Obesity is a global issue with detrimental health and economic impacts (1), whereof health consequences include increased risk of cancers, cardiovascular disease, and type 2 diabetes (2, 3). Such health consequences place a substantial direct and indirect burden on health systems and cost an estimated 2 trillion US dollars worldwide per annum (4). In Norway, results from the HUNT4 Survey (2017–2019), indicate that the prevalence of overweight and obesity among adults are 46% and 23%, respectively (5). As the rate of obesity escalates, the burden on available resources is expected to increase, requiring urgent intervention to manage the prevalence of obesity (4).

Conventional treatment of obesity has largely focused on short-term weight modification, with limited emphasis on the factors affecting longer-term weight maintenance (6) such as psychological factors. However, psychological factors often co-occur with obesity and may consequently maintain obesity (7). Although psychological factors such as depression, anxiety, and psychosocial stress have been shown to be associated with obesity (8–10), more research is required to clarify and elucidate how these psychological mechanisms relate to obesity.

Depression is a highly prevalent mental health disorder that has been found to be associated with obesity (10) across epidemiological, clinical, cross-sectional, and longitudinal studies (10–12). Researchers have theorised that this association may be due to increased weight stigma, social discrimination, and eating for the purpose of emotional regulation (13–15). Further, a bidirectional relationship between depression and obesity has also been suggested, where increasing severity of depression leads to an increase in body mass index (BMI) and increased BMI leads to an increase in severity of depression (12). Although a positive association between obesity and depression has been consistently found, sex and educational attainment may influence this relationship (16). For example, previous research has shown a negative association between depression and BMI in a low socioeconomic sample (16). Therefore, a comprehensive understanding of the relationship between depression and BMI should be further explored, taking into consideration potential mediating variables.

Anxiety is another major psychological disorder associated with obesity (9). The relationship between anxiety and BMI, however, appears to be inconsistent across the literature. Systematic reviews have demonstrated a moderate, positive relationship between anxiety severity and BMI (9, 17). In addition, while some findings indicate a non-linear U-shape association (18), others suggest the opposite, an inverted U-shape (19). As a result, there is evidence to suggest that anxiety is high at underweight, low at overweight, and then high again at obesity (20). Conversely, there is also evidence to indicate that anxiety is low at underweight, high at overweight, and low again at obesity (19). One reason for these differences may be confounding variables such as ethnicity, age and socioeconomic status. For example, while an inverse U-shape association has been shown in aged individuals (18), a U-shape association has been found in Caucasians and people of African descent, with no association in Asian or Hispanic populations (18). Moreover, as anxiety and depression are highly prevalent and linked with obesity, exploring their association is essential for building a clearer understanding of the multifaceted nature of obesity.

In addition to depression and anxiety, psychosocial stress may have important implications on obesity. For example, high levels of stress may change eating patterns (21) and may lead to excessive food intake toward a higher fat and sugar diet (22). Allostatic load, described as chronic exposure to stressors, has also been shown to reduce response and adaptation to stress (23). Importantly, a strong correlation between obesity and allostatic load has been found (24), highlighting the importance of further understanding the role of chronic stress in obesity.

Further, a history of trauma both in childhood and adulthood has been demonstrated to have an association with obesity (25, 26). Trauma refers to events that are perceived as sudden, uncontrollable, extremely negative, and continue to negatively impact individuals into the future (27). Hence, traumatic life events are associated with poorer physical and mental health, poverty, lower educational attainment, and obesity (28–32). With regards to obesity, traumatic events may dysregulate the body’s stress response system (i.e., hypothalamic-pituitary-adrenal [HPA] axis (33), resulting in a greater need to emotionally regulate. To reduce the stress-induced activation of the HPA axis, individuals exposed to traumatic life events may consume food high in sugar and fat through the form of disordered eating behaviours such as binge eating (34), which may contribute to weight gain (34, 35). Together, these processes may help to understand the relationship between psychosocial stress and obesity. However, as psychosocial stress is associated with several adverse outcomes including mental health difficulties, it is important to explore whether an independent relationship between psychosocial stress and BMI exists, controlling for depression, anxiety, age, sex, and educational attainment.

Furthermore, sex, educational attainment, and socioeconomic status have been found to relate to obesity, and may mediate the relationship between BMI, depression, anxiety, and psychosocial stress (36). Research has shown that individuals with lower education are more likely to have obesity compared to individuals with higher educational attainment (37). Married individuals are also more likely to have obesity than single individuals (38). Moreover, although there are mixed findings regarding sex differences (39), lower socioeconomic status has been linked with higher obesity levels (40). Therefore, it is important that these potentially mediating variables are accounted for when investigating the relationship between obesity and psychological factors.

The purpose of this study was to explore how depression, anxiety and psychosocial stress independently relate to BMI across four weight categories, including (1) normal weight (2), overweight (3), obesity class I, and (4) obesity classes II and III. As these psychological factors are likely to be mediated by other variables, we explored how they may relate to BMI independent or dependent of each other, controlling for age, sex, income, marital status, and educational attainment. Obesity classes II and III were combined for brevity (41) and the underweight group was not included as there were too few in this category in our sample. We hypothesised that as depression score, anxiety score and psychosocial stressors increase, so will the BMI class.

The final sample was 55% female and ranged in age between 20 to 99.6 years (M = 53.8, SD = 17.5), with an average BMI of 27.2 (SD = 4.6). The most common income range was 451 000–750 000 NOK (29.9%), the most common educational level was 3–4 years of vocational school or apprenticeship (21.6%), and 55.4% of the participants were married. See Table 1 for descriptive statistics. In total, 17.6% of all participants reported symptoms of depression, while 9.3% reported symptoms of anxiety ( Table 1 ). There was a positive correlation between HADS-A and HADS-D, N=23557, r=.552, p <.001.

A statistically significant relationship was found between depression and overweight (OR = 1.02, 95% CI [1.01, 1.04], p <.001), obesity I (OR = 1.05, 95% CI [1.03, 1.06], p <.001) and obesity II/III (OR = 1.10, 95% CI [1.06, 1.14], p <.001), among the adult participants, adjusting for sex, age, educational attainment, marital status, and income (see Table 3 ). The positive relationship between BMI and higher symptoms of depression increased in magnitude as symptoms of depression increased. No statistically significant relationship between depression and higher levels of BMI was found, after controlling for covariates among the elderly participants ( Table 3 ).

Among the adults, no statistically significant relationship between anxiety and higher levels of BMI was found, after controlling for covariates (see Table 3 ). For the elderly, a statistically significant relationship was found between anxiety and overweight (OR = 0.97, 95% CI [0.95, 0.99], p = .002), and obesity I (OR = [0.95, 0.99], p = .002) adjusting for sex, age, educational attainment, marital status, and income.

In the model, for the adult participants, considering anxiety and depression combined (HADS-total), the association with anxiety and depression was statistically significant with both obesity class I (OR = 1.02, 95% CI [1.01, 1.03], p < .001) and obesity II and III (OR = 1.04, 95% CI [1.02, 1.06], p <.001), but not for those with overweight (OR = 1.01, 95% CI [1.00, 1.01], p = .070). Among the elderly, the association with anxiety and depression was statistically significant with overweight (OR=0.99, 95% CI [0.97, 1.00], p = .006) but not with any of the obesity classes (see Table 3 ).

The majority (60.1%) of the participants reported having experienced 1-3 stressors, while 15.6% reported 4-10 stressors during their life course ( Table 1 ). Psychosocial stress significantly and positively related to all levels of BMI, with or without considering anxiety and depression, after controlling for covariates among all participants ( Tables 2 , 4 ). Among the adult participants, a positive relationship was found between exposure to psychosocial stress both in childhood/adolescence and through the life course with overweight and all obesity classes without considering anxiety and depression ( Table 2 ). The strongest relationship was found between exposure to psychosocial stress in childhood/adolescence and obesity class II and III without the HADS-total being included in the model (OR = 2.12, 95% CI [1.72, 2.61], p <.001), and when considering HADS-total (OR = 2.01, 95% CI [1.62, 2.49], p <.001) among participants <65 years old. The relationships showed the same trend for exposure to life course stressors with slightly weaker effects in the highest weight categories. For every one-unit of increase in number of psychosocial stressors, the odds of falling into a higher category of BMI increased. For example, among the adults, for every one-unit increase of psychosocial stressors in childhood, the odds of falling into the obesity class I category increased by 17.3%, while the odds of falling into the obesity class II and III increased by 34.4%, without adjusting for anxiety and depression ( Table 2 ). Similar, but weaker associations between psychosocial stress and BMI were found among the elderly, with the exception of exposure to psychosocial stress in childhood/adolescence and overweight ( Table 4 ). Adjusting for anxiety and depression did not change the association between psychosocial stress and BMI and was consistent across all three BMI categories in the full regression models in both age groups compared to normal weight ( Tables 2 , 4 ).

This study aimed to explore how depression, anxiety, and psychosocial stress individually relate to BMI across four weight categories: normal weight, overweight, obesity class I, and obesity classes II and III. Firstly, as hypothesised, results indicated that for every unit increase in depression, the odds of falling into a higher BMI class significantly increased after controlling for sex, age, educational attainment, marital status, and income. Secondly, anxiety was associated with overweight and obesity class I among the elderly participants. Thirdly, as hypothesised, after controlling for covariates, for every unit increase in number of psychosocial stressors, both life course and during childhood/adolescence, the odds of falling into obesity classes I, II and III increased significantly. This result remained consistent after controlling for depression and anxiety combined.

Previous research has demonstrated that as depression increases, so does BMI (10), which is supported by our results even after controlling for the potentially comorbid influences of anxiety and psychosocial stress. In addition, depression and obesity remained significantly and positively related, while controlling for the effect of sex, age, educational attainment, marital status, and income among participants <65 years. In contrast, however, Choi and colleagues (16) found a negative association between depression and BMI in a community sample with low socioeconomic status. This discrepancy may be because we controlled for educational attainment, which is often used as a proxy of socioeconomic status. Further, the propensity for individuals with depression to be sedentary may perpetuate both obesity and depression (50). For example, studies have shown an association between physical activity, depression, and obesity (50). As a result, this may increase the complexity of treating obesity independently of depression.

In contrast to previous research (9), our results suggested that anxiety was not positively associated with obesity among the adult participants. Statistically significant findings between anxiety and higher levels of BMI were, however, found among the elderly participants while controlling for multiple variables, potentially providing a more accurate relationship between anxiety and BMI by stratifying the age groups. Importantly, previous studies contain methodological limitations and weak associations (9). For instance, previous research is limited by poor confounder control (9, 19). Our results showing an association between anxiety and higher BMI among the elderly, may be due to the relationship between the body’s stress response system (HPA axis) (51) and anxiety-reducing behaviours (e.g., emotional eating) (52). Binge eating behavior is commonly associated with elevated body weight, and disordered eating patterns such as binge eating may lead to a temporary reduction in anxiety. Previous studies have found that anxiety is a stronger predictor of binge eating disorder (BED) than depression (53), and severe cases of binge eating can result in a clinical diagnosis of BED, which is characterised by greater preoccupations with food, poor dietary restraint, body dissatisfaction, psychological distress, and low self-esteem (53, 54). As anxiety disorders are commonly seen in individuals with BED (55), there is a need for further research to elucidate the temporal associations between BED, weight problems and anxiety.

Consistent with previous literature (28, 32), we found that as the number of past psychosocial stressors increases, so does the odds of falling into a higher BMI class. Even after controlling for the potentially confounding variables of depression, anxiety, sex, age, educational attainment, marital status, and income, this association remained consistent. Thus, to improve outcomes of lifestyle interventions, health care providers could incorporate routine screening of past and current psychosocial stressors experienced by a patient. Furthermore, psychosocial stress may contribute to the development of disordered eating behaviours such as binge eating that act as self-regulatory coping strategies, potentially contributing to difficulties with long-term weight management. Therefore, obesity treatment programs could incorporate psychological intervention to address psychosocial stress and replace maladaptive self-regulatory strategies with more adaptive coping methods. This holistic treatment approach may improve long-term weight maintenance following lifestyle intervention. Further research is required to assess the efficacy of incorporating treatment targeting psychosocial stress in obesity interventions.

Although not a primary focus of this study, there were notable results regarding the relationship between sex and BMI that would be relevant to explore in future research. Across all regression models, males were significantly more likely to be overweight than females; however, females were more likely to be classified in the obesity categories compared to males. These findings are supported across current literature (56, 57).

In addition, similar to previous research, our study showed that individuals with higher levels of education were less likely to be overweight and have obesity than individuals with less education (37). Future studies exploring mechanisms related to various domains of adversity, education, and the risk of obesity, may influence and improve prevention and treatment of obesity.

Substantial evidence is accumulating that identifies obesity as a multifaceted health problem. This study has shown that anxiety, depression and psychosocial stress are independently associated with BMI. Future research should continue to explore the complex nature of obesity and develop more appropriate assessments and interventions.

Publicly available datasets were analyzed in this study. Data used from the HUNT survey in research projects will, when reasonably requested by others, be made available on request to the HUNT Data Access Committee (hunt@medisin.ntnu.no). The HUNT data access information (available here: http://www.ntnu.edu/hunt/data) describes in detail the policy regarding data availability.

The studies involving human participants were reviewed and approved by Regional Committee for Medical Research Ethics Central Norway. The patients/participants provided their written informed consent to participate in this study.

TE-N contributed with design of the study, wrote the final manuscript and prepared data analysis and interpretation with KK. AT wrote the first draft of the manuscript, and provided critical feedback on the final manuscript. DS contributed to the study by providing critical feedback and helped shape the final manuscript. JR contributed to the study by conceiving and designing the study, and provided critical feedback and helped shape the final manuscript. KK contributed to the study by conceiving and designing the study, arranged material preparation and secondary data collection. All authors contributed to the article and approved the submitted version.

The Trøndelag Health Study (HUNT) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology NTNU), Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health. Our sincere appreciation to HUNT4 participants and HUNT researchers for providing valuable data and enabling the progression of knowledge in this important field.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.