Strengths and limitations of this study Measurement of body weight by health workers at both time points with a mean of 4.4 years of follow-up;

Ability to adjust for detailed lifestyle factors and chronic conditions;

The total number of antidepressant users was relatively small, which limited our power to conduct detailed subgroup analyses;

Dietary intake was only assessed at follow-up; therefore, we were unable to adjust for dietary change during follow-up.

Introduction Obesity is a major global health problem almost entirely caused by excess dietary intake and reduced energy expenditure. It is estimated that up to 205 million men and 297 million women over the age of 20 worldwide are obese.1 In Australia, the prevalence of obesity class I (body mass index (BMI) 30–34.9 kg/m2) and obesity class II or III (BMI ≥35 kg/m2) has respectively doubled and almost tripled since 1980.2 Currently it is estimated that 28.3% of Australian adults are obese.3 One of the most important health consequences of high and rising trends in global obesity prevalence has been the increased risk of developing depression.4 Indeed, data from the Global Burden of Disease study suggest that major depressive disorder was the second leading cause accounting for 8.2% of global years lived with disability in 2010.5 Several population-based cohort studies have consistently shown a positive relationship between antidepressant use and weight gain in countries such as the USA,6–8 Canada9 and Australia.10 This is valuable information for public health policy-makers and researchers given that the prevalence of antidepressant use is high in Australia and the USA (5%–12%),7 11 and frequently used by people without depressive or anxiety disorders.12 The underlying cause of weight gain due to long-term antidepressant use is poorly understood.13 In rodents, data from our laboratory have shown that the combination of chronic stress and short-term antidepressant treatment, followed by high-fat diet, results in long-term weight gain that is greater than that caused by stress and high-fat diet, without antidepressant exposure.14 In our animal paradigm, antidepressant exposure potentiated weight gain caused by obesity-promoting diet. Thus, increased antidepressant exposure might be a contributory factor to the obesity pandemic.13 It is supported by the change of energy intake related to antidepressant use. Data from a recent cross-sectional population-based study showed that antidepressant use was associated with increased energy intake.15 Poor diet, sedentary lifestyle, obesity and depression often cluster together; however, association studies between antidepressant use and obesity have been mostly based on registry data or short-term clinical trials, which limit their use to understand interactions.6–10 Therefore, whether specific antidepressant medications interaction with lifestyle risk factors (poor diet, inadequate physical activity and smoking) partially explain the development of human obesity is still unclear. Identifying the potential mechanism by which antidepressant medication increases the risk of obesity could help develop targeted strategies for prevention. This study was designed to specifically examine the association between antidepressant use and weight gain, as well as the interaction with diet and other lifestyle factors (eg, smoking, sedentary activity) in adults participating in a large-population based prospective cohort study.

Methods Data source and study participants This study was approved by the Queen Elizabeth Hospital Human Research Committee and, where appropriate, by the Aboriginal Health Research Ethics Committee, Adelaide, South Australia, Australia. The North West Adelaide Health Study (NWAHS) is an ongoing community-based cohort study among adults living in the North West region of Adelaide, South Australia. A detailed description of this cohort has been published elsewhere.16 The current study analysed data from both stage 2 (2004–2006) and stage 3 (2008–2010) data collections. A total of 2334 participants had information on body weight at both time points. Outcome variable—change in body weight At both stages 2 and 3, height and body weight were measured in light clothing and without shoes by trained clinic staff, to the nearest 0.1 cm and 0.1 kg, respectively. Annual weight gain was calculated by the difference of body weight (kg) between follow-up and baseline divided by the duration of follow-up (in years). Overweight and obesity were defined respectively as 25 kg/m2≥ BMI <30 kg/m2 and BMI ≥30 kg/m2. Exposure variable—prospective antidepressant use Information on medication use (based on prescription) according to the Anatomical Therapeutic Chemical (ATC) Classification was obtained from Medicare Australia (Pharmaceutical Benefits Scheme (PBS)) by confidential unit record linkage for the study period (between baseline and follow-up). Antidepressants (ATC code N06A) were categorised into three groups: tricyclic antidepressants (TCAs) (ATC code N06AA), selective serotonin reuptake inhibitors (SSRIs) (ATC code N06AB) and other antidepressants (ATC code N06AF, N06AG and N06AX). For each participant, the mean annual number of antidepressant prescriptions, calculated by adding the number of prescriptions and dividing it by the follow-up duration between stages 2 and 3, was categorised into three groups: non-user, low user (1–2 prescriptions/year) or high user (>2 prescriptions/year). Exposure of specific antidepressants was assessed independent of one or more antidepressants. Covariates Baseline and follow-up covariates The Centre for Epidemiologic Studies Depression Scale (CES-D) was used to measure depressive symptoms. CES-D scores were categorised as no depression (<16), mild depression (16-26) or moderate to severe depression (>26).17 Smoking behaviour was determined by self-report and coded as (1) non-smoker and (2) current or ex-smoker. Self-reported income was recoded into three levels (<$A20 000, $A20 000-$A60 000 or >$A60 000). Physical activity questions (baseline) from the Australian National Health Surveys were used to classify participants as sedentary, or having low, moderate or high levels of physical activity.18 Respondents were asked about the amount of walking, moderate and vigorous activity they had undertaken in the past 2 weeks. Follow-up only covariates Dietary intake during the previous 12 months was assessed by the Cancer Council Victoria Dietary Questionnaire for Epidemiological Studies V3.1 (modification of Food Frequency Questionnaire (FFQ)). The FFQ was previously validated in an Australian population, and is widely used in epidemiological studies. In the analysis, the daily intake of 128 food items were collapsed into 41 food groups as previously described.19 Dietary patterns were identified by factor analysis using the principal component method. Varimax rotation was used to assist the interpretability of the factor solution. Based on the Eigenvalue (>1), scree plot and interpretability, two dietary patterns were constructed: (1) the prudent pattern was characterised by high loadings of fruit and vegetable (see online supplementary figure S1) and (2) the Western pattern had high intake of processed meat, snacks and fast food. Scores of each dietary pattern were calculated as the sum of the products of factor loading coefficients and standardised daily intake of the food intake. Dietary pattern scores were dichotomised as low and high (ie, below or above zero). Supplementary file 1 [SP1.pdf] Statistical analyses Analysis of variance and χ2 test were used respectively to compare differences between categorical variables and in continuous variables between groups (gender, categories of antidepressant use). Linear regression models were used to assess the longitudinal association between antidepressant use and annual weight change. Three models were employed: model 1 was adjusted for age and gender, model 2 was further adjusted for income, smoking, physical activity and follow-up duration, and model 3 was further adjusted for depression status at baseline and follow-up and dietary patterns (continuous). Participants with missing information of depression were excluded in the corresponding analyses. As the association between antidepressant use and weight gain was mainly due to SSRI, we further looked at the interaction between SSRI use and lifestyle factors. Multiplicative interaction between SSRI use, lifestyle factors (categorical variables of dietary patterns (low or high), smoking (non-smoker, current or ex-smoker) and physical activity (sedentary, low, moderate/high)) was conducted by inputting the product terms of these variables and antidepressant use in the regression models. The analysis was subsequently stratified for lifestyle factors. The interaction between antidepressant use and age (continuous) was graphically represented using the marginsplot command in STATA V.14 (Stata Corporation). Sensitivity analyses were conducted using mixed linear modelling to assess the association between antidepressant use and body weight (baseline and follow-up), adjusted for age, income, depression and smoking status as time-varying variables, while considering antidepressant use, physical activity, dietary patterns and gender as time-invariant variables. We also assessed the association (incident rate ratio) between antidepressant use and 5% weight gain over 5 years using Poisson regression with robust variance. All analyses were performed using STATA V.14, and statistical significance was set at p<0.05 (two sided).

Results The mean age of the sample was 54.1 (SD 14.1) years (table 1). The mean duration of follow-up was 4.4 (SD 0.4) years. Women had a higher prevalence of depression and a higher mean level of antidepressant use than men. In the sample, 188 (8.1%) and 212 (9.1%) participants had a mean annual number of 1–2, and more than 2 antidepressant prescriptions, respectively. Information on antidepressant usage was based on prescription information; out of 400 antidepressant users, 225 (56.3%) were SSRI users, and in high SSRI users the mean annual number of SSRI prescriptions was 5.9 (SD 3.1) (see online supplementary table S1). The mean annual weight gain was 0.12, 0.18 and 0.28 kg in non-users, low and high antidepressant users, respectively. Table 1 Sample characteristic by sex* Compared with non-users, high antidepressant users had higher energy intake (9160 vs 8628 kJ/day) and higher Western dietary pattern scores after adjusting for age and gender (see online supplementary table S2). In multivariable regression models adjusted for age, gender, income, smoking, physical activity, follow-up duration and dietary patterns, antidepressant use was positively associated with weight gain. High users gained 0.22 kg (95% CI 0.00 to 0.44) per year when compared with non-users, and SSRI use was related to weight gain (table 2). In the fully adjusted model, high SSRI users gained 0.48 kg (95%CI 0.20 to 0.76) more than non-users. No association was found between TCA and other antidepressant use and weight gain. Table 2 Association (β 95% CI) between antidepressant use and annual weight gain In relation to annual weight gain, significant interactions were found between SSRI use and three lifestyle factors: Western dietary pattern, smoking and sedentary activity (table 3). The association between SSRI use and weight gain was mainly seen among those with unhealthy lifestyle; a strong dose–response relationship between SSRI use and weight gain was observed among those with high intake of Western diet: the regression coefficients were 0.00 kg, 0.46 kg (95% CI 0.05 to 0.88) and 0.84 kg (95%CI 0.43 to 1.24) for non-users, low users and high users, respectively. This association was not seen in those with low intake of Western diet. No significant interaction between SSRI use and the prudent dietary pattern was found. Among those with sedentary lifestyles, high SSRI use was associated with 1.01 kg (95%CI 0.52 to 1.50) higher weight gain per year than non-users. A consistent positive association between SSRI use and weight gain was only observed among smokers: low and high SSRI use was respectively associated with 0.44 kg (95% CI 0.05 to 0.84) and 0.66 kg (95% CI 0.23 to 1.10) higher weight gain per year than non-users. Table 3 Subgroup analyses of the association between SSRI use and annual weight gain* There was a significant interaction between antidepressant use and age in relation to weight gain (see online supplementary figure S2). The positive association between high antidepressant use and weight gain was mainly seen among those aged below 50 years. In the multivariable mixed regression model adjusted for time-varying depression status, smoking, age and income as well as time-invariant dietary patterns, physical activity and gender, antidepressant use was associated with body weight (baseline and follow-up)(see online supplementary table S3). Compared with non-use, high use was associated with an extra body weight of 4.40 kg (any antidepressant, p=0.002), 4.20 kg (SSRI, p=0.007) and 7.14 kg (other antidepressant, p<0.001), respectively. TCA use was not associated with body weight. No interaction between antidepressant use and gender was found (data not shown). Overall, 27.2% of the participants had weight gain above 5% over 5 years. In fully adjusted model, the incident rate ratio (IRR) for 5% weight gain were 1.00, 1.09 (95% CI 0.83 to 1.44) and 1.37 (95% CI 1.10 to 1.70) for non-users, low users and high users of antidepressant, respectively. A dose–response association between SSRI use and 5% weight gain was found in fully adjusted model: IRRs were 1.00, 1.37 (95% CI 1.03 to 1.81) and 1.43 (95% CI 1.10 to 1.86) (p trend <0.001) for non-users, low users and high users of SSRI, respectively (data not shown).