Retrospective self-reported weight changes during childhood and adolescence are not good predictors of metabolic syndrome risk factors in Mexican young adults

doi:10.4236/ojpm.2013.38064

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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: [email protected]

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

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

F. C. D. Andrade et al. / Open Journal of Preventive Medicine 3 (2013) 479-486

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%

F. C. D. Andrade et al. / Open Journal of Preventive Medicine 3 (2013) 479-486

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

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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