Vol.3, No.8, 479-486 (2013) Open Journal of Preventiv e Me dic ine
http://dx.doi.org/10.4236/ojpm.2013.38064
Retrospective self-reported weight changes during
childhood and adolescence are not good predictors
of metabolic syndrome risk factors
in Mexican young adults
Flávia C. D. Andrade1*, Michelle Jiménez1, Marcela Raffaelli2, Margarita Teran-García3,
Celia Aradillas-García4
1Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, USA;
*Corresponding Author:
2Department of Human and Community Development, University of Illinois at Urbana-Champaign, Urbana, USA
3Department of Food Sciences and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, USA
4Faculty of Medicine, University Autonomous of San Luis Potosí, San Luis Potosí, Mexico
Received 24 September 2013; revised 18 October 2013; accepted 27 October 2013
Copyright © 2013 Flávia C. D. Andrade et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The purpose of the study was to examine whether
retrospective self-reported weight changes dur-
ing childhood and adolescence were associated
with metabolic syndrome (MetS) risk factors in
Mexican young adults. Mexican college appli-
cants to the Universidad Autónoma de San Luis
Potosí, Mexico, 18 to 2 5 y ears old (n = 4187 ) who
had applied for the 2009 academic year were
included in the study. Participants underwent a
health screening—anthropometrics and blood
drawn—and completed a questionnaire. Five
major weight change categories were defined
based on self-reported weight during childhood
and adolescence: consistently normal, consis-
tently underweight, consistently overweight/
obese, weight gain, and weight loss. Most par-
ticipants self-reported being normal weight dur-
ing childhood (58.7%) and adolescence (58.3%).
Only a small proportion reported being over-
weight or obese during childhood (10.1%) or
adolescence (15.9%). Weight change patterns
during childhood and adolescence were marked
by overall stability: 40.1% of participants were
consistently normal, 15.6% underweight and
3.6% overweight/obese. Among those whose
weight changed, 25.0% gained weight and 15.7%
lost weight. In regression analyses, weight
change categories based on self-repo rted weight
statuses during childhood and adolescence
were not associated with current metabolic syn-
drome risk factors af ter controlling for measured
current BMI. Studies addressing the association
between weight gains in early life with met abolic
syndrome outcomes in early adulthood should
not rely on recalled weight status during early
life alone.
Keywords: Weight Change; Metaboli c Syndrome;
Metabolic Risk Factors’ Mexican Youn g Adults
1. INTRODUCTION
Obesity and excessive weight gain in early life repre-
sent major risk factors for metabolic syndrome (MetS) in
early adulthood [1-7]. In Mexico, as in other developing
countries, the prevalence of MetS is increasing due to the
rising prevalence of obesity [8,9]. Rates of MetS in Latin
America now equal those in the developed world, in-
creasing the burden of MetS-related diseases [10]. In
2006, prevalence of MetS in Mexico reached 22.2%
among men and 16.5% among women aged 25 to 34
years according to the National Cholesterol Education
Program Adult Treatment Panel III (NCEP-ATP III)
definition [11]. Although prevalence of MetS among
Mexican young adults is lower in comparison with their
older counterparts, it is likely to increase as they age.
The early identification of metabolic risk related to
weight gain and obesity represents an urgent strategy to
prevent premature deaths and disability due to chronic
diseases, as well as to reduce the economic burden [1,
12].
Given the association between excessive weight gain

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480
during childhood and adolescence on the risk of devel-
oping MetS, having a record of measured weight during
childhood and adolescence might allow early identifica-
tion of individuals at risk of metabolic abnormalities be-
fore the onset of chronic diseases. Some scholars have
argued that past weight may even be more important for
predicting chronic disease risk factors and development
than current weight [13-15]. However, past records of
weight status are not always available, particularly in
developing countries where longitudinal records of
weight are incomplete or inexistent due to the limited
access to medical care and difficulties and costs associ-
ated with longitudinal data collection necessary to track
weight change over time. Therefore, alternative options
such as the use of self-reported retrospective information
on weight status have been used in large scale studies.
For instance, previous studies have assessed the accuracy
of self-reported weight among Mexican Adolescents, and
also self-reported body mass index (BMI) and body per-
ception among Mexican adults; their findings suggested
that self-reported weight among these populations are
highly correlated with the measured BMI and thus may
be valid to estimate weight in epidemiological studies
[16,17].
Although several studies have found self-reported data
to be fairly accurate and useful for epidemiological pur-
poses, other studies using self-reported weight status has
shown controversial results. In a previous study, we
found that most (63%) college applicants in Mexico ac-
curately reported their current weight status categories,
with reporting accuracy lower among overweight and
obese participants [18]. A review of the literature showed
that for the most part, current weight tends to be under-
estimated and current height overestimated, with impor-
tant differences between men and women [19]. However
less is known about the validity and usefulness of re-
called weight, height, body mass index (BMI) or weight
status reports. Even less is known about their usefulness
in screening patients for MetS risks.
Despite the several limitations with recalled weight
status during childhood and adolescence, these may be
the only information that could be easily available to
assess weight change during childhood and adolescence
in some countries. Understanding such limitations is im-
portant when trying to access the potential usefulness of
self-reported weight status in early life among young
adults. For instance, some limitations related to retro-
spective data collection on self-reported weight status is
that participants may not recall past information correctly
and responses may be influenced for desirability (re-
sponse) bias [19]. However, other research has shown
that the accuracy of the recalled weight status can be
influenced by the elapsed time between the event and the
data collection [20,21]. In fact, many studies have fo-
cused on middle-aged or older adult populations with
long recall periods (see Bayomi et al., 2008 for a review)
[20]. It can be suggested that young adults may be more
likely to recall accurately information as they elapse pe-
riod is shorter. These studies have found that accuracy of
recalled data is also influenced by many factors such as
gender, race, current BMI and weight gain over the years
[22]. There are very few studies focusing on younger
adults and more recent recall periods. For example, Jen-
kins and colleagues developed an instrument to collect
information on individuals aged 26 - 29 years old about
their weight and height at ages 13 and 18 [13]. Results
indicated that recalled weight and height used to calcu-
late BMI underestimated their measured BMI values at
those ages, suggesting an instrument with moderate sen-
sitivity, but high specificity for obesity [13]. In general,
previous studies examining recalled anthropometric
measures have found that despite the limitations, recalled
information may be useful; but its use requires some
caution in the interpretation.
Given the high rates of obesity and MetS among
Mexican young adults, and given the limited access to
longitudinal data on their weight statues during child-
hood and adolescence is appropriate to explore the use-
fulness of recalled weight status to generate information
about weight change at early ages in order to early iden-
tify people at risk of developing chronic diseases in late
adulthood. Therefore, this study examined the associa-
tion between retrospective self-reported weight change
during childhood and adolescence, and current metabolic
risk factors among Mexican young adults. In addition,
we assessed whether these weight changes are associated
with current metabolic risk factors independent of cur-
rent BMI. This is because some of the effects of weight
gain during early life on metabolic syndrome factors may
be mediated through adult BMI [3]. In fact, weight and
BMI during childhood are associated with young adult
weight and BMI [4].
2. MATERIAL AND METHODS
2.1. Participants
We drew on data from a large-scale program of re-
search being conducted in collaboration between the
Universidad Autónoma de San Luis Potosí (UASLP) and
the University of Illinois at Urbana-Champaign. All par-
ticipants in this cross-sectional study were applicants to
UASLP for the 2009 academic year. In 2009, 9981 indi-
viduals ages 16 to 54 applied to UASLP. These indi-
viduals were invited to participate in a health screen at
the UASLP clinic. There were 9791 applicants who were
screened by trained health care professionals at the
UASLP clinic after an overnight fast. The health screen-
ing included anthropometric measurements and a blood

F. C. D. Andrade et al. / Open Journal of Preventive Medicine 3 (2013) 479-486 481
draw. After that, participants were invited to complete a
self-report questionnaire in Spanish about socioeconomic
and health conditions. A total of 7434 individuals com-
pleted the questionnaire; out of those 5858 were aged 18
- 25. For this study, we selected the 4187 participants
who were aged 18 - 25 who had complete information on
anthropometric measurements—height, weight, BMI,
waist circumference (WC), systolic blood pressure (SBP),
and diastolic blood pressure (DBP) and fasting glucose
(FG)—, self-reports of weight at childhood and adoles-
cence, and sociodemographic controls. Funding limita-
tions precluded lipids testing for all study respondents;
therefore, a subset of 353 participants was randomly se-
lected from the larger sample for high-density lipoprotein
cholesterol (HDL-C) and triglycerides (TG) testing. Fol-
lowing procedures approved by Institutional Review
Boards at both collaborating institutions, applicants who
chose to allow their data to be used for research purposes
provided written informed consent.
2.2. Variables
Sociodemographic information included age, gender,
smoking, drinking, and income. Age was calculated us-
ing the date of birth and date of questionnaire completion.
Gender was coded as 0 = male and 1 = female. Smoking
categories were: never smoked, current smoker, and pre-
vious smoker. Drinking was coded as never, current
drinker, previous drinker. Family monthly income was
categorized as (in thousands of pesos): less than $10, $10
- $14, $15 - $19, $20 - $24, $25 - $49, $50 or more, or
don’t know.
Weight change categories were defined based on par-
ticipants self-reported their weight status during child-
hood (5 to 10 years old) and adolescence. The response
categories were: very low weight, low weight, average
weight, overweight, or obese. Responses were recoded
into three categories: underweight (very low and low),
normal (average), and overweight/obese. Table 1 shows
the six weight change categories which were defined
from self-reported body weight statuses during childhood
and adolescence: 1) consistently normal, the reference
group, defined as self-reported normal weight during
childhood and adolescence; 2) consistently underweight,
defined as underweight during childhood and adoles-
cence; 3) consistently overweight/obese, defined as over-
weight or obese during childhood and adolescence; 4)
weight gain in early life, and 5) weight losses.
Anthropometric measurements included weight and
height, and were measured with bare feet and light
clothing. Weight was measured in the upright position to
the nearest 0.1 kg using a calibrated scale (Torino, Tecno
Lógica, Mexicana, Mexico). Height was measured using
a fix stadiometer and recorded to the nearest 0.5 cm.
BMI was calculated as kg/m2. WC was measured stand-
ing, immediately above the iliac crest and at the end of
normal expiration, using a flexible, non-stretching nylon
tape with accuracy to the nearest 0.1 cm.
Blood pressure (BP) was measured according to a
common protocol adapted from American Heart Asso-
ciation-recommended procedures. BP was taken on the
dominant arm in the seated position using appropriately
sized Welch Allyn cuffs.
Blood biomarkers included fasting blood glucose and
lipids profile. Fasting blood glucose was determined ac-
cording to the method of glucose oxidase peroxidase
GOD-PAP (Alcyon 300 autoanalyzer from Abbott, re-
agents from Biosystems). A subsample of participants
had serum lipid profiles carried out by an automatic ana-
lyzer for diagnostic use in vitro (Alcyon 300 autoana-
lyzer from Abbott, reagents from Biosystems). Results
are expressed in mg/dl. Serum triglycerides were deter-
mined according to the glycerol phosphate oxidase per-
oxidase method, based on a colorimetric enzymatic reac-
tion. HDL-C was determined by a direct method in which
Table 1. Definition of weight change patterns during childhood and adolescence.
Weight change patterns Self-reported weight during
childhood
Self-reported weight during
adolescence Percentage in the dataset
Consistently normal (CN) Normal Normal 40.10%
Consistently underweight (CUW) Underweight Underweight 15.64%
Consistently overweight/obese (COO) Overweight/obese Overweight/obese 3.56%
Weight gain in early ages (WG) Underweight Normal 12.59%
Underweight Overweight/obese 2.94%
Normal Overweight/obese 9.43%
Weight losses (WL) Normal Underweight 9.17%
Overweight Normal 5.59%
Overweight Underweight 0.98%

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482
a detergent solubilized the HDL-C, which was then
quantified spectrophotometrically according to the cho-
lesterol oxidase method.
Risk factors for MetS entitled elevated SPB and DBP,
WC, FG, and TG, but reduced levels of HDL-C as de-
fined by the ATP III criteria for MetS [23,24].
2.3. Statistical Analysis
Data analysis was performed using STATA S.E. 12.
All variables were assessed for normality, but results
showed no relevant variations. Descriptive statistics were
examined. Differences were assessed using Student’s
t-test for continuous variables. Regression analysis was
used to assess the association between weight change
categories as independent variable and each MetS risk
factor as an individual outcome. Analyses were per-
formed separately by gender. Model 1 included age, in-
come levels, smoking, and drinking behaviors as controls.
Model 2 included all variables from Model 1 and added
current BMI.
3. RESULTS
3.1. Descriptive Statistics
Females represented 51.5% of the sample. The mean
age was 19.0 years for males (95% CI 18.4 - 19.1) and
18.8 for females (95% CI 18.7 - 18.9). Although the dif-
ference was small, mean age was significantly different
by gender (p < 0.001). Most participants (58.7%) self-
reported being normal weight during childhood, 31.2%
underweight and 10.1% overweight or obese. At adoles-
cence, 58.3% self-reported being of normal weight,
25.8% underweight and 15.9% overweight or obese.
Turning to the description of weight change patterns
during childhood and adolescence, 40.1% of the parti-
cipants were consistently normal, 15.6% remained con-
sistently underweight and 3.6% were consistently over-
weight/obese. Among those who changed weight, 25.0%
had gained weight and 15.7% lost weight (Table 1).
The distribution of metabolic risk factors is displayed
in Table 2 for descriptive purposes. All MetS risk factors
were within normal levels, but the overall pattern shows
that males tended to be at higher metabolic risk than fe-
males. As shown in Ta ble 2, mean levels of SPB and
DBP were higher for males than females (p < 0.001).
Compared to females, males presented higher mean WC
and FG, and TG (p < 0.001) and lower HDL-C (p <
0.001) (Table 2).
3.2. Associations between Self-Reported
Weight Change and Current Metabolic
Risk Factors
Regression coefficients for the associations between
Table 2. Characteristics of MetS risk factors in Mexican young
adults, 20091.
Total Males Females
MetS risk
factors (n = 4187) (n = 2030) (n = 2157)
SBP (mmHg) 109.8 ± 10.5113.6 ± 10.1 106.2 ± 9.7
DBP (mmHg) 71.6 ± 8.4 73.9 ± 8.1 69.5 ± 8.2
WC (cm) 79.7 ± 11.6 82.5 ± 11.6 77.2 ± 10.9
FG (mg/dl) 84.8 ± 7.7 86.3 ± 7.7 83.5 ± 7.4
(n = 353) (n = 143) (n = 210)
HDL-C (mg/dl)47.4 ± 11.1 45.8 ± 11.1 48.5 ± 11.0
TG (mg/dl) 107.9 ± 50.8118.3 ± 57.5 100.8 ± 44.4
1Mean ± SD; MetS = metabolic syndrome, SBP = systolic blood pressure,
DBP = diastolic blood pressure, WC = waist circumference, FG = fasting
glucose, HDL-C = high density lipoprotein cholesterol, TG = triglycerides.
weight change categories and MetS risk factors are dis-
played in Table 3. Results for Model 1 indicate that
weight change patterns at younger ages were predictive
of SBP, DBP and WC for males and females, but not FG,
HDL or TG. However, almost all coefficients dropped to
non-significance when current BMI was added in Model
2.
4. DISCUSSION
Longitudinal records of weight change are rare in de-
veloping countries primarily due to the lack of complete
vital registration systems and limited access to stable
medical care, which could allow for the collection of
information at birth and during early stages in life [25,
26]. In addition, there are very few longitudinal studies
in developing countries given the time commitment and
high costs [26]. Therefore, we tested whether self-re-
ported weight status categories during childhood and
adolescence were useful in predicting metabolic risk
factors among Mexican young adults. There is evidence
of the validity of self-reported weight and body percep-
tion to estimate current weight in among Mexican ado-
lescents and adults [16,17]. However, we found little
evidence that weight change patterns based on recalled
weight status during childhood and adolescence contrib-
uted to the prediction of metabolic risk at young adult-
hood. We further explored the predictive value of the
recalled reports of body status at childhood and adoles-
cence on metabolic risk factors when these measures
were not combined into weight changes (data not shown).
According to this analysis, after controlling for current
BMI, weight statuses at childhood and adolescence did
not have a significant effect on metabolic risk factors in
this cohort (p > 0.05). Our findings indicate that the col-
lection of measured current BMI in these contexts could

F. C. D. Andrade et al. / Open Journal of Preventive Medicine 3 (2013) 479-486 483
Table 3. Regression coefficients assessing the association between weight change patterns and each individual component of the
MetS in Mexican young adults, 20091.
Males Females
Model 1 Model 2 Model 1 Model 2
MetS
risk
factors Coef. 95% CI Coef. 95% CI Coef.95% CI Coef. 95% CI
SBP (mmHg)
CUW −1.46 (−2.70 to −0.22) * 0.43 (−0.79, 1.64) −2.08 (−3.25 to −0.92) *** −0.20 (−1.30 to 0.91)
COO 3.47 (0.87 to 6.07) ** −0.71 (−3.05 to 1.63) 4.18 (1.36 to 6.99) ** −0.01 (−2.49 to 2.47)
WG 2.43 (1.24 to 3.63) *** −0.39 (−1.50 to 0.73) 1.94 (0.90 to 2.98) *** −0.08 (−1.09 to 0.93)
WL −1.49 (−2.63 to −0.35) * −0.79 (−1.86 to 0.29) −1.19 (−2.40 to 0.02) −0.08 (−1.27 to 1.10)
BMI 0.92 (0.79 to 1.04) *** 0.84 (0.73 to 0.95) ***
R2 4.0 17.0 3.0 16.0
DBP (mmHg)
CUW −0.49 (−1.49 to 0.50) 0.85 (−0.13 to 1.83) −0.85 (−1.84 to 0.13) 0.48 (−0.50 to 1.47)
COO 2.19 (0.33 to 4.05) * −0.79 (−2.56 to 0.99) 4.26 (1.84 to 6.68) *** 1.29 (−0.90 to 3.48)
WG 1.90 (0.95 to 2.84) *** −0.11 (−1.01 to 0.80) 1.57 (0.70 to 2.44) *** 0.13 (−0.73 to 0.99)
WL −0.24 (−1.22 to 0.73) 0.26 (−0.66 to 1.19) 0.02 (−1.03 to 1.08) 0.81 (−0.21 to 1.83)
BMI 0.65 (0.56 to 0.74) *** 0.60 (0.52 to 0.68) ***
R2 4.0 15.0 2.0 11.0
WC (cm)
CUW −4.72 (−5.76 to −3.69) *** −0.15 (−0.92 to 0.61) −4.42 (−5.55 to −3.30) *** −0.15 (−0.87 to 0.58)
COO 10.78 (7.79 to 13.76) *** 0.67 (−0.66 to 2.00) 12.85(9.80 to 15.89)*** 3.36 (1.65 to 5.07) ***
WG 7.06 (5.70 to 8.43) *** 0.25 (−0.41 to 0.91) 4.87 (3.70 to 6.04) *** 0.29 (−0.42 to 0.99)
WL −0.88 (−2.17 to 0.41) 0.83 (0.09 to 1.56) * −2.62 (−3.84 to −1.40) *** −0.11 (−1.01 to 0.79)
BMI 2.22 (2.12 to 2.31) *** 1.91 (1.81 to 2.01) ***
R2 18.0 78.0 14.0 66.0
FG (mg/dl)
CUW −0.26 (−1.26 to 0.75) 0.13 (−0.89 to 1.15) −0.26 (−1.16 to 0.63) 0.17 (−0.74 to 1.09)
COO −0.34 (−1.89 to 1.21) −1.20 (−2.78 to 0.39) −0.67 (−2.40 to 1.07) −1.64 (−3.37 to 0.09)
WG 0.64 (−0.25 to 1.53) 0.07 (−0.89 to 1.02) 0.38 (−0.46 to 1.22) −0.09 (−0.92 to 0.74)
WL −0.46 (−1.37 to 0.46) −0.31 (−1.22 to 0.60) −0.29 (−1.17 to 0.60) −0.03 (−0.91 to 0.85)
BMI 0.19 (0.10 to 0.28) *** 0.20 (0.11 to 0.28) ***
R2 2.0 3.0 3.0 4.0
HDL (mg/dl)
CUW 2.14 (−3.53 to 7.82) 1.7 (−3.98 to 7.38) −1.78 (−6.33 to 2.77) −2.72 (−7.20 to 1.75)
COO 8.03 (−1.02 to 17.08) 8.8 (0.43 to 17.16)* 0.36 (−6.18 to 6.90) 3.52 (−2.81 to 9.84)
WG −4.06 (−8.91 to 0.79) −2.76 (−8.03 to 2.51) −1.5 (−5.20 to 2.20) −0.15 (−3.98 to 3.68)
WL 3.08 (−3.45 to 9.61) 3.08 (−3.42 to 9.59) 6.39 (−0.15 to 12.93) 5.62 (−0.91 to 12.14)
BMI −0.32 (−0.84 to 0.20) −0.45 (−0.75 to −0.16) **

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484
Continued
R2 12.0 13.0 8.0 12.0
TG (mg/dl)
CUW −8.45 (−39.30 to 22.39) −4.36 (−34.94 to 26.22) −6.33 (−29.14 to 16.47) −1.19 (−23.69 to 21.31)
COO −24.4 (−53.71 to 4.86) −31.5 (−63.53 to 0.59) 12.31(−19.65 to 44.26) −4.88 (−39.37 to 29.61)
WG 21.56 (−2.29 to 45.41) 9.6 (−15.02 to 34.23) 2.22 (−12.00 to 16.45) −5.13 (−19.64 to 9.37)
WL −24.3 (−47.81 to −0.70) * −24.3 (−48.20 to −0.31) * −12.8(−27.18 to 1.59) −8.58 (−22.58 to 5.43)
BMI 2.93 (0.71 to 5.14) ** 2.48 (0.91 to 4.05) **
R2 12.0 15.0 10.0 16.0
1All analyses controlled for age, income levels, smoking, and drinking. 2Comparison with Consistently Normal category (reference group): *p < 0.05, **p < 0.01,
***p < 0.001. MetS = metabolic syndrome, SBP = systolic blood pressure, CUW = consistently underweight, COO = consistently overweight or obese, WG =
weight gain, WL = weight loss, BMI = body mass index, DBP = diastolic blood pressure, WC = waist circumference, FG = fasting glucose, HDL = high density
lipoprotein cholesterol, TG = triglycerides, Coef. = regression coefficient, 95% CI = 95% confidence interval. R2 = coefficient of determination.
be a better way to assess metabolic risk.
It is possible that young adults may not be completely
able to recall their weight statuses even with a short re-
called period. In our study, 10.1% of the participants
reported being overweight or obese during childhood;
however this percentage is lower than the 18.4% found
for Mexican children ages 5 - 11 using data from 1999
National Health and Nutrition Survey (ENSANUT 1999)
[27]. At adolescence, 15.9% of our participants reported
being overweight or obese, but this percentage is also
lower than the reports of 24.8% and 26.4% of excess
weight for boys and girls, respectively, aged 10 - 17
based on national estimates [28]. Some studies have
found that recall is better for shorter periods of time (e.g.
10 years) [22], but it is possible that recall of anthropom-
etric measurements for young ages may be problematic.
We used a self-reported measure of weight status to
assess body size at younger ages. Previous studies have
found moderate to low accuracy on reports of anthro-
pometric measures for older populations even when dif-
ferent methodologies, such as body images, were used to
collect the data [20,29]. A study comparing self-reports
of weights and heights with the use of body silhouettes
showed that body silhouettes were less precise for
women [30]. Further studies should address whether
other forms of measurement could yield measures that
are more precisely capture body size and body weight
changes at younger ages.
The findings need to be considered in light of several
limitations. The main limitation is the lack of data to
validate self-reports of weight status at younger ages.
There is evidence that recalled weight status may under-
estimate the prevalence of obesity [20,29], but we are not
able to test this possibility in our sample, given the lack
of past measured data. Second, even though the sample
was diverse in terms of parental education and income,
the respondents were college applicants in a single
Mexican state and therefore findings may not generalize
to all Mexican young adults. However, findings may be
useful for targeted intervention among this specific
population.
In conclusion, our study indicates that self-reported
weight changes during childhood and adolescence are
not predictive of metabolic risk beyond what can be ob-
tained with measured current BMI. Future studies should
examine whether other retrospective measures of self-
reported weight status are better associated with current
(and future) metabolic abnormalities, particularly in set-
tings where longitudinal data on weight changes are lim-
ited.
5. ACKNOWLEDGEMENTS
Up Amigos acknowledges the contributions of research staff and
study participants. Funding was provided by grants from the UASLP
Hormones Laboratory at the School of Medicine, Clinical Biochemistry
Laboratory at the Chemical Sciences School, and the University Health
Center under agreement support C09-PIFI-030606 (to C. Aradillas-
Garcia); the University of Illinois at Urbana-Champaign Research
Board (#09070) and Center on Health, Aging, and Disability (to F.
Andrade); and the USDA National Institute of Food and Agriculture,
Hatch Projects #600108-793000-793323 (to M. Raffaelli) and
#600109-698000-698354 (to M. Terán-Garcia).
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