Perceived Weight, Not Obesity, Increases Risk for Major Depression Among Adolescents

Abstract

This study examined the association between major depression, obesity and body image among adolescents. Methods: Participants were 4,175 youths 11–17 years of age sampled from the community who were interviewed using the Diagnostic Interview Schedule for Children and Adolescents, Version IV, completed a self-report questionnaire, and had their weight and height measured. There were 2 measures of body image: perceived weight and body satisfaction. Obesity was associated with increased risk of depression, with no controls for covariates. However, when the association was examined in models which included weight, major depression, and body image measures and covariates, there was no association between major depression and body weight, nor between body satisfaction and major depression. Perceived overweight was strongly and independently associated with body weight (O.R. = 2.62). We found no independent association between major depression and body weight. If there is an etiologic link between major depression and body weight among adolescents, it most likely operates through processes involving components of body image. Future research should focus on the role of depression and body image in the etiology of obesity.

Introduction

Obesity is a significant public health problem because of the high prevalence of the condition (Odgen, et al., 2008), as well as the morbidity and mortality attributed to it (Must, et al., 1999; NHLBI, 1998) and the medical expenditures resulting from obesity-related conditions (Finkelstein, et al., 2004; Finkelstein, et al., 2009) The increasing prevalence of obesity has been noted in children as well (Ogden, et al., 2006; 2008). In terms of the etiology of adolescent obesity, we now have considerable evidence for the role of behavioral determinants such as physical activity, sedentary lifestyles, and a high calorie diet. We have considerable less data on the role of social and psychological factors in obesity. For example, the relation between obesity and depression is unclear, with mixed evidence thus far. For example, a review by Atlantis and Baker (2008) found that among adults, obesity modestly increased risk for depression among women but not men, and that studies from outside the United States found no association between obesity and depression. Most studies have been cross-sectional, both for adults and adolescents.

Historically, obesity has been a stigmatized condition with reports documenting social exclusion, occupational and educational discrimination, and expression attributed to negative attitudes (Puhl and Brownell, 2001). This is true for adults (Puhl & Heuer, 2009) as well as children and adolescents (Puhl & Latner, 2007. The multiple, adverse social and interpersonal consequences of obesity have resulted in the widespread assumption that the obese suffer psychologically both as a direct consequence of social adversities and indirectly through negative “reflected appraisal” (Wardle et al., 2006). However, epidemiologic studies of adolescents have yielded mixed results for an association between obesity and the most common mental health outcome examined – depression (see Wardle et al., 2006; Zametkin et al., 2004).

In their recent meta-analytic study, Luppino et al. (2010) identified only 6 papers that met their criteria for prospective studies of the relation between obesity and depression involving adolescents, that is, subjects were under the age of 18 at baseline. They found that the pooled data from the studies of adolescents which examined obesity as a risk factor for subsequent depression found little increased risk. Herva et al. (2006) and Anderson et al. (2007) found that obesity at baseline was not related to depression at follow-up for adolescents. Bardone et al. (1998) found obesity at baseline predicted subsequent depression only marginally. Other studies also have found mixed or no support for increased risk of depression associated with obesity (Wardle et al., 2006). Frisco et al. (2009) found no association between weight and depressive symptoms among girls, but did for boys. In their recent paper, Merikangas et al. (2012) found obesity was not associated with obesity among adolescents overall, but there was elevated risk for boys.

Merikangas et al. (2012) note that evidence thus far on obesity and depression among adolescents has been confounded by diverse sample characteristics and methodologic differences in measuring both depression and weight. Many studies have relied on self-reports of weight, which appears to contribute to confirmation bias (Ge et al., 2001). Many studies also have focused on symptoms of depressed mood, rather than diagnosed depression, thus ignoring more severe manifestations and more rigorously measured depression status.

While there are limited coherent epidemiologic data linking obesity to clinical depression among adolescents, there is evidence from the literature suggesting that the primary effect of obesity is indirect rather than direct. That is, the association between obesity and depression may be mediated by other factors. One of these factors is body image, in particular perceived weight and body satisfaction.

Longitudinal studies indicate that poor body image is associated with greater psychological distress, more disordered eating, binge eating and fewer health-promoting behaviors such as physical activity and consumption of fruits and vegetables (Holsen, et al., 2001; Stice, 2001; Stice and Bearman, 2001; Neumark-Sztainer et al., 2006). ter Bogt et al. (2006) report that body image is a better predictor of internalizing problems (but not externalizing problems) than weight. Daniels (2005) finds no relationship between obesity and depressive symptoms, but poor body image was related to depressive symptoms. Neumark-Sztainer et al. (2007) report that poor body image was one of the strongest prospective predictors of obesity, binge eating, and extreme weight control behaviors. None of these papers focused on clinical depression.

Thus far, to our knowledge, no paper has examined the association between weight, body image, and diagnosed depression among adolescents. The purpose of this paper is to reexamine the association between obesity and DSM-IV major depression, taking into account the effects of body image. To this end, we use data from Teen Health 2000 (TH2K), a large community-based sample of adolescents 11–17 years of age which assessed DSM-IV major depression (among other psychiatric disorders) and also measured height and weight and included two measures of body image: perceived weight and satisfaction with one’s body.

Our hypothesis is that there is an association between obesity and major depression and this relationship is mediated by body image, e.g., negative body image better accounts for risk of major depression than body weight per se.

Methods

Sample

The sample was selected from households in the Houston metropolitan area enrolled in two local health maintenance organizations. One youth, aged 11 to 17 years, was sampled from each eligible household, oversampling for ethnic minority households. Initial recruitment was by telephone contact with parents. A brief screener was administered on ethnic status of the sample youths and to confirm data on age and sex of youths. Every household with a child 11 to 17 years of age was eligible. Because there were proportionately fewer minority subscriber households, sample weights were developed and adjusted by poststratification to reflect the age, ethnic, and sex distribution of the 5-county Houston metropolitan area in 2000. The precision of estimates are thereby improved and sample selection bias reduced to the extent that it is related to demographic composition (Andrews et al., 1973). Thus, the weighted estimates generalize to the population 11 to 17 years of age in a metropolitan area of 4.7 million people.

Data were collected on sample youths and one adult caregiver in 2000–2001 using computer-assisted personal interviews and self-administered questionnaires. The computerized interview contained the structured psychiatric interview (see below) and demographic data on the youths and the household. Height and weight measures were conducted after the completion of the interviews. The interviews and measurements were conducted by trained, lay interviewers. The interviews took on average 1 to 2 hours, depending on the number of psychiatric problems present. Interviews, questionnaires, and measurements were completed with 4175 youths at baseline, representing 66% of the eligible households. There were no significant differences in gender, age or among ethnic groups in completion rates. All youths and parents gave written informed consent prior to participation. All study forms and procedures were approved by the University of Texas Health Science Center Committee for Protection of Human Subjects.

Sample characteristics are presented in Table 1. As can be seen, the sample, in addition to being representative, was also diverse in terms of age, ethnic status, and family income (reported by parent).

Table 1.

Unweighted Sample Characteristics, Teen Health 2000

Characteristics		Wave I N=4175
		%
Gender of Youth	Male	51.1
	Female	48.9
Age of Youth	16 +	24.9
	Between 13 and 15	48.1
	12 or less	27.0
Ethnicity of Youth	White American	35.4
	African American	35.4
	Latino American	24.4
	Others	4.6
Family Income	$65,000 +	35.3
	$ 35,000 – $ 64,999	40.7
	< $35,000	24.0
Major Depression	Yes	1.7
	No	98.3
Weight	Healthy Weight	61.0
	Overweight (95th>BMI≥85th)	18.2
	Obese (BMI≥95th)	20.8

Measures

Major Depression

Data on psychiatric disorders were collected using the youth version of the Diagnostic Interview Schedule for Children, Version 4 (DISC-IV), a highly structured instrument with demonstrated reliability and validity (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Interviews were conducted by college-educated, lay interviewer who had been extensively trained using protocols provided by Columbia University. Interviews with the DISC-IV were administered using laptop computers.

We examine the association between obesity and major depression using DSM-IV diagnostic criteria (American Psychiatric Association, 2000). Thus, we define major depression as at least one major depressive episode in the previous 12 months (prevalence was 1.7%).

BMI and weight status

Height and weight were measured using standard field procedures such as a Tanita digital scale (see Armstrong & Welsman, 1997; Lohman, Roche & Martorelli, 1991; National Institutes of Health, 1998). BMI is defined as weight in kg/squared height in meter (kg/m2). Weight status is categorized as healthy weight (BMI <85^th percentile), overweight (95^th>BMI≥85^th percentile), and obese (BMI>95^th percentile) (CDC, 2006; Ogden et al., 2008). For the overall sample at baseline, 18.2% were overweight (85th≤BMI>95th) and 20.8% were obese (BMI≥95th). Thus, 39% were overweight or obese. Younger youths were heavier, as were males, minority youths, and those from lower income families (Table 2). For purposes of analyses, due to low prevalence of major depression, weight is dichotomized: healthy weight vs. other.

Table 2.

Distribution of Weight Status, Perceived Weight and Body Satisfaction in Sample

			Prevalence (%, 95%CI)
Demographic factors		BMI ≥85th	BMI≥95th	Perceived overweight	Body dissatisfaction
Overall		38.99 (37.39–40.59)	20.79 19.47–22.11)
Age
12 or less		42.55 (39.40–45.69)	22.75 (20.14–25.36)	23.08 (20.58–25.58)	33.89 (31.07–36.71)
Between 13–15		39.78 (37.50–42.06)	20.84 (18.96–22.72)	25.50 (23.57– 27.42)	36.47 (34.34–38.59)
16 +		34.20 (31.07–37.32)	18.72 (16.13–21.31)	25.05 (22.38–27.72)	42.30 (39.26–45.33)
p-value		p<0.001	p>0.05	p>0.05	p<0.001
Gender
male		41.51 (39.25–43.78)	22.96 (21.02–24.89)	21.39 (19.63–23.15)	30.83 (28.85–32.82)
Female		36.34 (34.08–38.60)	18.51 (16.71–20.31)	28.25 (26.27–30.23)	43.93 (41.75–46.11)
p-value		p<0.01	p<0.01	p<0.001	p<0.001
Ethnicity
White American		31.17 (28.76–33.57)	13.93 (12.14–15.71)	22.79 (20.63–24.95)	37.82 (35.33–40.31)
African American		44.35 (41.77–46.92)	25.01 (22.77–27.24)	22.97 (20.80–25.13)	32.43 (30.00–34.85)
Latino American		44.86 (41.74–47.99)	27.68 (24.87–30.48)	28.76 (25.93–31.59)	41.73 (38.67–44.80)
p-value		p<0.0001	p<0.0001	p<0.001	p<0.001
Family income
< $35,000		46.68 (43.11–50.25)	26.26 (23.14–29.38)	25.33 (22.50–28.17)	36.75 (33.60–39.90)
35,000 – $ 64,999		41.67 (39.02–44.32)	22.12 (19.90–24.35)	25.39 (23.21–27.57)	36.84 (34.43–39.25)
$65,000 +		31.92 (29.31–34.54)	16.13 (14.08–18.18)	23.58 (21.31–25.86)	37.70 (35.11–40.29)
p-value		p<0.0001	p<0.0001	p>0.05	p>0.05

Body Image

Body Image is measured with two items. One item assesses perceived weight and inquires whether youths perceive themselves as: (a) skinny; (b) somewhat skinny; (c) average weight; (d) somewhat overweight, or (e) overweight. The other assesses body satisfaction and inquires how satisfied youths are with their body: (a) very dissatisfied; (b) somewhat dissatisfied; (c) neither; (d) somewhat satisfied; or (e) very satisfied. Youths who rate themselves as somewhat or overweight are scored as having poor body image, in this case perceived overweight. Those who rate themselves as somewhat or very dissatisfied with their body also are scored as having poor body image, or in this case, body dissatisfaction. As can be seen in Table 2, females and Latinos were more likely to perceive themselves as overweight. Older youths and females were more likely to express dissatisfaction with their body while African Americans were less likely to be dissatisfied. Unlike body weight, there was no association between family income and either perceived weight or body satisfaction.

Covariates

Faith et al. (2002) have noted many studies of obesity and depression fail to control for relevant covariates that might mediate or moderate the observed association. Another problem is that there is little consistency across studies. Luppino et al. (2010) found only age and gender were consistent across studies. As a result, the direction and magnitude of the effects of covariates remain unclear. We include as covariates known correlates of both depression and obesity: age, gender, family income, physical activity, and dieting behavior (see Roberts & Roberts, 2007; Frisco et al., 2010; Merikangas et al., 2012).

Age is categorized as 12 or younger, 13 – 15, and 16 or older.

Family income is categorized as less than $35,000, $35,000 – $64,999, and $65,000 or more.

Physical activity is measured with an item that asks on how many days in the past week youths walk or bicycled at least 30 minutes at a time. Responses are 0, 1, 2, 3, 4, 5, 6 or 7 days. Scores were dichotomized as “0 days” and 1 day or more in the past week.

Dieting behavior is considered to be important in the context of weight and psychological functioning (Needham and Crosnoe, 2005; Ross, 1994). Our items include (1) refused to eat foods you thought would make you fat; (2) made yourself throw up; (3) took pills to lose weight; (4) took laxatives; (5) fasted for at least 24 hours. The time referent is past month. The items are summed, yielding a score of 0 – 5, and dichotomized as 0 and 1 or more days.

Analyses

The relationship between weight status (healthy weight vs. overweight/obese) and depression (yes, no) is examined, using odds ratios controlling for covariates. Healthy weight group (BMI <85^th percentile) is the referent. In our analyses, body weight is considered the exposure and depression is considered the outcome, That is, greater body weight is hypothesized to increase risk for major depression, all other things equal.

We also examine the association between weight and depression separately for males and females, given that some studies have reported that gender modifies this association (see Mustillo et al., 2003; Blaine, 2008; Frisco et al., 2009; Merikangas et al., 2012).

The estimated odds ratios and their 95% confidence limits were calculated using survey logistic regression (Proc Surveylogistic) procedures in SAS V9.1 (SAS Institute, 2004) and Taylor series approximation to compute the standard error of the odds ratio. Lepkowski and Bowles (1996) have indicated that the difference in computing standard error between this method and other repeated replication methods such as the jackknife is very small. In examining multivariate models including weight, perceived weight, body satisfaction and depression (see Table 4), the adjusted R-square was used to compare non-nested models. The adjusted R-square is appropriate for comparing models with different numbers of independent variables. Larger R-square values indicate a better model fit (see Wooldridge, 2008, 201 – 203).

Table 4.

Adjusted Odds Ratios for the Association Between Actual Weight, Weight Perception, Body Satisfaction and DSM – IV Major Depress.

Major depress	Model 1	Model 2	Model 3	Model 4	Model 5
Overall*	N=3816	N=3741	N=3746	N=3738	N=3695
Healthy weight	1			1	1
Overweight or obese	2.51 (1.47–4.29)			1.17 (0.57–2.40)	1.26 (0.62–2.55)
Perceived normal weight		1		1	1
Perceived overweight		4.08 (2.36–7.06)		3.74 (1.84–7.62)	2.63 (1.25–5.57)
Body satisfaction			1		1
Body dissatisfaction			2.66 (1.51–4.68)		1.75 (0.94–3.25)
Model fit statistics: Adjusted R-Square	0.100	0.128	0.100	0.129	0.126
Male+	N=1960	N=1924	N=1921	N=1923	N=1899
Healthy weight	1			1	1
Overweight or obese	3.05 (1.32–7.01)			1.03 (0.35–3.00)	1.03 (0.34–3.17)
Perceived normal weight		1		1	1
Perceived overweight		9.07 (3.78–21.75)		8.95 (3.33–24.07)	7.01 (2.62–18.73)
Body satisfaction			1		1
Body dissatisfaction			3.13 (1.23–7.99)		1.83 (0.73–4.56)
Model fit statistics: Adjusted R-Square	0.105	0.173	0.110	0.173	0.181
Female+	N=1856	N=1817	N=1825	N=1815	N=1796
Healthy weight	1			1	1
Overweight or obese	2.21 (1.12–4.38)			1.28 (0.46–3.57)	1.47 (0.54–4.01)
Perceived normal weight		1		1	1
Perceived overweight		2.67 (1.38–5.18)		2.31 (0.86–6.26)	1.48 (0.52–4.20)
Body satisfaction			1		1
Body dissatisfaction			2.35 (1.16–4.78)		1.74 (0.77–3.91)
Model fit statistics: Adjusted R-Square	0.092	0.105	0.091	0.106	0.102

Model 1: Major depress = weight status (BMI)

Model 2: Major depress = perceived weight

Model 3: Major depress =body satisfaction

Model 4: Major depress = weight status (BMI)+ perceived weight

Model 5: Major depress = weight status (BMI)+ perceived weight + body satisfaction

^*Adjusted for age, gender, family income, diet and physical activity.

⁺Adjusted for age, family income, diet and physical activity.

Results

Further data on weight status, depression and body image are presented in Table 2. Males are more likely to be obese or overweight, but females are more likely to perceive themselves as overweight and to be dissatisfied with their body. White American youths are less likely to be obese or overweight, but Latino Americans perceive themselves as overweight and are more dissatisfied with their bodies. Lower income youths are more likely to be obese or overweight, but there is no association between family income and either measure of body image. The age effect is even more mixed. Younger youths tend to be more overweight but there is no age effect for obesity. There is no age effect for perceived weight, but older youths are more likely to be dissatisfied with their weight.

Odds of major depression by weight and body image are presented in Table 3. In Table 3, three sets of multivariate models are examined: (1) the relationship between actual weight and depression; (2) the relationship between combination of actual weight and perceived weight and depression; and (3) the relationship between combination of actual weight and body satisfaction and depression. For example, those in category “Perceived normal weight, Healthy weight” having healthy weight (BMI < 85^th) and perceive themselves as skinny; somewhat skinny or average weight. In all analyses, adjustments are made for covariates. Controlling for the covariates, those who are overweight/obese are at greater risk of major depression, overall and by gender. For perceived weight, those who perceive themselves as overweight, regardless of weight status, are at increased risk of major depression.. The association of body satisfaction with major depression, however, is limited to those who are overweight or obese.

Table 3.

Adjusted Odds Ratios for the Association between Actual Weight, Weight Perception, Body Satisfaction and DSM – IV Major Depression.

Actual Weight, Weight Perception, Body Satisfaction	Total sample* N=3816	Males+ N=1960	Females+ N=1856
	OR, 95% C.I.	OR, 95% C.I.	OR, 95% C.I.
Healthy weight	1	1	1
Overweight or obese	2.51 (1.47–4.29)	3.05 (1.32–7.01)	2.21 (1.12–4.38)
Perceived normal weight, Healthy weight	1	1	1
Perceived normal weight, Overweight or obese	1.48 (0.60–3.67)	0.53 (0.06–4.40)	2.66 (0.95–7.43)
Perceived overweight, Healthy weight	4.39 (1.88–10.28)	6.24 (1.33–29.26)	3.83 (1.41–10.44)
Perceived overweight, Overweight or obese	4.35 (2.32–8.16)	9.02 (3.32–24.54)	2.85 (1.29–6.30)
Body satisfaction, Healthy weight	1	1	1
Body satisfaction, Overweight or obese	1.14 (0.40–3.21)	0.85 (0.16–4.44)	1.62 (0.44–5.91)
Body dissatisfaction, Healthy weight	1.60 (0.75–3.41)	1.04 (0.28–3.89)	1.83 (0.72–4.67)
Body dissatisfaction, Overweight or obese	4.40 (2.31–8.41)	6.57 (2.39–18.06)	3.51 (1.53–8.05)

^*: Adjusting for age, gender, family income, diet, and physical activity.

⁺: Adjusting for age, family income, diet, and physical activity.

Bold: Odds ratios are statistically significant (p<0.05).

In the overall sample, regardless of body weight, perceived overweight increases the risk for major depression (OR = 4.4). The same pattern is observed for males and females. For example, compared with those who are healthy weight with perceived normal weight, the odds are 4.35 for the overall sample, 9.02 for males and 2.85 for females who are overweight/obese and perceive themselves as overweight.

On the other hand, the effects of body satisfaction are less robust. For those who are overweight or obese, and who also are dissatisfied with their body, there is increased risk for major depression, with odds of 6.57 for males and 3.51 for females who are dissatisfied with their body and also are overweight or obese.

Further examination of Table 3 reveals another interesting pattern. The perception of being overweight increases risk of major depression more for males than females, regardless of weight status. For body dissatisfaction, the gender difference is much less dramatic.

Table 4 presents additional results of multivariate analyses in which Model 1 examines the association between weight and major depression. Model 2 focuses just on perceived weight and the odds of depression among those who perceive themselves as overweight are 2.7 for females, 4.1 for the overall sample and 9.1 for males. Model 3 repeats those analyses focusing on body dissatisfaction. In all models, adjustments are made for covariates.

Again, all of the odds are significant but not as strong as for perceived weight. Model 4 examines weight and perceived weight simultaneously. Actual weight becomes nonsignificant, as hypothesized, for the overall sample and for males, but not for females. The odds of depression are 3.74 for the overall sample and 8.95 for males. Model 5 is the full model, entering weight, perceived weight and body satisfaction. The results confirm our hypotheses for perceived weight, but not body satisfaction, for the overall sample and for males but not females. That is, it is perceived weight rather than body satisfaction that is associated with greater risk of depression. Actual weight plays no role.

All models for males have higher values of adjusted R-square than those in models for the overall sample, indicating a better fit. Models for females have the lowest values of adjusted R-square, indicating less fit. When comparing models for overall, the model with actual weight and perceived weight (Model 4) has the best fit with adjusted R-square of 0.129. Fit of Model 2 (perceived weight only) is not much different from that of Model 4 (adjusted R-square = 0.128). A similar trend is found for models of females. For males, Model 5, including actual weight, perceived weight and body satisfaction, has the best fit (adjusted R-square = 0.181). Fit of this model is slightly higher than that of Model 2 which includes only perceived weight (adjusted R-square = 0.173). The model including only perceived weight appears to be most parsimonious for overall and for each gender.

Discussion

To our knowledge, this is the first study to examine weight, body image and major depression among adolescents. We found that, as hypothesized, body image mediated the association initially observed between body weight and major depression. Further, when both perceived weight and body satisfaction were entered into the same model (Table 4, Model 5), only perceived weight was associated with major depression. Interestingly, the effect size for this association is consistent with what has been reported for obesity and depression among adolescents with odds in the range of 2.5 (see Blaine, 2008).

Our results bear on work by Frisco et al. (2010). In that paper, the authors draw upon the concept of health congruency (Chipperfield, 1993; Idler, Hudson & Leventhal, 1999) to posit that adolescent depressive symptoms result both from negative self-schema concerning weight and actual weight. Frisco et al. (2010) examined weight pessimists (perceived weight worse than actual weight), weight realists (accurate weight perceptions) and weight optimists (perceived weight better than actual weight). They found weight pessimists to be at greater risk for depressive symptoms. We could not confirm their findings. Our results show that perceived overweight, regardless of weight status, was associated with greater risk for major depression. Congruency was irrelevant. We extended their research by including another measure of body image, body satisfaction, and again could not confirm their results. Both overweight and obese males and obese females with body dissatisfaction had greater risk of major depression. Furthermore, when we examined a model in which weight, perceived weight and body satisfaction were examined in multivariate analyses, neither weight nor body satisfaction were associated with major depression, only perceived weight. Thus, our results suggest that it is not incongruent self-schemas, but accurate self-schemas of adolescents regarding overweight and obesity that have negative mental outcomes, in this case major depression. Frisco et al. (2010) also tested and rejected hypotheses derived from double jeopardy theory (McLeod & Owens, 2004), e.g., that being overweight and perceiving one’s self to be overweight is more stressful in terms of adolescent mental health (Miller & Kaiser, 2001; Strauss & Pollack, 2003). Our results do support this argument, in that we found that those adolescents who were overweight or obese and perceived themselves as such had greater odds of major depression. However, these results were tempered by our multivariate models which found only perceived overweight was associated with major depression.

What our data indicate is that perceptions of being overweight or obese are associated with risk for major depression, that this negative self-schema is ipso facto stressful, engendering adverse mental health outcomes. The marginalization and stigma associated with being overweight and obese appears to be the most likely process (see Puhl & Latner, 2007). The theoretical perspective most germane to this argument is stress theory, in particularly the model emphasized by sociologists (see Dohrenwend, 2000; Turner, 2010; Thoits, 2010).

Our results are also consistent with a recent review by Luppino et al. (2010) who found little or no association between obesity and subsequent depression in three prospective studies of adolescents. None of these studies examined body image.

On a descriptive note, we found that body image varied systematically across subgroups, much as body weight. That is, we found measures of perceived weight and body satisfaction varied by age, gender, and ethnicity as did Martin et al. (2009). Unlike body weight, in which lower income youths are at increased risk, we found no association between measures of body image and family income.

Limitations

Our study had limitations. Our sample was not, strictly speaking, a community-based sample. We were able to complete assessments with only 66% of those sampled at baseline, raising the possibility of nonresponse bias. We have carefully examined potential biases from these design characteristics in previous publications (Roberts & Roberts, 2007; Roberts et al., 2009) and demonstrated that bias from these sources was minimal. We should note that the response rate for adolescents in the National Comorbidity Survey Replication Adolescent Survey was 75% (Kessler, Avenevoli, Costello, Green et al., 2009).

Our estimate of the association between weight and depression may be attenuated because we could not examine lifetime trajectories of either major depression or weight. Data on these factors from early childhood through adolescence are needed, but such studies have not been done, to our knowledge.

Another potential issue is that we did not include data from parent reports of outcomes or predictors (with the exception of family income). While there is argument that data from multiple informants is desirable, many studies attest to considerable discordance in parent-child reports of psychopathology and functioning (see Roberts et al., 2005). In a previous paper, we have demonstrated substantial differences in parent-child concordance across ethnic groups, such that minority parents reported fewer problems but there were no differences among youths across ethnic groups (Roberts et al., 2005). This suggests that reliance on youth reports may be less problematic that use of parent reports, particularly for major depression.

The TH2K study was not designed to assess the roles of family history or genetics in the epidemiology of psychiatric disorders or obesity among youths. As a result, we could not examine the contribution of these factors relative to those factors examined. Reviews by Rutter and colleagues (Rutter et al., 1999a, 1999b) as well as Kendler (1998) suggest that considerable variance in risk of psychiatric disorders is explained by family history and or genetics. Studies of monozygotic twins have found that there is a genetic influence on obesity (Stunkard et al., 1986), but no single gene linked to obesity has been identified. It is possible, indeed plausible, that a gene X environment interaction might better account for the association between depression and obesity (Silventoinen et al., 2010).

We also did not examine directly the role of weight-related stigma, including measures of weight-related prejudice, discrimination, and teasing/victimization (Puhl & Latner, 2007; Puhl & Heuer, 2009).

Conclusion

The literature suggests two possibilities for a link between depression and obesity (see Luppino et al., 2010). One is that a depressed person, through dysregulated stress systems or through unhealthy life styles, develops more obesity over time. The other is that obesity, through its negative effects on self-image or somatic consequences, results in development of depression over time. In the case of adolescents, which of these explanations better fit the data depends on a question of fact. In this case, using cross-sectional data, we tested whether increased risk of major depression was associated with obesity. Initial analyses suggested such an association.

However our data, though admittedly preliminary, suggest a third hypothesis - that the etiologic link, if there is one, between body weight and depression operates through a third factor, body image, which acts as a mediator. There is a substantial literature suggesting that negative body image is related to poor psychological functioning. Indeed, body image appears to be a better predictor of mental health outcomes than weight per se (Herbozo & Thompson, 2010). In the case of our data from TH2K, it appears that perceived weight is more important than weight in terms of risk or major depression, and so our data fit better with the second hypothesis.

More prospective studies are needed which examine the association between clinical or diagnosed depression, obesity and body image within the adolescent population. These studies should also focus on potential moderators and mediators of this association. Based on our results, one of these factors should be stigma and processes involving weight-related prejudice and discrimination.

It is premature, based on the evidence this far, to make recommendations regarding clinical or public health practice. However, the data thus far support the recommendation by Blaine (2008) that adolescent depression screening and intervention strategies, alongside nutritional and exercise-based interventions, may be an important component in the prevention of obesity, both in adolescents and adults. To this, we would add interventions which focus on how youths feel about their weight and body more generally.

The American Academy of Child and Adolescent Psychiatry (2007) in discussing practice parameters for youths recommends screening all children and adolescents for key depressive symptoms such as depressive or sad mood, irritability, and anhedonia. Diagnosis of a depressive disorder should be considered if these symptoms are present most of the time and exceed normative standards.

From a medical perspective, physicians faced with youths who are clinically depressed and overweight may want to consider treatment plans which incorporate strategies that focus on both depression and reduction in caloric intake, since overnutrition is the primary target for prevention of or reduction in overeating (Bray et al., 2012; Sacks et al., 2009). Efficacions treatments for adolescent depression exist in terms of both pharmacological and psychological and social/environmental modalities (see Kazdin, 2004; Biederman et al., 2004; American Academy of Child and Adolescent Psychiatry, 2007; The TADS Team, 2007). Successful interventions for obesity also are available (Faith & Wrotniak, 2009). Guidelines exist for determining which youths are obese and should receive treatment (see Himes & Dietz, 1994; Barlow & Dietz, 1998; Faith & Wrotniak, 2009; Apovian & Lenders, 2007) as well as reviews of efficacy of weight control interventions (August et al., 2008).