History theory [16], and the
59
immunocompetence handicap hypothesis (ICHH [1719]). Body height growth is a life history
60
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
4
component [1,20], that involves important costs for its expression and maintenance, which may originate
61
trade-offs on other costly components such as reproduction [21] or immunity [22].
62
The costs in height can be measure in terms of survival and physiological expenditure [22]. For
63
example, it has been shown that shorter people are more likely to be more longevous and less likely to
64
suffer from age-related chronic diseases [22,23]. With some exceptions, we have a limited number of
65
cell replications during our lifetimes. A minimal increment in body height necessary involves more
66
cells, maybe trillions, and more replications during the life. This higher number of cell replications
67
demands greater number of proteins to maintain taller, larger bodies [22], which together with an
68
increase on free radicals generated by the corresponding energy consumption, may lead to greater
69
likelihood of DNA damage [24], thus increasing the incidence of cancer and reducing longevity [22].
70
Trade-offs between these life-history components could be mediated by sexual hormones. Trade-
71
off with reproduction occurs because at the beginning of sexual maturity sexual hormones are
72
responsible to reallocate energetic and physiological resources to this function, instead of somatic
73
growth. For instance, an increment in estrogen production leads to the onset of menstrual bleeding in
74
women, but also slows the process of growth, and eventually causes it to cease [25]; estrogen stimulates
75
mineral deposition in the growth plates at the ends of the long bones, thus terminating cell proliferation,
76
and resulting in the fusion of the growth plates to the shaft of the bone [26, see also 27]. In turn, trade-
77
off with immunity occurs because the same increment in sexual steroids , usually has suppressive effects
78
on several immune components [17]. For example, testosterone may increase the severity of malaria,
79
leishmaniasis, amebiasis [28], and perhaps tuberculosis [see 29,30].
80
Therefore, as consequence of these life-history trade-offs, height could be considered as a
81
reliable indicator of individuals’ condition in terms of (1) the amount and quality of nutritional resources
82
that were acquired until sexual maturity, (2) the RHP to obtain resources for the somatic maintenance in
83
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
5
adult stage, and (3) the current immunocompetence to afford the immune cost imposed by sexual
84
steroids. Thus, according with ICHH height can be used for potential partners to receive information
85
about the quality of potential mate; only high-quality individuals could afford to allocate resources to
86
better immunity and attractive secondary sexual traits simultaneously [18], which would result in
87
increased sexual preference towards taller individuals.
88
Despite the association between height and attractiveness being widely tested, whether immunity
89
might be linking this relation is poorly studied. Moreover, most studies have been done using high-
90
income developed populations (often samples characterized as Western, Educated, and from
91
Industrialized, Rich, and Democratic [WEIRD] societies [31]), which has led to a lack of information of
92
what is occurring in other populations with important socio ecological differences. Considering these
93
ecological pressures is important because although genetic allelic expression could be the main factor
94
that determines individual height differences [25], height is also the most sensible human anatomical
95
feature that respond to environmental and socioeconomic conditions [21,32]. For instance, variation in
96
height across social classes is known to be greater in poorer countries [33], but much reduced where
97
standards of living are higher [34]. Economic inequality not only affects population nutritional patterns,
98
which are especially important during childhood to stablish adult height, but also the presence of
99
infectious diseases [35]. Childhood disease is known to adversely affect growth: mounting an immune
100
response to fight infection increases metabolic requirements and can thus affect net nutrition, and hence
101
reduce productivity. Disease also prevents food intake, impairs nutrient absorption, and causes nutrient
102
loss [36,37]. Therefore, comparing with high-income, developed populations, habitants from sites with
103
stronger ecological pressures imposed by pathogens, or greater nutritional deficiencies, would face
104
greater costs to robustly express this trait, and in consequence could show a stronger sexual selective
105
pressure over height, since it would more accurately signal growth rates, life-history trajectories, and
106
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
6
health status. This phenotypic variation is described as developmental plasticity, which is a part of the
107
phenotypic plasticity related to growth and development, in response to social, nutritional, and
108
demographic conditions, among others [38]. In fact, during the last century, and given a general
109
improvement in nutrition, height has increased around the world [39], but maintaining the level of
110
dimorphism in favor of men.
111
Colombia and Mexico are two of the most socioeconomically heterogeneous countries in the
112
world; although both countries have a high Human Development Index [40], and have relatively good
113
health compared to global standards, attaining respective scores of 68 and 66 in the Healthcare Access
114
and Quality (HAQ) Index [41], Colombia and Mexico have GINI coefficients of 50.8 and 43.4,
115
respectively, making them the 12th and 43th most unequal countries in the world (GINI index World
116
Bank estimate; https://data.worldbank.org/indicator/SI.POV.GINI). These national-level statistics,
117
however, hide important within-country differences. In particular, in Latin-America people in rural areas
118
tend to be poorer and have less access to basic services such as health and education than people in
119
urban areas.
120
According to data from the World Bank and the Colombian National Administrative Department
121
of Statistics, in 2017 Colombia was the second most unequal country in Latin-America after Brazil; in
122
rural areas 36% of people were living in poverty, and 15.4% in extreme poverty, while in urban areas
123
these values were only 15.7% and 2.7%, respectively [for a summary, see 42].
124
In addition to rural communities, in Latin-America, indigenous people tend to have high rates of
125
poverty and extreme poverty [43], and have poorer health [44] less susceptible to improve by national
126
income growth [45]. In Mexico, there are at least 56 independent indigenous peoples, whose lifestyle
127
practices differ in varying degrees from the typical “urbanized” lifestyle. Among these groups, the
128
Me’Phaa people, from an isolated region known as the “Montaña Alta” of the state of Guerrero, is one
129
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
7
of the groups whose lifestyle most dramatically differs from the westernized lifestyle typical of more
130
urbanized areas [46]. Me’Phaa communities are small groups, composed of fifty to eighty families, each
131
with five to ten family members. Most communities are based largely on subsistence farming of legumes
132
such as beans and lentils, and the only grain cultivated is corn. Animal protein is acquired by hunting
133
and raising some fowl, but meat is consumed almost entirely during special occasions and is not part of
134
the daily diet. There is almost no access to allopathic medications, and there is no health service,
135
plumbing, or water purification system. Water for washing and drinking is obtained from small wells.
136
Most Me’Phaa speak only their native language [47]. In consequence, these communities have some of
137
the lowest income and economic development in the country, and the highest child morbidity and
138
mortality due to chronic infectious diseases [46].
139
These three Latin-American populations can provide an interesting indication about how
140
regional socioeconomic conditions, and the intensity of ecological pressures by pathogens, may
141
modulate the function of height as an informative sexually selected trait of health and individual
142
condition. Therefore, the aim of the present study was to evaluate whether human height is related to
143
health measured by both, self-perception, and relevant nutritional and health anthropometric indicators
144
in three Latin-American populations that widely differ in socioeconomic and ecological conditions: two
145
urbanized samples from Bogota (Colombia) and Mexico City (Mexico), and one isolated indigenous
146
population (Me´Phaa, Mexico).
147
Materials and Methods
148
Ethics Statement
149
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
8
All procedures for testing and recruitment were approved by El Bosque University Institutional
150
Committee on Research Ethics (PCI.2017-9444) and National Autonomous University of Mexico
151
Committee on Research Ethics (FPSI/CE/01/2016). All participants read and signed a written informed
152
consent.
153
Participants
154
A total of 251 (120 women and 131 men) adults took part in the study. They were from three
155
different samples: (1) Mexican indigenous population, (2) Mexican urban population, and (3)
156
Colombian urban population.
157
The first sample consisted of 75 subjects (mean age ± SD = 33.60 ± 9.51 years old) from the
158
small Me’Phaa community – Plan de Gatica” from a region known as the “Montaña Alta” of the state
159
of Guerrero in Southwest Mexico. In this group, 24 participants were women (33.46 ± 8.61) and 39 were
160
men (33.74 ± 10.41), who were participating in a larger study about immunocompetence. Both sexes
161
were aged above 18 years old. In Mexico, people from this age is considered as Adult. We collected all
162
measurements in the own community. Me’Phaa communities are about 20 kilometers apart, and it takes
163
about three hours traveling on rural dirt roads to reach the nearest large town, about 80 km away.
164
Mexico City is about 850 kilometers away and the trip takes about twelve hours by road. This
165
community has the lowest income in Mexico, the highest index of child morbidity and mortality by
166
gastrointestinal and respiratory diseases (children's age from 0 to 8 years old, which is the highest
167
vulnerability and death risk age; [46]), and the lowest access to health services. These conditions were
168
determined by last 10 years of statistical information obtained from the last record of the national system
169
of access to health information in 2016 [46].
170
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
9
The second sample consisted of 66 subjects (20.67 ± 2.32) over 18 years old of general
171
community from Mexico City, of whom 36 were women (20.2 ± 2.27) and 30 were men (21.13 ± 2.36).
172
Finally, the third sample consisted of 122 undergraduate students with ages ranging from 18 to 30 years
173
old (30.23 ± 4.27), 60 were women (20.2 ± 2.27) and 62 were men (21.13 ± 2.36) from Bogota,
174
Colombia. All urban participants were recruited through public advertisements.
175
Participants from both urban population samples were taking part in two different, larger studies
176
in each country. In Colombia, all data were collected in the morning, between 7 and 11 am, because
177
saliva samples (for hormonal analysis), as well as voice recordings, odor samples, and facial
178
photographs, were also collected as part of a separate project. Additionally, women in the Colombian
179
sample were not hormonal contraception users, and all data were collected within the first three days of
180
their menses.
181
Participants who were under allopathic treatment, and hormonal contraception female users from
182
both countries were excluded from data collection. All participants completed a sociodemographic data
183
questionnaire, which included medical and psychiatric history.
184
Procedure
185
All participants signed the informed consent and completed the health and background
186
questionnaires. For participants from the indigenous population, the whole procedure was carried out
187
within their own community, and participants from the urban population attended a university laboratory
188
from each country on individual appointments.
189
First, participants were asked to complete the health and sociodemographic data questionnaires.
190
Subsequently the anthropometric measurements were taken.
191
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
10
Self-reported health
192
We used a Spanish language validated version of the SF-36 questionnaire [48]. The used version
193
was validated in Colombia [49]. The SF-36 produces eight factors, calculated by averaging the recoded
194
scores of individual items: 1) Physical functioning (items 3 to 12), 2) Role limitations due to physical
195
health (items 13 to 16), 3) Role limitations due to emotional problems (items 17 to 19), 4)
196
Energy/fatigue (items 23, 27, 29 and 31), 5) Emotional well-being (items 24, 25, 26, 28 and 30), 6)
197
Social functioning (items 20 and 32), 7) Pain (items 21 and 22), and 8) General health (items 1, 33, 34,
198
35 and 36).
199
To calculate this factors, all items were recoded following the instructions on how to score SF-36
200
[48].We calculated final factor averaging the recoded items. To make this data compatible with the
201
Mexican database, and because item 35 cannot be answered by the Mexican Indigenous population, this
202
item was excluded and the health factor was calculated averaging only items 1, 33, 34, and 36.
203
Anthropometric measurements
204
All anthropometric measurements were measured three times, consecutively, and then averaged
205
(for agreement statistics between the three measurements of each characteristic, see section 1.3 on S1
206
File). All participants were in light clothes and had their shoes removed. The same observer repeated the
207
three measurements.
208
We measured the body height in centimeters, to the nearest millimeter, using a 220cm Zaude
209
stadiometer, with the participant’s head aligned according to the Frankfurt horizontal plane and with feet
210
together against the wall.
211
Anthropomorphic measurements also included waist circumference (cm), weight (kg), fat
212
percentage, visceral fat level, muscle percentage, and BMI. Circumference of waist was measured in
213
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
11
centimeters using a flexible tape, midway between the lowest rib and the iliac crest, and was recorded to
214
the nearest millimeter. These anthropomorphic measures have been used as an accurate index of
215
nutritional status and health, especially waist circumference. Metabolic syndrome is associated with
216
visceral adiposity, blood lipid disorders, inflammation, insulin resistance or full-blown diabetes, and
217
increased risk of developing cardiovascular disease [50,51, for a review see 52], including Latin-
218
American populations [53]. Waist circumference has been proposed as a crude anthropometric correlate
219
of abdominal and visceral adiposity, and it is the simplest and accurate screening variable used to
220
identify people with the presence of the features of metabolic syndrome [54,55]. Hence, In the presence
221
of the clinical criteria of metabolic syndrome, an increased waist circumference does provide relevant
222
pathophysiological information insofar as it defines the prevalent form of the syndrome resulting from
223
abdominal obesity [51].
224
Weight (kg), fat percentage, visceral fat level, muscle percentage and BMI were obtained using
225
an Omron Healthcare HBF-510 body composition analyzer, calibrated before each participant’s
226
measurements were obtained.
227
Statistical analysis
228
To test the association between height and health, we fitted general a Linear Mixed Model
229
(LMM). The dependent variable in this model were the self-reported health factor and the predictor
230
variables included participant sex, age, population (indigenous, urban), height and waist as fixed, main
231
effects, as well as anthropometric measurements (hip, weight, fat percentage, BMI and muscle
232
percentage). Interactions between height and population, height and sex, and height and waist
233
circumference were also included. Country was always included as a random factor, with random
234
intercepts.
235
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
12
Although allowing slopes to vary randomly is recommended [56], we only included random
236
intercepts in the models because there is only one data-point per subject. Population (indigenous, urban)
237
was always included as a fixed effect because while there are important differences in health (and self-
238
reported health) between indigenous and urban populations in Latin-America, while no such differences
239
were expected by country. General LMM were fitted to test residual distribution. In all cases, residuals
240
were closer to a normal or gamma (inverse link) distribution, for each population/country. Models was
241
fitted using the lmer function from the lmerTest package [57;
242
https://www.rdocumentation.org/packages/lmerTest] in R, version 3.5.2 [58].
243
The most parameterized initial model was then reduced based on the Akaike Information
244
Criterion (AIC) and the best supported model (i.e. the model with the lowest AIC with a ΔAIC higher
245
than 2 units from the second most adequate model) is reported [see 59]. To accomplish this, we
246
implemented the ICtab function from the bbmle package [60;
247
http://www.rdocumentation.org/packages/bbmle]. Once a final model was selected, model diagnostics
248
were performed (collinearity, residual distribution, and linearity of residuals in each single term effect;
249
see section 3 in S1 File).
250
Results
251
All analysis, data manipulation, tables and figures, as well as the code to produce them, can be
252
reproduced and explored in more detail using R scripts in Markdown format (S2 File) using the are
253
available as Supplementary Files, as well as the output, S1 File (in HTML format), where all
254
Supplementary tables and figures can also be found. All data are available at the Open Science
255
Framework (https://doi.org/10.17605/OSF.IO/KGR5X).
256
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
13
Figure 1 shows the distribution of age, waist, height, visceral fat and self-reported health, which
257
strongly varies in both women (Fig 1A) and men (Fig 1B), sex, population (indigenous, urban) and
258
country (Colombia, Mexico).
259
260
Fig 1. Distribution of all measured variables by sex, population and country. (A) Female participants. (B) Male
261
participants. For descriptives (mean, SD, median, minimum, and maximum values), see S2 Table (female participants) and
262
S3 Table (male participants), of the Supplemental Material.
263
264
To establish the relationship between height and self-reported health, we fitted three mixed
265
models (Table 1).
266
267
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
14
Table 1. Results of separate linear mixed models testing effects of independent variables on self-
268
reported health.
269
Model 1
Model 2
Estimate
df
p
Estimate
df
p
Estimate
df
p
(Intercept)
-97.01
226.83
0.520
-166.17
233.81
0.198
-181.41
234.65
0.153
Age
0.07
224.16
0.660
0.11
231.11
0.488
.
.
.
BMI (kg/m2)
-0.03
226.02
0.990
.
.
.
.
.
.
Fat (%)
-0.21
226.00
0.650
.
.
.
.
.
.
Height (cm)
1.13
226.58
0.240
1.49
233.27
0.064
1.59
234.01
0.043
Height:PopulationUrban
0.30
226.00
0.300
.
.
.
.
.
.
Height:SexMale
0.02
226.01
0.930
.
.
.
.
.
.
Height:Waist
-0.02
226.37
0.180
-0.02
233.25
0.064
-0.02
234.00
0.041
Hip (cm)
-0.05
226.98
0.830
.
.
.
.
.
.
Muscle (%)
-0.32
226.81
0.570
.
.
.
.
.
.
PopulationUrban
-38.67
226.02
0.400
8.42
233.98
0.009
8.24
234.38
0.010
SexMale
3.18
226.09
0.940
6.01
233.07
0.034
5.82
234.00
0.039
Waist (cm)
2.60
226.19
0.220
2.66
233.18
0.094
2.91
234.01
0.061
Weight (kg)
0.03
226.06
0.970
.
.
.
.
.
.
Note. Indigenous population and females were used as reference for categorical predictors. Significant effects are in bold. For
270
a full version of this table, including standard errors and t-values, see S7 Table, and for an ANOVA-like table of random
271
effects, see S8 Table in the Supplemental Material, available online.
272
273
In the first model we included, as predictors, all measured variables as main effects, as well as
274
the interactions between height and population, height and sex, and height and waist. In the second
275
model, we included age, height, population, sex, waist, and the interaction between height and waist. For
276
the final, third model, we removed age since this predictor did not have any influence on self-reported
277
health factor in the previous models.
278
These three models were compared using the Akaike Information Criterion (AIC) as well as
279
Akaike weights (wi AIC), and ΔAIC (Table 2). The analyses revealed that Model 3 is not only the most
280
parsimonious model, but has a lower AIC and higher Akaike weight [see 59] than the previous two
281
models; in fact, Model 3 is 5.66 times more likely to be the best model compared to Model 2, and more
282
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
15
than 4000 compared to Model 1 (in comparison to Model 1, Model 2 is close to 750 times more likely to
283
be the best model).
284
285
Table 2. Performance criteria of LME models.
286
Model
AIC
ΔAIC
df
wi(AIC)
Model 3
1981.4
.
8
0.85
Model 2
1984.87
3.47
9
0.15
Model 1
1998.09
16.69
16
<0.001
Note. Models are in descending order from the best, to the worst fitting. ΔAIC is the change in AIC between each model and
287
the previous. Akaike weights wi(AIC) are conditional probabilities for each model being the best model [59].
288
289
Nevertheless, for Model 3 (the minimum adequate model), Variance Inflation Factors (VIF)
290
revealed extreme collinearity for height, waist, and the interaction between height and waist (VIF > 75
291
in those cases; S9 Table). This problem, however, has solved after centering and rescaling both height
292
and waist measures (VIF < 3 in all cases; S10 Table). In addition, this centered and rescaled version of
293
Model 3 had no issues regarding its residual distribution (i.e. for all samples it resembled a normal
294
distribution) or linearity of residuals (see S2 Fig), and each single term predictor was linearly related to
295
self-rated health (see S3 Fig).
296
Furthermore, the final, centered and rescaled version of Model 3, had a lower AIC than model 3
297
(1962 vs 1981), and was over 1400 times more likely to be the best model, as revealed by Akaike
298
weights (see S11 Table).
299
The final model (Table 3; Fig 2) showed a significant, negative main effect waist circumference
300
(t = -3.01, β = -3.27, p < 0.001), as well as a significant effect of population (urban samples rated their
301
health 8.24 points higher than indigenous participants; t = 2.60, p = 0.01), and sex (men rated their
302
health 5.82 points higher than women t = 2.07, p = 0.039). In addition, this model (Table 3) revealed that
303
Colombians reported better heath than Mexicans (Fig 2B).
304
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
16
305
Table 3. Results of the final linear mixed model testing effects of independent variables on self-
306
reported health
307
Estimate
SE
df
t
p
(Intercept)
53.64
6.2
1.88
8.65
0.016
Height_cs
-0.17
1.57
234.16
-0.11
0.914
Waist_cs
-3.27
1.08
234.29
-3.01
0.003
SexMale
5.82
2.81
234
2.07
0.039
PopulationUrban
8.24
3.17
234.38
2.6
0.01
Height_cs:Waist_cs
-2.28
1.11
234
-2.06
0.041
Note. Indigenous population and females were used as reference for categorical predictors. Significant effects are in bold.
308
Both waist and height were centered and rescaled (identified by the suffix _cs).
309
310
311
Fig 2. Final model estimates. Forest-plot of estimates for each fixed factor with 95% CI. (A) Fixed effects. (B) Random
312
effects. For categorical fixed predictors, indigenous population and female participants were used as reference. Both waist
313
and height were centered and rescaled (identified by the suffix _cs).
314
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
17
315
Moreover, a significant interaction between waist and height (Table 3; t = -2.06, p = 0.041) was
316
exposed, indicating that the associated health costs of a larger waist circumference were different for
317
people of different heights (Fig 3); the best predicted self-rated health was for tall participants with small
318
waists, and the worst was for (again) tall participants, but with large waist circumferences. The model
319
also revealed that for shorter people, there are no predicted significant associated costs of having a large
320
waist. In other words, the association between height and self-rated health is positive for people with
321
small waist circumferences, but negative for people with large waists.
322
In addition, age, waist circumference, height, visceral fat, BMI, and muscle percentage, were
323
significantly correlated with self-rated health (r > 0.20, in all cases), for men and women (for bivariate
324
Pearson correlations between all measured variables see S4 Table for all participants combined, S5
325
Table for women, and S6 Table for men).
326
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
18
327
Fig 3. Interaction between height and waist. Model predictions were split by (A) sex, and ((B) population. To simplify
328
interpretation, raw (instead of centred and rescaled) values of height and waist were used. As waist reference, minimum,
329
quartiles (lower, median and upper), and maximum waist circumference values were used, showed on a blue to red colour
330
scale. For an interactive 3D plot of the interaction between height and waist, see S4 Fig, or the 3D animated version
331
contained in S1 File.
332
Discussion
333
The present study provides new insights into the nature of the relationship between height and
334
health, in both men and women, by studying three Latin American samples, which included urban and
335
indigenous populations with marked differences in access to basic needs and services like food and
336
health.
337
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
19
Contrary to our initial hypothesis, we did not find height by itself to be a significant predictor of
338
self-perceived health but by an interaction with waist circumference in all populations studied. Most
339
results in favor of a direct relationship between height itself and health were carried out more than
340
twenty years ago, in small samples, from modern societies, and in specific Western ethnic groups. New
341
studies with non-traditional population groups have failed to verify the positive relationship between
342
height and health, especially associated with cardiovascular and autoimmune diseases [61,62]. For
343
example, studies in groups of Native Americans, Japanese, Indians and Pakistanis showed that lower
344
people had a lower prevalence of cardiovascular disease than the highest people in each group [62].
345
These findings were similar in a group of inhabitants of Sardinia, a European population with the lowest
346
physical stature recorded in Europe in recent years [61].
347
Interestingly, our results suggest that although there is a main effect of waist size on self-
348
perceived health, the associated costs of a large abdominal circumference are differential depending on
349
stature; this is, waist circumference predicted self-reported health differently for people of different
350
heights: while being taller predicts better self-rated health for taller people with relatively small waists,
351
being taller was found to be associated with poorer perceptions of their of health in people with larger
352
waist circumferences. Furthermore, while there is a cost of abdominal and visceral adiposity for tall
353
people, there is no predicted cost for shorter persons. Therefore, these results argue the importance of
354
consider a phenotypic integration of different human features that could be involved in health or
355
physiological condition, when a possible sexually selected trait is being evaluated as a signal of
356
immunocompetence.
357
On the other hand, given that height is the most sensible human anatomical feature to
358
environmental and socioeconomic conditions [21,32], we expected stronger relation between health and
359
height for indigenous population, where the cost to produce and maintain this costly trait is greater than
360
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
20
for habitants from urbanized areas. Nevertheless, we did not find inter-population differences in the
361
magnitude of this relation, urban populations reported better health than the indigenous sample, and the
362
shortest participants tended to be from the indigenous Me’Phaa sample. These results could in fact
363
suggests different life history strategies. In harsh environments, compared to modern Western societies,
364
different life strategies could take place [63], like investing relatively less energy in growth and
365
reallocating it towards reproduction [21]. In addition, a relative increase in the intensity or number of
366
infectious diseases (including child disease, like in the case of the Me’Phaa) and a tendency to early
367
sexual maturity, could have negative effects on growth, resulting in lower average height values [64,65].
368
These trends could be a compensation between life history components [25]. Finally, fast and prolonged
369
growth imply high costs for the organism [1]; rapid growth seems to influence mortality risk [66], and
370
growing for a longer time, delays the onset of reproduction, increasing the risk of dying and producing
371
fewer offspring [1]. This perspective of life strategies allows us to understand the relationship between
372
height, health, and reproduction. It suggests the importance of addressing factors such as ethnicity,
373
socio-economic status, level of urbanization, especially in populations where there is great heterogeneity
374
of access to food, health and pressure resources for pathogens, as in Latin American populations in
375
which this relationship has barely been directly explored.
376
Although our study did not directly evaluate any immunological marker but a self-perception of
377
health, the implementation of a physiological immune indicator of adaptive immune system appears to
378
be consistent with our results. It has been found that men but not women show a curvilinear relationship
379
between antibody response to a hepatitis-B vaccine and body height, with a positive relationship up to a
380
height of 185 cm, but an inverse relationship in taller men [19]. In our three populations, the maximum
381
height was lower than 185 cm, which could explain the linear but not curvilinear relation found. In
382
addition, the fact that self-perception in our study and antibody response in previous studies are both
383
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
21
positively associated with body height could contribute to the knowledge about the reliability of self-
384
perception of health as an indicator of immunological condition.
385
Finally, in relation with sex differences women reported lower health in average than men in all
386
communities, which is concordant with reports and normative SF-36 data in other populations, and
387
especially in younger people [e.g. 67,68]. These results could add support to the idea that height is a
388
reliable signal of health in men [25], while for women it could reflect reproductive success [69] in terms
389
of labor and birth, and to a lesser extend function as an indicator of health [70]. It has been seen that
390
taller women experience fewer problem during this process, because of a lower risk of a mismatch
391
between fetal head size and the size of the birth canal [70]. Nevertheless, this idea is only speculative
392
and more studies comparing health, reproductive success and female height need to be done.
393
The present study contributes with information that could be important in the framework of
394
human sexual selection. If health and genetic quality cues play an important role in human mate choice
395
[e.g. 71], and height and waist interact to signal health, its evolutionary consequences, including its
396
cognitive and behavioral effects, should be addressed in future research. This could be done by studying
397
the interaction between waist circumference and height, in relation to reproductive and/or mating
398
success, as well as mate preferences and perceived attractiveness, in samples with both Westernized and
399
non-Westernized lifestyles.
400
Acknowledgments
401
We are grateful to L. Rojas, A. Ramos, A. Valderrama, V. West. S. Camelo, L. Quintero, P.
402
Garzón, M. Aguirre, A. Pastrana y N. Caro for their help in data collection, and all our participants.
403
References
404
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
22
1. Sear R. Height and reproductive success: is bigger always better? In: Frey UJ, Störmer C,
405
Willführ KP, editors. Homo Novus: A Human Without Illusions. Berlin, Heidelberg: Springer
406
Berlin Heidelberg; 2010. pp. 0103. doi:10.1007/978-3-642-12142-5
407
2. Pawlowski B, Dunbar RIM, Lipowicz A. Tall men have more reproductive success. Nature.
408
2000;403: 156. doi:10.1038/35003107
409
3. Salska I, Frederick DA, Pawlowski B, Reilly AH, Laird KT, Rudd NA. Conditional mate
410
preferences: Factors influencing preferences for height. Pers Individ Dif. 2008;44: 203215.
411
doi:10.1016/j.paid.2007.08.008
412
4. Sear R, Allal N, Mace R. Height, marriage and reproductive success in Gambian women. Res
413
Econ Anthropol. 2004;23: 203224. doi:10.1007/s12110-006-1003-1
414
5. Silventoinen K, Lahelma E, Rahkonen O. Social background, adult body-height and health. Int J
415
Epidemiol. 1999;28: 911918. doi:10.1093/ije/28.5.911
416
6. Manning JT. Fluctuating asymmetry and body weight in men and women: Implications for sexual
417
selection. Ethol Sociobiol. 1995;16: 145153. doi:10.1016/0162-3095(94)00074-H
418
7. Pawlowski B, Jasienska G. Women’s preferences for sexual dimorphism in height depend on
419
menstrual cycle phase and expected duration of relationship. Biol Psychol. 2005;70: 3843.
420
doi:10.1016/j.biopsycho.2005.02.002
421
8. Melamed T. Personality correlates of physical height. Pers Individ Dif. 1992;13: 13491350.
422
doi:10.1016/0191-8869(92)90179-S
423
9. Blaker NM, Rompa I, Dessing IH, Vriend AF, Herschberg C, van Vugt M. The height leadership
424
advantage in men and women: Testing evolutionary psychology predictions about the perceptions
425
of tall leaders. Gr Process Intergr Relations. 2013;16: 1727. doi:10.1177/1368430212437211
426
10. Peck MN, Lundberg O. Short stature as an effect of economic and social conditions in childhood.
427
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
23
Soc Sci Med. 1995;41: 733738. doi:10.1016/0277-9536(94)00379-8
428
11. Mueller U, Mazur A. Evidence of unconstrained directional selection for male tallness. Behav
429
Ecol Sociobiol. 2001;50: 302311. doi:10.1007/s002650100370
430
12. Nettle D. Height and reproductive success in a cohort of british men. Hum Nat. 2002;13: 473
431
491. doi:10.1007/s12110-002-1004-7
432
13. Nettle D. Women’s height, reproductive success and the evolution of sexual dimorphism in
433
modern humans. Proc R Soc London Ser B Biol Sci. 2002;269: 19191923.
434
doi:10.1098/rspb.2002.2111
435
14. Pawlowski B. Variable preferences for sexual dimorphism in height as a strategy for increasing
436
the pool of potential partners in humans. Proc R Soc London Ser B Biol Sci. 2003;270: 709712.
437
doi:10.1098/rspb.2002.2294
438
15. Re DE, Perrett DI. Concordant preferences for actual height and facial cues to height. Pers Individ
439
Dif. 2012;53: 901906. doi:10.1016/j.paid.2012.07.001
440
16. Stearns SC. Life history evolution: successes, limitations, and prospects. Naturwissenschaften.
441
2000;87: 476486. doi:10.1007/s001140050763
442
17. Folstad I, Karter AJ. Parasites, bright males, and the immunocompetence handicap. Am Nat.
443
1992;139: 603622. doi:10.1086/285346
444
18. Sheldon BC, Verhulst S. Ecological immunology: Costly parasite defences and trade-offs in
445
evolutionary ecology. Trends Ecol Evol. 1996;11: 317321. doi:10.1016/0169-5347(96)10039-2
446
19. Krams IA, Skrinda I, Kecko S, Moore FR, Krama T, Kaasik A, et al. Body height affects the
447
strength of immune response in young men, but not young women. Sci Rep. 2014;4: 13.
448
doi:10.1038/srep06223
449
20. Wells J. The Thrifty Phenotype Hypothesis: Thrifty Offspring or Thrifty Mother? J Theor Biol.
450
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
24
2003;221: 143161. doi:10.1006/jtbi.2003.3183
451
21. Walker R, Gurven M, Hill K, Migliano A, Chagnon N, De Souza R, et al. Growth rates and life
452
histories in twenty-two small-scale societies. Am J Hum Biol. 2006;18: 295311.
453
doi:10.1002/ajhb.20510
454
22. Samaras TT. How height is related to our health and longevity: A review. Nutr Health. 2012;21:
455
247261. doi:10.1177/0260106013510996
456
23. Samaras TT, Elrick H. Height, body size, and longevity: is smaller better for the human body?
457
West J Med. 2002;176: 2068. Available: http://www.ncbi.nlm.nih.gov/pubmed/12016250
458
24. Giovannelli L, Saieva C, Masala G, Salvini S, Pitozzi V, Riboli E, et al. Nutritional and lifestyle
459
determinants of DNA oxidative damage : a study in a Mediterranean population. Carcinogenesis.
460
2002;23: 14831489.
461
25. Stulp G, Barrett L. Evolutionary perspectives on human height variation. Biol Rev. 2016;91: 206
462
234. doi:10.1111/brv.12165
463
26. Ellison PT. On fertile ground: A natural history of human reproduction. Cambridge, MA: Harvard
464
University Press; 2009.
465
27. Iravani M, Lagerquist M, Ohlsson C, Sävendahl L. Regulation of bone growth via ligand-specific
466
activation of estrogen receptor alpha. J Endocrinol. 2017;232: 403410. doi:10.1530/JOE-16-
467
0263
468
28. Bernin H, Lotter H. Sex bias in the outcome of human tropical infectious diseases: Influence of
469
steroid hormones. J Infect Dis. 2014;209. doi:10.1093/infdis/jit610
470
29. Neyrolles O, Quintana-Murci L. Sexual Inequality in Tuberculosis. PLoS Med. 2009;6:
471
e1000199. doi:10.1371/journal.pmed.1000199
472
30. Nhamoyebonde S, Leslie A. Biological Differences Between the Sexes and Susceptibility to
473
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
25
Tuberculosis. J Infect Dis. 2014;209: S100S106. doi:10.1093/infdis/jiu147
474
31. Henrich J, Heine SJ, Norenzayan A. The weirdest people in the world? Behav Brain Sci. 2010;33:
475
6183. doi:10.1017/S0140525X0999152X
476
32. Walker R, Hamilton MJ. Life‐History Consequences of Density Dependence and the Evolution of
477
Human Body Size. Curr Anthropol. 2008;49: 115122. doi:10.1086/524763
478
33. Deaton A. Height, health, and development. Proc Natl Acad Sci. 2007;104: 1323213237.
479
doi:10.1073/pnas.0611500104
480
34. Garcia J, Quintana-Domeque C. The evolution of adult height in Europe: A brief note. Econ Hum
481
Biol. 2007;5: 340349. doi:10.1016/j.ehb.2007.02.002
482
35. Lim SS, Allen K, Bhutta ZA, Dandona L, Forouzanfar MH, Fullman N, et al. Measuring the
483
health-related Sustainable Development Goals in 188 countries: a baseline analysis from the
484
Global Burden of Disease Study 2015. Lancet. 2016;388: 18131850. doi:10.1016/S0140-
485
6736(16)31467-2
486
36. Silventoinen K. Determinants of variation in adult body height. J Biosoc Sci. 2003;35: 263285.
487
doi:10.1017/S0021932003002633
488
37. Dowd JB, Zajacova A, Aiello A. Early origins of health disparities: Burden of infection, health,
489
and socioeconomic status in U.S. children. Soc Sci Med. Elsevier Ltd; 2009;68: 699707.
490
doi:10.1016/j.socscimed.2008.12.010
491
38. Kuzawa CW, Bragg JM. Plasticity in Human Life History Strategy. Curr Anthropol. 2012;53:
492
S369S382. doi:10.1086/667410
493
39. Bentham J, Di Cesare M, Stevens GA, Zhou B, Bixby H, Cowan M, et al. A century of trends in
494
adult human height. Elife. 2016;5: e13410. doi:10.7554/eLife.13410
495
40. Human Development Report Office. Human Development Indicators and Indices: 2018 Statistical
496
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
26
Update [Internet]. New York, NY; 2018. Available:
497
http://hdr.undp.org/sites/default/files/2018_human_development_statistical_update.pdf
498
41. Fullman N, Yearwood J, Abay SM, Abbafati C, Abd-Allah F, Abdela J, et al. Measuring
499
performance on the Healthcare Access and Quality Index for 195 countries and territories and
500
selected subnational locations: a systematic analysis from the Global Burden of Disease Study
501
2016. Lancet. 2018;391: 22362271. doi:10.1016/S0140-6736(18)30994-2
502
42. Poverty and inequality. Colombia Reports. 17 Nov 2018. Available:
503
https://data.colombiareports.com/colombia-poverty-inequality-statistics/
504
43. Hall G, Patrinos HA, editors. Indigenous Peoples, Poverty and Human Development in Latin
505
America [Internet]. The World Bank; 2004. doi:10.1596/978-1-4039-9938-2
506
44. Montenegro RA, Stephens C. Indigenous health in Latin America and the Caribbean. Lancet.
507
2006;367: 18591869. doi:10.1016/S0140-6736(06)68808-9
508
45. Biggs B, King L, Basu S, Stuckler D. Is wealthier always healthier? The impact of national
509
income level, inequality, and poverty on public health in Latin America. Soc Sci Med. 2010;71:
510
266273. doi:10.1016/j.socscimed.2010.04.002
511
46. SINAIS. Sistema Nacional de Informacion en Salud [Internet]. 2016. Available:
512
http://www.sinais.salud.gob.mx
513
47. Miramontes O, DeSouza O, Hernández D, Ceccon E. Non-Lévy Mobility Patterns of Mexican
514
Me’Phaa Peasants Searching for Fuel Wood. Hum Ecol. 2012;40: 167–174. doi:10.1007/s10745-
515
012-9465-8
516
48. Ware JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual
517
framework and item selection. Med Care. 1992;30: 47383.
518
49. Lugo A LH, García E HI, Gómez R C. Confiabilidad del cuestionario de calidad de vida en salud
519
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
27
SF-36 en Colombia. Rev Fac Nac Salud Publica. 2006;24: 3750.
520
50. Czernichow S, Kengne A-P, Stamatakis E, Hamer M, Batty GD. Body mass index, waist
521
circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease
522
mortality risk? Evidence from an individual-participant meta-analysis of 82 864 participants from
523
nine cohort studies. Obes Rev. 2011;12: 680687. doi:10.1111/j.1467-789X.2011.00879.x
524
51. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444: 881887.
525
doi:10.1038/nature05488
526
52. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference
527
and waist:hip ratio as predictors of cardiovascular riska review of the literature. Eur J Clin
528
Nutr. 2010;64: 1622. doi:10.1038/ejcn.2009.68
529
53. Knowles KM, Paiva LL, Sanchez SE, Revilla L, Lopez T, Yasuda MB, et al. Waist
530
Circumference, Body Mass Index, and Other Measures of Adiposity in Predicting Cardiovascular
531
Disease Risk Factors among Peruvian Adults. Int J Hypertens. 2011;2011: 110.
532
doi:10.4061/2011/931402
533
54. Alberti KGM, Zimmet P, Shaw J. The metabolic syndromea new worldwide definition. Lancet.
534
2005;366: 10591062. doi:10.1016/S0140-6736(05)67402-8
535
55. Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults.
536
Executive Summary of The Third Report of The National Cholesterol Education Program
537
(NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In
538
Adults (Adult Treatment Panel III). JAMA. 2001;285: 24862497. Available:
539
http://www.ncbi.nlm.nih.gov/pubmed/11368702
540
56. Barr DJ, Levy R, Scheepers C, Tily HJ. Random effects structure for confirmatory hypothesis
541
testing: Keep it maximal. J Mem Lang. 2013;68: 255278. doi:10.1016/j.jml.2012.11.001
542
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
28
57. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed
543
Effects Models. J Stat Softw. 2017;82: 126. doi:10.18637/jss.v082.i13
544
58. R Core Team. R: A language and environment for statistical computing. [Internet]. Vienna,
545
Austria: R Foundation for Statistical Computing.; 2018. Available: http://www.r-project.org/
546
59. Wagenmakers E-J, Farrell S. AIC model selection using Akaike weights. Psychon Bull Rev.
547
2004;11: 192196. doi:10.3758/BF03206482
548
60. Bolker B. Package ‘bbmle’. Tools for General Maximum Likelihood Estimation [Internet]. R
549
CRAN Repository; 2017. Available: http://cran.r-project.org/web/packages/bbmle/index.html
550
61. Pes GM, Ganau A, Tognotti E, Errigo A, Rocchi C, Dore MP. The association of adult height
551
with the risk of cardiovascular disease and cancer in the population of Sardinia. Schooling CM,
552
editor. PLoS One. 2018;13: e0190888. doi:10.1371/journal.pone.0190888
553
62. Samaras TT, Elrick H, Storms LH. Is short height really a risk factor for coronary heart disease
554
and stroke mortality? A review. Med Sci Monit. 2004;10: RA63-76.
555
63. Perry GH, Dominy NJ. Evolution of the human pygmy phenotype. Trends Ecol Evol. 2009;24:
556
218225. doi:10.1016/j.tree.2008.11.008
557
64. Harvey PH, Clutton-Brock TH. Life History Variation in Primates. Evolution (N Y). 1985;39:
558
559581. doi:10.2307/2408653
559
65. Promislow DEL, Harvey PH. Living fast and dying young: A comparative analysis of life-history
560
variation among mammals. J Zool. 1990;220: 417437. doi:10.1111/j.1469-7998.1990.tb04316.x
561
66. Rollo CD. Growth negatively impacts the life span of mammals. Evol Dev. 2002;4: 5561.
562
doi:10.1046/j.1525-142x.2002.01053.x
563
67. Hopman WM, Towheed T, Anastassiades T, Tenenhouse A, Poliquin S, Berger C, et al. Canadian
564
normative data for the SF-36 health survey. CMAJ. 2000;163: 26571. Available:
565
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
29
http://www.ncbi.nlm.nih.gov/pubmed/10951722
566
68. Watson EK, Firman DW, Baade PD, Ring I. Telephone administration of the SF-36 health
567
survey: validation studies and population norms for adults in Queensland. Aust N Z J Public
568
Health. 1996;20: 359363. doi:10.1111/j.1467-842X.1996.tb01046.x
569
69. Gluckman PD, Hanson MA. Evolution, development and timing of puberty. Trends Endocrinol
570
Metab. 2006;17: 712. doi:10.1016/j.tem.2005.11.006
571
70. Wells JCK, DeSilva JM, Stock JT. The obstetric dilemma: An ancient game of Russian roulette,
572
or a variable dilemma sensitive to ecology? Am J Phys Anthropol. 2012;149: 4071.
573
doi:10.1002/ajpa.22160
574
71. Roberts SC, Little AC. Good genes, complementary genes and human mate preferences.
575
Genetica. 2008;132: 309321. doi:10.1007/s10709-007-9174-1
576
Supporting Information
577
S1 File. HTML output for R Markdown. This file contains the script and output for all analyses, data
578
manipulation and compilation, tables and figures. This file was created using R scripts in Markdown
579
format (Rmd file) to promote transparency and ensure reproducibility.
580
S2 File. R Markdown source file for HTML output. R Markdown file used to generate S1 File.
581
S1 Table. Intraclass correlation of anthropometric characteristics measurements.
582
S2 Table. Descriptive statistics of measured variables of female participants.
583
S3 Table. Descriptive statistics of measured variables of male participants.
584
S4 Table. Correlations between measured variables for all participants
585
S5 Table. Correlations between measured variables for female participants
586
S6 Table. Correlations between measured variables for male participants
587
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
30
S7 Table. Results of separate linear mixed models testing effects of independent variables on self-
588
reported health. Full table including standard errors and t-values.
589
S8 Table. ANOVA-like table with tests of random-effect terms.
590
S9 Table. Variance Inflation Factors of Model 3 predictors.
591
S10 Table. Variance Inflation Factors of the Final Model (Model 3 centered and rescaled)
592
predictors.
593
S11 Table. Information criteria for Model 3 and Model 3 (centered and rescaled).
594
S1 Fig. Sexual dimorphism of height, waist and health for all samples (A) Self-perceived health. (B)
595
Height. (C) Waist. Comparisons between female and male participants for each sample, were performed
596
using t-tests, adjusted for multiple tests. **** p < 0.0001.
597
S2 Fig. Model diagnostics. (A) Residual distribution for each sample. (B) Linearity in each (single
598
term) fixed factor. Centered and rescaled variables are identified by the suffix _cs.
599
S3 Fig. Single term predictor slopes. Slope of coefficients for each (single term) fixed predictor,
600
against self-rated health (linear relationship between each model term and response). For Population, 1 =
601
Indigenous, and 2 = Urban. For sex, 1 = female, and 2 = male. For simplicity, raw (instead of centered
602
and rescaled) values of height and waist were used.
603
S4 Fig. Interaction between height and waist (interactive, animated 3D version). For simplicity, raw
604
(instead of centered and rescaled) values of height and waist were used. Click and drag the plot to
605
change its orientation. Scroll to zoom. In S1 File, where this figure is also included, you can also use the
606
buttons below the figure to control the animation.
607
.CC-BY 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/562942doi: bioRxiv preprint first posted online Feb. 27, 2019;
Article
Full-text available
If you think you are in control of your behavior, think again. Evidence suggests that behavioral modifications, as development and persistence of depression, maybe the consequence of a complex network of communication between macro and micro-organisms capable of modifying the physiological axis of the host. Some parasites cause significant nutritional deficiencies for the host and impair the effectiveness of cognitive processes such as memory, teaching or non-verbal intelligence. Bacterial communities mediate the establishment of parasites and vice versa but this complexity approach remains little explored. We study the gut microbiota-parasite interactions using novel techniques of network analysis using data of individuals from two indigenous communities in Guerrero, Mexico. Our results suggest that Ascaris lumbricoides induce a gut microbiota perturbation affecting its network properties and also subnetworks of key species related to depression, translating in a loss of emergence. Studying these network properties changes is particularly important because recent research has shown that human health is characterized by a dynamic trade-off between emergence and self-organization, called criticality. Emergence allows the systems to generate novel information meanwhile self-organization is related to the system’s order and structure. In this way, the loss of emergence means a depart from criticality and ultimately loss of health.
Article
Full-text available
A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8-98·1) in Iceland, followed by 96·6 (94·9-97·9) in Norway and 96·1 (94·5-97·3) in the Netherlands, to values as low as 18·6 (13·1-24·4) in the Central African Republic, 19·0 (14·3-23·7) in Somalia, and 23·4 (20·2-26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1-93·6) in Beijing to 48·0 (43·4-53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6-68·8) in Goa to 34·0 (30·3-38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view-and subsequent provision-of quality health care for all populations. Bill & Melinda Gates Foundation.
Article
Full-text available
The relationship between body height and the risk of non‒communicable diseases such as cardiovascular disease and cancer has been the subject of much debate in the epidemiological literature. Concerns have recently arisen over spurious associations due to confounding factors like birth cohort, especially in the context of epidemiological transition. The population of Sardinia represents an interesting case study, as the average physical stature of inhabitants was the lowest recorded in Europe until a few decades ago. In this population we tested whether height is an independent risk factor for cardiovascular disease and cancer. We analysed the stature of 10,427 patients undergoing endoscopy for any reason, for whom a detailed clinical history of cardiovascular disease and/or malignancies had been documented. Poisson regression modelling was used to test the association between stature and disease risk. When patients were subdivided according to sex and height tertiles, the risk of cardiovascular disease proved significantly greater for subjects in the lowest tertile irrespective of sex (men: 1.87; 95%CI 1.41‒2.47; women: 1.23; 95%CI 0.92‒1.66) and smaller for those in the highest tertile (men: 0.51; 95%CI 0.35‒0.75; women: 0.41; 95%CI 0.27‒0.61). However, after adjusting the risk for birth cohort and established risk factors, it mostly resulted in non-significant values, although the overall trend persisted. Similar results were obtained for all-cancer risk (relative risk for men and women in the lowest tertile: 1.44; 95%CI 1.09–1.90 and 1.17; 95%CI 0.93–1.48, in the highest tertile: 0.51; 95%CI 0.36–0.72 and 0.62; 95%CI 0.47–0.81, respectively) as well as for some of the most common types of cancer. We concluded that the risk of developing cardiovascular disease and malignancies does not vary significantly with stature in the Sardinian population, after adjusting for birth cohort and more obvious risk factors.
Article
Full-text available
Body height and other body attributes of humans may be associated with a diverse range of social outcomes such as attractiveness to potential mates. Despite evidence that each parameter plays a role in mate choice, we have little understanding of the relative role of each, and relationships between indices of physical appearance and general health. In this study we tested relationships between immune function and body height of young men and women. In men, we report a non-linear relationship between antibody response to a hepatitis-B vaccine and body height, with a positive relationship up to a height of 185 cm, but an inverse relationship in taller men. We did not find any significant relationship between body height and immune function in women. Our results demonstrate the potential of vaccination research to reveal costly traits that govern evolution of mate choice in humans and the importance of trade-offs among these traits.
Article
Full-text available
Research suggests that tall individuals have an advantage over short individuals in terms of status, prestige, and leadership, though it is not clear why. Applying an evolutionary psychology perspective, we predicted that taller individuals are seen as more leader-like because they are perceived as more dominant, healthy, and intelligent. Being fit and physically imposing were arguably important leadership qualities in ancestral human environments—perhaps especially for males—where being a leader entailed considerable physical risks. In line with our expectations, our results demonstrate that by manipulating an individual’s stature height positively influences leadership perception for both men and women, though the effect is stronger for men. For male leaders this height leadership advantage is mediated by their perceived dominance, health, and intelligence; while for female leaders this effect is only mediated by perceived intelligence.
Book
Full-text available
Height is of great interest to the general public and academics alike. It is an easily observable and easily measurable characteristic, and one that appears to be correlated with a number of important outcomes, from survival to intelligence to employment and marriage prospects. It is also of interest to evolutionary biologists, as the end product of life history decisions made during the period of growth. Such decisions will depend at least partly on the payoffs to size in adulthood. This chapter surveys the costs and benefits of height during adulthood: what are the consequences of height in terms of mortality rate, mating success and fertility outcomes for each sex, and how much do these differ between environments? It is clear from this survey that relationships between height and fitness correlates show considerable variation between populations, suggesting that the costs and benefits of height depend on environmental conditions. If any tentative conclusion can be drawn it is that while short height is rarely advantageous, particularly for men, tall height is not univer- sally beneficial, particularly for women. We can also conclude that height is clearly still important for fitness correlates in modern environments, thereby demonstrating that we have yet to leave our biological imperative behind.
Article
Human height is a highly variable trait, both within and between populations, has a high heritability, and influences the manner in which people behave and are treated in society. Although we know much about human height, this information has rarely been brought together in a comprehensive, systematic fashion. Here, we present a synthetic review of the literature on human height from an explicit evolutionary perspective, addressing its phylogenetic history, development, and environmental and genetic influences on growth and stature. In addition to presenting evidence to suggest the past action of natural selection on human height, we also assess the evidence that natural and sexual selection continues to act on height in contemporary populations. Although there is clear evidence to suggest that selection acts on height, mainly through life-history processes but perhaps also directly, it is also apparent that methodological factors reduce the confidence with which such inferences can be drawn, and there remain surprising gaps in our knowledge. The inability to draw firm conclusions about the adaptiveness of such a highly visible and easily measured trait suggests we should show an appropriate degree of caution when dealing with other human traits in evolutionary perspective.
Article
Numerous investigations have revealed a bias toward males in the susceptibility to and severity of a variety of infectious diseases, especially parasitic diseases. Although different external factors may influence the exposure to infection sources among males and females, one recurrent phenomenon indicative of a hormonal influence is the simultaneous increase in disease occurrence and hormonal activity during the aging process. Substantial evidence to support the influence of hormones on disease requires rigorously controlled human population studies, as well as the same sex dimorphism being observed under controlled laboratory conditions. To date, only very few studies conducted have fulfilled these criteria. Herein, we introduce tropical infectious diseases, including amebiasis, malaria, leishmaniasis, toxoplasmosis, schistosomiasis, and paracoccidioidomycosis, in which hormones are suspected to play a role in disease processes. We summarize the most recent findings from epidemiologic studies in humans and from hormone replacement studies in animal models, as well as data regarding the influence of hormones on immune responses underlying the pathology of the diseases.
Article
Physical height has a well-documented effect on human mate preferences. In general, both sexes prefer opposite-sex romantic relationships in which the man is taller than the woman, while individual preferences for height are affected by a person’s own height. Research in human mate choice has demonstrated that attraction to facial characteristics, such as facial adiposity, may reflect preferences for body characteristics. Here, we tested preferences for facial cues to height. In general, increasing apparent height in men’s faces and slightly decreasing apparent height in women’s faces maximizes perceived attractiveness. Individual preferences for facial cues to height were predicted by self-reported preferences for actual height. Furthermore, women’s own height predicted opposite-sex preferences for facial cues to apparent height, though this finding did not extend to male participants. These findings validate the use of facial cues to height and demonstrate a further component of facial attractiveness that reflects preferences for body characteristics.
Article
Extensive variation in life-history patterns is documented across primate species. Variables included are gestation length, neonatal weight, litter size, age at weaning, age at sexual maturity, age at first breeding, longevity, and length of the estrous cycle. Species within genera and genera within subfamilies tend to be very similar on most measures, and about 85% of the variation remains when the subfamily is used as the level for statistical analysis. Variation in most life-history measures is highly correlated with variation in body size, and differences in body size are associated with differences in behavior and ecology. Allometric relationships between life-history variables and adult body weight are described; subfamily deviations from best-fit lines do not reveal strong correlations with behavior or ecology. However, for their body size, some subfamilies show consistently fast development across life-history stages while others are characteristically slow. One exception to the tendency for relative values to be positively correlated is brain growth: those primates with relatively large brains at birth have relatively less postnatal brain growth. Humans are a notable exception, with large brains at birth and high postnatal brain growth.