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. Author manuscript; available in PMC: 2014 Jul 1.
Published in final edited form as: Br J Psychiatry. 2013 May 9;203:24–34. doi: 10.1192/bjp.bp.112.123299

An Epidemiologic Study of Adult Attention-Deficit/Hyperactivity Disorder and Obesity

Samuele Cortese 1,2,3, Stephen V Faraone 4, Silvia Bernardi 5, Shuai Wang 5, Carlos Blanco 5
PMCID: PMC3696877  NIHMSID: NIHMS484697  PMID: 23661765

Abstract

Background

A significant association between ADHD and obesity has been reported. This study addresses unexplored aspects of this relationship.

Aims

To evaluate the association between adult obesity and: 1) persistent, remitted, or lifetime ADHD; 2) number of childhood ADHD symptoms, controlling for socio-economic status and mood, anxiety, and substance use disorders.

Method

Face-to-face psychiatric interviews in 34,653 U.S. adults from the National Epidemiologic Study on Alcohol and Related Conditions. Obesity was defined as a body mass index ≥ 30.

Results

Persistent, lifetime or remitted ADHD were not associated with obesity after controlling for confounders. The number of childhood ADHD symptoms was significantly associated with adult obesity, even after adjustment, in women.

Conclusions

Childhood ADHD symptoms are associated with obesity in women even after comorbid psychiatric disorders are accounted for. This provides a rationale for longitudinal studies assessing the impact of the treatment of childhood ADHD symptoms on obesity in women.

Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most frequent childhood-onset psychiatric conditions, with an estimated worldwide prevalence exceeding 5% in school-age children1. Impairing symptoms of ADHD persist into adulthood in ~ 65% of childhood-onset cases2. ADHD imposes an enormous burden on society in terms of psychological dysfunction, adverse vocational outcomes, stress on families, and societal financial costs3.

Whereas the comorbidity between ADHD and psychiatric disorders has been extensively investigated3, the association with general medical conditions has received much less attention. Among medical disorders, there is increasing but in part mixed evidence that points to a significant association between obesity and ADHD in children4. Although literature in adults is limited, initial evidence from population samples suggests a persistence of a significant association in adulthood as well4.

Defined as a body mass index (BMI) ≥ 305, obesity is a chronic condition currently affecting one adult in three in the U.S.6. It is considered one of the major causes of morbidity (including cardiovascular risk and diabetes) and mortality in adults6. Given the significant impairment associated with both ADHD and obesity, adults with both conditions represent a portion of the population particularly vulnerable from a medical and psychosocial standpoint. Interestingly, preliminary data suggest that the treatment of ADHD may contribute also to significant weight reduction in adults with ADHD and obesity7. However, before implementing screening and intervention programs aimed at preventing or reducing obesity in adults through ADHD treatment, it is necessary to extend current preliminary literature, addressing unexplored important aspects that may inform such programs.

A question that remains unknown is if the association between ADHD and obesity in adults holds after controlling for possible confounders. In particular, it is unclear if ADHD is associated with obesity after taking into account the effect of comorbid mental disorders that have been shown to impact on weight, i.e., mood, anxiety, and substance use disorders (SUDs)8. Determining if adult ADHD per se is associated with obesity or if the link is mediated by other factors, including comorbid psychiatric disorders, is crucial before implementing possible interventions strategies for the management of obesity based on the specific treatment of ADHD.

Moreover, while there is evidence of a relationship between persistent ADHD and obesity in adulthood4, it is not known if ADHD in remission is associated with obesity as well. This would be highly relevant from a clinical and public health perspective, setting the groundwork for prospective studies assessing the effects of ADHD treatment in youth as a way to prevent obesity in adulthood.

Additionally, focusing only on a categorical definition of ADHD may limit our ability to detect significant association with obesity. While both impulsive and inattentive symptoms of ADHD have been hypothesized to lead to dysregulated eating behaviors that contribute to weight gain and obesity, the role of hyperactivity is unclear4. Considering the three symptom dimensions of ADHD separately (i.e., inattentive, impulsive, and hyperactive symptoms) may allow to better understand specific behavioral and neurobiological pathways underlying the possible association between ADHD symptoms and obesity. Taking into account the three ADHD symptom dimensions separately may also be more informative for specific preventive and intervention strategies addressing one or more of these dimensions. So far, only one study9 addressed the relationship between ADHD symptoms (lumping together hyperactive and impulsive symptoms) in childhood and obesity in early adulthood, but it has not been explored whether childhood ADHD symptoms are associated with obesity in mid-adulthood and later on, when the prevalence of obesity is the highest6.

Finally, there is some evidence that the association between ADHD and obesity depends upon gender, with one study in children and adolescents reporting a significant association between ADHD symptoms and obesity only in adolescents females10, and two other studies11,12 pointing to higher odds ratios for the association between ADHD and obesity in unmedicated female adolescents than in male adolescents. Therefore, there is a need to better understand possible gender differences in the association between ADHD and adult obesity.

To address these unexplored issues, we draw on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)13, a large representative sample of the U.S. adult population. The NESARC assessed, by means of face-to-face interviews, a broad range of Axis I and II disorders as well as several general medical conditions, including obesity. The aims of the present study were: 1) to assess the relationship between persistent, lifetime, or remitted ADHD and obesity in adulthood, controlling for relevant sociodemographic variables as well as for mood, anxiety, and substance use disorders; and 2) to evaluate the relationship of obesity in adulthood to each of ADHD’s symptom dimensions in childhood, controlling for the aforementioned possible confounders. Analyses were performed in the entire sample (i.e., men plus women), as well as in men and women separately. Based on prior literature4, we hypothesized: 1) a significant positive association between obesity in adulthood and persistent, as well as lifetime and remitted ADHD, even after controlling for relevant sociodemographic characteristics and comorbid mental disorders, in women but not in men; and 2) a significant relationship of obesity in adulthood to impulsive and inattentive, but not hyperactive symptoms of ADHD in childhood, that holds after controlling for relevant sociodemographic and psychiatric confounders, in women but not in men.

Methods

Sample and procedures

The NESARC13 target population at Wave 1 (2001–2002) was the civilian noninstitutionalized population 18 years and older residing in households and group quarters in the United States, including Alaska and Hawaii. One sample person from each household or group quarters’ unit was randomly selected for interview. Blacks, Hispanics, and adults 18 to 24 years were oversampled, with data adjusted for oversampling and household and person-level non-response. Black and Hispanic persons and young adults were oversampled because these subgroups have been underrepresented in previous comorbidity surveys in the U.S.14. Interviews were conducted with 43,093 participants by experienced lay interviewers with extensive training and supervision. All procedures, including informed consent, received full human subjects review and approval from the U.S. Census Bureau and the U.S. Office of Management and Budget. The Wave 2 interview was conducted approximately 3 years later (2004–2005, mean [SE] interval: 36.6 [2.6] months). Excluding ineligible respondents (e.g., deceased), the Wave 2 response rate was 86.7%, reflecting 34,653 completed interviews15. Participants were ≥ 20 years old (individuals aged 90 or older were coded as 90 for privacy purposes). Wave 2 NESARC weights include a component that adjusts for nonresponse, demographic factors, and psychiatric diagnoses to ensure that the Wave 2 sample approximated the target population, that is, the original sample minus attrition between the two waves. As reported elsewhere15, adjustment for nonresponse was successful, as the Wave 2 respondents and the original target population did not differ on age, race/ethnicity, gender, socioeconomic status, or the presence of any substance, mood, anxiety, or personality disorder. The analyses of the present study refer to participants in Wave 2.

Measures

Sociodemographic and socioeconomic variables

Sociodemographic measures presented in this study include age, sex, race/ethnicity, nativity, education and individual income.

DSM-IV diagnoses

The diagnostic interview Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version (AUDADIS-IV) Wave 2 version16 was used to generate DSM-IV diagnoses. The AUDADIS-IV is a valid and reliable fully structured diagnostic interview designed for use by professional interviewers who are not clinicians.

ADHD

Respondents were asked the symptoms of DSM-IV ADHD in Wave 2 only. Twenty symptom items operationalized the 18 ADHD items of DSM-IV criterion A. For the diagnosis of ADHD, six or more inattention and/or impulsive-hyperactive symptoms had to be present for at least 6 months (criterion A), be associated with impairment in two or more settings (criterion C) and interfere significantly with social, school, or work functioning (criterion D). To be consistent with empirical data showing validity of late onset diagnosis and with the recommendation of the DSM-5 committee17, symptoms had to be present before the age of 12, rather than before age 7 as currently required by DSM-IV(-TR). Persistent ADHD was defined by the current presence of the aforementioned criteria at Wave 2. Remitted ADHD was defined by at least 2 months without symptoms at Wave 2. Lifetime ADHD was defined as persistent plus remitted ADHD. Test-retest reliability for ADHD was good (k=0.71)18. Internal consistency reliability of the ADHD symptom items (Cronbach’s α=0.89) was excellent18.

Other DSM-IV diagnoses

Mood disorders assessed by the AUDADIS included DSM-IV major depressive disorder (MDD), dysthymia, and bipolar I and II disorder. Anxiety disorders included DSM-IV panic disorder, social anxiety disorder, specific phobia, generalized anxiety disorder and posttraumatic stress disorder (PTSD). Personality disorders were assessed on a lifetime basis at Wave 1 and included avoidant, dependent, obsessive-compulsive, paranoid, schizoid, histrionic and antisocial personality disorders. Borderline, schizotypal and narcissistic personality disorders were measured at Wave 2. Test-retest reliabilities for AUDADIS-IV mood, anxiety, impulsive and personality disorders in the general population and clinical settings were fair to good (k=0.40–0.77)18. Convergent validity was good to excellent for all affective, anxiety, and personality diagnoses, and selected diagnoses showed good agreement (k=0.64–0.68) with psychiatrist reappraisals18. The AUDADIS-IV also has good to excellent (k=0.70–0.91) test-retest reliability for substance use disorder diagnoses18.

Obesity

Current (i.e., at Wave 2) weight (in pounds) and height (in inches) were self-reported at Wave 2. BMI was calculated as follows5: [weight (pounds)/height (inches)2] × 703. Obesity was defined as BMI ≥ 30, consistent with WHO criteria19.

Statistical analysis

Weighted percentages and means were computed to derive sociodemographic and clinical characteristics, including DSM-IV diagnoses, as well as current weight, height, BMI, and obesity rates of respondents with and without a diagnosis of ADHD (persistent, remitted, or lifetime). Data were weighted to adjust for the sample design, for the effects of non-response, and to correct for survey undercoverage error (adjusting by age, race, sex, and ethnicity).

To assess the first aim of the study, logistic regression yielded odds ratios (ORs) and 95% CI indicating measures of association between ADHD diagnosis (persistent, remitted, or lifetime) and obesity (as well as height, weight, and BMI). To control for sociodemographic and psychiatric variables, two sets of logistic regressions, yielding adjusted ORs (AORs) and 95% CI, were conducted. The first included current obesity status as the outcome variable and persistent ADHD, as well as 12-month mood disorders, anxiety disorders, SUDs, race/ethnicity, and individual income as predictors. All these variables have been found to be associated with ADHD in a previous report on this sample20 and with obesity in a prior national survey8. With regard to SUD, we used “Nicotine dependence” and “Any SUDs, other than nicotine dependence” as independent variables, to specifically assess the role of nicotine dependence. The second logistic regression model was similar to the previous one, but included lifetime, instead of persistent ADHD, and lifetime rather than 12-month mood disorders, anxiety disorders, and SUDs (again, considering “Nicotine dependence” and “Any SUDs, other than nicotine dependence”).

To evaluate the second aim of the study, a logistic regression model was developed including current obesity as the outcome variable and number of inattentive, hyperactive, and impulsive symptoms (separately) before age 18, along with the other variables included in the aforementioned second model, as predictors. All regression models, and the prevalence of anthropometric measures, are presented in the overall sample (i.e., men plus women) as well as stratified by gender. We consider significant ORs those whose CI does not include 1. Standard errors and 95% CIs for all analyses were estimated by using SUDAAN (version 9.0; Research Triangle Institute, USA), to adjust for the design effects of the NESARC.

Results

Sociodemographic, socioeconomic, and clinical characteristics

Sociodemographic, socioeconomic, and clinical characteristics of the sample are shown in Table 1 and Supplemental eTable 1. There were 14,564 males in the full sample (47.92%, weighted percentage). The prevalence of persistent, remitted, and lifetime ADHD were 1.02%, 0.91%, and 1.93%, respectively. Details about the clinical characteristics of ADHD adults in this sample have been previously described20.

Table 1.

Sociodemographic and socioeconomic characteristics of adults without and with ADHD (lifetime, persistent, and remitted) in the National Epidemiologic Survey on Alcohol and Related Conditions.

Pairwise comparisons

Lifetime ADHD Persistent ADHD Remitted ADHD Non ADHD Lifetime ADHD vs. Non ADHD Persistent ADHD vs. Non ADHD Remitted ADHD vs. Non ADHD Persistent ADHD vs. Remitted ADHD
(n=616, 1.93%) (n=340, 1.02%) (n=276, 0.91%) (n=34037, 98.07%)
% (S.E.) % (S.E.) % (S.E.) % (S.E.) OR (95% CI) OR (95% CI) OR (%% CI) OR (95% CI)
Sex
 Male 57.20 (2.24) 53.06 (3.10) 61.83 (3.25) 47.73 (0.34) 1.46 (1.22–1.76) 1.24 (0.96–1.59) 1.77 (1.35–2.34) 0.70 (0.48–1.01)
 Female 42.80 (2.24) 46.94 (3.10) 38.17 (3.25) 52.27 (0.34) 1 (1-1) 1 (1-1) 1 (1-1) 1 (1-1)
Race/Ethnicity
 White 80.76 (1.96) 79.13 (3.00) 82.59 (2.23) 70.72 (1.55) 1 (1-1) 1 (1-1) 1 (1-1) 1 (1-1)
 Black 6.40 (0.95) 5.85 (1.18) 7.02 (1.48) 11.14 (0.67) 0.50 (0.38–0.67) 0.47 (0.30–0.72) 0.54 (0.36–0.82) 0.87 (0.47–1.62)
 Native American 3.59 (0.90) 4.29 (1.48) 2.80 (0.99) 2.16 (0.18) 1.45 (0.85–2.47) 1.77 (0.87–3.62) 1.11 (0.52–2.37) 1.60 (0.56–9.61)
 Asian 1.32 (0.55) 1.64 (0.93) 0.96 (0.57) 4.33 (0.52) 0.27 (0.12–0.58) 0.34 (0.11–1.04) 0.19 (0.06–0.58) 1.79 (0.33–9.61)
 Hispanic 7.93 (1.39) 9.09 (2.22) 6.63 (1.40) 11.65 (1.20) 0.60 (0.43–0.83) 0.70 (0.41–1.18) 0.49 (0.34–0.70) 1.43 (0.72–2.85)
Nativity
 US-born 95.09 (1.02) 94.87 (1.53) 95.34 (1.40) 85.96 (1.39) 1 (1-1) 1 (1-1) 1 (1-1) 1 (1-1)
 Foreign-born 4.91 (1.02) 5.13 (1.53) 4.66 (1.40) 14.04 (1.39) 0.32 (0.21–0.48) 0.33 (0.18–0.60) 0.30 (0.16–0.56) 1.11 (0.45–2.74)
Age, years
 18–29 31.09 (2.53) 33.58 (3.29) 28.31 (3.64) 16.05 (0.32) 1 (1-1) 1 (1-1) 1 (1-1) 1 (1-1)
 30–44 34.20 (2.32) 33.11 (2.98) 35.42 (3.48) 29.65 (0.36) 0.60 (0.45–0.78) 0.53 (0.38–0.74) 0.68 (0.45–1.01) 0.79 (0.48–1.29)
 45–64 31.81 (2.32) 31.00 (3.05) 32.73 (3.55) 34.67 (0.32) 0.47 (0.36–0.62) 0.43 (0.30–0.61) 0.54 (0.35–0.81) 0.80 (0.47–1.35)
 65+ 2.89 (0.66) 2.31 (0.78) 3.55 (1.05) 19.64 (0.35) 0.08 (0.05–0.12) 0.06 (0.03–0.11) 0.10 (0.05–0.20) 0.55 (0.21–1.42)
Education
 < High School 14.68 (1.90) 13.61 (2.45) 15.88 (2.73) 14.01 (0.45) 1.10 (0.80–1.50) 1.06 (0.69–1.62) 1.14 (0.75–1.74) 0.93 (0.52–1.65)
 High School 29.52 (2.36) 32.71 (3.25) 25.96 (2.25) 27.44 (0.53) 1.13 (0.89–1.43) 1.30 (0.96–1.76) 0.95 (0.67–1.35) 1.37 (0.87–2.14)
 College 55.79 (2.59) 53.68 (3.49) 58.16 (3.58) 58.55 (0.63) 1 (1-1) 1 (1-1) 1 (1-1) 1 (1-1)
Individual Income
 0–19K 50.06 (2.40) 58.34 (3.24) 40.80 (3.58) 42.03 (0.56) 1 (1-1) 1 (1-1) 1 (1-1) 1 (1-1)
 20–34K 22.84 (1.86) 20.29 (2.41) 25.70 (3.05) 23.12 (0.35) 0.83 (0.66–1.05) 0.63 (0.46–0.86) 1.15 (0.80–1.64) 0.55 (0.34–0.89)
 35–69K 21.76 (1.93) 17.17 (2.60) 26.90 (3.31) 24.34 (0.39) 0.75 (0.58–0.97) 0.51 (0.34–0.75) 1.14 (0.78–1.67) 0.45 (0.26–0.78)
 >70K 5.34 (1.21) 4.21 (1.56) 6.60 (1.81) 10.51 (0.44) 0.43 (0.26–0.69) 0.29 (0.13–0.63) 0.65 (0.36–1.18) 0.45 (0.17–1.18)

All estimates are based on weighted data taking into account the study design.

Significant OR (95% CI not including 1) are indicated in bold.

Obesity rates in adults with persistent, remitted, and lifetime ADHD

Considering the entire sample (men plus women), obesity rates, as well as BMI (Table 2a), were significantly higher in adults with persistent ADHD than in those without ADHD (OR =1.44 [95% CI: 1.06–1.95] and p=0.015, respectively). BMI (p=0.032), but not obesity rates, was also significantly higher in adults with lifetime ADHD vs. non ADHD. Adults with ADHD (persistent, remitted, or lifetime) had significantly higher weight than non ADHD individuals (p=0.005, p=0.001, and p=0.001, respectively). However, results were different when stratifying by gender. In men, BMI and obesity rates did not differ significantly across groups (Table 2b). On the contrary, women with lifetime ADHD presented with significantly higher BMI (p=0.019) and obesity rates (OR=1.41 [95% CI: 1.05–1.89]) than women without ADHD (Table 2c). In addition, women with persistent ADHD had significantly higher obesity rates (OR=1.48 [95% CI: 1.01–2.17]) than women without ADHD (Table 2c). Moreover, weight was significantly higher in men with persistent ADHD compared to men without ADHD (p=0.045, Table 2b) and in women with lifetime ADHD in relation to women without ADHD (p=0.019, Table 2c).

Table 2a.

Anthropometric data (values at Wave 2) of individuals (men plus women) without and with ADHD (lifetime, persistent, and remitted) in the National Epidemiologic Survey on Alcohol and Related Conditions.

Pairwise comparisons

Lifetime ADHD Persistent ADHD Remitted ADHD Non ADHD Lifetime ADHD vs. Non ADHD Persistent ADHD vs. Non ADHD Remitted ADHD vs. Non ADHD Persistent ADHD vs. Remitted ADHD
(n=616, 1.93%) (n=340, 1.02%) (n=276, 0.91%) (n=34037, 98.07%)
M or % (S.E.) M or % (S.E.) M or % (S.E.) M or % (S.E.) p or OR [95% CI] p or OR [95% CI] p or OR [95% CI] p or OR [95% CI]
Weight (pounds) 186.19 (2.39) 186.82 (3.62) 185.49 (2.68) 176.40 (0.50) 0.001 0.005 0.001 0.75
Height (inches) 67.96 (0.20) 67.41 (0.24) 68.58 (0.31) 66.94 (0.05) <0.001 0.057 <0.0001 0.002
BMI* 28.29 (0.33) 28.79 (0.49) 27.73 (0.39) 27.56 (0.06) 0.032 0.015 0.66 0.09
Obesity** 31.16 (2.55) 34.88 (3.42) 26.99 (3.03) 27.13 (0.40) 1.22 (0.96–1.54) 1.44 (1.06–1.95) 0.99 (0.73–1.35) 1.45 (0.99–2.12)
*

Computed as: [weight (pounds)/height (inches)2] × 703.

**

BMI ≥ 30.

All estimates are based on weighted data taking into account the study design.

Significant p (<0.05) or OR (95% CI not including 1) are indicated in bold.

Table 2b.

Anthropometric data (values at Wave 2) of men without and with ADHD (lifetime, persistent, and remitted) in the National Epidemiologic Survey on Alcohol and Related Conditions.

Pairwise comparisons

Lifetime ADHD Persistent ADHD Remitted ADHD Non ADHD Lifetime ADHD vs. Non ADHD Persistent ADHD vs. Non ADHD Remitted ADHD vs. Non ADHD Persistent ADHD vs. Remitted ADHD
(n=322, 2.31%) (n=162, 1.13%) (n=160, 1.18%) (n=14242, 97.69%)
M or % (S.E.) M or % (S.E.) M or % (S.E.) M or % (S.E.) p or OR [95% CI] p or OR [95% CI] p or OR [95% CI] p or OR [95% CI]
Weight (pounds) 200.00 (3.09) 204.17 (5.04) 196.00 (3.32) 193.93 (0.65) 0.045 0.535 0.171
Height (inches) 70.66 (0.21) 70.12 (0.30) 71.18 (0.27) 69.88 (0.06) 0.0003 0.426 <0.0001 0.012
BMI* 28.17 (0.44) 29.10 (0.69) 27.28 (0.49) 27.85 (0.07) 0.473 0.075 0.257 0.034
Obesity** 28.30 (3.42) 33.74 (4.77) 23.08 (3.97) 26.54 (0.54) 1.09 (0.78–1.53) 1.41 (0.92–2.16) 0.83 (0.53–1.30) 1.70 (0.97–2.97)
*

Computed as: [weight (pounds)/height (inches)2] × 703.

**

BMI ≥ 30.

All estimates are based on weighted data taking into account the study design.

Significant p (<0.05) or OR (95% CI not including 1) are indicated in bold.

Table 2c.

Anthropometric data (values at Wave 2) of women without and with ADHD (lifetime, persistent, and remitted) in the National Epidemiologic Survey on Alcohol and Related Conditions.

Pairwise comparisons

Lifetime ADHD Persistent ADHD Remitted ADHD Non ADHD Lifetime ADHD vs. Non ADHD Persistent ADHD vs. Non ADHD Remitted ADHD vs. Non ADHD Persistent ADHD vs. Remitted ADHD
(n=294, 1.59%) (n=178, 0.92%) (n=116, 0.67%) (n=19795, 98.41%)
M or % (S.E.) M or % (S.E.) M or % (S.E.) M or % (S.E.) p or OR [95% CI] p or OR [95% CI] p or OR [95% CI] p or OR [95% CI]
Weight (pounds) 167.70 (3.07) 167.14 (3.94) 168.47 (4.43) 160.22 (0.48) 0.019 0.086 0.069 0.815
Height (inches) 64.34 (0.16) 64.32 (0.20) 64.36 (0.24) 64.25 (0.04) 0.595 0.741 0.658 0.900
BMI* 28.44 (0.48) 28.43 (0.62) 28.46 (0.69) 27.29 (0.07) 0.019 0.072 0.094 0.974
Obesity** 34.97 (3.34) 36.17 (4.37) 33.33 (4.94) 27.67 (0.49) 1.41 (1.05–1.89) 1.48 (1.01–2.17) 1.31 (0.84–2.03) 1.13 (0.64–2.01)
*

Computed as: [weight (pounds)/height (inches)2] × 703.

**

BMI ≥ 30.

All estimates are based on weighted data taking into account the study design.

Significant p (<0.05) or OR (95% CI not including 1) are indicated in bold.

In the entire sample, after controlling for sociodemographic and 12-month DSM-IV diagnoses (Table 3a), persistent ADHD was no longer significantly associated with current obesity (p=0.075). The lack of significant association between persistent ADHD and current obesity after controlling for the aforementioned factors was confirmed when limiting the analyses to men or women, separately (Table 3b and 3c, respectively). After entering each of the confounding variables in the model separately, persistent ADHD was still significantly associated with obesity controlling for SUDs, or race, or income, but was not significantly associated with obesity controlling for mood or anxiety disorders (data available upon request). There was no significant association either between lifetime ADHD and obesity after controlling for other lifetime DSM-IV diagnoses, in the entire sample as well as when considering the two genders separately (Supplemental eTables 2a–2c).

Table 3a.

Logistic regression model in the entire sample (men plus women). Dependent variable: Obesity (at Wave 2). Independent variables: persistent ADHD and 12-month mood disorders, anxiety disorders, SUDs (nicotine dependence and any SUDs other than nicotine dependence), race/ethnicity, and individual income.

b S.E. AOR 95% CI p (2-tailed)
Predictors (independent variables)
Persistent ADHD 0.27 0.15 1.31 0.97–1.78 0.075
Mood disorder 12-month 0.27 0.05 1.30 1.18–1.44 <0.0001
Anxiety disorder 12-month 0.24 0.04 1.27 1.16–1.39 <0.0001
Any SUDs other than nicotine dependence 12-month −0.20 0.05 0.82 0.74–0.91 0.0004
Nicotine dependence 12-month −0.11 0.05 0.89 0.81–0.99 0.0302
Race/Ethnicity
 White 0.00 0.00 1.00 1.00-1.00 .
 Black 0.59 0.04 1.80 1.66–1.94 <0.0001
 Native American 0.50 0.11 1.64 1.33–2.03 <0.0001
 Asian −1.16 0.14 0.31 0.24–0.42 <0.0001
 Hispanic 0.21 0.05 1.24 1.12–1.37 0.0001
Individual Income
 0–19K 0.00 0.00 1.00 1.00-1.00 .
 20–34K 0.00 0.04 1.00 0.92–1.09 0.982
 35–69K 0.02 0.04 1.02 0.95–1.09 0.655
 >70K −0.14 0.06 0.87 0.78–0.98 0.0201

Significant 95% CI and p (<0.05) are indicated in bold.

Table 3b.

Logistic regression model in men. Dependent variable: Obesity (at Wave 2). Independent variables: persistent ADHD and 12-month mood disorders, anxiety disorders, SUDs (nicotine dependence and any SUDs other than nicotine dependence), race/ethnicity, and individual income.

b S.E. AOR 95% CI p (2-tailed)
Predictors (independent variables)
Persistent ADHD 0.29 0.21 1.33 0.88–2.03 0.176
Mood disorder 12-month 0.11 0.10 1.11 0.92–1.35 0.277
Anxiety disorder 12-month 0.26 0.08 1.30 1.11–1.52 0.001
Any SUDs (other than nicotine dependence) 12-month −0.10 0.06 0.91 0.80–1.03 0.132
Nicotine dependence 12-month −0.10 0.07 0.91 0.79–1.05 0.188
Race/Ethnicity
 White 0.00 0.00 1.00 1.00-1.00 .
 Black 0.27 0.07 1.31 1.15–1.50 0.0001
 Native American 0.59 0.16 1.80 1.30–2.48 0.0005
 Asian −0.98 0.20 0.38 0.25–0.56 <0.0001
 Hispanic 0.14 0.06 1.15 1.02–1.30 0.0237
Individual Income
 0–19K 0.00 0.00 1.00 1.00-1.00 .
 20–34K 0.09 0.06 1.10 0.97–1.24 0.1449
 35–69K 0.19 0.05 1.21 1.09–1.35 0.0005
 >70K 0.02 0.08 1.02 0.87–1.20 0.8236

Significant 95% CI and p (<0.05) are indicated in bold.

Table 3c.

Logistic regression model in women. Dependent variable: Obesity (at Wave 2). Independent variables: persistent ADHD and 12-month mood disorders, anxiety disorders, SUDs (nicotine dependence and any SUDs other than nicotine dependence), race/ethnicity, and individual income.

b S.E. AOR 95% CI p (2-tailed)
Predictors (independent variables)
Persistent ADHD 0.26 0.20 1.29 0.87–1.92 0.1950
Mood disorder 12-month 0.37 0.06 1.44 1.28–1.62 <0.0001
Anxiety disorder 12-month 0.23 0.05 1.26 1.14–1.40 <0.0001
Any SUDs (other than nicotine dependence) 12-month −0.42 0.09 0.66 0.55–0.79 <0.0001
Nicotine dependence 12-month −0.12 0.07 0.88 0.77–1.01 0.0667
Race/Ethnicity
 White 0.00 0.00 1.00 1.00-1.00 .
 Black 0.84 0.04 2.32 2.12–2.54 <0.0001
 Native American 0.42 0.14 1.52 1.16–2.01 0.0033
 Asian −1.36 0.18 0.26 0.18–0.37 <0.0001
 Hispanic 0.28 0.07 1.32 1.15–1.52 0.0002
Individual Income
 0–19K 0.00 0.00 1.00 1.00-1.00 .
 20–34K −0.05 0.06 0.95 0.85–1.06 0.3827
 35–69K −0.18 0.05 0.84 0.75–0.93 0.0017
 >70K −0.50 0.10 0.61 0.50–0.74 <0.0001

Significant 95% CI and p (<0.05) are indicated in bold.

Relationship of ADHD symptom dimensions in childhood to obesity in adulthood

In the entire sample, after controlling for lifetime DSM-IV diagnoses and sociodemographic characteristics, the number of impulsive (p=0.001) and inattentive (p=0.036), but not of hyperactive symptoms before age 18, was significantly associated with current obesity in adulthood (Table 4a). However, a different pattern emerged when considering the two genders separately. In men, the number of impulsive, inattentive, or hyperactive symptoms before 18 years was not significantly associated with current obesity rates (the association with the number of impulsive symptoms approached significance: p=0.06) (Table 4b). By contrast, in women, the number of each of the three symptom dimensions before 18 years was significantly associated with current obesity (Table 4c).

Table 4a.

Logistic regression model in the entire sample (men plus women). Dependent variable: Obesity rate (at Wave 2); Independent variables: number of hyperactive symptoms before age 18, number of impulsive symptoms before age 18, number of inattentive symptoms before age 18, any mood disorders lifetime, any anxiety disorders lifetime, SUDs lifetime (nicotine dependence and any SUDs other than nicotine dependence), race/ethnicity, and individual income.

b S.E. AOR 95% CI p (2-sided)
Predictors (independent variables)
Number of inattentive symptoms 0.02 0.01 1.02 1.00–1.05 0.036
Number of impulsive symptoms 0.06 0.02 1.06 1.02–1.10 0.001
Number of hyperactive symptoms −0.02 0.01 0.98 0.95–1.00 0.092
Any mood disorders lifetime 0.26 0.03 1.30 1.21–1.39 <0.0001
Any anxiety disorders lifetime 0.20 0.04 1.22 1.13–1.31 <0.0001
Any SUDs (other than nicotine dependence) lifetime −0.08 0.04 0.92 0.86–0.99 0.028
Nicotine dependence lifetime −0.06 0.04 0.94 0.87–1.02 0.121
Race/Ethnicity
 White 0.00 0.00 1.00 1.00-1.00 .
 Black 0.60 0.04 1.82 1.68–1.96 <0.0001
 Native American 0.48 0.11 1.61 1.30–1.99 <0.0001
 Asian −1.17 0.15 0.31 0.23–0.42 <0.0001
 Hispanic 0.22 0.05 1.25 1.13–1.38 <0.0001
Individual Income
 0–19K 0.00 0.00 1.00 1.00-1.00 .
 20–34K 0.02 0.04 1.02 0.94–1.11 0.646
 35–69K 0.03 0.04 1.04 0.96–1.12 0.358
 >70K −0.10 0.06 0.90 0.80–1.01 0.071

Significant 95% CI and p (<0.05) are indicated in bold.

Table 4b.

Logistic regression model in men. Dependent variable: Obesity rate (at Wave 2); Independent variables: number of hyperactive symptoms before age 18, number of impulsive symptoms before age 18, number of inattentive symptoms before age 18, any mood disorders lifetime, any anxiety disorders lifetime, SUDs lifetime (nicotine dependence and any SUDs other than nicotine dependence), race/ethnicity, and individual income.

b S.E. AOR 95% CI p (2-sided)
Predictors (independent variables)
Number of inattentive symptoms 0.02 0.02 1.02 0.99–1.05 0.260
Number of impulsive symptoms 0.05 0.03 1.05 1.00–1.11 0.061
Number of hyperactive symptoms −0.02 0.02 0.98 0.95–1.02 0.378
Any mood disorders lifetime 0.21 0.06 1.23 1.08–1.40 0.001
Any anxiety disorders lifetime 0.12 0.06 1.12 1.00–1.26 0.052
Any SUDs (other than nicotine dependence) lifetime 0.00 0.05 1.00 0.91–1.10 0.981
Nicotine dependence lifetime −0.07 0.06 0.94 0.83–1.05 0.258
Race/Ethnicity
 White 0.00 0.00 1.00 1.00-1.00
 Black 0.29 0.07 1.34 1.17–1.53 0.0001
 Native American 0.56 0.16 1.75 1.26–2.41 0.001
 Asian −0.98 0.20 0.37 0.25–0.56 <0.0001
 Hispanic 0.15 0.06 1.16 1.03–1.32 0.019
Individual Income
 0–19K 0.00 0.00 1.00 1.00-1.00 .
 20–34K 0.12 0.06 1.13 0.99–1.28 0.065
 35–69K 0.21 0.05 1.24 1.11–1.38 0.0001
 >70K 0.05 0.08 1.05 0.89–1.23 0.548

Significant 95% CI and p (<0.05) are indicated in bold.

Table 4c.

Logistic regression model in women. Dependent variable: Obesity rate (at Wave 2); Independent variables: number of hyperactive symptoms before age 18, number of impulsive symptoms before age 18, number of inattentive symptoms before age 18, any mood disorders lifetime, any anxiety disorders lifetime, SUDs lifetime (nicotine dependence and any SUDs other than nicotine dependence), race/ethnicity, and individual income.

b S.E. AOR 95% CI p (2-sided)
Predictors (independent variables)
Number of inattentive symptoms 0.03 0.01 1.03 1.00–1.05 0.0372
Number of impulsive symptoms 0.08 0.02 1.09 1.03–1.14 0.0014
Number of hyperactive symptoms −0.04 0.02 0.96 0.92–1.00 0.0454
Any mood disorders lifetime 0.33 0.05 1.39 1.27–1.52 <0.0001
Any anxiety disorders lifetime 0.27 0.05 1.31 1.19–1.43 <0.0001
Any SUDs (other than nicotine dependence) lifetime −0.20 0.05 0.81 0.73–0.90 0.0002
Nicotine dependence lifetime −0.06 0.05 0.94 0.85–1.05 0.242
Race/Ethnicity
 White 0.00 0.00 1.00 1.00-1.00
 Black 0.84 0.04 2.33 2.13–2.54 0.0001
 Native American 0.41 0.14 1.51 1.13–2.00 0.0052
 Asian −1.36 0.18 0.26 0.18–0.37 <0.0001
 Hispanic 0.28 0.07 1.32 1.15–1.52 0.0002
Individual Income
 0–19K 0.00 0.00 1.00 1.00-1.00 .
 20–34K −0.04 0.06 0.96 0.85–1.07 0.441
 35–69K 0.18 0.06 0.84 0.75–0.94 0.0025
 >70K −0.49 0.10 0.61 0.51–0.75 <0.0001

Significant 95% CI and p (<0.05) are indicated in bold.

Discussion

This is the largest study to examine the relationship between ADHD and obesity. It is also the first one to relate ADHD as categorical construct and each ADHD symptom dimension in childhood to obesity in adulthood controlling for an extensive set of confounding factors, including sociodemographic variables and DSM diagnoses (mood disorders, anxiety disorders, and SUDs) and to perform these analyses stratified by gender. Our study provides insights into unexplored issues that may inform further screening and treatment programs aimed at decreasing obesity by means of ADHD treatment.

The results of this study did not support our hypothesis of a significant association, in women, between ADHD and obesity after controlling for a broad range of covariates. While, in the unadjusted model, persistent ADHD was significantly associated with obesity in adulthood in the entire sample as well as in women, the relationship did not hold after controlling for sociodemographic characteristics and DSM-IV diagnoses associated with both ADHD and obesity. Lifetime ADHD was associated with current obesity only in women and only in the unadjusted model. However, when considering ADHD symptom dimensions, the number of lifetime inattentive, impulsive, and hyperactive symptoms (separately) before age 18 was significantly associated with obesity in adulthood in women, even after controlling for confounding variables, as predicted by our second hypothesis. In men, no symptom dimension was significantly associated with current obesity in adulthood.

Relationship between ADHD as a categorical diagnosis and obesity

The association, in the unadjusted model, between persistent ADHD and obesity in the entire sample is in line with prior epidemiologic studies in adults and in children, summarized in 4 (with the exception of one epidemiologic study in early adolescents21), including mixed samples of males and females. Our study, as the one by Pagoto et al.22, used face-to-face interviews to generate a formal diagnosis of ADHD. Other epidemiologic studies relied on self-ratings in adult samples, parent/teacher rating scales in children, interviews assessing ADHD symptoms without a formal diagnosis of ADHD or, in childhood samples, on a single question such as “Has your doctor ever told you that your child has ADHD?”4. Both self-ratings and the use of a single question have lower reliability compared to face-to-face interviews23. As Pagoto et al.22, we found that remitted ADHD was not significantly associated with current obesity, while persistent ADHD was. This suggests that the presence of a categorical diagnosis of ADHD in the past would not be sufficient, per se, to account for the increased likelihood of current obesity in adulthood. This finding concurs with the report by Barkley et al. who, in their longitudinal cohort, found significantly higher BMI in individuals with persistent, but not remitted, ADHD, compared to non ADHD individuals (obesity rates were not reported in that study)24. We note that the prevalence of obesity in our non-ADHD sample (27.1% [95% CI: 26.35–27.91]) was close to the overall self-reported obesity rate in the U.S. (26.7% [95% CI: 26.4%–27.0%]) in 200925. Therefore, it is unlikely that the significantly higher obesity rates in individuals with ADHD in our sample were due to unusually low obesity rate in non-ADHD individuals.

While we confirmed the significant association between persistent ADHD and obesity at the unadjusted level, as already reported in prior smaller epidemiologic studies (summarized in 4), this association did not hold after controlling for a broad range of variables including sociodemographic and a wide range of DSM-IV diagnoses (mood disorders, anxiety disorders, and SUDs), in the entire sample as well as when stratifying by gender. These results did not significantly change when we considered lifetime ADHD and lifetime mental disorders instead of 12-month diagnoses. Prior studies controlled for comorbid major depressive disorder22 or for symptoms of depression/anxiety (without a formal diagnosis)26. However, to our knowledge, our study is the first to include, as covariates, the broader range of mood disorders as well as anxiety disorders and SUDs diagnosed with face-to-face interviews.

Our results suggest that, while adults with ADHD may be at risk of obesity, comorbid mood and depressive disorders might be more directly linked to obesity than ADHD per se. Therefore, if further longitudinal studies confirm a causal relationship between comorbid mood and anxiety disorders and obesity in adults with ADHD, specific screening and intervention programs for the management of obesity in ADHD adults should take into account comorbid mood and anxiety disorders.

Relationship between ADHD symptom dimensions and obesity

In the entire sample, hyperactive symptoms before age 18 were not significantly associated with obesity after controlling for relevant covariates; on the other hand, the numbers of inattentive or impulsive symptoms, considered separately, were. However, these results reflect a different pattern in men and women: while the number of hyperactive, impulsive, or inattentive symptoms before 18 years was significantly associated with current obesity in females, none of the three symptom dimensions was significantly associated with obesity in men, although the association with the number of impulsive symptoms approached significance (p=0.061). Given the current debate on how best to operationalize ADHD diagnostic criteria for adults27, our results highlight the value of considering a dimensional approach.

Although the retrospective report of ADHD symptoms cannot strongly support causality, our results are consistent with the hypothesized role of both impulsivity and inattention in increasing the risk of obesity, at least in females. As for impulsivity, it has been proposed that both deficient inhibitory control and delay aversion reinforce abnormal eating behaviours, which, in turn, would increase the likelihood of obesity28. Inattention and associated poor planning capacities might cause difficulties in adhering to regular eating patterns, favouring abnormal eating behaviors28. Individuals with ADHD may also be relatively inattentive to internal signs of hunger and satiety4. Inattention and associated deficient planning capacities may also interfere with dietary regimes in individuals with obesity who wish to decrease their body weight4. Contrary to our expectation, we found that also the number of hyperactive symptoms before age 18 was significantly associated with obesity in women, even after controlling for possible confounders. This association could seem counterintuitive if one assumes that motor hyperactivity increases energy expenditure, thus favouring weight loss. However, it is well known that the motor hyperactivity of ADHD is not constant but is modulated by the context. Of note, little difference in hyperactivity levels between children with ADHD and healthy comparisons was detected while watching television29 and children with ADHD watch more television and engage in less physical activity than comparisons12. Further studies are needed to better understand if and how the hyperactive dimension of ADHD can contribute to weight gain in women.

The association between ADHD symptoms before age 18 and current obesity in women but not in men is consistent with a previous study by van Egmond-Frohlich et al. in adolescents where separate analyses by gender were performed10. As van Egmond-Frohlich and coworkers suggest, this gender difference may be accounted for by several factors, including: 1) The prevalence of dysregulated eating disorders and, in particualr, of binge eating disorder is higher in girls than in boys30; 2) Girls are under higher cultural pressure to be thin and thus to diet than boys30. Since ADHD has been shown as a significant barrier to weigth loss7, girls with high levels of ADHD symptoms are more prone to fail during their efforts to lose weight; 3) Restrained eating is more frequent in girls compared to boys30. High levels of impulsivity are associated with rebound effect after restrained eating, and there is evidence showing that in girls, but not in boys, higher preoccupation with weight predicted the development of overweight/obesity31. Therefore, impulsivity may lead to excessive overeating after food restriction, favoring weight gain in girls.

Our results have relevant clinical and public health implications, providing a strong rationale for longitudinal studies assessing the value of screening and treatment interventions aimed at preventing obesity by addressing impulsivity, inattention and hyperactivity in girls, regardless of the presence of a categorical diagnosis of ADHD. Inattention and related impaired executive functions, as well as impulsivity that hamper the appropriate adherence to a regular diet regime, might be mistakenly attributed to laziness and “character problems”, adding to the stigmatization of individuals with obesity4. Therefore, treatments focused on these symptoms are paramount to decrease stigma associated with obesity. The pharmacological treatment of comorbid ADHD with psychostimulants leads to significant long-term weight loss in individuals with a lengthy history of weight loss failure, because of the positive effects of treatment on self-directedness, persistence and novelty-seeking behaviors rather than the temporary anorexigenic effects of psychostimulants7. As a complement to pharmacological treatment, cognitive behavioural (CBT) interventions have been also reported to be effective for ADHD in adults32. Our study provides a rationale to assess the efficacy of pharmacological treatment as well as of CBT in girls addressing ADHD symptoms, regardless of the presence of a categorical diagnosis, in order to prevent weight gain in adulthood.

From a research standpoint, our findings suggest that: 1) a dimensional approach focused on ADHD symptom dimensions, rather than on ADHD as a category, might be a fruitful avenue in future research on the psychopathological and neurobiological mechanisms underlying the link between ADHD and obesity. This is in line with the recent Research Domain Criteria initiative of the National Institute of Mental Health33, which aims to establish the genetic and neural correlates of dimensions of observable behaviours, extending and complementing current categorical diagnoses; 2) it is pivotal to consider gender differences when assessing the relationship between ADHD symptoms and risk for obesity.

Limitations

Our findings should be considered in the light of study limitations. The assessment of childhood ADHD, as well as of remission/persistence of ADHD symptoms, was retrospective. However, reliability and validity of the retrospective report of ADHD symptoms have been shown in several studies (e.g., 34,35), although others have shown that retrospective report can lead to underestimation36 or overestimation of ADHD prevalence37. ADHD symptoms and related impairments were endorsed directly by the participants, without additional information from a third person or school reports, although the appropriateness of the use of external reports for the diagnosis of ADHD in adults is still debated in the field27. This may help explain the lower prevalence of ADHD found in this study compared to the rate (~4%) reported when additional information from other persons is sought, as discussed in our previous study20. The items of the AUDADIS-IV interview operationalised the 18 items of the DSM-IV criterion A, which were originally developed to diagnose ADHD in school-aged children and may not be sensitive enough to diagnose ADHD in adults. This may have led to an underestimation of the ADHD prevalence rate. The availability, in the next future, of the upcoming ADHD DSM-5 criteria, providing example of ADHD symptoms applicable across the lifespan, will allow addressing this issue. Weight and height were self-reported. It is unlikely and we are not aware of any empirical evidence that self-report biases reports of weight and height by individuals with and without ADHD differentially. Moreover, there is evidence that in the U.S. population < 60 years, self-reported weight and height are an accurate estimation of directly measured values38. Another limitation is that the NESARC did not include a systematic assessment of eating disorders, which may have provided useful insight into the pathways linking ADHD and obesity, as well as of sleep disturbances, which may be associated with ADHD39 and have been shown to contribute to weight gain40. Finally, we could not control for the possible effect of psychotropic drugs on weight status. However, given the anorexigenic effects of psychostimulant treatment40, the most common ADHD treatment, their effects would not be expected to lead to a spurious association between ADHD and obesity.

Conclusions and future perspectives

Our results may have important implications for clinical practice, public health policy, and future research. Although ADHD in adulthood was associated with obesity, the association did not hold after controlling for sociodemographic factors and psychiatric comorbidities. This suggests that programs aimed at reducing obesity in ADHD adults should target comorbid disorders. On the other hand, the association of ADHD symptoms before age 18 with obesity rates in women, even after controlling for sociodemographic characteristics and comorbid mental disorders, should prompt clinicians to systematically monitor for obesity risk in girls with high levels of ADHD symptoms, regardless the presence of a formal diagnosis of ADHD and other comorbid psychiatric disorders. Longitudinal studies should be conducted to assess the efficacy of the treatment of ADHD symptoms in girls in order to prevent weight gain in adulthood.

Future research adopting a dimensional approach to ADHD, complementing a categorical one, could advance our knowledge on the relationship between ADHD symptoms and obesity. Given the clinical relevance and the impact of both ADHD and obesity in terms of public health, further research on their association and their management when they co-occur should be encouraged.

Supplementary Material

Acknowledgments

The National Epidemiologic Survey on Alcohol and Related Conditions was sponsored by the National Institute on Alcohol Abuse and Alcoholism with supplemental support from the National Institute on Drug Abuse. Work on this manuscript was supported by “Marie Curie” grant PIOF-253103 from the European Commission (Dr. Cortese), NIH grants DA019606 and DA023200 (Dr. Blanco), and New York State Psychiatric Institute and NIH grant R25MH086466 (Dr. Bernardi).

Footnotes

Contributor’s Statement

Samuele Cortese: 1) Substantial contribution to conception, analysis and interpretation of data; 2) Drafting the article; 3) Final approval of the version to be submitted. Stephen V. Faraone: 1) Substantial contribution to interpretation of data; 2) Revising the article critically; 3) Final approval of the version to be submitted. Silvia Bernardi: 1) Substantial contribution to interpretation of data; 2) Revising the article critically; 3) Final approval of the version to be submitted. Shuai Wang: 1) Substantial contribution to analysis of data; 2) Revising the article critically; 3) Final approval of the version to be submitted. Carlos Blanco: 1) Substantial contribution to conception and design, acquisition of data, and interpretation of data; 2) Revising the article critically; 3) Final approval of the version to be submitted.

Declaration of interest: The National Epidemiologic Survey on Alcohol and Related Conditions was sponsored by the National Institute on Alcohol Abuse and Alcoholism with supplemental support from the National Institute on Drug Abuse. Work on this manuscript was supported by “Marie Curie” grant PIOF-253103 from the European Commission (Dr. Cortese), NIH grants DA019606 and DA023200 (Dr. Blanco), and New York State Psychiatric Institute and NIH grant R25MH086466 (Dr. Bernardi). Conflicts of interest: Dr. Cortese has served as scientific consultant for Shire Pharmaceuticals (2009–2010). In the past year, Dr. Faraone received consulting income and/or research support from Shire, Otsuka and Alcobra and research support from the National Institutes of Health (NIH). He is also on the Clinical Advisory Board for Akili Interactive Labs. In previous years, he received consulting fees or was on Advisory Boards or participated in continuing medical education programs sponsored by: Shire, McNeil, Janssen, Novartis, Pfizer and Eli Lilly. Dr. Faraone receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health and Oxford University Press: Schizophrenia: The Facts. Dr. Bernardi, Dr. Shuai and Dr. Blanco report no conflicts of interest.

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