Accuracy of Fetal Growth Indicators as Surrogate Measures of Steroid Hormone Levels during Pregnancy

Jennifer David Peck1,, Barbara S. Hulka1, David A. Savitz1, Donna Baird2, Charles Poole1 and Barbara E. Richardson3

1 Department of Epidemiology, University of North Carolina School of Public Health, Chapel Hill, NC.
2 Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC.
3 Department of Veterinary Anatomy and Public Health, Division of Public Health, Texas A&M University, College Station, TX.

Received for publication August 29, 2001; accepted for publication August 9, 2002.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
This study evaluates the use of fetal growth characteristics as surrogate measures for steroid hormone exposures during pregnancy. The validity of using birth weight, birth length, ponderal index, and placental weight as indicators of pregnancy hormone exposures was assessed using third-trimester serum samples from 568 pregnant women who participated in the Child Health and Development Studies, Berkeley, California (1959–1966). The magnitude of the associations between birth characteristics and hormone concentrations was assessed using geometric means, Pearson’s correlations, and linear and logistic regression. Accuracy was evaluated using sensitivity, specificity, and receiver operating characteristic curve analyses. The strongest and most consistent association observed was between birth weight and estriol levels. Despite a positive correlation (r = 0.32) and strong associations with high estriol levels (odds ratio for highest compared with lowest birth weight quartile = 6.63, 95% confidence interval: 3.20, 12.5), the predictive performance of birth weight as a proxy for estriol levels was poor (area under the receiver operating characteristic curve = 0.66, 95% confidence interval: 0.61, 0.71). Likewise, all fetal growth measures revealed little discriminatory ability as indicators of estriol, estrone, estradiol, or progesterone levels. Thus, observed associations with these surrogate measures may not be a reflection of pregnancy hormone exposure and should be interpreted with caution.

biological markers; birth weight; growth; hormones; placenta; pregnancy

Abbreviations: Abbreviations: CHDS, Child Health and Development Studies; ROC, receiver operating characteristic.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
There is growing interest in pregnancy hormone concentrations as potential determinants of breast cancer in the mother (114) and her offspring (1520). However, few opportunities exist to evaluate this association directly because pregnancy typically occurs decades before a breast cancer diagnosis. Studies evaluating maternal and fetal exposure to pregnancy hormones and subsequent risk of breast cancer have used fetal growth indicators such as birth weight, birth length, ponderal index, and placental weight as substitutes for pregnancy hormone exposure (12, 13, 17, 18, 20, 21). However, the accuracy of these indices as surrogate measures has not been thoroughly examined.

The placenta is the primary endocrine gland for the production of pregnancy steroids, but the maternal and fetal adrenal glands and maternal ovary also contribute to steroid synthesis (22). The placenta forms estriol predominately (approximately 90 percent) from dehydroepiandrosterone sulfate, which is secreted by the fetal adrenal gland and converted by the fetal liver to 16{alpha}-hydroxydehydroepiandrosterone sulfate (23). Estrone and estradiol are produced by the placenta from the dehydroepiandrosterone sulfate precursors provided by the maternal and fetal adrenals at a 40:60 approximate ratio, respectively (24, 25). Progesterone is produced mainly by the placenta from cholesterol and pregnenolone obtained from the maternal bloodstream (26).

Estrogens are generally acknowledged to stimulate cell proliferation and are, thus, important determinants of fetal growth (27). Because of this biologic relation and consistent positive correlations between pregnancy estrogens and fetal size (2731), estrogens and placental mass (2830, 32, 33), and progesterone levels and placental weight (34, 35), researchers have considered fetal growth indicators to be useful markers of pregnancy hormone exposure. In an effort to evaluate the accuracy of this approach, this study assesses the validity of birth weight, birth length, ponderal index, and placental weight as surrogate measures of estriol, estrone, estradiol, and progesterone levels during pregnancy. Identifying the extent of exposure misclassification related to the use of these surrogate measures will have important implications for the interpretation of existing studies and future use of such proxies.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The data were collected from pregnant women who participated in the Child Health and Development Studies (CHDS), Berkeley, California. The CHDS enrolled 15,528 women from 1959 to 1966 to study the development of children from pregnancy through childhood (36). All participants consented to participate in the CHDS at the time of enrollment. Information on sociodemographic characteristics, reproductive history, health-related behaviors, pregnancy, and fetal characteristics was obtained from personal interviews during the index pregnancy and medical record abstraction for 12,552 women. Maternal blood samples were also collected during each trimester of pregnancy and postpartum and stored as serum at –20°C.

The current study was approved by the Texas A&M Institutional Review Board for Human Subjects in Research. The study population consists of breast cancer cases and controls previously selected from the CHDS participants in 1992 for a nested case-control study on alpha-fetoprotein levels during pregnancy and breast cancer (37). Cases were identified as all CHDS women with histologically confirmed primary in situ or invasive breast carcinoma identified in the California Cancer Registry between 1969 and 1991. Randomized recruitment (38) was used to match approximately two controls to every case by 5-year groupings of age at index pregnancy. The index pregnancy preceded breast cancer diagnosis by 20 years on average, making it unlikely for future disease to affect the association between fetal growth and hormones. Analyses restricted to controls confirmed that case status did not alter the findings.

Of the 710 women (247 cases and 463 controls) eligible for the original case-control study, 594 had a third-trimester serum sample available for hormone analysis at the time of the current study (75 had only second-trimester samples available, most likely because of early delivery, 39 had insufficient third-trimester serum for study, two were lost in laboratory accidents). Of the 594 with available serum, 26 were excluded because of fetal death, twin pregnancy, gestation greater than 44 weeks, insufficient sera, missing date of blood draw, blood draw on the same day as delivery, or blood draw more than 100 days before delivery. The final analysis included a total of 568 women (194 breast cancer cases and 394 controls).

Steroid hormone measurements
Assays for total unconjugated estradiol, estrone, estriol, and progesterone were completed in 1998 using third-trimester serum samples from the last pregnancy occurring within the CHDS enrollment period. Free and albumin-bound estrogens are the bioavailable fractions of primary interest in breast cancer studies (39). We measured only total levels because free levels can increase with length of storage due to the dissociation of weak bonds (40). All assays were conducted by Quest Diagnostics/Nichols Institute, San Juan Capistrano, California. Radioimmunoassays were preceded by organic extraction and celite chromatography (estradiol and estrone only).

Laboratory personnel were blinded to the case-control status of the specimens. All samples were assayed in duplicate in batches of approximately 60 samples per assay. Three quality control pools (low, medium, high) were included in the front, middle, and back of each assay batch to identify significant laboratory drift. A seven-point standard curve was transferred by pipet at the beginning and end of all batches to identify and correct for any shifts in measurements. Assays were repeated if less than 50 percent of the sample was recovered or if the coefficient of variation was greater than 20 percent. The results from duplicate assays were averaged. The within-assay coefficients of variation ranged from 8.5 to 13.6, and the between-assay coefficients of variations were between 9.0 and 10.8.

Gestational age-adjusted hormone levels
One blood sample was available for each participant during the third trimester of pregnancy. The timing of the blood draw ranged from 26 to 42 (mean, 34.5) weeks of gestation based on the date of the last normal menstrual period and preceded delivery by an average of 36 (range, 1–100) days. For data analysis, hormone measurements were adjusted for the timing of the blood draw to account for the increasing estrogen and progesterone levels that accompany advancing gestation. Hormone levels (on the log10 scale) were predicted for each day preceding delivery by estimating generalized additive models (41) for each hormone among the control group. Linearity was assessed by fitting an additive model with associations estimated using a locally weighted regression smoothing technique (LOESS) (42). The chi-square test for nonparametric effects was used to test for differences between the linear fit and the smooth fit of each term. Significant nonlinearity was detected only for the association between progesterone and number of days preceding delivery. A linear relation between days preceding delivery and hormone level was confirmed for estradiol, estrone, and estriol.

Using the results of the linear and LOESS regression models, we calculated predicted hormone levels for all subjects according to the timing of the blood draw. To depict how far the observed (log) hormone measurement was above or below the expected (log) value at a given point during pregnancy, we calculated residual values by subtracting the predicted (log) value from the actual (log) hormone measurement observed on a given day of blood draw. For ease of interpretation, all residual values were adjusted to the same point of reference by adding a constant value equivalent to the predicted (log) hormone level occurring 35 days prior to parturition (i.e., 2.52, 3.13, 2.12, and 5.19 for estriol, estradiol, estrone, and progesterone, respectively). Finally, the date-adjusted hormone levels were transformed back to their original units in nanograms per milliliter (ng/ml).

Fetal growth indicators
Birth weight and birth length were abstracted from the offspring’s medical records by CHDS personnel. Placental weight was recorded using the Benirschke protocol for gross placental examination (43). All examinations were performed by trained CHDS staff physicians and graduate students in the biologic sciences. Placental weight is missing for some participants (n = 88) because of lack of available trained personnel at the time of delivery. Ponderal index, a composite indicator of fetal size and nutritional status (44), was calculated as birth weight (kg) divided by birth length cubed (cm3).

Statistical analysis
Geometric mean hormone levels (adjusted for the timing of blood draw) and 95 percent confidence intervals were calculated across the categories of each fetal growth indicator. The association among the continuous values was estimated using Pearson’s product moment correlations. The linear associations between log10-transformed pregnancy hormone levels and the four fetal growth indicators were estimated using linear regression models. Using logistic regression, we assessed the probability that each fetal growth indicator was associated with an increased occurrence of elevated or depressed steroid hormone levels. Fetal growth indicators were categorized into fourths and entered into logistic regression models predicting steroid hormone levels above the 75th percentile and levels below the 25th percentile. Odds ratios and 95 percent confidence intervals are reported.

Because there are few established determinants of pregnancy hormone levels, namely, maternal age, parity, and tobacco smoking (4548), confounding was assessed by examining the impact of various covariates on the point estimates. The covariates included age at index pregnancy, number of previous pregnancies, age at first full-term pregnancy, race, height, prepregnancy body mass index, number of cigarettes smoked per day, alcohol intake, caffeine consumption, and fetal sex. Changes in effect estimates and standard errors were evaluated by starting with models that included all the covariates and assessing the effect of removing one variable from the model at a time. Variables that changed the parameters by more than 10 percent upon elimination were controlled in the final regression analyses. These included age at index pregnancy, number of previous full-term pregnancies, race, height, and number of cigarettes smoked per day at the time of interview.

The sensitivity and specificity of birth weight as a predictor of the highest and lowest quartiles of estriol exposure were evaluated by comparing the distribution of predicted and observed (log) estriol levels for agreement. The predicted values were calculated using the linear regression equation predicting (log) estriol levels by birth weight adjusted for the covariates specified above. The distribution of the predicted and observed (log) estriol values was categorized into quartiles for comparison.

Receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of birth weight, birth length, ponderal index, and placental weight as indicators of steroid hormone levels during pregnancy. True elevated steroid hormone levels were defined as the top 25 percent of each hormone distribution. All ROC calculations were completed using Intercooled Stata 6.0 for Windows (49). This technique plots sensitivity (true-positive rate) against 1 – specificity (false-positive rate) to evaluate the predictive performance of continuous variables across the range of possible cutpoints. The accuracy of the indicator is reflected by the position of the curve and the area under the curve. The area under the curve is a summary measure ranging from 0.5 to 1.0 that depicts the discriminatory ability of the indicator. It is interpreted as the probability that a randomly selected individual from a specified outcome group (e.g., high estriol levels) will have an indicator value that is higher than a randomly selected individual from the remaining outcome group (e.g., lower estriol levels). The recommended guidelines for the area under the curve maintain that 0.5–0.69 represents no to low discriminatory power, 0.70–0.9 represents moderate discriminatory power, and greater than 0.9 represents high discriminatory power (50). The nonparametric ROC procedure was applied to compare the area under the curve for two or more indicators among the same study population (51).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Geometric mean estriol, estrone, estradiol, and progesterone levels are presented in table 1 by fetal characteristics. Of the fetal growth indicators evaluated in this analysis, birth weight was the only indicator that exhibited a dose-response pattern for all the hormones examined. The mean levels of all hormones increased between 32 percent and over 100 percent when comparing birth weights of less than 2,500 g with those of 4,500 g or more. Greater mean levels of estriol were observed with increasing birth length, ponderal index, and placental weight, but these changes were more modest. Although a dose-response gradient was not evident between birth length and progesterone levels, mothers of babies in the highest birth length category had notably higher mean levels of progesterone. Mean progesterone levels were highest in the top category of ponderal index, but they did not exhibit the small incremental increases noted for estriol, estrone, and estradiol. A pattern of higher progesterone levels was also observed with increasing placental weight.


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TABLE 1. Geometric mean estriol, estrone, estradiol, and progesterone levels by fetal characteristics, Child Health and Development Studies, Berkeley, California, 1959–1966
 
Pearson’s product moment correlations between hormone levels and fetal growth indicators ranged from weak to moderate levels (range, 0.06–0.32), with the strongest association noted between estriol and birth weight (table 2). Similarly, birth length and ponderal index were more highly correlated with estriol levels than with the remaining steroid hormones. Placental weight, however, was most highly correlated with progesterone levels (r = 0.24). The fetal growth indicators were also significantly correlated with one another (data not shown). All correlations were positive except the association between birth length and ponderal index (r = –0.35). The magnitude of the associations ranged from a moderate correlation between placental weight and ponderal index (r = 0.24) to a fairly strong correlation between birth weight and birth length (r = 0.74). Similarly, all hormones were positively correlated, with the strongest association noted between estrone and estradiol (r = 0.71) and the weakest between estrone and progesterone (r = 0.25). When assessed independently using linear regression, all fetal growth indicators were linearly associated with each (log-transformed) steroid hormone, with the exception of birth length and placental weight as predictors of (log) estrone levels (table 3).


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TABLE 2. Pearson’s correlation coefficients for fetal growth indicators and estriol, estrone, estradiol, and progesterone levels, Child Health and Development Studies, Berkeley, California, 1959–1966
 

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TABLE 3. Linear regression parameter estimates* and 95% confidence intervals for (log) hormone levels as predicted by birth weight, birth length, ponderal index, and placental weight, Child Health and Development Studies, Berkeley, California, 1959–1966
 
Using logistic regression, we found that the most notable association with elevated hormone levels was between birth weight and estriol (table 4). Mothers giving birth to infants with the highest birth weights were approximately six times more likely to have high estriol levels compared with those with birth weights in the bottom quarter of the distribution (95 percent confidence interval: 3.20, 12.5). Increasing birth length and placental weight were also associated with elevated estriol levels, but the magnitude of the associations was somewhat reduced. Similarly, higher birth weight, ponderal index, and placental weight were moderately associated with elevated estradiol levels, and those with the highest placental weights appeared to be modestly associated with high progesterone levels. Associations between the fetal growth indicators and low hormone levels essentially mirrored the results obtained when evaluating elevated hormone levels (data not shown). For low estriol levels, all fetal growth indicators exhibited a trend of increasing odds ratios with decreasing fetal growth measurements, with the lowest birth weight category revealing the strongest association (odds ratio = 6.81, 95 percent confidence interval: 3.45, 13.47). Controlling for gestational age in the linear and logistic regression models had little impact on the results, modestly decreasing the precision and point estimates.


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TABLE 4. Odds ratios* and 95% confidence intervals for the top quartile of steroid hormone levels by quartiles of birth weight, birth length, ponderal index, and placental weight, Child Health and Development Studies, Berkeley, California, 1959–1966
 
Because birth weight appeared to be the indicator most closely associated with estriol levels during pregnancy, the distribution of observed (log) estriol levels was compared with levels predicted by birth weight (adjusted for smoking, number of previous full-term pregnancies, age at index pregnancy, race, and height) (table 5). The proportion of women truly exposed to elevated estriol levels (top 25 percent) who were identified as such using birth weight (i.e., sensitivity) was 0.36 (50/138). The proportion of women truly unexposed to elevated estriol levels who were classified correctly (i.e., specificity) was 0.79 (325/413). Conversely, the sensitivity and specificity of birth weight as an indicator of low estriol levels (bottom 25 percent) were 0.42 (57/137) and 0.81 (334/414), respectively. The results were essentially unchanged when the predictive equations were generated using birth weight alone or when adjusted for gestational age.


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TABLE 5. Agreement of observed quartiles of estriol levels and expected values as predicted by birth weight,* Child Health and Development Studies, Berkeley, California, 1959–1966
 
ROC curve analyses demonstrated that birth weight, birth length, ponderal index, and placental weight were all poor discriminators of elevated steroid hormone levels during pregnancy, defined as values above the 75th percentile (table 6). The calculated area under the curve for all steroid hormones was below 0.70, the recommended cutpoint for moderate discriminatory power (50). Of the indicators evaluated, birth weight performed the best, but with an area under the curve of 0.66 (95 percent confidence interval: 0.61, 0.71), its performance was unsatisfactory (figure 1). In addition to all curves lying close to the diagonal, the majority of the confidence intervals for the area under the curves included 0.50, which represents the value for no discriminatory ability. Furthermore, performance was not improved when true hormone exposure was defined as levels above the 25th percentile, the median, or the 90th percentile. Restricting the analysis to the controls also did not affect the results.


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TABLE 6. Area under the receiver operating characteristic curve for fetal growth indicators as measures of elevated steroid hormone levels* during pregnancy, Child Health and Development Studies, Berkeley, California, 1959–1966
 


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FIGURE 1. Receiver operating characteristic (ROC) curve for birth weight as a predictor of high estriol exposure, defined as levels above the 75th percentile, Child Health and Development Studies, Berkeley, California, 1959–1966.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
This study evaluated the associations and predictive performance of birth weight, birth length, ponderal index, and placental weight in relation to third-trimester serum concentrations of estriol, estrone, estradiol, and progesterone. The strongest and most consistent association observed between the fetal growth indicators and pregnancy hormones was the association between birth weight and estriol levels. Yet, birth weight was not an effective surrogate measure of high estriol exposure during pregnancy as demonstrated by ROC curve analysis.

One other study has evaluated measures of fetal size as proxies for pregnancy estrogens. Kaijser et al. (52) assessed birth weight, ponderal index, and placental weight as predictors of total estriol levels (free and glucuronic acid conjugates). To estimate a total estriol load throughout the last half of pregnancy, they took multiple blood samples between 17 and 37 weeks of gestation. Despite methodological differences, our assessments of the association between birth weight and estriol levels were similar. Like Kaijser et al., we found mean levels of estriol to be twice as high for mothers with infants in the highest birth weight category (>4,500 g) compared with those in the lowest birth weight category (<2,500 g). Additionally, our assessment of the sensitivity of birth weight as a predictor of the top quartile of the estriol distribution was very similar at 0.36 compared with their calculation of 0.28, while our specificity calculation of 0.79 was somewhat lower than the previously reported estimate of 0.93. The sensitivity and specificity for predicting the lowest estriol exposure were 0.42 and 0.81 compared with 0.23 and 0.94 reported by Kaijser et al.

Hormone measurements using a single serum sample for each woman are subject to within-person measurement error due to biologic variation. Studies have reported little diurnal or day-to-day variation for pregnancy estrogens with between-day coefficients of variation across 5–6 consecutive days ranging from 7.4 percent for total estradiol (53) to 13.2 percent for estriol (54). Progesterone levels have been reported to be somewhat more variable with a between-day coefficient of variation of 21.9 percent (55). Because error in the steroid hormone measurement should be unrelated to measures of fetal growth, any bias introduced would be toward the null (56). Hence, measurements repeated throughout pregnancy may potentially improve the precision of hormone exposure assessment and the observed predictive ability of birth weight.

Given the unknown impact of long-term freezer storage, the stability of serum components and the potential for volume loss should be considered when evaluating these results. In these data, the majority (91–96 percent) of the steroid hormone measurements were within the normal ranges reported for third-trimester levels (5760). The stability and individual rankings of serum steroid hormone concentrations have been reported to be essentially unaffected after up to 8 years of low temperature freezer storage (6164). The effects of several decades of storage, however, remain unknown. Markers of sample desiccation such as serum sodium levels were not available for these samples. A 1994 study of serum cotinine levels in 100 randomly selected serum samples from the CHDS cohort detected some evidence of evaporation, with slightly elevated mean sodium levels in samples stored at a facility in Frederick, Maryland, and higher sodium levels in samples housed in another storage facility (65). Volume loss would not be expected to be differential with respect to fetal growth characteristics, but it could be affected by differences in length of storage. When mean hormone levels were compared across year of blood draw, we observed slightly increased levels in samples collected in the last 2 years of study. Upon further inspection, 518 of our samples were stored in the Frederick facility, and the remaining 50 were collected in the last 2 years of study. When these 50 observations were removed, differences in mean hormone levels were no longer observed by year of blood draw. Even so, excluding the 50 samples from the analysis did not change the main sensitivity, specificity, or ROC curve results. Overall, error introduced by volume loss or degradation would diminish the ability of fetal growth indicators to predict pregnancy hormone levels.

Increased concentrations of pregnancy estrogens have been implicated as a possible risk factor for the development of maternal breast cancer (814) and gonadal germ-cell tumors (6670) and breast cancer in the adult offspring (1520). A number of studies of intrauterine (1620) and maternal (113) exposures have used known correlates of pregnancy hormone levels as proxy variables for serum hormone measurements that were unattainable retrospectively. These studies proceeded on the assumption that correlates of pregnancy hormones are suitable alternatives to direct serum measurements. The results of the present study confirm some degree of association between steroid hormone concentrations of estradiol, estrone, estriol, and progesterone and each potential fetal growth surrogate, with the exception of estrone’s lack of association with birth length and placental weight. Positive correlations, however, did not correspond to accurate predictive performance for any indicator of fetal growth. The sensitivity and specificity of birth weight as a marker for high estriol levels demonstrated substantial misclassification of hormone exposure, with a 64 percent chance that an exposed subject would be incorrectly classified as unexposed and a 21 percent chance that an unexposed subject would be incorrectly classified as exposed. Under the assumption that the misclassification for this dichotomous surrogate exposure is nondifferential, the sensitivity and specificity average to approximately 50 percent (sensitivity = 0.36, specificity = 0.79), indicating that the result of the exposure-disease association will be close to null regardless of the true magnitude of the effect (56). According to our results, misclassification associated with low estriol exposure would be equally pronounced (sensitivity = 0.42, specificity = 0.81). Thus, while the effects observed between fetal growth indicators and the outcomes of interest may be real, they may not be a reflection of pregnancy hormone exposures. Furthermore, negative studies using such proxies cannot rule out the possibility of true hormonal effects.


    ACKNOWLEDGMENTS
 
This research was supported by grants from the Susan G. Komen Foundation and the National Cancer Institute, National Institutes of Health, Department of Health and Human Services (RO3 CA 75953). The CHDS efforts were supported by the National Institute of Child Health and Development (N01HD63258).

The authors would also like to thank Dr. Barbara Cohn, Roberta Christianson, and Dr. Barbara van den Berg and the staff of the Child Health and Development Studies for their collaboration, data file assembly, and technical assistance providing serum samples.


    NOTES
 
Correspondence to Dr. Jennifer David Peck, Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M University System Health Science Center, 3000 Briarcrest Dr., Suite 310, College Station, TX 77802 (e-mail: jdpeck{at}srph.tamushsc.edu). Back


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 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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