[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 54.163.92.62. Please contact the publisher to request reinstatement.
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Download PDF
Figure. Flow of Current Analytical Sample in the National Longitudinal Study of Adolescent Health
Figure. Flow of Current Analytical Sample in the National Longitudinal Study of Adolescent Health

aWave I participants not included in the current analysis because it used self-reported height and weight data.

bCases added in the field, selected as part of a paired subsample, or without a sample flag.

Table 1. Selected Characteristics of Participants by Longitudinal Severe Obesity Statusa
Table 1. Selected Characteristics of Participants by Longitudinal Severe Obesity Statusa
Table 2. Incidence of Severe Obesity by Adolescent Weight Status, Stratified by Sex and Race/Ethnicity, National Longitudinal Study of Adolescent Healtha
Table 2. Incidence of Severe Obesity by Adolescent Weight Status, Stratified by Sex and Race/Ethnicity, National Longitudinal Study of Adolescent Healtha
Table 3. Association Between Adolescent Obesity and Incident Severe Obesity in Adulthood, National Longitudinal Study of Adolescent Healtha
Table 3. Association Between Adolescent Obesity and Incident Severe Obesity in Adulthood, National Longitudinal Study of Adolescent Healtha
1.
Mokdad AH, Ford ES, Bowman BA,  et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001.  JAMA. 2003;289(1):76-79PubMedArticle
2.
Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years of life lost due to obesity.  JAMA. 2003;289(2):187-193PubMedArticle
3.
Freedman DS, Khan LK, Serdula MK, Galuska DA, Dietz WH. Trends and correlates of class 3 obesity in the United States from 1990 through 2000.  JAMA. 2002;288(14):1758-1761PubMedArticle
4.
Skelton JA, Cook SR, Auinger P, Klein JD, Barlow  SE. Prevalence and trends of severe obesity among US children and adolescents.  Acad Pediatr. 2009;9(5):322-329PubMedArticle
5.
Sturm R. Increases in clinically severe obesity in the United States, 1986-2000.  Arch Intern Med. 2003;163(18):2146-2148PubMedArticle
6.
Sturm R. Increases in morbid obesity in the USA: 2000-2005.  Public Health. 2007;121(7):492-496PubMedArticle
7.
Wang YC, Gortmaker SL, Taveras EM. Trends and racial/ethnic disparities in severe obesity among US children and adolescents, 1976-2006 [published online ahead of print March 17, 2010].  Int J Pediatr ObesPubMedArticle
8.
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000.  JAMA. 2002;288(14):1723-1727PubMedArticle
9.
Arterburn DE, Maciejewski ML, Tsevat J. Impact of morbid obesity on medical expenditures in adults.  Int J Obes (Lond). 2005;29(3):334-339PubMedArticle
10.
National Institutes of Health.  The Practical Guide: Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Bethesda, MD: National Institutes of Health; 2000
11.
Bray GA. Drug treatment of obesity.  Rev Endocr Metab Disord. 2001;2(4):403-418PubMedArticle
12.
Buchwald H, Avidor Y, Braunwald E,  et al.  Bariatric surgery: a systematic review and meta-analysis.  JAMA. 2004;292(14):1724-1737PubMedArticle
13.
Colquitt JL, Picot J, Loveman E, Clegg AJ. Surgery for obesity.  Cochrane Database Syst Rev. 2009;(2):CD003641PubMed
14.
Tanner BD, Allen JW. Complications of bariatric surgery: implications for the covering physician.  Am Surg. 2009;75(2):103-112PubMed
15.
Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008.  JAMA. 2010;303(3):235-241PubMedArticle
16.
Popkin BM, Udry JR. Adolescent obesity increases significantly in second and third generation US immigrants: the National Longitudinal Study of Adolescent Health.  J Nutr. 1998;128(4):701-706PubMed
17.
Ogden CL, Kuczmarski RJ, Flegal KM,  et al.  Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version.  Pediatrics. 2002;109(1):45-60PubMedArticle
18.
Must A, Anderson SE. Body mass index in children and adolescents: considerations for population-based applications.  Int J Obes (Lond). 2006;30(4):590-594PubMedArticle
19.
Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts.  Am J Clin Nutr. 2009;90(5):1314-1320PubMedArticle
20.
The NS, Gordon-Larsen P. Entry into romantic partnership is associated with obesity.  Obesity (Silver Spring). 2009;17(7):1441-1447PubMed
21.
Gordon-Larsen P, Adair LS, Suchindran CM. Maternal obesity is associated with younger age at obesity onset in US adolescent offspring followed into adulthood.  Obesity (Silver Spring). 2007;15(11):2790-2796PubMedArticle
22.
Chantala K, Kalsbeek D, Andrace E. Non-response in Wave III of the Add Health Study. http://www.cpc.unc.edu/projects/addhealth/data/guides/W3nonres.pdf. Accessed July 7, 2010
23.
Tourangeau R, Shin HC. National Longitudinal Study of Adolescent Health: Grand Sample Weight.  October 1999. http://www.cpc.unc.edu/projects/addhealth/data/guides/weights.pdf. Accessed July 7, 2010
24.
Prentice RL, Gloeckler LA. Regression analysis of grouped survival data with application to breast cancer data.  Biometrics. 1978;34(1):57-67PubMedArticle
25.
Richardson DB. Discrete time hazards models for occupational and environmental cohort analyses.  Occup Environ Med. 2010;67(1):67-71PubMedArticle
26.
McTigue KM, Garrett JM, Popkin BM. The natural history of the development of obesity in a cohort of young US adults between 1981 and 1998.  Ann Intern Med. 2002;136(12):857-864PubMedArticle
27.
Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? a review of the literature.  Prev Med. 1993;22(2):167-177PubMedArticle
28.
Stevens J. Ethnic-specific cutpoints for obesity vs country-specific guidelines for action.  Int J Obes Relat Metab Disord. 2003;27(3):287-288PubMedArticle
29.
Zheng H, Lenard NR, Shin AC, Berthoud HR. Appetite control and energy balance regulation in the modern world: reward-driven brain overrides repletion signals.  Int J Obes (Lond). 2009;33:(suppl 2)  S8-S13PubMedArticle
30.
Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here?  Science. 2003;299(5608):853-855PubMedArticle
31.
Freedman DS, Mei Z, Srinivasan SR, Berenson GS, Dietz WH. Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study.  J Pediatr. 2007;150(1):12-17, e2PubMedArticle
32.
Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity.  JAMA. 1999;282(16):1523-1529PubMedArticle
Original Contribution
November 10, 2010

Association of Adolescent Obesity With Risk of Severe Obesity in Adulthood

Author Affiliations

Author Affiliations: Carolina Population Center (Drs The, Suchindran, Popkin, and Gordon-Larsen) and Departments of Nutrition (Drs The, Popkin, and Gordon-Larsen), Biostatistics (Dr Suchindran), and Epidemiology (Dr North), Gillings School of Global Public Health, University of North Carolina, Chapel Hill.

JAMA. 2010;304(18):2042-2047. doi:10.1001/jama.2010.1635
Abstract

Context Although the prevalence of obesity has increased in recent years, individuals who are obese early in life have not been studied over time to determine whether they develop severe obesity in adulthood, thus limiting effective interventions to reduce severe obesity incidence and its potentially life-threatening associated conditions.

Objective To determine incidence and risk of severe obesity in adulthood by adolescent weight status.

Design, Setting, and Participants A cohort of 8834 individuals aged 12 to 21 years enrolled in 1996 in wave II of the US National Longitudinal Study of Adolescent Health, followed up into adulthood (ages 18-27 years during wave III [2001-2002] and ages 24-33 years during wave IV [2007-2009]). Height and weight were obtained via anthropometry and surveys administered in study participants' homes using standardized procedures.

Main Outcome Measures New cases of adult-onset severe obesity were calculated by sex, race/ethnicity, and adolescent weight status. Sex-stratified, discrete time hazard models estimated the net effect of adolescent obesity (aged <20 years; body mass index [BMI] ≥95th percentile of the sex-specific BMI-for-age growth chart or BMI ≥30.0) on risk of severe obesity incidence in adulthood (aged ≥20 years; BMI ≥40.0), adjusting for race/ethnicity and age and weighted for national representation.

Results In 1996, 79 (1.0%; 95% confidence interval [CI], 0.7%-1.4%) adolescents were severely obese; 60 (70.5%; 95% CI, 57.2%-83.9%) remained severely obese in adulthood. By 2009, 703 (7.9%; 95% CI, 7.4%-8.5%) non–severely obese adolescents had become severely obese in adulthood, with the highest rates for non-Hispanic black women. Obese adolescents were significantly more likely to develop severe obesity in young adulthood than normal-weight or overweight adolescents (hazard ratio, 16.0; 95% CI, 12.4-20.5).

Conclusion In this cohort, obesity in adolescence was significantly associated with increased risk of incident severe obesity in adulthood, with variations by sex and race/ethnicity.

Individuals with severe obesity (body mass index [BMI] ≥40) encounter serious and potentially life-threatening complications, including diabetes, hypertension, hyperlipidemia, asthma, and arthritis,1 and substantial reductions in life expectancy.2 Repeated cross-sectional and self-reported data suggest that severe obesity prevalence has increased substantially over the past few decades,37 potentially increasing at a faster rate than moderate obesity.6 In 2000, an estimated 2.2% of adults,3 or 4.8 million individuals, were severely obese,8,9 with a disproportionately higher prevalence in women and racial/ethnic minorities.3,4,7 Yet, few national studies track individuals over time to understand the progression of obesity to severe obesity.

Diet, exercise, and behavioral modification are recommended as initial treatments for severe obesity, resulting in short-term weight loss, which, when combined with pharmacotherapy, can be associated with a 5% to 10% reduction in weight.10,11 However, antiobesity pharmacological agents have substantial adverse effects, and discontinuation often results in weight regain.11 In contrast, bariatric surgery results in weight loss ranging from 60% to 70% for at least 10 years and commonly results in complete resolution or improvement in comorbidities after surgery.12 As such, bariatric surgery is the only treatment that has been shown to have long-term success,13 yet this procedure has major potential complications, including leakage, pneumonia, pulmonary embolism, band slippage, and band erosion.14 Given the lack of successful treatment options, risks associated with treatment, and numerous health consequences of severe obesity, primary prevention is critical.

Understanding which individuals are at risk of severe obesity is essential for determining when interventions would need to be implemented to prevent obese individuals from progressing to severe obesity. Although observational studies have reported that the prevalences of overweight, obesity, and severe obesity have increased in recent years,8,15 individuals who are obese early in life have not been studied longitudinally to determine their risk of developing severe obesity in adulthood. To this end, we used a US nationally representative, longitudinal cohort to determine the incidence and risk of severe obesity in adulthood among individuals who were obese during adolescence.

METHODS
National Longitudinal Study of Adolescent Health

The National Longitudinal Study of Adolescent Health (Add Health) is a cohort of adolescents (20 745 individuals aged 11-20 years at baseline; mean age, 15.9 years) drawn from a sample of 80 high schools and 52 middle schools in the United States with unequal probability of selection. Incorporating systematic sampling methods and implicit stratification into the Add Health study design ensured that this sample is representative of US schools in 1994-1995 with respect to region, urbanicity, school size, school type, and ethnicity.

Poststratification sample weights ensure that population estimates at each wave conform to population estimates from individuals eligible for each interview; thus, the respondents are representative of the US school population in grades 7 through 12 in 1994-1995 (wave I) as they are followed into adulthood. Wave II, conducted in 1996 (n = 14 738; mean age, 16.5 years) included wave I adolescents still of school age by design (including those currently in high school and high school dropouts). Wave III, conducted in 2001-2002 (n = 15 197; mean age, 22.3 years) and wave IV, conducted in 2007-2009 (n = 15 701; mean age, 28.9 years) included all wave I respondents, regardless of wave II participation. The most recent data collection (wave IV) includes follow-up interviews from 15 701 wave I respondents drawn from 19 962 of the original 20 745 wave I respondents (exclusions: 96 deceased at wave III and 687 not sampled at wave III), with 80.25% of the eligible respondents (ineligible: 184 who moved out of the country, 87 military stationed out of the country, and 126 deceased at wave IV) consenting to participate in wave IV.

Written informed consent was obtained for all wave I participants. Survey procedures have been described elsewhere and were approved by the institutional review board at the University of North Carolina at Chapel Hill.16

Measures

Weight and height were measured in waves II through IV during in-home surveys using standardized procedures. Wave I used self-reported height and weight data, which were excluded from this analysis because the gain in 1 additional year of follow-up was not an acceptable trade-off for the error that would have been introduced with use of a combination of self-report (wave I) and measured (waves II-IV) height and weight data.

Body mass index (calculated as weight in kilograms divided by height in meters squared) and BMI percentiles from measured height and weight were derived for age and sex using the Centers for Disease Control and Prevention National Center for Health Statistics growth charts.17 Given that adolescent BMI (wave II; 1996) was not linearly associated with incident severe obesity, BMI was categorized using the recommended definitions for comparability across adolescence and adulthood.18 These categories were defined as: (1) normal weight (≥5th to <85th percentile on BMI-for-age growth chart or BMI of ≥18.5 to <25 for individuals aged <20 years; BMI of ≥18.5 to <25 for individuals aged ≥20 years); (2) overweight (≥85th to <95th percentile or BMI of ≥25 to <30 for individuals aged <20 years; BMI of ≥25 to <30 for individuals aged ≥20 years); (3) obesity (≥95th to <120% of 95th percentile or BMI of ≥30 to <40 for individuals aged <20 years; BMI of ≥30 to <40 for individuals aged ≥20 years); and (4) severe obesity (≥120% of 95th percentile for individuals aged <20 years19; BMI of ≥40 for individuals aged ≥20 years). Respondents who exceeded scale capacity (for wave III: 330 lb [148.5 kg] [n = 12]; for wave IV: 440 lb [198 kg] [n = 2]) were classified as severely obese. Incident severe obesity in adulthood was classified as nonsevere obesity at adolescence (wave II) and severe obesity at adulthood (wave III or IV; 2001-2009).

Age was recorded as the respondent's age on the date of examination. Age at onset of severe obesity was defined as age at the wave in which the individual was initially classified as severely obese. We observed a nonlinear relationship between age at onset of severe obesity and development of severe obesity in young adulthood; thus, we categorized age at onset of severe obesity as younger than 20 years (reference), 20 to 24.9 years, 25 to 29.9 years, and 30 years or older.

Consistent with previous Add Health research,20,21 race/ethnicity was obtained from a combination of in-home surveys from parents and adolescents and was categorized as non-Hispanic white, non-Hispanic black, Hispanic (Cuban, Puerto Rican, Central/South American, Mexican, or other Hispanic), or Asian American (Chinese, Filipino, or other Asian).

Statistical Analyses

Statistical analyses were conducted using Stata software, version 10.1 (Stata Corp, College Station, Texas). To account for Add Health's stratified sampling strategy, clustered sampling design, and nonresponse bias,22,23 sample weights and survey analysis techniques were used in all analyses. All results are nationally representative of adolescents who were enrolled in grades 7 through 12 in 1994 and followed up into adulthood.

For descriptive analyses, percentages were calculated for categorical variables, while means were calculated for continuous variables. To compare individuals with incident severe obesity with individuals without severe obesity, a 2-sided t test and F statistic were used to test for statistical differences (P < .05). Incidence rates of severe obesity during the transition from adolescence to adulthood were calculated by sex, race/ethnicity, and adolescent weight status (normal, overweight, and obese). A 2-sided F statistic was used to compare the incidence of severe obesity by these categories, and the Bonferroni correction (P = .0167) was applied for multiple comparisons.

Discrete time hazard models (with a complementary log-log link), a type of a survival analysis model appropriate when the outcome is ascertained at periodic measurements,24,25 were used to determine the relationship between adolescent obesity and incidence of severe obesity in adulthood. Given the relatively low incidence of severe obesity in individuals who had normal weight as adolescents, the 3 categories used to obtain absolute incidence rates (normal weight, overweight, and obese) were condensed to 2 categories, obese vs nonobese (ie, collapsing normal weight and overweight into the nonobese category) for the hazard analyses. Given the particular discrete time interval based on the examination dates and obesity data, models were conditioned on time as a unit of analysis, with age at the examination during which severe obesity was first recorded serving as the primary time variable in all models. Age-specific hazard ratios (incidence rate ratios) were calculated for the probability of becoming severely obese during a given age range, conditioned on no severe obesity at the beginning of that interval. Discrete time hazard models assume that once individuals become severely obese, they remain severely obese and, thus, while included in models, they no longer contribute to the analysis.

The hazard models included only race and sex to provide net effects of risk rather than causal modeling of these relationships. Thus, a parsimonious model was used to describe the relationship between adolescent obesity (vs nonobesity) and risk of severe obesity in adulthood. To determine whether the relationship between adolescent obesity and severe obesity risk varied by sex and race/ethnicity, a 3-way interaction was used to examine effect modification using Wald tests (P = .10). Despite borderline significance (P = .14), differences in the associations across race/ethnicity are clinically important given the racial/ethnic disparities in the prevalence of obesity and its comorbidities. Thus, final models were sex-stratified and included interactions between adolescent obesity and race/ethnicity. Additionally, effect measure modification by age at severe obesity onset with adolescent obesity and age at severe obesity onset with race/ethnicity was tested, but neither showed effect modification.

RESULTS

Data from the initial 14 738 participants measured at wave II (Figure) were included in the analytic sample frame, with a total of 29 476 observations spanning 1996 (wave II) to 2009 (wave IV), excluding participants of Native American race/ethnicity (n = 45); individuals missing sampling weights (needed to correct for nonresponse bias and sample design) (n = 3699), height and weight data at wave II (n = 46) or wave III or 4 (n = 436), or race/ethnicity (n = 74); individuals who were underweight (because the amount of weight gain necessary to shift from underweight to severe obesity in the 13-year time frame of the study could indicate a different phenotype or surrogate for other metabolic conditions) (n = 1381); and girls/women who were pregnant at baseline (n = 144). Given interest in incident severe obesity, individuals who were already severely obese at baseline (n = 79 [1.0%; 95% confidence interval {CI}, 0.7%-1.4%]) were excluded; these 79 individuals were more likely to be racial/ethnic minorities than participants included in the analytic sample and most (n = 60 [70.5%; 95% CI, 57.2%-83.9%]) remained severely obese in adulthood (result not shown).

The final analytic sample included all available exposure, outcome, and covariate data across waves II, III, and IV, totaling 15 598 observations across 8834 individuals. The analytic sample included significantly more whites, older individuals, and individuals of higher parental education than those excluded. However, inverse probability weighting showed no evidence of selection bias by these factors in final models.

Over the 13-year period between adolescence (1996) and adulthood (2007-2009), a total of 703 incident cases of severe obesity in adulthood were observed, indicating a total incidence rate of 7.9% (95% CI, 7.4%-8.5%) (Table 1). Individuals with incident severe obesity in adulthood had a higher adolescent BMI, were older, and were more likely to be racial/ethnic minorities compared with individuals without severe obesity.

A substantial proportion of obese adolescents became severely obese by their early 30s, with significant variation by sex (Table 2). Among individuals who were obese as adolescents, incident severe obesity was 37.1% (95% CI, 30.6%-43.6%) in men and 51.3% (95% CI, 44.8%-57.8%) in women. Incident severe obesity was highest among black women at 52.4% (95% CI, 40.9%-63.8%). Across all sex and racial/ethnic groups, less than 5% of individuals who were at a normal weight in adolescence became severely obese in adulthood.

In analysis using multivariate, discrete hazard models, obese adolescents were significantly more likely to develop severe obesity than normal-weight or overweight adolescents (hazard ratio, 16.0; 95% CI, 12.4-20.5), with variation across race/ethnicity and sex (Table 3). While the hazard ratio for men was higher than for women, the incidence of severe obesity in adulthood was higher among women (51.3%; 95% CI, 44.8%-57.8%) than men (37.1%; 95% CI, 30.6%-43.6%). Thus, the male-female differences in risk must be interpreted relative to the difference in rates of incidence.

COMMENT

Taking advantage of a nationally representative longitudinal data set, we observed high rates of incident severe obesity in adulthood among individuals who were obese earlier in life, with a higher incidence in women (vs men) and with the highest risk for black women. These nationally representative estimates suggest that approximately 125 000 individuals may have been severely obese during adolescence, while another 1 million adolescents may have become severely obese by the time they reached their early 30s. Over the 13-year study period, individuals who never developed severe obesity gained an average of 5.1 BMI units, whereas individuals who developed severe obesity as adults gained an average of 14.2 BMI units. Furthermore, obese adolescents were at substantially higher risk of developing severe obesity in adulthood than normal-weight or overweight adolescents.

Rates of obesity have increased across all age groups, with cross-sectional National Health and Nutrition Examination Survey data suggesting a severe obesity prevalence of 4.2% for men and 7.6% for women in young adulthood (aged 20-39 years) in 2008.15 Short-term self-reported data suggest that severe obesity might be increasing at a faster rate than moderate obesity.6 This increase is particularly concerning given the serious and potentially life-threatening complications associated with severe obesity. Yet, little is known regarding the persistence of severe obesity, the progression of obesity to severe obesity, and how risk differs by sex and race/ethnicity. Understanding these patterns is critical for reducing the burden of obesity and for implementing interventions to prevent the progression of obesity to severe obesity. Findings from current research suggest that interventions designed to prevent adult-onset severe obesity would best be implemented among obese adolescents, particularly black girls.

Although previous studies have shown a persistence of obesity from childhood and adolescence to adulthood,26,27 there are no known studies that have examined persistence and development of severe obesity. The current findings indicate that (1) there is strong persistence of severe obesity from adolescence to young adulthood; (2) there is a relatively high incidence rate of severe obesity during the transition from adolescence to adulthood; and (3) individuals who were obese as adolescents were significantly more likely to become severely obese in adulthood, highlighting the need for primary and secondary prevention of severe obesity early in the life course. In particular, primary prevention efforts should focus on the prevention of obesity prior to adolescence, while secondary prevention efforts should focus on the identification and treatment of high-risk groups in adolescence, including overweight and obese adolescents.

There are a few limitations to this analysis. The main objective of this study was to determine the incidence of severe obesity during the transition from adolescence to adulthood and to determine which groups are at highest risk. As such, the analytic strategy was designed to test the net effects of race/ethnicity and sex on severe obesity rather than undertake causal modeling of these relationships. Clearly, several other biological, sociocultural, and environmental factors associated with race/ethnicity and sex are likely to affect severe obesity incidence. Future research should address the specific factors associated with onset of severe obesity. A second limitation to this research is the use of conventional albeit somewhat arbitrary BMI cut points. These cut points do not capture differences in incidence of obesity comorbidities that exist on the continuum of BMI values.28 Likewise, cut points do not capture the complex process of body weight regulation29 or the gradual process of weight gain.30 However, cut points are needed for clinical guidance28 and for comparative purposes.18 Finally, while unique longitudinal data were available for the period from adolescence to young adulthood, these data are nationally representative of the school-aged population in 1994-1995 that were followed up over time into adulthood and, thus, are not nationally representative of the population aged 24 to 33 years at follow-up.

In summary, data from a nationally representative, ethnically diverse longitudinal sample suggest a high incidence of severe obesity during the transition from adolescence to adulthood. The clinical implications of these observed trends are concerning given the comorbidities and chronic disease associated with severe obesity.4,31,32 Findings highlight the need for interventions prior to adulthood to prevent the progression of obesity to severe obesity, which may reduce severe obesity incidence and its potentially life-threatening consequences.

Back to top
Article Information

Corresponding Author: Penny Gordon-Larsen, PhD, University of North Carolina at Chapel Hill, Carolina Population Center, University Square, 123 W Franklin St, Chapel Hill, NC 27516-3997 (pglarsen@unc.edu).

Author Contributions: Dr Gordon-Larsen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: The, Gordon-Larsen.

Acquisition of data: Popkin, Gordon-Larsen.

Analysis and interpretation of data: The, Suchindran, North, Gordon-Larsen.

Drafting of the manuscript: The, Suchindran.

Critical revision of the manuscript for important intellectual content: The, North, Popkin, Gordon-Larsen.

Statistical analysis: The, Suchindran, Gordon-Larsen.

Obtained funding: Gordon-Larsen.

Administrative, technical, or material support: Popkin, Gordon-Larsen.

Study supervision: North, Gordon-Larsen.

Financial Disclosures: None reported.

Funding/Support: This work was supported by National Institutes of Health grant R01-HD057194.

Role of the Sponsor: The National Institutes of Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

Additional Information: This research uses data from Add Health, a program project directed by Kathleen Mullan Harris, PhD, and designed by J. Richard Udry, PhD, Peter S. Bearman, PhD, and Kathleen Mullan Harris, PhD at the University of North Carolina, Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss, PhD, and Barbara Entwisle, PhD, both from the University of North Carolina, Chapel Hill, for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health Web site (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. None of the acknowledged individuals received compensation for their assistance.

Additional Contributions: We thank Linda S. Adair, PhD, and Elizabeth J. Mayer-Davis, PhD, University of North Carolina, Chapel Hill, for their advice and Frances Dancy, BS, University of North Carolina Carolina Population Center for her helpful administrative assistance. No compensation was received.

References
1.
Mokdad AH, Ford ES, Bowman BA,  et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001.  JAMA. 2003;289(1):76-79PubMedArticle
2.
Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years of life lost due to obesity.  JAMA. 2003;289(2):187-193PubMedArticle
3.
Freedman DS, Khan LK, Serdula MK, Galuska DA, Dietz WH. Trends and correlates of class 3 obesity in the United States from 1990 through 2000.  JAMA. 2002;288(14):1758-1761PubMedArticle
4.
Skelton JA, Cook SR, Auinger P, Klein JD, Barlow  SE. Prevalence and trends of severe obesity among US children and adolescents.  Acad Pediatr. 2009;9(5):322-329PubMedArticle
5.
Sturm R. Increases in clinically severe obesity in the United States, 1986-2000.  Arch Intern Med. 2003;163(18):2146-2148PubMedArticle
6.
Sturm R. Increases in morbid obesity in the USA: 2000-2005.  Public Health. 2007;121(7):492-496PubMedArticle
7.
Wang YC, Gortmaker SL, Taveras EM. Trends and racial/ethnic disparities in severe obesity among US children and adolescents, 1976-2006 [published online ahead of print March 17, 2010].  Int J Pediatr ObesPubMedArticle
8.
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000.  JAMA. 2002;288(14):1723-1727PubMedArticle
9.
Arterburn DE, Maciejewski ML, Tsevat J. Impact of morbid obesity on medical expenditures in adults.  Int J Obes (Lond). 2005;29(3):334-339PubMedArticle
10.
National Institutes of Health.  The Practical Guide: Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Bethesda, MD: National Institutes of Health; 2000
11.
Bray GA. Drug treatment of obesity.  Rev Endocr Metab Disord. 2001;2(4):403-418PubMedArticle
12.
Buchwald H, Avidor Y, Braunwald E,  et al.  Bariatric surgery: a systematic review and meta-analysis.  JAMA. 2004;292(14):1724-1737PubMedArticle
13.
Colquitt JL, Picot J, Loveman E, Clegg AJ. Surgery for obesity.  Cochrane Database Syst Rev. 2009;(2):CD003641PubMed
14.
Tanner BD, Allen JW. Complications of bariatric surgery: implications for the covering physician.  Am Surg. 2009;75(2):103-112PubMed
15.
Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008.  JAMA. 2010;303(3):235-241PubMedArticle
16.
Popkin BM, Udry JR. Adolescent obesity increases significantly in second and third generation US immigrants: the National Longitudinal Study of Adolescent Health.  J Nutr. 1998;128(4):701-706PubMed
17.
Ogden CL, Kuczmarski RJ, Flegal KM,  et al.  Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version.  Pediatrics. 2002;109(1):45-60PubMedArticle
18.
Must A, Anderson SE. Body mass index in children and adolescents: considerations for population-based applications.  Int J Obes (Lond). 2006;30(4):590-594PubMedArticle
19.
Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts.  Am J Clin Nutr. 2009;90(5):1314-1320PubMedArticle
20.
The NS, Gordon-Larsen P. Entry into romantic partnership is associated with obesity.  Obesity (Silver Spring). 2009;17(7):1441-1447PubMed
21.
Gordon-Larsen P, Adair LS, Suchindran CM. Maternal obesity is associated with younger age at obesity onset in US adolescent offspring followed into adulthood.  Obesity (Silver Spring). 2007;15(11):2790-2796PubMedArticle
22.
Chantala K, Kalsbeek D, Andrace E. Non-response in Wave III of the Add Health Study. http://www.cpc.unc.edu/projects/addhealth/data/guides/W3nonres.pdf. Accessed July 7, 2010
23.
Tourangeau R, Shin HC. National Longitudinal Study of Adolescent Health: Grand Sample Weight.  October 1999. http://www.cpc.unc.edu/projects/addhealth/data/guides/weights.pdf. Accessed July 7, 2010
24.
Prentice RL, Gloeckler LA. Regression analysis of grouped survival data with application to breast cancer data.  Biometrics. 1978;34(1):57-67PubMedArticle
25.
Richardson DB. Discrete time hazards models for occupational and environmental cohort analyses.  Occup Environ Med. 2010;67(1):67-71PubMedArticle
26.
McTigue KM, Garrett JM, Popkin BM. The natural history of the development of obesity in a cohort of young US adults between 1981 and 1998.  Ann Intern Med. 2002;136(12):857-864PubMedArticle
27.
Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? a review of the literature.  Prev Med. 1993;22(2):167-177PubMedArticle
28.
Stevens J. Ethnic-specific cutpoints for obesity vs country-specific guidelines for action.  Int J Obes Relat Metab Disord. 2003;27(3):287-288PubMedArticle
29.
Zheng H, Lenard NR, Shin AC, Berthoud HR. Appetite control and energy balance regulation in the modern world: reward-driven brain overrides repletion signals.  Int J Obes (Lond). 2009;33:(suppl 2)  S8-S13PubMedArticle
30.
Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here?  Science. 2003;299(5608):853-855PubMedArticle
31.
Freedman DS, Mei Z, Srinivasan SR, Berenson GS, Dietz WH. Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study.  J Pediatr. 2007;150(1):12-17, e2PubMedArticle
32.
Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity.  JAMA. 1999;282(16):1523-1529PubMedArticle
×