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O2 max decrement from sea level to 4,338 m in Peruvian Quechua
1Department of Anthropology, The University at Albany, State University of New York, Albany, New York 12222; 2Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6; 3Department of Anthropology, The Pennsylvania State University, University Park, Pennsylvania 16804; 4Departamento de Ciencias Biológicas y Fisiológicas, Universidad Peruana Cayetano Heredia, San Martin de Porras, Peru; and 5Instituto Boliviano de Biologia de Altura, Casilla 717, La Paz, Bolivia
Submitted 27 November 2002 ; accepted in final form 7 April 2003
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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O2 max). This
hypothesis was tested via repeated measures of
O2 max (sea level vs.
4,338 m) in 30 men of mixed Spanish and Quechua origins. Individual genetic
admixture level (%Spanish ancestry) was estimated by using
ancestry-informative DNA markers. Genetic admixture explained a significant
proportion of the variability in
O2 max after
control for covariate effects, including sea level
O2 max and the decrement
in arterial O2 saturation measured at
O2 max
(SpO2 max) (R2 for admixture
and covariate effects 
0.80). The genetic effect reflected a main effect
of admixture on O2
max (P = 0.041) and an interaction between admixture and
SpO2 max (P = 0.018). Admixture
predicted O2 max
only in subjects with a large SpO2 max
(P = 0.031). In such subjects,
O2 max was
12–18% larger in a subgroup of subjects with high vs. low Spanish
ancestry, with least squares mean values (±SE) of 739 ± 71 vs.
606 ± 68 ml/min, respectively. A trend for interaction (P =
0.095) was also noted between admixture and the decrease in ventilatory
threshold at 4,338 m. As previously, admixture predicted
O2 max only in
subjects with a large decrease in ventilatory threshold. These findings
suggest that the genetic effect on
O2 max depends on
a subject's aerobic fitness. Genetic effects may be more important (or easier
to detect) in athletic subjects who are more likely to show gas-exchange
impairment during exercise. The results of this study are consistent with the
evolutionary hypothesis and point to a better gas-exchange system in
Quechua. deoxyribonucleic acid; genetic markers; aerobic performance; Andes; hypoxia; altitude
10,000 years ago
(31). Given the extremes of
altitude that are now permanently inhabited in the region [up to 5,200 m
(50)], it seems reasonable to
hypothesize that these populations derive in part from groups who experienced
natural selection in the past favoring superior oxygen transport phenotypes.
If so, one functional consequence of genetic adaptation might be an ability
(in current populations) to limit normal impairments in oxygen uptake and/or
utilization that occurs during strenuous exercise in hypoxia. Indeed, many
previous studies have made this argument, pointing to the relatively high
maximal oxygen consumption
(O2;
O2 max in ml ·
min-1 · kg-1) in hypoxia of Andean study groups
(3,
11,
14,
29,
34,
35), or suggesting that such
groups, compared with lowland groups, experience only a small decrement in sea
level O2 max [change
() in O2 max] when
exposed to hypoxia (2,
11,
14,
23,
45,
47,
49). Regarding the latter,
only a few studies have directly measured the
O2 max in such
groups using a repeated-measures design
(2,
23,
47), and these report
O2 max decrements in
Andean natives that are between 30 and 80% of the decrement seen in
lowland comparison groups. In this regard, the smaller
O2 max decrement in
Quechua may reflect an integrated functional response to hypobaric hypoxia
that involves multiple physiological and biochemical systems
(22). However, the reported
differences in the magnitude of the
O2 max decrement may be
exaggerated. In two of the studies cited above, the lowland comparison groups
were trained athletes, and aerobic fitness has a well-known positive effect on
the O2 max decrement
(12,
17,
28,
30,
32,
41,
42,
44).
In the present study, we assessed
O2 max decrement via a
repeated-measures design in a large group (n = 30) of young Peruvian
men of mixed Quechua and Spanish ancestry who were born and raised in Lima,
Peru (sea level). These subjects were first measured in Lima and then were
transported to Cerro de Pasco, Peru (4,338 m) for measurement after 12 h
of exposure to hypobaric hypoxia. The main objective of our study was to
assess the influence of Quechua vs. Spanish genetic admixture on the
O2 max decrement within
this study group, controlling for variation in aerobic fitness that may impact
the magnitude of decrement experienced. Our focus on individual genetic
admixture level as a study-independent variable represents a new research
strategy to detect the effects of genetic adaptation on physiological
phenotypes. The approach is made possible by new molecular genetic techniques
that give admixture estimates for individuals descendant from two or more
parental populations. Admixture in the Andes is historically well documented
and began 500 yr ago with the contact of Andean Native American, Spanish,
and, to a lesser extent, West African groups. Thus the approach represents an
alternative to the more typical study comparing an Andean native group with a
lowland "control" group. Such studies ignore the reality of Andean
population history and may be confounded by many factors, including unknown
admixture levels in the so-called native group.
To estimate the extent of Spanish ancestry (admixture) for each individual
within the sample, we used a panel of 22 ancestry-informative DNA genetic
markers. This revealed an admixture range within the sample from <1 to 64%
Spanish ancestry. From this, we hypothesized a positive relationship between
the extent of Spanish ancestry and the magnitude of the
O2 max decrement, as
would be expected if natural selection had favored hypoxia-tolerant phenotypes
in past Quechua populations. Importantly, for the approach to be useful, the
individual genetic markers need not be associated (i.e., linked) with whatever
genes determine exercise capacity in Quechua. The markers are simply used to
produce a probability estimate of the proportionate ancestry of an individual.
As a construct that reflects ancestry (and not specific genes or markers), the
admixture estimate may be associated with a specific physiological phenotype,
even if the individual genetic markers are not. In this regard, the approach
may be seen as a first step to identify physiological phenotypes for further
genetic study.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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The subjects for this study were young men (18–35 yr), who gave written, informed consent according to guidelines approved by the Institutional Review Boards at the University at Albany, State University of New York, and the Universidad Cayetano Heredia, Lima, Peru. Subjects were identified as nonsmokers and screened via a brief clinical history and medical examination for conditions contraindicating participation in the study protocols, including chronic obstructive respiratory diseases, cardiovascular disease, and renal disease. At screening, a venous blood sample was drawn from the antecubital vein, and Hb concentration (g/dl) was immediately determined by a Hemocue blood Hb analyzer (Angelholm, Sweden). Subjects with Hb <13.4 g/dl were considered anemic and were excluded from the study.
The subjects were recruited from within a specific district of Lima, Peru
(Barrios Altos district, population 150,000). In this district, 10%
of inhabitants are recent down-migrants from highland Peru. Individuals
accepted into the study were born and raised in Lima or near sea level, and
both sets of their parents and grandparents were born at an altitude >3,000
m. Thus all study subjects were first- or second-generation down-migrants. No
specific attempt was made to recruit subjects based on surname composition or
skin reflectance measures, both of which have been used in the past to assess
ancestry. The majority of subjects described themselves as
"Peruvians," but acknowledged both their Quechua and Spanish
origins.
Study Design
Subjects were recruited into the study during the last 2 wk of July 2001 in
Lima, Peru. Each subject completed baseline studies in Lima requiring 4 h
of participation. These subjects were studied again within 2 wk at 4,338 m in
the town of Cerro de Pasco, Peru. Cerro de Pasco is a 6- to 10-h bus ride from
Lima on paved road. The first 4–6 h of the trip involve a steady
gain in altitude to a high mountain pass (4,800 m). The road then
descends to the Peruvian Altiplano (3,600–4,300 m) for the next
3–4 h of the trip. Subjects arrived in Cerro de Pasco from Lima in
groups of four to five per day over a 2-wk period and rested in the laboratory
for 2–4 h before studies were initiated. Thus these subjects were
studied after 10–12 h of acute exposure to hypobaric hypoxia. Of 32
subjects measured in Lima, two were not measured in Cerro de Pasco. One was
unable to make the trip for personal reasons, and the other was diagnosed with
acute mountain sickness on arrival to Cerro de Pasco.
Anthropometry and Pulmonary Function
Standard anthropometry was performed on each subject by the same investigator. Measurements included height, weight, and skinfolds at subscapular, suprailiac, biceps, and triceps sites. Body density was calculated according to age and sex-specific equations given by Durnin and Womersley (10). Kashiwazaki et al. (26) have tested the validity of a number of reference equations against doubly labeled water measures of body composition in Bolivian Aymara and concluded that the reference equations above are the best available for use in Andean native populations. Percent body fat was calculated from the Siri equation. Pulmonary function was assessed on each subject in Cerro de Pasco by using a VS400 volumetric spirometer (Puritan-Bennett, Mallinckrodt, Hazelwood, MO), calibrated daily with a 3-liter calibration syringe. Each subject performed a maximal inspiration, followed immediately by a forced maximal expiration while in a standing position. From this procedure, the forced vital capacity (FVC) and forced expiratory volume made in 1 s were determined based on the best of at least two efforts. FVC and forced expiratory volume in 1 s measures were corrected for BTPS.
Admixture Rate
Genetic markers. Individual Spanish admixture proportion was estimated by using a panel of 22 informative genetic markers (MID-575, TSC1102055, WI-11153, MID-52, SGC30610, WI-17163, WI-9231, WI-4019, WI-11909, D11S429, TYR-192, DRD2 TaqD, DRD2 BclI, WI-14319, CYP19, PV92, WI-7423, CKM, MID-161, MID-93, FY, and F13B). The first 20 markers were selected because they show high-frequency differences (>30%) between Native American and Spanish populations, which are a priori the main parental populations in this sample. We also included in the panel two markers (FY and F13B) providing information on African ancestry. Details of these markers, including allele frequencies in all parental populations, DNA sequences, exact positions of single-nucleotide polymorphisms (SNPs), and the PCR primers and amplification conditions used are available from the dbSNP database (www.ncbi.nlm.nih.gov/SNP) under the submitter handle PSU-ANTH.
Genotyping. SNPs and insertion and deletion markers were scored by a melting-curve assay, in which the target sequence containing the SNP is amplified by PCR by using a mismatched primer where necessary to create an artificial restriction site polymorphism. PCR products were digested with a restriction enzyme, and the resulting restriction fragment-length polymorphisms are scored by their melting curves in a Hybaid DASH machine (ThermoHybaid). More details about this genotyping method can be found in Akey et al. (1). PCR products of the PV92 Alu insertion polymorphism were scored by conventional 2% agarose gel electrophoresis.
Admixture estimation. Usually, in samples from admixed populations, admixture is estimated by using the frequencies of samples representing the contributing parental populations as a contrasting reference. In this case, although there is information on the allele frequencies for these markers in several European populations, no such information is available for the Quechua parental population. Thus, to estimate admixture, we have used a strategy in which information on all parental frequencies is not required. We have used the program STRUCTURE, developed by Jonathan Pritchard, to infer admixture proportions in the samples from Lima and Cerro de Pasco. This program has been designed to infer the presence of genetic structure and to estimate the admixture proportions in samples with unknown genetic structure. This program can be used to estimate the number of subpopulations present in a sample and to assign individuals to each of those subpopulations, including estimates of individual admixture from each subpopulation. To estimate admixture in our sample, we prepared an input file, which included the genotype data for the 32 samples from Lima, 39 samples of highland-born subjects from Cerro de Pasco (not reported here), and 72 additional samples from Spain. We then ran the STRUCTURE program with K = 2 as the predefined setting for the number of populations, using 30,000 iterations for the burn-in period and 70,000 additional iterations to obtain parameter estimates. The output file provides an estimate of the European and Native American ancestry for each individual in the sample. We ran the program several times, with consistent results. More information about the program STRUCTURE can be found in Pritchard et al. (38). Using STRUCTURE in this manner has been shown to result in individual admixture estimates that are highly correlated with estimates made by using maximum likelihood and parental allele frequencies from both parental populations.
The estimates of individual admixture range from 0 (Quechua) to 1 (Spanish) and reflect the proportionate contribution of two different population histories to the genetic makeup of an individual.
Exercise
Identical protocols to measure
O2 max (l/min) were
administered in Lima and Cerro de Pasco. To begin,
O2 was measured at rest
(5 min) with the subject seated. After resting measurements,
O2 max was measured on a
mechanically braked Monarch 818e research ergometer. Subjects started with a
workload of 1.0-kg resistance at 60 rpm, and resistance was incremented by 0.5
kg at constant rpm every 3 min until subject volitional fatigue. Subjects were
given verbal encouragement, and
O2 max was defined as the
highest level of O2
averaged over the final minute of the test, concomitant with at least one of
the following: a nonlinear increase in exercise ventilation, resulting in a
respiratory exchange ratio (RER) >1.10, a plateau in the
O2-work rate
relationship, or a maximal heart rate (HR) within 10% of the age-predicted
maximum.
During O2 testing,
subjects breathed through a low-resistance breathing valve, and expired
ventilation (E, l/min
BTPS), as well as the fractional concentrations of O2
and CO2 in expired air, was processed by a Parvo-medics True Max
metabolic measuring system (Sandy, UT) to produce 1-min-interval calculations
of O2, carbon dioxide
production (CO2), the
RER, and the ventilatory equivalents for oxygen and carbon dioxide
(E/O2
and
E/CO2,
respectively). Gas analyzers were calibrated with standard gases before each
exercise test. The pneumotach used to measure ventilatory flow was also
calibrated before each test with a 3-liter calibration syringe. HR was
continuously monitored via telemetry (Polar Electric Oy, Sweden) interfaced
with the metabolic measuring system. Arterial oxygen saturation by pulse
oximetry (SpO2) was continuously monitored by an Ohmeda
5740 pulse oximeter by using a finger-tip sensor (subjects were instructed not
to grip with that finger). The pulse oximetry signal was acquired by an
REM/400M data-acquisition system (CB Sciences) and recorded every 15 s during
O2 measurements.
Ventilatory threshold
(Ethresh) was determined
graphically from the E,
E/O2,
and
E/CO2,
according to the method of Caiozzo et al.
(7).
Statistics
All variables were evaluated for normality by using the Kolmogorov-Smirnov
test against a standard normal distribution by using the Lilliefors two-tail
probability. The admixture variable was transformed by the natural logarithm
[log(e) admixture] to achieve normality for statistical testing
purposes. Differences between exercise response variables in Lima and Cerro de
Pasco were evaluated by paired Student's t-test, whereas differences
between subgroups established within the data set were evaluated by
t-test for independent samples. The general linear model procedure
from SYTAT version 5.1a (Macintosh) or version 9.0 (personal computer) was
used to construct univariate and multivariate models predicting the
O2 max (decrement)
from sea level to 4,338 m. Such analyses are potentially problematic because
change () depends on the initial or baseline value. Baseline, in this
regard, is the value achieved at sea level for a given measure. To address
this issue, we have adopted the statistical approach recommended by a number
of authors (21,
25). That is, when analyzing a
-dependent variable, the baseline value must be entered as a covariate,
despite the obvious mathematical relationship between dependent and
independent variables. Similarly, a baseline value must be entered as a
covariate (control) when analyzing a -independent variable, despite the
obvious mathematical collinearity between the baseline and -independent
variables. This procedure results in a nonbiased removal of baseline effects.
In the present context, it allowed the evaluation of the effect of admixture
on O2 max decrement,
independent of the level of O2
max achieved at sea level.
Our analytic strategy to detect genetic variance in
O2 max was as
follows. First, multivariate models were constructed that controlled for
baseline and other important covariate effects on
O2 max. Then,
genetic main effects were evaluated by the addition of the log(e)
admixture variable. Last, interaction effects between log(e)
admixture and relevant covariates were evaluated.
Values are expressed as means ± SD, unless otherwise indicated.
Statistical significance criteria was P 
0.05 for all tests.
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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Subject characteristics are given in
Table 1. Compared with US
National Health and Nutrition Examination Surveys reference standards, these
subjects fell below the 5th percentile for stature by age, and near the 15th
and 50th percentiles for weight by age and stature by weight, respectively. In
this respect, they are typical of both highland- and lowland-born healthy
Andean native men. Compared with a highland-born control population from Cerro
de Pasco, they were slightly taller and fatter and had 15% smaller FVC, as
expected (comparative data not shown). The average admixture rate was 10%
Spanish ancestry and ranged from <1 to 64%.
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Maximal exercise response data are given in
Table 2. These subjects were
well motivated to perform the exercise test, and all achieved the criteria for
a true O2 max during
exercise at sea level. Even in Cerro de Pasco, where exercise was subjectively
unpleasant, most subjects showed nonlinear increases in
E near the end of the
O2 max test, resulting in
a high RER, and a majority (21 out of 30 subjects) had maximal HR levels
within 10% of their age-predicted maximum.
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The O2 max decreased
from sea level to 4,338 m by 19% (P < 0.001). Maximal HR was
significantly lower at 4,338 m compared with sea level.
E BTPS was unchanged, but the
E BTPS/O2 was
significantly higher at 4,338 m. The RER was unchanged from sea level to 4,338
m. The mean Ethresh,
expressed in liters per minute of
O2, was significantly
lower at 4,338 m compared with sea level, i.e., 2.27 vs. 1.64 l/min
(P < 0.01).
Ethresh was also
significantly lower at altitude when expressed as a percentage of
O2 max, i.e., 74.1 vs.
66.1% of O2 max at sea
level and 4,338 m, respectively.
Table 3 gives the matrix of
correlation coefficients between variables that were used in subsequent
multivariate analyses to model the effect of genetic admixture on
O2 max. Variables
that were not significantly related to
O2 max, and thus
not included in multivariate analyses, included the fat-free mass and Hb
concentration. Sea level O2
max, sea level
Ethresh,
SpO2 measured at
O2 max
(SpO2 max), and
Ethresh were all
strongly correlated to
O2 max, i.e.,
correlation coefficients between 0.59 and 0.81. Figures
1 and
2 show the relationship between
O2 max and sea
level O2 max and
SpO2 max, respectively, as both relationships are
central to the multivariate analyses that follow.
Figure 1 reveals minimal
O2 max decrement for
individuals with <2.5 l/min sea level
O2 max, but substantially
larger decrements (25%) for individuals approaching 4.0 l/min sea level
O2 max. Similarly,
Fig. 2 reveals only minimal
O2 max decrements for
individuals with <10% decrease in SpO2 max and larger
O2 max decrements for
individuals decreasing SpO2 max by >20%.
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Table 4 gives results of
multivariate analyses modeling the decrease in
O2 max from sea level to
4,338 m. Model 1 (Table
4) is the simplest model and again demonstrates that the majority
of the variability in O2
max is explained by sea level
O2 max (
= 0.516,
R2 = 0.669, P < 0.001). Model building
continued with the addition of the SpO2 max
variable, controlling for the sea level value of SpO2
max (model 2, Table
4). This model reveals a significant independent association of
SpO2 max on
O2 max ( =
0.022, P = 0.033), increasing the R2 over
model 1 by 0.058. The addition of log(e) admixture
(model 3, Table 4)
reveals a significant main effect of log(e) admixture ( =
-0.205, P = 0.041) and a significant interaction of log(e)
admixture with SpO2 max ( = 0.014,
P = 0.018).
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The nature of this interaction is best understood by viewing plots that
show the O2 max as
a function of admixture and SpO2 max in various
study subgroups (Figs. 3 and
4). Subgroups were created by
splitting the data sample above and below the median of admixture and
SpO2 max. The
O2 max residuals
were used as the dependent variable in these plots from the regression of
O2 max on both sea
level O2 max and sea
level SpO2 max. This allows a nonbiased assessment of
the individual deviation from the overall group mean
O2 max, adjusting
for baseline (i.e., sea level) effects. Thus positive and negative values
specify individuals with larger and smaller than average decrements in
O2 max, respectively,
independent of the sea level O2
max and the baseline SpO2 max. In fact, control
for baseline SpO2 max (sea level SpO2
max) makes little difference in this regard, because sea level
saturation was uniformly high and showed little variation relative to the
SpO2 max observed in Cerro de Pasco. Nevertheless, as
described in MATERIALS AND METHODS, control for baseline is the
correct approach to view the effect of admixture or
SpO2 max on
O2 max.
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Figure 3 shows the
relationship between the O2
max residuals and log(e) admixture in subgroups with the
smallest (Fig. 3A) and
largest (Fig. 3B)
decreases in SpO2 max. Log(e) admixture was
not related to O2
max in the subgroup with the smallest SpO2
max (mean decrease 14.7 percentage points), but significantly correlated
to O2 max in the
subgroup with the largest SpO2 max (mean decrease
20.8 percentage points) (R = 0.557, P = 0.031). Whereas
subgroup analysis allows the visualization of this interaction effect, the
transformation of a continuous variable (SpO2
max) into a dichotomous variable results in a loss of information. Thus
the reader should refer back to model 3
(Table 2) when gauging the true
strength of association between admixture and
O2 max.
Alternately, the interaction between admixture and
SpO2 max may be seen in
Fig. 4, which shows the
relationship between the
O2 max residuals
and SpO2 max in subgroups with the lowest
(Fig. 4A) and highest
(Fig. 4B) levels of
genetic admixture. SpO2 max was not related to
O2 max in the
subgroup with the lowest genetic admixture (mean 1.1% European genetic
influence), but was highly significantly correlated to
O2 max in the
high-admixture subgroup (mean 18.4% European genetic influence) (R =
0.652, P = 0.008).
To address the potential for confounding or spurious correlation, it is
important to establish the general similarity between subgroups used in the
analyses above. Comparative data in Table
5 show no significant differences for potential confounding
variables between subgroups, with the exception of a significantly higher
relative O2 max (ml
· min-1 · kg-1) at sea level in the low-
vs. high-admixture subgroups. However, this difference does not explain (as a
positive confounder) the interaction described above. That is, model
3 (Table 4) explicitly
controls for sea level O2
max when testing for the main and interaction effects of admixture on
the O2 max decrement. A
model substituting relative O2
max for absolute O2
max yields the same qualitative result.
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Models 4 and 5 (Table
4) test for the effects of
Ethresh,
log(e) admixture, and the interaction of these two variables on
O2 max. Model
4 reveals a significant positive association between
O2 max and
Ethresh,
controlling for sea level values of both ( = 0.297, P = 0.006).
However, model 5 (Table
4) shows no main effect of log(e) admixture on
O2 max (P
= 0.319) and only a trend for interaction between log(e) admixture
and Ethresh
(P = 0.095). It is worth noting that the interpretation of this
interaction, if significant, would be the same as that described above between
admixture and SpO2 max. That is, the trend
suggests an effect of admixture that is evident only in individuals with a
large decrease in
Ethresh. Similarly, the
positive association between
O2 max and
Ethresh is only
evident in individuals with high Spanish admixture, but not evident in
individuals with low Spanish admixture.
Additional multivariate models were run, controlling for
SpO2 max and
Ethresh
simultaneously. These models do not change the basic results presented in
models 3 and 5 (Table
4). That is, the interaction between log(e) admixture
and SpO2 max remained significant after control
for Ethresh
( = 0.011, P = 0.037). Similarly, the interaction between
log(e) admixture and
Ethresh remained
nonsignificant after control for SpO2 max,
although the P value once again reached a relatively low level
( = 0.086, P = 0.114). A final model testing for the three-way
interaction among log(e) admixture,
Ethresh, and
SpO2 max revealed a marginally significant
interaction in this regard ( = 0.004, P = 0.06). Interpretation
of this interaction is similar to the interpretation given above for two-way
interaction. That is, the admixture effect is evident in individuals with
large decrements in SpO2 max and
Ethresh, but absent in
individuals showing only small decreases in SpO2 max and
Ethresh from sea level to
4,338 m.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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O2 max decrement in a
subset of subjects showing larger than average altitude-related decreases in
SpO2 max. In these subjects, high Spanish ancestry was
associated with larger
O2 max, consistent
with the general hypothesis that Quechua natives of the highland Andes are
adapted to high altitude based on population-specific genetic factors that
have arisen as a consequence of natural selection. The strength of this
evolutionary inference depends on the novel research design and on the choice
of O2 max as the
study-dependent variable. A repeated-measures design, which exploits
intragroup variability in genetic admixture, has clear advantages over the
traditional comparative approach i.e., Andean native vs. lowland
"control." Comparisons between groups may be confounded by many
factors, including unknown levels of admixture in the study populations
(4). Regarding the
O2 max, this
phenotype has long been considered an important marker of hypoxia sensitivity
or tolerance (2,
23,
47).
The study approach depends on the well-documented (and different)
population history of exposure to hypobaric hypoxia between Quechua and
Spanish. In essence, the admixture estimate is a construct describing the
proportionate contribution of alternate population histories to the genetic
makeup of an individual. This is an important point because the association
between individual admixture and
O2 max is to be
interpreted relative to population history, not relative to the specific
genetic markers that were used to derive the estimates. In other words, for
the approach to work, it is not necessary that there be a direct linkage
between the genetic markers and the
O2 max phenotype,
especially as the markers used represent only a small fraction of the total
genome. Our laboratory has previously reported that admixture can show a
significant correlation with specific phenotypes, even if the markers
informative for admixture are not physically linked to the trait in question
(36,
43). The process of admixture
does produce allelic associations between unlinked and linked loci, as a
function of the level of frequency differences between the parental
populations and admixture rate
(8). However, it is important
to note that, several generations after the admixture event, the association
between linked markers will be much higher than the background association
between unlinked markers. This could be exploited to map the genetic factors
that contribute to hypoxia tolerance in the Quechua population. Presently, the
genes involved are unknown.
The Association Between Admixture and
O2
max
By itself, genetic admixture level was not related to variability in
O2 max. However,
after control for both sea level
O2 max and
SpO2 max, strong associations between genetic
admixture and O2
max were revealed. Covariate control is an important step because, in
this and previous studies (12,
17,
28,
30,
32,
41,
42,
44), the majority of the
variability in the O2
max was explained by the sea level
O2 max. This effect is
impressive as aerobically fit subjects typically lose two to four times more
of their sea level O2 max
and show decrements in aerobic performance at even modest altitudes, i.e.,
<600 m (17). Similarly,
this and other studies (12,
32,
41,
42,
44) demonstrate that much of
the variability in the
O2 max is
explained by the SpO2 max. Sea level
O2 max and
SpO2 max together explained nearly 73% of the
variability in the O2
max within our study sample. Of the remaining variability, another 7%
was explained by genetic factors assessed by the admixture variable
(P = 0.041) and an admixture-by-SpO2 max
interaction effect (P = 0.018, model 3,
Table 4). Thus the effect of
admixture on O2
max depends on the magnitude of SpO2 max.
Admixture effects were large between subjects with large
SpO2 max (Fig.
3B), but not detectable between individuals who showed
only modest decreases in SpO2 max
(Fig. 3A). In the
subset of individuals showing larger than average SpO2
max, adjusted mean values for
O2 max (ml/min)
were 18% larger in the highest vs. lowest subgroups of Spanish ancestry, i.e.,
least squares mean values, adjusted for sea level
O2 max, were 739 and
606 ml/min, respectively.
One interpretation of this finding is that genetic effects are more
important (i.e., easier to detect) across the range of admixture when subjects
are aerobically fit. This makes sense because aerobically fit individuals are,
a priori, more hypoxia sensitive, given their larger
O2 max (see
references cited above). In addition, athletes vs. nonathletes show a wider
alveolar-arterial partial pressure difference during exercise and show
impairments in gas exchange, even in normoxia
(9,
40). These differences suggest
a pulmonary limitation to exercise in athletes via ventilation-perfusion
mismatch and/or diffusion limitation, both of which increase as problems with
increasing exercise intensity and/or hypoxia
(15,
19). Thus better pulmonary
function in Quechua may be the basis for part of the admixture effect
described, particularly as Andean natives are characterized by large lungs
(6,
13,
18,
24) and high-pulmonary
diffusion capacities (39,
46). One previous study is
consistent with this hypothesis, showing higher arterial oxygen saturation
during exercise in Andean natives compared with a highlandborn control group
of lowland ancestry (5).
This interpretation is further supported by the
Ethresh data. In this and
previous studies (28,
41),
Ethresh explained
a large proportion of the variability in
O2 max. We also
noted a trend for interaction
(admixture-by-Ethresh)
that paralleled the admixture-by-SpO2 max
interaction (P = 0.095). That is, the admixture effect tended to be
evident only in individuals who showed a large decrease in
Ethresh. This may be a
reflection of pulmonary limitation and a decrease in arterial O2
saturation in the most fit subjects, as experimental studies demonstrate an
increase in blood lactate concentration when arterial saturation is lowered
(27). In fact,
Ethresh and
SpO2 max were strongly correlated (R =
0.522, P < 0.01, Table
3).
Alternately, the relationship between
O2 max and
SpO2 max may also be seen to depend on the
genetic admixture level (Fig.
4). Theoretically,
O2 max and
SpO2 max should show a positive linear
relationship (as in Fig.
4B) because of the shape of the Hb-O2
equilibrium curve (OEC). This has been demonstrated by Ferretti et al.
(12), who show nonlinear
relationships between the
O2 max and the
fractional concentration of inspired oxygen from 0.1 (hypoxia) to 0.3
(hyperoxia) and also between the arterial saturation measured at
O2 max and fractional
concentration of inspired oxygen. The nonlinearity in both relationships
mirrors the OEC, and, consequently, a plot of
O2 max vs.
arterial saturation (or SpO2 max) yields a linear
plot. Interestingly, both aerobically fit and sedentary subjects fall on the
same line (12), although
aerobically fit subjects have both larger
O2 max and
SpO2 max. This suggests that athletes vs.
nonathletes operate on the steep part of the OEC at maximal exercise and, as a
consequence, have little margin to increase oxygen flow conductance when
oxygen levels decrease (12,
17,
30,
44). The absence of a positive
relationship between O2
max and SpO2 max in low-admixture subjects
is thus unexpected (Fig.
4A).
In Quechua, it may be that variability in
O2 max is not
dependent on the OEC or that arterial desaturation is not a major factor
driving the O2
max. If so, then other factors independent of O2 content may
be implicated, including the cardiac output, the peripheral O2
diffusion (capillary to mitochondria), or the mitochondrial oxidative capacity
itself. However, this study was not designed to address these specific
possibilities. Also, O2
max has been described as an integrated functional response variable,
not defined (or limited) by a single factor, but rather set by the interaction
of multiple O2 transport conductances in the lungs, circulatory
system, and skeletal muscle
(48). In this sense, it might
be more realistic to consider the possibility that genetic adaptation in
Quechua is the result of an integrated evolutionary response involving
multiple interacting components of the O2 transport chain.
Limitations of the Present Study
The estimates of individual admixture were based on 22 genetic markers. These markers were selected to estimate admixture because they differed greatly in the frequency of specific alleles between Native American and European populations. They have been validated by genotyping diverse samples of Native American populations (Mayan, Southwestern Native Americans, Native Mexican, and Aymara) and European populations (Europe, Germany, and European Americans). Although this panel of markers is sufficient to obtain a precise estimate of group mean admixture (typically, with a standard error <3%), the application of these markers does not provide as precise an estimate of admixture at the individual level. The issue of quantifying the exact error around these estimates is a complicated one, and standard errors are not constant across the range of admixture. For example, standard errors are larger for individuals with high Spanish admixture. We estimate that, to obtain a precision of <3% in the individual admixture estimates, at least 80–100 ancestry-informative markers would be required. Thus it is important to mention that the estimates of individual admixture for each person show a wide confidence interval. A partial solution to this problem is to ensure a large sample size with a wide range of admixture across the study sample, as we have done here.
One weakness of this study is that arterial oxygen saturation was measured via pulse oximetry rather than from blood sampled during exercise. Fortunately, the study partially compensates for error in the SpO2 measure via a relatively large sample size. A large sample ensures accurate group mean values, assuming that there is no bias in the measurement. While a number of studies demonstrate the general validity and accuracy of SpO2 measures at rest and during exercise vs. blood-gas measures (20, 33, 41, 51), some studies suggest bias, especially during maximal exercise (37, 52). This external bias, or the idea that the "true" saturation cannot be measured via pulse oximetry at maximal exercise, is a minor problem in the present context. A larger potential problem would be the presence of internal bias, or the idea that measurement validity depends on conditions internal to the study. For example, it would be problematic if SpO2 validity depended on altitude or fitness level. There is no indication that this is the case, and, moreover, the repeated-measures approach minimizes some of the potential problems in this regard. Nevertheless, blood-gas studies in Andean natives will be necessary to confirm the results of this study and to further explore the specific mechanisms that explain hypoxia tolerance in Quechua natives.
Summary
The results of this study are consistent with the hypothesis that Quechua
are genetically adapted to hypobaric hypoxia. We have employed a novel
research strategy that is based on estimating the individual genetic admixture
level within a specific study group using a panel of ancestry-informative
genetic markers. We demonstrate an association between the extent of Spanish
ancestry and an important functional phenotype
(O2 max) that may
be considered to define hypoxia tolerance. Whereas all comparative studies
have some inherent problems that limit evolutionary inference
(4,
16), the admixture approach
applied here holds great promise. This is particularly true because the
library of ancestry-informative genetic markers is expected to grow in the
future, leading to increased precision in admixture estimation.
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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O2 max and
O2 max decrement during
exposure to acute hypoxia. J Appl Physiol
64: 1486-1492,
1988.
O2 max of trained and
untrained subjects. J Sports Sci
11: 37-42,
1993.
