Abstract
Background:
Depression and obesity have a bidirectional relationship making the management of one, without the other, problematic. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are a preferred medication class for diabetes and obesity treatment given their weight loss effect; however, it is not known how antidepressants impact this effect.
Objective:
To assess the impact of antidepressant use on GLP-1 RA-associated weight loss in patients with or without type 2 diabetes mellitus.
Methods:
This was a retrospective, propensity matched, cohort study conducted using TriNetX. The study identified patients initiating a GLP-1 RA being treated with citalopram/escitalopram, bupropion, or no antidepressant. Cohorts were propensity score matched to analyze the primary outcome of mean end-of-study (77-371 days) body weight.
Results:
An initial query identified 31 273 patients eligible for analysis (30 160 receiving no antidepressant, 311 receiving bupropion, and 802 receiving citalopram/escitalopram). After propensity score matching, the study found patients receiving citalopram/escitalopram were taking more antidiabetic therapies at baseline compared with patients not treated with an antidepressant. Patients in the antidepressant cohorts experienced less weight loss compared with their respective matched cohorts not receiving antidepressants (citalopram/escitalopram −0.73 kg versus −1.74 kg; bupropion −0.84 kg versus −3.46 kg). Only the bupropion cohort was significantly heavier at end-of-study versus the non-antidepressant cohort (108 kg versus 103 kg, P = 0.018).
Conclusion and Relevance:
Antidepressants may diminish the weight loss effect of GLP-1 RAs. Additional research is needed to assess whether all GLP-1 RAs are affected similarly and the optimal weight loss strategies in patients receiving antidiabetic therapy with comorbid depression.
Keywords: glucagon-like peptide-1 receptor agonist, antidepressant, weight loss, obesity, diabetes
Background
The number of adults who are overweight or obese continues to rise in the United States, and many recent social factors (eg, social unrest, war, inflation, pandemic) may well raise concerns for related increases in depression and anxiety.1,2 Moreover, as there is a bidirectional association between depression and obesity, it is vital that both are addressed to improve patient outcomes, 3 particularly as obesity is associated with poorer health outcomes in myriad diseases.4,5 In a large meta-analysis, obesity increased the risk for depression more among Americans versus Europeans and among people with depressive disorder at least 20 years of age. 6 Concurrently, baseline depression increased the odds of developing obesity. 3 As such, there is a growing need for the combined use of antidepressants and weight loss medications or medications for diabetes which facilitate weight loss in the comorbid treatment of depression and obesity/diabetes.
Commonly used selective serotonin reuptake inhibitors (SSRIs) include escitalopram and citalopram. 7 Both have generally favorable side effect profiles, but are associated with a risk of gaining 5% or more in body weight, or approximately 5 kg, the mechanism of which remains unclear.8,9 Bupropion, another commonly used antidepressant (a dopamine/norepinephrine reuptake inhibitor), has been associated with weight loss and is included in a combination medication for the treatment of obesity (ie, Contrave, bupropion/naltrexone). 10 Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have been shown to reduce weight in obese patients with type 2 diabetes, an effect magnified in obese patients without type 2 diabetes. 11 However, the real-world impact of how antidepressant use influences the weight loss effect of GLP-1 RAs is unknown.
Therefore, the objective of this study is to assess the real-world impact of antidepressant use on GLP-1 RA-associated weight loss. To accomplish this objective, the study identified patients starting a GLP-1 RA with commonly used background antidepressant therapy to assess the impact on mean body weight in comparison with patients not treated with antidepressants. While other antidepressant classes (eg, tricyclic antidepressants) or other SSRIs (eg, paroxetine) may increase body weight, citalopram/escitalopram were chosen as they are common, first-line antidepressants that generally have little impact on body weight. Bupropion was also assessed as an agent potentially associated with weight loss, noted by its inclusion in a combination medication indicated for weight loss. Together, these medications were selected as they are reasonable first-line agents, particularly among patients who may need and/or desire weight loss following the initiation of a GLP-1 RA.
Methods
This retrospective, propensity score–matched (PSM), cohort study was conducted using TriNetX (Cambridge, Massachusetts), a federated network of de-identified medical records from patients in 19 different countries. Data are de-identified in the TriNetX platform and only displayed in aggregate. On the TriNetX platform, cohorts are created by setting specific inclusion and exclusion criteria individually, or in relation to multiple factors and/or time periods. Factors included in establishing cohorts include demographics, diagnoses, and procedures (based on International Classification of Diseases, Tenth Revision [ICD-10] codes), and medications and laboratory values (based on Current Procedural Terminology [CPT] codes). This study was considered exempt from institutional review. The work was conducted in compliance with the requirements of the University of South Florida’s Institutional Review Board.
A search was performed of patients for their first instance of GLP-1 RA use, including exenatide, liraglutide, dulaglutide, semaglutide, lixisenatide, or albigultide on July 3, 2021 without date restriction. To be included in the analyses, patients had to be at least 30 years of age and have a baseline body weight available between 68.18 kg (150 pounds) and 181.81 kg (400 pounds). The age cutoff was based on current age, not age at index. Thus, this age cutoff was selected to avoid assessing pediatric use when looking back to index date, which was confirmed on data assessment. The weight cutoff was selected based on the initial steps of cohort formation which found significant frequencies outliers at the low and high end-of-study body weight ranges, indicating potential miscoding in electronic health records. We excluded patients with type 1 diabetes mellitus and nicotine dependence, and those taking other antidepressants (apart from the antidepressants included in the planned analysis), antipsychotics, naltrexone, or naloxone. Nicotine dependence was a specific exclusion criterion because of its alternative indication for bupropion. Therefore, to limit the study to a single population, we excluded nicotine dependence to ensure internal validity. The index event (ie, baseline time point) was defined as the first occurrence of being started on a GLP-1 RA in patients already meeting (or on the same day meeting) the defined inclusion/exclusion criteria.
Statistical analyses were performed on the TriNetX platform including PSM to facilitate cohort comparisons, and outcome comparisons, over defined time periods. Propensity score matching was done on a 1:1 basis as this is what the statistical programming allows on the platform to balance cohorts. The platform-based calculation for PSM used logistic regression via a scikit-learn package in Python. This used a greedy nearest neighbor matching algorithm with a caliper of 0.1 pooled standard deviations. Covariates included in the PSM included depression (ICD-10: F32-33), hemoglobin A1c (HbA1c), body weight, age, sex, race/ethnicity, and anxiety (ICD-10: F40-48). Covariates for PSM were limited to avoid overadjustment and were selected based on originally planned outcomes (ie, HbA1c and body weight), key demographic factors (ie, age, sex, race/ethnicity), and key population determinant of the study (ie, depression, anxiety). The primary outcome was the difference in end-of-study body weight between cohorts after the index event, thus assessing the impact of GLP-1 RA initiation. Data quality inspection identified that too large of a proportion of missing data existed to assess HbA1c or body mass index as outcomes. These data, with notation of specific sample size, are reported among patients with data available at baseline. A 2-sided Student t test was used to compare continuous data. The chi-square test was used to compare categorical data.
Baseline characteristics of each cohort (within 1 year of the index date) and outcomes in each cohort within a time period defined by researchers were assessed from the index date (77-371 days postindex event). In the event of multiple data points being available in the time window for a given variable, the most recent value was collected (relative to the present date). Data were reported as missing if a patient did not have a value within the researcher defined time period. For continuous outcomes, difference in mean change between groups can be calculated on TriNetX; however, the variance in that change is not calculable. As such, baseline and follow-up weights are compared, yet the change in weight difference between groups is reported without inferential statistics.
Results
The cohort searches resulted in identifying 30 160 patients in the no antidepressant cohort, 311 patients in the bupropion cohort, and 802 patients in the citalopram/escitalopram cohort. Final cohort size following propensity score matching and baseline characteristics are reported in Table 1.
Table 1.
Baseline Patient Characteristics in Propensity Score–Matched Cohorts.
| Baseline characteristics | Cohort 1: GLP-1 RA ± citalopram/escitalopram | P value | Cohort 2: GLP-1 RA ± bupropion | P value | ||
|---|---|---|---|---|---|---|
| Citalopram/escitalopram cohort (N = 792) | No antidepressant (GLP-1 RA only) therapy cohort (N = 792) |
Bupropion cohort (N = 306) |
No antidepressant (GLP-1 RA only) therapy cohort (N = 306) |
|||
| Age at index, mean (SD) | 57.7 (11.4) | 57.8 (11.2) | 0.878 | 56.2 (11.3) | 56.6 (11.5) | 0.662 |
| Female, No. (%) | 503 (63.51) | 532 (67.17) | 0.126 | 190 (62.09) | 198 (64.71) | 0.502 |
| White, No. (%) | 616 (77.78) | 613 (77.40) | 0.857 | 222 (72.55) | 218 (71.24) | 0.719 |
| Not Hispanic or Latino, No. (%) | 528 (66.67) | 526 (66.41) | 0.915 | 193 (63.07) | 196 (64.05) | 0.801 |
| Black or African American, No. (%) | 65 (8.21) | 64 (8.08) | 0.927 | 40 (13.07) | 39 (12.75) | 0.904 |
| Hispanic or Latino, No. (%) | 21 (2.65) | 25 (3.16) | 0.549 | 13 (4.25) | 19 (6.21) | 0.276 |
| Asian, No. (%) | 10 (1.26) | 10 (1.26) | 1.000 | 10 (3.27) | 10 (3.27) | 1.000 |
| HbA1c, %, mean (SD) | 8.5 (2.1) a | 8.3 (2.0) b | 0.185 | 7.9 (2.0) c | 7.9 (2.1) d | 0.939 |
| SBP, mmHg, mean (SD) | 128.6 (16.8) e | 130.0 (16.5) f | 0.104 | 129.5 (16.2) g | 132.1 (17.0) h | 0.060 |
| Type 2 diabetes mellitus, No. (%) | 509 (64.27) | 531 (67.05) | 0.244 | 191 (62.42) | 197 (64.38) | 0.615 |
| Anxiety, dissociative, stress-related, somatoform, and other nonpsychotic mental disorders, No. (%) | 180 (22.73) | 185 (23.36) | 0.765 | 63 (20.59) | 67 (21.90) | 0.693 |
| Major depressive disorder, single episode, No. (%) | 146 (18.43) | 131 (16.54) | 0.321 | 77 (25.16) | 75 (24.51) | 0.852 |
| Major depressive disorder, recurrent, No. (%) | 39 (4.92) | 30 (3.79) | 0.268 | 21 (6.86) | 17 (5.56) | 0.503 |
| Metformin, No. (%) | 485 (61.24) | 398 (50.25) | <0.001 | 167 (54.58) | 162 (52.94) | 0.685 |
| SGLT-2 inhibitors | ||||||
| Empagliflozin, No. (%) | 66 (8.33) | 51 (6.44) | 0.150 | 16 (5.23) | 24 (7.84) | 0.191 |
| Canagliflozin, No. (%) | 45 (5.68) | 39 (4.92) | 0.501 | 10 (3.27) | 18 (5.88) | 0.122 |
| Dapagliflozin, No. (%) | 39 (4.92) | 23 (2.90) | 0.038 | 10 (3.27) | 19 (6.21) | 0.087 |
| Ertugliflozin, No. (%) | 10 (1.26) | 0 (0) | 0.002 | 10 (3.27) | 10 (3.27) | 1.000 |
| Sulfonylureas | ||||||
| Glipizide, No. (%) | 93 (11.74) | 90 (11.36) | 0.814 | 31 (10.13) | 33 (10.78) | 0.792 |
| Glyburide, No. (%) | 32 (4.04) | 14 (1.77) | 0.007 | 10 (3.27) | 10 (3.27) | 1.000 |
| Glimepiride, No. (%) | 104 (13.13) | 84 (10.61) | 0.120 | 36 (11.77) | 38 (12.42) | 0.804 |
| Basal insulin | ||||||
| Insulin glargine, No. (%) | 184 (23.23) | 162 (20.46) | 0.181 | 54 (17.65) | 53 (17.32) | 0.915 |
| Insulin detemir, No. (%) | 74 (9.34) | 48 (6.06) | 0.014 | 20 (6.54) | 18 (5.88) | 0.738 |
| Insulin degludec, No. (%) | 47 (5.93) | 34 (4.29) | 0.138 | 10 (3.27) | 10 (3.27) | 1.000 |
| Insulin isophane, No. (%) | 31 (3.91) | 21 (2.65) | 0.159 | 11 (3.60) | 10 (3.27) | 0.824 |
| Insulin lispro protamine, No. (%) | 20 (2.53) | 14 (1.77) | 0.298 | 10 (3.27) | 10 (3.27) | 1.000 |
| Insulin aspart protamine, No. (%) | 22 (2.78) | 15 (1.89) | 0.244 | 10 (3.27) | 10 (3.27) | 1.000 |
| Prandial insulin | ||||||
| Insulin lispro, No. (%) | 89 (11.24) | 60 (7.58) | 0.013 | 25 (8.17) | 29 (9.48) | 0.569 |
| Insulin aspart, No. (%) | 89 (11.24) | 70 (8.84) | 0.112 | 27 (8.82) | 25 (8.17) | 0.772 |
| Insulin glulisine, No. (%) | 18 (2.27) | 15 (1.89) | 0.598 | 10 (3.27) | 10 (3.27) | 1.000 |
| Insulin, regular, human, No. (%) | 59 (7.45) | 47 (5.93) | 0.228 | 23 (7.52) | 19 (6.21) | 0.522 |
| GLP-1 RAs | ||||||
| Exenatide, No. (%) | 122 (15.40) | 99 (12.50) | 0.095 | 52 (16.99) | 42 (13.73) | 0.262 |
| Liraglutide, No. (%) | 334 (42.17) | 337 (42.55) | 0.879 | 140 (45.75) | 130 (42.48) | 0.416 |
| Semaglutide, No. (%) | 96 (12.12) | 99 (12.50) | 0.819 | 33 (10.78) | 45 (14.71) | 0.146 |
| Dulaglutide, No. (%) | 285 (35.99) | 261 (32.96) | 0.205 | 95 (31.05) | 101 (33.01) | 0.603 |
| Lixisenatide, No. (%) | 11 (1.39) | 10 (1.26) | 0.826 | 10 (3.27) | 10 (3.27) | 1.000 |
| Antidepressants | ||||||
| Bupropion, No. (%) | 0 (0) | 0 (0) | — | 301 (98.37) | 0 (0) | 0.000 |
| Escitalopram, No. (%) | 459 (57.96) | 0 (0) | 0.000 | 0 (0) | 0 (0) | — |
| Citalopram, No. (%) | 351 (44.32) | 0 (0) | 0.000 | 0 (0) | 0 (0) | — |
| Thyroid supplements, No. (%) | 160 (20.20) | 144 (18.18) | 0.307 | 65 (21.24) | 52 (16.99) | 0.181 |
| Glucocorticoids, No. (%) | 173 (21.84) | 152 (19.19) | 0.191 | 72 (23.53) | 57 (18.63) | 0.137 |
Abbreviations: GLP-1 RA, glucagon-like peptide-1 receptor agonist; HbA1c, hemoglobin A1c; SBP, systolic blood pressure.
N = 314.
N = 292.
N = 127.
N = 118.
N = 759.
N = 762.
N = 293.
N = 300.
Baseline Findings
Regarding the comparison of the no antidepressant cohort versus the citalopram/escitalopram cohort, baseline characteristics were generally well balanced. Patients receiving citalopram/escitalopram required greater intensity of antidiabetic therapy at baseline (eg, higher proportion of patients receiving insulins and secretagogues), although rarely was the difference statistically significant (Table 1). No trend to this effect was observed in the comparison of the no antidepressant versus bupropion cohorts.
Outcomes
Overall, patients in the antidepressant cohorts lost less weight compared with patients receiving no antidepressants (Table 2). In PSM cohort 1 (GLP-1 RA ± citalopram/escitalopram), patients lost an average of 0.73 kg (GLP-1 RA + citalopram/escitalopram) versus 1.74 kg (GLP-1 RA only). In PSM cohort 2 (GLP-1 RA ± bupropion), patients lost an average 0.84 kg (GLP-1 RA + bupropion) versus 3.46 kg (GLP-1 RA only). Only the end-of-study mean body weight was significantly different in the PSM cohort 2 (GLP-1 RA + bupropion, 108 kg; GLP-1 RA only, 103 kg; P = 0.018) (escitalopram/citalopram, 101 kg; no antidepressant, 99 kg; P = 0.099).
Table 2.
Baseline and End-of-Study Body Weight in Propensity Score–Matched Cohorts.
| Outcomes | Citalopram/escitalopram cohort | No antidepressant (GLP-1 RA only) therapy cohort | P value | Bupropion cohort | No antidepressant (GLP-1 RA only) therapy cohort | P value |
|---|---|---|---|---|---|---|
| Baseline body weight, kg, mean (SD) | 101.7 (27.1)n = 791 | 100.5 (25.8) n = 792 |
0.373 | 108.8 (25.3) n = 306 |
106.5 (25.7) n = 306 |
0.267 |
| Study end body weight, kg, mean (SD) | 101.0 (26.9) n = 752 |
98.8 (25.1) n = 778 |
0.099 | 108.0 (25.1) n = 295 |
103.1 (25.5) n = 301 |
0.018 |
| Change from baseline, kg | −0.73 | −1.74 | — | −0.84 | −3.46 | — |
Abbreviations: GLP-1 RA, glucagon-like peptide-1 receptor agonist.
Discussion
This is the first study, to our knowledge, to assess the impact of antidepressants on the body weight effect of GLP-1 RAs using real-world health record data. Prior studies have assessed variation in weight impact among antidiabetic agents in patients treated with antidepressants.6,12 However, this time course differs from what was assessed herein, where patients were on antidepressants at baseline and the study assessed body weight from the first instance of GLP-1 RA use. Overall, we found less evidence of weight loss among new GLP-1 RA users treated with an antidepressant versus patients not treated with an antidepressant. In addition, we identified a greater baseline antidiabetic treatment burden among patients treated with citalopram/escitalopram versus no antidepressant, which may have impacted the observed change in weight. This finding may be related to antidepressants, such as citalopram/escitalopram, being associated with weight gain.8,9 However, the finding may also be related to factors the study was unable to account for, such as duration of diabetes. Indeed, extended SSRI use has been associated with a greater risk of starting insulin therapy among patients with type 2 diabetes. 13 However, the potential mechanism (decreased insulin secretion) was identified among patients without diabetes. A possible explanation for why patients taking bupropion had a different mean body weight at end of study versus patients not taking an antidepressant is selection bias. That being, patients with greater need for weight loss may have been preferentially prescribed bupropion in the course of typical clinical practice. Therefore, what may appear as less weight loss capacity while taking bupropion may actually represent patients at baseline with greater difficulty managing body weight. In addition, the overall weight change between bupropion and citalopram/escitalopram may have been similar as the patients receiving bupropion may also be those tending to struggle with body weight more. Of note, while bupropion is a component of the Food and Drug Administration (FDA)-approved weight loss medication Contrave, the bupropion dosing in the present study was unknown and naltrexone use was excluded.
Results from large meta-analyses show GLP-1 RA use results in an average 3 kg weight loss, without regard to type 2 diabetes diagnosis.13,14 There are notable differences between GLP-1 RA agents, long- versus short-acting, standard versus high doses, potency, and the resultant effect on weight loss which is impacted by type 2 diabetes status. 15 In our analysis, patients receiving a GLP-1 RA without an antidepressant were observed to have weight loss similar to what would be expected based on other studies. 15 However, given the real-world data set used in our analysis, it should be considered that the observed effect of the GLP-1 RA on weight loss is impacted by these differences between agents, and further may have been diminished due to medication nonadherence. Indeed, nonadherence to GLP-1 RAs is more common versus noninjectable antidiabetic medications,16-18 and poor adherence to GLP-1 RAs is associated with significantly less weight loss compared with high adherence. 19
Study limitations include the observational design based on a retrospective data extraction of electronic health records. As such, it is possible that some data are miscoded or omitted. Noteworthy, we expect type 2 diabetes to have been significantly undercoded based on the results, however, not different between groups secondary to PSM. Therefore, we do not expect the possibility of a minority of patients using these agents for weight loss in the absence of type 2 diabetes to have impacted the results. In addition, to avoid substantial missing data or limiting sample size, a sufficient time window from which to extract baseline characteristics was applied. The TriNetX platform preferentially collects the most recent data point. Therefore, we expect this to have limited effect on the measurement of body weight as this is routinely measured at physician office visits. In addition, the time window enabled assessment of characteristics which are not measured/assessed as consistently in provider office visits and/or laboratory assessments. Moreover, with regard to body weight, there is no reason to expect, based on the study populations, that one group might be more predisposed to outdated body weight assessment. Also, in relation to body weight, the study was dependent on assessing pre and post weight, but was unable to assess the variance in the change of weight, thus precluding inferential statistics on mean change in body weight. In addition, there is the possibility for confounding by indication, which is somewhat mitigated by PSM, to produce a baseline study population with a similar rate of comorbidities and medication use. However, it is not possible to match for all known confounders on the TriNetX platform (eg, severity of depression or anxiety, specific psychiatric diagnoses, socioeconomic status, lifestyle habits, or concurrent lifestyle interventions or counseling). We were not able to fully account for use of medications which may result in weight gain or diminished weight loss (eg, insulins, sulfonylureas) among escitalopram/citalopram users, for the duration of antidepressant use prior the initiation of the index GLP-1 RA, medication dosing, or medication adherence. In considering how escitalopram/citalopram use may impact the body weight change associated with GLP-1 RA therapy, there are 2, not mutually exclusive, possibilities. First, there is the potential for escitalopram/citalopram to increase antidiabetic treatment burden secondary to weight gain, thus diminishing the weight loss effect of GLP-1 RAs. Alternately, there may be a diminished capacity to lose weight with GLP-1 RAs as a result of the interaction. It is worth noting that the present analysis did not assess a traditionally weight-neutral antidepressant, such as fluoxetine, as fluoxetine is less frequently used first line and thus would have been more likely to create substantial differences in the baseline population. Also, medication dosing was not assessed, and higher doses of GLP-1 RAs are known to be associated with greater weight loss. As such, the overall small magnitude of weight change is not unexpected, yet the difference between groups is potentially clinically relevant. Finally, the follow-up time range, which may impact the weight change observed, was selected as the impact of GLP-1 RAs in clinical trials on weight is typically seen in the first several months and then maintained through a year. 20 So that, while we do not expect a difference in follow-up time between groups, were one present, we would not expect it to impact the results of the study.
Conclusion and Relevance
Overall, results suggest that antidepressant use may reduce the weight loss effect from GLP-1 RA therapy. Additional analyses are needed to confirm this finding, assess whether all GLP-1 RAs are equally affected, and assess concurrent strategies to optimize weight loss in this setting. Future directions can also assess the use of guideline directed follow-up for lifestyle interventions to promote healthy habits in conjunction with medication therapy among patients with depression needing to lose weight. 4
Footnotes
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Prior Presentation: None
ORCID iD: Nicholas W. Carris 
https://orcid.org/0000-0001-8904-5034
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