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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Previous studies have yielded inconsistent results when documenting the association between key dietary factors and adolescent weight change over time. The purpose of this study was to examine the extent to which changes in adolescent sugar-sweetened beverage (SSB), diet soda, breakfast, and fast-food consumption were associated with changes in BMI and percent body fat (PBF). This study analyzed data from a sample of 693 Minnesota adolescents followed over 2 years. Random coefficient models were used to examine the relationship between dietary intake and BMI and PBF and to separate cross-sectional and longitudinal associations. Adjusting for total physical activity, total energy intake, puberty, race, socioeconomic status, and age, cross-sectional findings indicated that for both males and females, breakfast consumption was significantly and inversely associated with BMI and PBF, and diet soda intake was significantly and positively associated with BMI and PBF among females. In longitudinal analyses, however, there were fewer significant associations. Among males there was evidence of a significant longitudinal association between SSB consumption and PBF; after adjustment for energy intake, an increase of one serving of SSB per day was associated with an increase of 0.7 units of PBF among males. This study adds to previous research through its methodological strengths, including adjustment for physical activity and energy intake assessed using state-of-the-art methods (i.e., accelerometers and 24-h dietary recalls), as well as its evaluation of both BMI and PBF. Additional research is needed to better understand the complex constellation of factors that contribute to adolescent weight gain over time.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Obesity is a major public health concern (1,2). The transition through adolescence and into early adulthood is recognized as an influential age for excess weight gain, marked by poor dietary patterns and physical inactivity (3). Adolescence is generally characterized as a period of growing independence, where individuals are increasingly beginning to make their own decisions about their day-to-day life, including what to eat. Recently, several national health organizations have identified a number of key dietary behaviors associated with excess weight gain among youth and adolescents, including frequent intake of sweetened beverages and fast food, and infrequent breakfast consumption (4). A wide array of cross-sectional studies have been published in this area, many of which support these dietary behaviors as important correlates of weight-related outcomes (5). However, longitudinal studies assessing the associations between these factors and weight outcomes have produced inconsistent results, and many have had significant methodological limitations (6).

For example, a number of large, longitudinal adolescent cohort studies have examined the association between sugar-sweetened beverage (SSB) intake and BMI over time (7,8,9,10,11,12,13,14). Some of these studies have found no longitudinal association (8,10,14) while others have found a positive association (7,9,11,12,13). Importantly, not all studies adjusted for potential confounders, such as physical activity (8,9,13). In fact, none of these studies adjusted for objectively measured physical activity; most relied on self-reported measures, which can be subject to high levels of error and bias (15).

A number of these longitudinal adolescent cohort studies have also assessed the relationship between diet soda consumption and weight status; paradoxically, most findings suggest a positive association, indicating that more diet soda consumption is associated with greater BMI gains over time (7,8,14). However, in a number of studies, these positive associations were not straightforward (i.e., significant associations were only observed for boys, but not girls, or associations were attenuated by adjusting for various covariates). In addition, Striegel-Moore and colleagues (13) found that diet soda intake was not associated with weight status over time among 2,371 girls enrolled in the National Heart, Lung and Blood Institute Growth and Health Study. In contrast, a study by Ludwig et al. (11) among 548 Massachusetts adolescents found that change in diet soda intake over 19 months was inversely associated with obesity incidence (odds ratio: 0.44). In addition, most (7,8,11,13,16), but not all (14), of these studies have adjusted for factors such as total energy intake. A majority of studies in this area have used food frequency questionnaires to assess dietary intake (7,11,14,16); however, food frequency questionnaires have been shown to provide substantially less accurate estimates of total energy intake than other methods of dietary assessment (17).

Additional analyses from similar adolescent cohorts have examined other key dietary behaviors, such as breakfast consumption, in relation to weight status. Five studies reported no significant association between the frequency of breakfast consumption and weight status in fully adjusted analyses (18,19,20,21,22). Three studies found an inverse association (i.e., less frequency breakfast intake being associated with greater weight gain; refs. 16,23,24), though one study observed this association only among overweight participants (23). In addition, one longitudinal study of 159 first-year college students (ages 18–19 years) found a positive association between breakfast consumption and weight gain (25).

Finally, few adolescent cohort studies have examined longitudinal associations between fast-food intake and weight change. Two studies reported positive associations, where greater fast-food consumption was associated with greater weight gain (26,27). Two other studies found a combination of positive and null effects among varying subgroups of their samples and across different types of analyses (16,24). None of these studies controlled for physical activity using objective measures, and several used measures of physical activity that have not been validated (24,26).

Overall, this growing body of research presents inconsistencies in the relationships between key dietary factors and adolescent weight change over time, and additional research in this area is needed. The purpose of our current study was to examine the extent to which changes in SSB, diet soda, breakfast, and fast-food consumption were associated with changes in BMI in a cohort of nearly 700 adolescents over a 2-year period. Given that previous studies in this area have primarily focused on the relationship between these key dietary factors and changes in BMI, we also sought to expand this focus by assessing the relationship between diet and objectively measured percent body fat (PBF). In addition, our aim was to quantify these associations independent of a number of important possible confounding factors, including objectively measured physical activity. Finally, although many longitudinal studies in this area have examined baseline dietary patterns as predictors of future weight status or weight change, we sought to examine the extent to which change in dietary patterns over time is associated with change in body mass and body composition. There is a subtle, yet valuable, distinction between these research questions that may have important implications for public health practice and clinical recommendations in addressing excess weight gain during adolescence.

Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

The adolescents enrolled in this study were participants in two longitudinal cohort studies examining the etiology of childhood obesity (28): (i) Identifying Determinants of Eating and Activity (IDEA) and (ii) the Etiology of Childhood Obesity (ECHO). Baseline data from IDEA participants were collected in 2006–2007, with a follow-up assessment in 2008–2009 (24 months after baseline). Baseline data from ECHO participants were collected in 2007–2008 with a follow-up in 2009–2010 (24 months after baseline). Both studies were conducted within the seven-county metropolitan area of Minneapolis-St Paul, Minnesota, and included identical measurement protocols.

For IDEA, 6th through 11th grade students and one parent or guardian were recruited via an existing cohort examining tobacco use among adolescents (29), an application list from the State department of motor vehicles, and a convenience sample from within the community. For ECHO, 6th through 11th grade students and one parent or guardian were recruited from the membership base of HealthPartners, a large health maintenance organization in Minnesota. Recruitment for ECHO was designed to yield a racially/ethnically diverse sample with the intent to obtain a distribution of children and parents that matched national prevalence for healthy and unhealthy weight status. Combining the two samples, as was done in the present analysis, resulted in a larger and more diverse sample. An indicator variable representing the study (IDEA or ECHO) was included in all analytic models to account for any unmeasured confounding. Study protocols were approved by the University of Minnesota and Ohio State University institutional review boards.

Body composition

Body composition was measured by trained staff during clinic visits, which were conducted in the University's Epidemiology Clinical Research Center (ECRC). Before conducting body composition measurements, participants dressed in study-issued t-shirts and shorts. Height was measured without shoes using a Shorr Height Board to the nearest 0.1 cm (Shorr Productions, Olney, MD). Weight (to the nearest 0.1 kg) and percent body fat (to the nearest 0.1%) were assessed using a digital bioelectrical impedance scale (Tanita TBF-300A Body Composition Analyzer/Scale; Tanita, Tokyo, Japan).

Dietary intake

Adolescents completed telephone-administered 24-h dietary recalls. Trained and certified staff from the University of Minnesota Nutrition Coordination Center administered the recalls, using the Nutrition Data System-Research with an interactive, interview format with direct data entry linked to a nutrient database. Most participants completed three 24-h recalls (2 weekdays and 1 weekend day), though a limited number (2.4%) of participants completed only two. Dietary recall data were used to estimate the frequency of SSB, diet beverage (artificially sweetened), and breakfast consumption. SSB were defined as sweetened: soft drinks, fruit drinks, tea, coffee, and/or coffee substitutes. Diet drinks were artificially sweetened: soft drinks, fruit drinks, tea, coffee, and/or coffee substitutes. Breakfast consumption was defined as the percent of recall days on which participants reported a meal called breakfast that contained ≥50 calories (30).

We did not include prompts to obtain information on where foods were obtained, so it was not possible to identify fast-food items in the 24-h recalls. As a result, fast-food intake was assessed via a survey item asking: 1) “In the past month. how many times did you buy food at a restaurant where food is ordered at a counter or at a drive-through window (there is no waiter/waitress)?” Numerous examples of fast-food facility types were provided. For these items assessing beverage and fast-food intake, nine response options ranged from “never or rarely” to “3 or more times per day.”

Physical activity

The ActiGraph accelerometer, model 7164 (ActiGraph, Pensacola, FL) was used to collect 7 days of physical activity data using 30-s epochs (data collection intervals). The monitor is an objective measure of physical activity and has been validated for use with children in laboratory and field settings (31,32). At monitor distribution, trained research staff fit an elastic belt with an attached monitor to each adolescent, according to a standardized protocol. Participants were given written and verbal instructions on the use and care of the monitors and were instructed to wear the monitor during all waking hours except when swimming or bathing for the rest of the day on which they received the monitor and for the next 6 days.

Methods developed in the Trial for Activity in Adolescent Girls (TAAG) study were used to define compliance with use of the accelerometer (33). TAAG defined compliance as providing nonmissing data for 80% of the time that 70% of the sample wore the accelerometers; for weekdays, that was 11.2 h/day, and for weekends, that was 7.2 h/day. In the present study, 79.5 and 86.6% were compliant for at least 4 of 7 days in IDEA and ECHO, respectively. Ten students were excluded because they did not have at least one compliant day.

Accelerometry data were reduced using methods previously shown to provide valid results even under conditions of informative missingness (34). We used imputation (33,35) to replace missing data in time blocks that did not meet the criteria for compliance; for time blocks that did meet the criteria for compliance, missing data were not imputed. On average, participants provided data for 82.0 h over 7 days. On average, 22.3 additional hours of data were imputed for each participant in the IDEA sample and 18.6 h of data were imputed for each participant in the ECHO sample. Thus on average, 102.8 h of data were available for analysis per participant, with 20.1% of those data generated via the imputation procedure.

Total physical activity was defined as the sum of light (50–1,499 counts/30 s), moderate (1,500–2,600 counts/30 s), and vigorous activity (>2,600 counts/30 s), excluding sedentary behavior (<50 counts/30 s; ref. 36). In this sample, sedentary behavior represented 66.3% of the time included in the analysis while total activity represented the remaining 33.7%.

Other covariates

Self-report demographic data were obtained at the initial study visit from adolescents and parents. Adolescents reported gender, age, and race/ethnicity; parents reported whether their child qualified for free or reduced priced lunch, and the highest level of education among the adults living in the household (college graduate, Y/N). Adolescents also completed the self-report Pubertal Development Scale (37). The Pubertal Development Scale is a 5-question summed score with good internal consistency (Cronbach's α = 0.77) and reasonable associations with physician ratings (r = 0.61–0.67; ref. 37).

Analyses

Before analysis, gender was coded 1 = male, 0 = female. Race was coded 1 = white, 0 = other. Parent education was coded 1 = college graduate, 0 = other. Eligibility for free or reduced price school lunch was coded 1 = free and reduced lunch program, 0 = other. Puberty, activity, and calories were modeled as continuous variables. BMI (kg/m2) was calculated using height and weight measurements; BMI (instead of BMI z-score) was used in all modeling, as it is recommended practice for longitudinal research (38,39).

The exposure variables were servings per day of SSB and diet soda, days per week purchasing fast food, and proportion of recalls reporting eating breakfast. For each of the independent variables, a mean across the two measurement visits (baseline and 24 months) was calculated; in addition, the deviation between the value observed at a given visit and that participant's mean was calculated. The coefficient for the mean score estimated the cross-sectional difference in the outcome between groups of youth who differed by one unit of exposure. The coefficient for the deviation score estimated the longitudinal change in the outcome within youth who changed by one unit of exposure. We have used this decomposition scheme in previous studies related to obesity (40,41); there is a general discussion of this approach in a recent text on longitudinal data analysis (42).

Because we expected substantial differences between males and females in BMI and PBF, and potentially in the relationships of interest, analyses were conducted separately for males and females. To facilitate interpretation, all variables except the deviation scores were centered before analysis by subtracting the gender-specific mean from each observed value. That was not necessary for the deviation scores, as they had a mean of zero by definition.

Random coefficient models were used to examine the relationship between exposures and BMI and PBF; these models fit a random slope and intercept for each participant (42,43,44). Consistent with the recommendations of Singer and Willett (42), we used age as the index for time. For each dependent variable (BMI, PBF), we ran separate models for each exposure. In each model, we included age and the mean and deviation exposure variables. Those models provided a test of whether there were cross-sectional or longitudinal associations across the age range. We used empirical sandwich standard errors to accommodate the complex pattern of correlation in the data due to the nesting of youth within schools and neighborhoods and to the nesting of repeat observations within youth. We ran each model twice; in the first, we adjusted for race, grade, parent education, school lunch, puberty, total physical activity measured at baseline, and study (ECHO vs. IDEA). In the second, we included energy intake measured at baseline as an additional covariate.

Tables 2 and 3 report 64 tests, with 16 in each gender × design combination. As a result, we used an α of 0.05/16 = 0.003125 to indicate statistical significance.

Table 2.  Cross-sectional associations between key diet factors, BMI, and percent body fat among adolescents
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Table 3.  Longitudinal associations between key diet factors, BMI, and percent body fat among adolescents over time
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Descriptive statistics were prepared using SAS PROC MEANS and SAS PROC FREQ. Regression models were run in SAS PROC MIXED. All analyses were run in SAS, version 9.1 (ref. 45).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Of 723 participants measured at baseline, participants were excluded who at either the baseline or the 2-year follow-up survey: (i) had less than one full day of accelerometry data (n = 13), (ii) had less than 2 days of dietary recall data (n = 11), or (c) were missing data on other variables used in the regression analyses (n = 6), reducing the number of participants included in the analyses to 693. Considering the pattern of missing data over time, 131 participants contributed to the analysis only at baseline (e.g., missing some variables at 24 months follow-up), 535 contributed to the analysis at both baseline and 24 months follow-up (e.g., no missing variables), and 27 contributed to the analysis only at 24 months follow-up (e.g., missing some variables at baseline).

The average age of the sample at baseline was 14.6, and consisted of 49% males. At the first visit, participants reported an average daily consumption of 0.85 servings of SSB and 0.17 servings of diet soda. They reported purchasing fast food 0.9 times per day and eating breakfast on 89% of days when dietary recalls were conducted. Average BMI was 22.0 kg/m2, increasing to 23.1 kg/m2 at the 2-year visit. Average PBF was 21.3, increasing to 22.0 at the 2-year visit. The average self-reported energy intake was 1,982 kcal, increasing to 1,994 kcal at the 2-year visit. The average minutes of daily physical activity was 310 at baseline, decreasing to 292 at the 2-year visit.

Table 1 summarizes the characteristics of the sample at each measurement visit separately for males and females. Overall, males increased in BMI more rapidly than females. Males had higher self-reported energy intake and higher activity levels than girls. Females had higher puberty scores than males. Male and female participants were predominantly white, with few participants in the free or reduced lunch program at school, and most of the participants had at least one parent who had graduated from college.

Table 1.  Descriptive characteristics of the sample at each data collection period
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Combining males and females, the average BMI change from baseline to 24 months was +1.1 kg/m2 (95% CI: −0.83, 1.40), while the average PBF change was +0.72 (95% CI: 0.13, 1.31). For dietary variables, the average changes were: −0.02 servings/day for SSB (95% CI: −0.11, 0.07), +0.03 servings/day for diet soda (95% CI: −0.02, 0.08), +0.24 purchases/day for fast food (95% CI: 0.12, 0.37), and −0.6% recall days with breakfast reported (95% CI: −0.08, −0.03).

Table 2 summarizes the cross-sectional results of the regression analyses. Among males and females, there was evidence of inverse associations between breakfast consumption and BMI and PBF. Among females there were consistent positive associations between self-reported diet soda consumption and both BMI and PBF. For example, among females the β-coefficient in the fully adjusted model (model 2) indicates that for each one serving per day increase in diet soda consumption, BMI and PBF increase by 2.5 and 3.6 units, respectively.

Table 3 summarizes the longitudinal results of the regression analyses. Among males there was some evidence of a positive, longitudinal association between SSB consumption and both BMI and PBF. After adjustment for energy intake, an increase of one serving of SSB per day was associated with an increase of 0.3 and 0.7 units of BMI and PBF, respectively. However, the only association that was significant at the 0.003125 α level (used to correct for the number of tests) was that of SSB and PBF among males after adjusting for total caloric intake.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Findings from this cohort of adolescents yielded evidence for cross-sectional associations between breakfast consumption and BMI and PBF among both males and females, Specifically, youth who consumed breakfast more frequently were more likely to have a lower BMI and PBF compared to those consuming breakfast less frequently. In addition, females who consumed diet soda more frequently were more likely to have a higher BMI and PBF compared to females who did not. Interestingly, we did not observe any cross-sectional associations between SSB intake or fast-food consumption and either BMI or PBF, as have been identified in previous research.

Cross-sectional findings provide no insight into the temporality of these relationships, and reverse causality is a major concern. For example, the positive association found between diet soda consumption and BMI in the cross-sectional analyses likely reflects the effect of excess body weight on eating habits (e.g., those struggling with their weight may begin drinking diet soda).

Longitudinal studies provide better evidence to support causality. In our longitudinal analyses, we found little consistent evidence of associations between our four dietary intake measures and BMI or PBF after taking into account the number of analyses performed. We found the strongest support for a relationship between increased SSB intake and increased PBF among males. With a less conservative α level (0.05), increased SSB intake was also associated with increased BMI in males, and increased fast food was associated with increased PBF in females. However, given the many tests examined in this study, these results should be interpreted with caution.

Previous research has yielded inconsistent evidence of longitudinal relationships between these key dietary factors and changes in adolescent weight status over time. Limitations in study designs and measurement tools may, in part, account for this wide range of inconsistent findings. For example, to our knowledge, there have been no adolescent cohort studies to date that have assessed these important dietary factors and weight change over time, independent of objectively measured physical activity. It is imperative that these associations be examined independent of physical activity, due to the large body of literature supporting the argument that physical activity drives appetite and food consumption, perhaps particularly during these adolescent years, and that physical activity is also associated with weight status (46,47). Accurately assessing physical activity in these adolescent cohort studies (e.g., using objective assessment methods) may be of paramount importance. Furthermore, it may be important to also examine the influence of total energy intake in these associations. We presented our models using a two-stage approach, both with and without energy adjustment. Although we would expect overall energy intake to be on the causal pathway in some of these relationships (and thus would not want to control for it), it has become standard practice to adjust for energy intake in these types of analyses as a means of adjusting for bias in food and nutrient intake reporting by weight status (7,8,9,11,13,18,19,20,22,23,27). For these reasons, it is important to obtain an accurate estimate of energy intake, using state-of-the-art methods, such as 24-h dietary recalls, and to report analyses both with and without adjustment for energy intake.

Another potential reason for inconsistent results in previous literature is the number of variables that are often examined and the number of statistical tests that are often reported. It is well known that the probability of a significant result increases directly as the number of tests performed increases. The probability of a significant result given 10 independent tests is 0.4, not 0.05. With studies in this area, tests are not likely to be independent, because the exposures and outcomes are correlated, so that the probability of a significant association would be even higher. We have attempted to control for this problem by using a more conservative α level to evaluate our tests, reflecting the fact that Tables 2 and 3 report 16 tests for each gender × design combination. Studies that fail to control their type 1 error rate are likely to report as significant associations that are due instead to chance.

Our study is also uncommon in the research questions we have posed. We examined the extent to which change in dietary patterns over time is associated with change in body mass. In contrast, many longitudinal studies in this area have examined baseline dietary patterns as predictors of future weight status or weight change. Although documenting the relationship between early risk exposure and a later health outcome is an important research question, we also feel that examining the association between change in dietary factors and change in body mass is important in helping to understand the extent to which improvements in adolescent dietary intakes might result in body mass changes. In addition, we have examined the longitudinal associations between these dietary factors and PBF, and few studies to date have examined associations between adolescent dietary changes and changes in body composition over time. Although BMI is often used as a proxy for body fatness, it can be subject to error and misclassification of body fatness (48), and thus it is also important to assess these diet-weight relationships within the context of other, complimentary measures of body composition.

Given the strengths of our methods, it is important to consider possible reasons for the contrasts between the cross-sectional and longitudinal findings. It is possible that our measures of dietary intake were inadequate; however, the significant associations observed in the cross-sectional analyses undermine that explanation. It is possible that the relationships are nonlinear, and so missed in the linear models that we fit to the longitudinal data; that is a possibility, but we think it is unlikely, given the short time period involved. Limited changes in diet in the cohort over the 2-year follow-up period (limited range in exposure) could be another explanation for the null findings. Also, limited changes in body weight and PBF over the 2-year period (outside of normal growth at this age) could be an issue. It is possible that the cohort may begin to experience more pronounced heterogeneity in these exposures and outcomes as participants begin to transition from adolescence to young adulthood. If so, longitudinal relationships may be more readily observed at that time; we hope to continue to follow the cohort so that we can address this possibility directly in future analyses.

The strengths of this study include the longitudinal cohort design, the substantial sample size, and the ability to adjust for a number of important potential confounders, such as objectively measured physical activity. Despite the strengths of this work, however, our findings should be interpreted with several caveats in mind. For example, our sample was drawn from one geographic metropolitan region in the Midwestern United States, which may limit generalizability. Although there was some diversity within the sample, participants were primarily white with few coming from relatively low-socioeconomic backgrounds. It is possible that longitudinal dietary influences on weight change may be more readily apparent among more high-risk groups of adolescents (e.g., those who are gaining excess weight at a more rapid pace). In addition, we conducted multiple testing of two dependent and four independent variables; therefore, individual P values should be assessed in a more conservative manner, and robust patterns in findings should be highlighted, rather than individual associations. Finally, despite our use of state-of-art dietary assessment methods (i.e., 24-h recalls), there may be substantial error and/or bias associated with current dietary assessment tools that may have influenced our findings, as well as have accounted for some of the notable inconsistency reported in previous research in this area.

To date, many adolescents and young adults fail to meet national dietary recommendations for health, and this may have important implications for a wide range of long-term chronic disease outcomes. In that adolescents and young adults are among the most frequent consumers of energy-dense food products, such as soda and fast food, and that they are also among the most heavily targeted age groups for food and beverage marketing, there is a critical need for clinical and public health efforts that target this age group. Overall, there is also an urgent need for a better understanding of the ways in which adolescent dietary patterns contribute to obesity and the avenues through which we can prevent excess weight gain over time. It is most likely that no single dietary factor may dramatically contribute alone to weight gain over time, but rather it is a constellation of factors that occur throughout childhood and adolescence that impact excess weight gain and long-term weight trajectories.

ACKNOWLEDGEMENT

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

This research was funded by the National Cancer Institute Transdisciplinary Research in Energetics and Cancer Initiative (NCI Grant 1 U54 CA116849-01, Examining the Obesity Epidemic Through Youth, Family & Young Adults, PI: Robert Jeffery) and the National Heart, Lung and Blood Institute (R01HL085978, PI: Leslie Lytle). Additional salary support was also provided by Award Number K07CA126837 from the National Cancer Institute (PI: M.L.). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Heart, Lung and Blood Institute. We acknowledge and thank Stacey Moe, Pamela Carr, Anne Samuelson, Dawn Nelson, Megan Treziok, Kian Farbakhsh, Mary Hearst, Bill Baker, and the other members of the IDEA and ECHO study teams for their important contributions to the study.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References
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