Effects of the selective serotonin reuptake inhibitors citalopram and escitalopram on glucolipid metabolism: a systematic review

Objectives: Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) frequently co-occur, highlighting the need to understand the metabolic effects of antidepressants. This systematic review evaluated the impact of citalopram and escitalopram on glucose and lipid metabolism, focusing on glycemic control.

Methods: A comprehensive search of PubMed, Embase, Web of Science, PsycINFO, the Cochrane Library and Google Scholar was conducted. Primary outcomes included changes in glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG). Secondary outcomes assessed lipid profiles (triglycerides, cholesterol, high-density lipoprotein, and low-density lipoprotein) and depressive symptom scales. Subgroup analyses were conducted to evaluate outcomes in patients with comorbid T2DM and MDD and those with MDD only.

Results: Thirteen studies involving 502 participants met the inclusion criteria. Six randomized controlled trials, four prospective studies, one cohort trial, one single-arm trial and one three-arm trial. The findings suggest that both citalopram and escitalopram tend to reduce HbA1c and FBG levels. No significant effects on lipid profiles were observed across the included studies.

Conclusion: Citalopram and escitalopram appear to exert beneficial effects on glycemic control, as evidenced by reductions in HbA1c and FBG. Further high-quality investigations are warranted to validate these findings and guide individualized treatment strategies.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024544963, identifier CRD42024544963.

1 Introduction

The global prevalence of type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) has increased substantially, posing a significant public health concern (1). In individuals with T2DM, the prevalence of depression is approximately twice that observed in those without T2DM, with women exhibiting a higher rate than men, irrespective of diabetes status (2). This relationship between T2DM and MDD is bidirectional, influenced by a complex interplay of biological, psychological, and social factors (3). These factors worsen depressive symptoms and impair glycemic control, thereby complicating the management of both conditions.

Insulin, a peptide hormone produced by pancreatic beta cells, crosses the blood-brain barrier and binds to endothelial cell receptors, initiating tyrosine kinases-dependent signaling cascades (4). These cascades regulate central and peripheral metabolic processes, including synaptic plasticity, neurotransmitter modulation, and neuro cognitive functions (5, 6). Insulin resistance, an early hallmark of T2DM, disrupts glucose metabolism in muscle, adipose, and hepatic tissues and impairs dopaminergic (DA) signaling and reward-related behaviors, highlighting a critical link between T2DM and MDD (7, 8). Improving central insulin signaling can peripheral insulin sensitivity, reduce glucose production, and optimize metabolic outcomes (9). Chronic stress further aggravates insulin resistance and depressive symptoms through dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and excessive innate immune activation (10). Oxidative stress augments these pathologies by damaging lipids and proteins and by reducing antioxidant enzyme activity in the brain and pancreas (11).

Selective serotonin reuptake inhibitors (SSRIs) have been shown to improve blood glucose levels compared to placebo, with Fluoxetine and escitalopram/citalopram demonstrating particular efficacy (12). Escitalopram, the S-enantiomer of citalopram, exhibits superior tolerability and safety compared to other antidepressants (13, 14). It may also enhance glycemic control, by modulating the HPA axis (15), increasing insulin sensitivity in hepatic and muscular tissues, and normalizing neural connectivity in regions such as the hippocampus, and nucleus accumbens (NAc) (16–18). Moreover, escitalopram has been reported to exert antioxidant and anti-inflammatory effects and to reduce lipid levels (19–21).

These findings suggest that escitalopram may confer therapeutic benefits for glycemic control in patients with comorbid T2DM and MDD. The present study aims to systematically review current evidence on the effects of citalopram and escitalopram on glucose and lipid metabolism in this population.

2 Methods

2.1 Research registration

The study protocol was pre-registered with PROSPERO (CRD42024544963) prior to initiating the literature review.

2.2 Data source and search methodology

A systematic literature review was conducted utilizing databases including PubMed, Embase, Web of Science, Google Scholar, PsycINFO and Cochrane databases. The search, limited to English-language publications and focused on clinical trials investigating the effects of citalopram or escitalopram on glucose metabolism in patients with T2DM from January 2000 to December 2024. Boolean search terms included: (“escitalopram” OR “escitalopram oxalate” OR “citalopram hydrobromide”) AND (“diabetes mellitus, type 2” OR “diabetes” OR “type 2 diabetes” OR “diabetes” OR “serum glucose” OR “glucose metabolism” OR “hyperglycemia” OR “hypoglycemia” OR “glucose metabolism disorder” OR “glycosylated hemoglobin” OR “glucose intolerance” OR “insulin resistance” OR “impaired glucose metabolism”). Title and abstract screening were conducted independently by two reviewers (YJD and ML). Relevant studies were further assessed as detailed in Figure 1. Any discrepancies were resolved through discussion, with mediation by SGL when necessary.

Figure 1

Figure 1. Flowchart of study identification and selection.

2.3 Inclusion and exclusion criteria

Inclusion criteria: (1) clinical trials assessing the impact of citalopram or escitalopram on glucose metabolism; (2) studies involving T2DM patients with clinical glucose-related outcomes; (3) English-language publications; and (4) research published between January 2000 and December 2024.

Exclusion criteria: (1) reviews, letters, case reports, cross-sectional studies, and other non-original literature; (2) trials lacking adequate statistical data; (3) studies involving T2DM patients with comorbid severe mental disorders other than MDD; and (4) research on type 1 diabetes, gestational diabetes, or non-T2DM conditions.

2.4 Data extraction

Extracted data included authorship, publication year, geographic region, participant demographics (sex, age), study design, sample size, and follow-up duration. Primary outcomes were changes in glycosylated hemoglobin (HbA1c, %) and fasting blood glucose (FBG, mg/dl). Secondary outcomes included lipid profiles including triglycerides (TG, mg/dl), cholesterol (CH, mg/dl), high-density lipoprotein (HDL, mg/dl), and low-density lipoprotein (LDL, mg/dl), and assessments of depressive symptoms.

2.5 Quality assessment

The methodological quality of included studies was assessed using the Newcastle-Ottawa Scale, which evaluates three key domains: selection, comparability, and outcome/exposure assessment. Surveys with overall scores of 0–3, 4–6, and 7–9 were categorized as being of poor, fair, or good quality, in that order. We used the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to assess the evidence quality (22).

2.6 Statistical analysis

Data were summarized as means with standard deviations, where applicable. Study heterogeneity was evaluated using Higgins’ I² statistic and p value. The I² statistic was interpreted as follows: no heterogeneity was defined as <25%, mild heterogeneity as 25–50%, moderate heterogeneity as 50–75%, and high heterogeneity as >75% (23). A fixed-effects model was used for low heterogeneity (p > 0.1, I² ≤ 50%), and a random-effects model for high heterogeneity (p < 0.1, I² > 50%). Funnel plots were generated to inspect plot asymmetry visually. Begg’s and Egger’s regression tests were used.

Sensitivity analyses were conducted to evaluate the robustness of the results. Subgroup analyses were stratified by disease subgroup (T2DM-MDD vs MDD-only) and pharmacological classification (escitalopram vs citalopram), with additional stratification by geographic region and age strata. All statistical analyses were performed using Stata 17.0.

3 Results

3.1 Characteristics of included studies

The study selection process is shown in Figure 1. Of the 231 screened articles, 13 studies met the inclusion criteria, six randomized controlled trials, four prospective studies, one cohort study, one single-arm trial, and one three-arm trial. Quality assessments are illustrated in Supplementary Figure S1; Supplementary Table S1. The GRADE assessments are listed in Supplementary Table S2. Collectively, these studies involved 502 middle-aged and older adults, with 23 participants loss to follow-up. Participants were stratified into two groups: those with comorbid T2DM-MDD and those with MDD only. Two studies assessed citalopram (20–40 mg/day for 8–26 weeks), while nine evaluated escitalopram (5–30 mg/day for 1–52 weeks). Further categorization was based on study region (Middle East, South Asia, East Asia, Western Europe, and North America) and age (<50 years, 50-60 years, >60 years). See Table 1.

Table 1

Table 1. Summary of baseline characteristics.

Due to differences in study designs and outcomes, a formal meta-analysis was not feasible, prompting a systematic review instead. Given substantial between-study heterogeneity (I² > 50%), the results were restricted to outcomes demonstrating low-to-moderate heterogeneity (I² ≤ 50%), with exploratory pooled estimates derived from random-effects models (Figures 2, 3; S2). The results of sensitivity analyses are listed in Supplementary Table S3. Publication bias was systematically evaluated through funnel plot asymmetry assessments using Begg’s rank correlation and Egger’s weighted regression tests, with graphical representations in Supplementary Figures S3, S4 and quantitative results in Supplementary Table S4.

Figure 2

Figure 2. Subgroup analysis of study regions. (A) HbA1c (glycosylated hemoglobin) levels before and after treatment. (B) FBG (fasting blood glucose) levels before and after treatment. Notes: Santi N. (32)¹ represents T2DM comorbid with MDD group, and Santi N. (32)² represents MDD only group.

Figure 3

Figure 3. Subgroup analysis of participant age. (A) HbA1c levels before and after treatment. (B) FBG levels before and after treatment. Notes: Santi N. (32)¹ represents T2DM comorbid with MDD group, and Santi N. (32)² represents MDD only group.

3.2 Primary outcome measures

3.2.1 HbA1c

HbA1c, a crucial marker of glycemic control was assessed in ten studies involving 409 participants, with treatment durations ranging from 8 weeks to 12 months (24–27, 29–35). Significant reductions in HbA1c levels were observed in specific subgroups, notably in a South African cohort (standardized mean difference [SMD] = 0.63, 95% CI: 0.34-0.92) (Figure 2A). Among participants under 60 years, the SMD was 0.48 (95% CI: 0.09-0.87) for those under 50 years and 1.05 (95% CI: 0.66-1.45) for the 50-60 years group (Figure 3A). The forest plot indicates that both citalopram and escitalopram may lower HbA1c levels (Supplementary Figure S5A), as supported by disease-specific (Supplementary Figure S6A) and drug subgroup analyses (Supplementary Figure S7A). High heterogeneity (I² > 50%) likely arise from differences in sample characteristics and study designs.

Four studies reported statistically significant HbA1c reductions. Khazaie et al. (24) documented a 1.59% ± 1.03 decrease (P < 0.001), while Gehlawat et al. (25)Tiwary et al. (34) and Israt et al. (30) also reported significant improvements. Although six studies (26, 27, 29, 31–33, 35) found no significant HbA1c changes, a trend toward improvement was noted.

3.2.2 FBG

FBG was evaluated a primary marker of glycemic fluctuation, was evaluated in ten studies involving 327 participants (24, 25, 27, 28, 28, 30–33, 35, 36). These studies compared the effects of escitalopram or citalopram versus placebo on FBG, over treatment durations of 1 to 12 weeks. Eight studies specifically targeting patients with T2DM-MDD (24, 25, 27, 28, 30–33) reported significant post-treatment improvements in FBG (Supplementary Figure S5B). In contrast, no significant FBG changes were noted in patients with MDD only, suggesting that the T2DM-MDD comorbidity uniquely influences glucose metabolism. The forest plot (Supplementary Figure S6B) suggests that these medications may reduce FBG levels, supported by study region, age and drug subgroup analyses (Figures 2B, 3B, and S7B). High heterogeneity (I² > 50%) may be attributed to variations in sample characteristics and study designs.

Khazaie et al. (24) observed a significant FBG reduction (39.95 ± 25.66 mg/dL, P < 0.001) in T2DM-MDD patients treated with citalopram (40 mg/d). Similarly, Israt et al. (30) found that 12 weeks of escitalopram significantly improved FBG levels in patients with T2DM-MDD (P < 0.001). Additional studies (Wei et al. (31) [P = 0.027] Gehlawat et al. (25) [P < 0.05] Sebedi et al. (28) [P < 0.001]) also reported statistically significant effects, whereas Santi et al. (32) did not observe significant changes (P > 0.05).

3.3 Second outcome measures

3.3.1 Lipid profile

Four studies. (25, 29, 32, 35) assessed TG and CH levels before and after escitalopram treatment, finding no statistically significant changes (Supplementary Table S5; Supplementary Figure S8). Disease subgroup analyses yielded similar results (Supplementary Figure S9). Additionally, three studies (25, 29, 35) examined the effects of escitalopram on HDL and LDL levels, reporting no significant differences between escitalopram and placebo. These findings suggest that escitalopram exerts a negligible impact on lipid profiles, including TG, CH, HDL, or LDL level.

3.3.2 Clinical depression assessment

Nine studies assessed changes in depressive symptoms using instruments including the Hamilton Depression Rating Scale (HAMD), the Beck Depression Inventory (BDI), and other relevant depression scales before and after treatment. Although overall interventions alleviated depressive symptoms, statistically significant was not reached across all studies (Supplementary Figure S10A). Nonetheless, four studies (25, 32, 34, 36) reported significant reductions in HAMD total scores (Supplementary Figure S10B), while two studies (24, 26) observed significant decreases in BDI scores (Supplementary Figure S10C), indicating a favorable therapeutic treatment.

4 Discussion

This systematic review represents the first comprehensive analysis of the effects of citalopram and escitalopram on glucolipid metabolism. Synthesized data from thirteen studies—primarily involving adults with T2DM-MDD and individuals with MDD only. Our findings suggest that both citalopram and escitalopram tend to reduce HbA1c and FBG levels. However, no significant effects on lipid profiles were observed across the included studies.

These results are consistent with previous reports (25, 30), which demonstrated significant improvements in glycemic control, particularly reductions in FBG and HbA1c, among patients with T2DM and comorbid MDD. The underlying hypothesis guiding this review posits that poor glycemic control in patients with comorbid T2DM and MDD is linked to insulin resistance, which contributes to MDD pathophysiology. A GWAS utilizing data from the UK Biobank identified 496 shared risk SNPs, implicating critical biological pathways involved in both disorders, including glycolipid metabolism (PPAP2B, DGKB, LIPC), adipocytokine signaling (LEPR, PPARGC1A), T2DM (GCK, CACNA1C), long-term depression (ITPR2, IGF1), and immune pathways (NFATC3, NFATC2) (37). Rodent models of insulin resistance exhibit impaired dopaminergic signaling and disrupted reward-related behaviors (7, 38), reflecting detrimental effects on emotional well-being. A hyperdopaminergic state in the amygdala may underlie increased risk of mood disorder observed in insulin-resistant, diabetic rats (39). Moreover, the high expression of insulin receptor in the dopaminergic neurons of midbrain, which encodes reward-seeking behavior, underscores the interplay between insulin signaling and mood regulation (29, 40, 41). In diabetic patients, peripheral insulin administration markedly impairs glucose metabolism in appetite- and reward-related regions, such as the mesostriatal system, compared to healthy controls (42).

The genotyping of the CYP2C19 gene is essential for personalizing escitalopram therapy, as the metabolizer status significantly influences drug concentrations and therapeutic efficacy (43). Escitalopram response is intricately to reward processing, with early increases in frontostriatal connectivity during reward anticipation correlating significantly with reduced depressive symptoms after eight weeks of treatment (16). Moreover, in patients with MDD, improvements in depressive symptoms after two weeks of escitalopram treatment were positively correlated with increased functional connectivity between the left hippocampus and the inferior frontal gyrus, suggesting an early predictor of antidepressant efficacy (18). These findings imply that escitalopram may ameliorate both cognitive and emotional functions that are compromised by diabetes. Notably, escitalopram is reported to enhance synaptic plasticity within three to five weeks in healthy individuals (44), positioning it as a promising candidate for improving insulin sensitivity while and simultaneously addressing the dual challenges posed by T2DM and MDD.

Psychological factors, such as stress and depression, also play a significant role in glucose regulation, suggesting a complex interplay between mental health and metabolic processes (45). From an endocrinological perspective, it is essential to explore how antidepressants might affect insulin sensitivity and glucose metabolism. The relationship between T2DM and MDD is primarily mediated through the HPA axis, which drives elevated cortisol levels that adversely affect brain regions rich in glucocorticoid receptors (46–48). Escitalopram has been demonstrated to enhance cognitive function in stressed rodent models by modulating the HPA axis and the insulin receptor substrate/Glycogen Synthase Kinase 3 (GSK-3β) signaling pathway (46), suggesting that escitalopram may represent a viable therapeutic strategy for the concurrent management of T2DM and MDD. Insulin resistance and hyperglycemia contribute to mitochondrial dysfunction, generating reactive oxygen species (ROS) that disrupt energy metabolism and initiate apoptosis (49). In models of chronic stress-induced depression, escitalopram has demonstrated efficacy in mitigating oxidative damage, enhancing antioxidant defenses, and modulating brain-derived neurotrophic factor (BDNF) levels, thereby promoting neuronal healthy (19). Moreover, n-3 polyunsaturated fatty acids (PUFAs) and escitalopram may work synergistically enhance adenylyl cyclase activity and BDNF expression, further reinforcing their antidepressant effects (50). In summary, antidepressants such as escitalopram and citalopram may affect glucose metabolism by addressing stress and depression, which are recognized factors affecting insulin sensitivity and glucose regulation.

Escitalopram therapy significantly reduced CH, TG, LDL and malondialdehyde levels, while increasing HDL compared to the atherosclerosis model group (21). In contrast, antidepressants such as citalopram and escitalopram have been associated with adverse alterations in lipid profiles, including elevated triglyceride levels, increased LDL cholesterol, and decreased HDL cholesterol (51). A 24-month observational study revealed that the use of antidepressants such as escitalopram, paroxetine, and duloxetine was associated with a 10-15% increased risk of weight gain of at least 5% from baseline weight (52). Additionally, atypical depression has been correlated with heightened insulin resistance, characterized by increased appetite and subsequent weight gain (53).

Our results demonstrated no significant alterations in lipid homeostasis, contrasts with prior observational studies reporting modest elevations in TG and CH among individuals T2DM-MDD (29). This discrepancy may be attributed to several factors: (i) population-specific pathophysiological characteristics, (ii) limited longitudinal assessment windows, and (iii) differences in baseline glycemic control. Moreover, obesity-related neuroinflammation has been shown to impair serotonin transporter (SERT) expression in the hippocampus, potentially elucidating the diminished responsiveness to SSRIs observed in obese individuals with comorbid depression (54). Understanding the complex interactions between mental health and lipid metabolism is crucial for developing comprehensive treatment strategies, particularly in patients presenting with comorbid obesity and depression.

Given the pathophysiological insights into depression, the exploration of novel therapeutic strategies is paramount importance. Chronic unpredictable mild stress induces been shown to induce depressive-like behaviors and neuroinflammation in leptin-deficient mice, effects that were reversed by pioglitazone, a peroxisome proliferator-activated receptor gamma (PPARγ) agonist, likely through the enhancement of plasma glucose levels (55). Pioglitazone has emerged as a promising adjunctive treatment for non-diabetic MDD, demonstrating early improvements and potential remission (56). Clinical investigations further substantiate its efficacy and safety, positioning pioglitazone as an augmentation strategy for patients with moderate to severe MDD (57). Moreover, the 5-HT3 receptor antagonist 3-methoxy-N-p-tolylquinoxalin-2-carboxamide (QCM-4) has exhibited considerable promise in improving insulin sensitivity and mitigating depressive-like behaviors in mice subjected to a high-fat diet, while also normalizing glucose and lipid profiles (58, 59).

This study has several limitations. First, significant variability exists among the included studies due to differences in baseline metabolic profiles, medication dosages, and statistical methods. Second, the relatively small sample sizes underscore the need for larger clinical trials to confirm these findings. Third, the exclusion of non-English publications may introduce publication biases. Moreover, individual responses to SSRIs vary, with some patients experiencing dyslipidemia and weight gain as side effects.

5 Conclusion

Despite methodological and sample size limitations precluding a formal meta-analysis, the available evidence suggests that both citalopram and escitalopram are effective in reducing FBG and HbA1c levels. To strengthen the evidence base, future research should prioritize large-scale, multicenter RCTs utilizing standardized protocols for treatment duration and dose titration. These studies should include diverse patient populations, stratified by obesity status, diabetes severity, and depression subtypes, to identify subgroups most likely to benefit. Such efforts are essential for validating the efficacy of these medications and for drawing more definitive conclusions regarding their effects on glycemic control.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

YD: Data curation, Writing – original draft. ML: Data curation, Writing – review & editing. JD: Writing – review & editing. MY: Writing – review & editing. ZT: Writing – review & editing. MZ: Writing – review & editing, Funding acquisition, Project administration. SL: Writing – review & editing, Funding acquisition, Project administration.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. The work was supported by the National Natural Science Foundation of China (82301709 to MZ); the Zhejiang Provincial Natural Science Foundation (LTGY24H0900112 to SL).

Acknowledgments

The author appreciates the support of all individuals who contributed to this research.

Conflict of interest

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.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2025.1578326/full#supplementary-material

References

1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. (2018) 392:1789–858. doi: 10.1016/S0140-6736(18)32279-7

PubMed Abstract | Crossref Full Text | Google Scholar

2. Roy T and Lloyd CE. Epidemiology of depression and diabetes: a systematic review. J Affect Disord. (2012) 42 Suppl:S8–S21. doi: 10.1016/S0165-0327(12)70004-6

PubMed Abstract | Crossref Full Text | Google Scholar

3. Liu Y, Huang SY, Liu DL, Zeng XX, Pan XR, and Peng J. Bidirectional relationship between diabetes mellitus and depression: Mechanisms and epidemiology. World J Psychiatry. (2024) 14:1429–36. doi: 10.5498/wjp.v14.i10.1429

PubMed Abstract | Crossref Full Text | Google Scholar

4. Ito S, Yanai M, Yamaguchi S, Couraud PO, and Ohtsuki S. Regulation of tight-junction integrity by insulin in an in vitro model of human blood-brain barrier. J Pharm Sci. (2017) 106:2599–605. doi: 10.1016/j.xphs.2017.04.036

PubMed Abstract | Crossref Full Text | Google Scholar

5. Banks WA, Owen JB, and Erickson MA. Insulin in the brain: there and back again. Pharmacol Ther. (2012) 136:82–93. doi: 10.1016/j.pharmthera.2012.07.006

PubMed Abstract | Crossref Full Text | Google Scholar

6. De Bartolomeis A, De Simone G, De Prisco M, Barone A, Napoli R, Beguinot F, et al. Insulin effects on core neurotransmitter pathways involved in schizophrenia neurobiology: a meta-analysis of preclinical studies. Implications for the treatment. Mol Psychiatry. (2023) 28:2811–25. doi: 10.1038/s41380-023-02065-4

PubMed Abstract | Crossref Full Text | Google Scholar

7. Gruber J, Hanssen R, Qubad M, Bouzouina A, Schack V, Sochor H, et al. Impact of insulin and insulin resistance on brain dopamine signalling and reward processing - An underexplored mechanism in the pathophysiology of depression? Neurosci Biobehav Rev. (2023) 149:105179. doi: 10.1016/j.neubiorev.2023.105179

PubMed Abstract | Crossref Full Text | Google Scholar

8. James DE, Stöckli J, and Birnbaum MJ. The aetiology and molecular landscape of insulin resistance. Nat Rev Mol Cell Biol. (2021) 22:751–71. doi: 10.1038/s41580-021-00390-6

PubMed Abstract | Crossref Full Text | Google Scholar

9. Kullmann S, Kleinridders A, Small DM, Fritsche A, Häring HU, Preissl H, et al. Central nervous pathways of insulin action in the control of metabolism and food intake. Lancet Diabetes Endocrinol. (2020) 8:524–34. doi: 10.1016/S2213-8587(20)30113-3

PubMed Abstract | Crossref Full Text | Google Scholar

10. Su WJ, Peng W, Gong H, Liu YZ, Zhang Y, Lian YJ, et al. Antidiabetic drug glyburide modulates depressive-like behavior comorbid with insulin resistance. J Neuroinflamm. (2017) 14:210. doi: 10.1186/s12974-017-0985-4

PubMed Abstract | Crossref Full Text | Google Scholar

11. Réus GZ, Dos Santos MA, Abelaira HM, Titus SE, Carlessi AS, Matias BI, et al. Antioxidant treatment ameliorates experimental diabetes-induced depressive-like behaviour and reduces oxidative stress in brain and pancreas. Diabetes Metab Res Rev. (2016) 32:278–88. doi: 10.1002/dmrr.2732

PubMed Abstract | Crossref Full Text | Google Scholar

12. Tharmaraja T, Stahl D, Hopkins CWP, Persaud SJ, Jones PM, Ismail K, et al. The association between selective serotonin reuptake inhibitors and glycemia: A systematic review and meta-analysis of randomized controlled trials. Psychosom Med. (2019) 81:570–83. doi: 10.1097/PSY.0000000000000707

PubMed Abstract | Crossref Full Text | Google Scholar

13. Hyttel J, Bøgesø KP, Perregaard J, and Sánchez C. The pharmacological effect of citalopram residues in the (S)-(+)-enantiomer. J Neural Transm Gen Sect. (1992) 88:157–60. doi: 10.1007/BF01244820

PubMed Abstract | Crossref Full Text | Google Scholar

14. Yin J, Song X, Wang C, Lin X, and Miao M. Escitalopram versus other antidepressive agents for major depressive disorder: a systematic review and meta-analysis. BMC Psychiatry. (2023) 23:876. doi: 10.1186/s12888-023-05382-8

PubMed Abstract | Crossref Full Text | Google Scholar

15. Buhl ES, Jensen TK, Jessen N, Elfving B, Buhl CS, Kristiansen SB, et al. Treatment with an SSRI antidepressant restores hippocampo-hypothalamic corticosteroid feedback and reverses insulin resistance in low-birth-weight rats. Am J Physiol Endocrinol Metab. (2010) 298:E920–9. doi: 10.1152/ajpendo.00606.2009

PubMed Abstract | Crossref Full Text | Google Scholar

16. Dunlop K, Rizvi SJ, Kennedy SH, Hassel S, Strother SC, Harris JK, et al. Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report. Neuropsychopharmacology. (2020) 45:1390–7. doi: 10.1038/s41386-020-0688-x

PubMed Abstract | Crossref Full Text | Google Scholar

17. Song W, Shen Y, Zhang Y, Peng S, Zhang R, Ning A, et al. Expression alteration of microRNAs in Nucleus Accumbens is associated with chronic stress and antidepressant treatment in rats. BMC Med Inform Decis Mak. (2019) 19:271. doi: 10.1186/s12911-019-0964-z

PubMed Abstract | Crossref Full Text | Google Scholar

18. Xiao H, Yuan M, Li H, Li S, Du Y, Wang M, et al. Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder. J Affect Disord. (2021) 291:315–21. doi: 10.1016/j.jad.2021.05.013

PubMed Abstract | Crossref Full Text | Google Scholar

19. Dionisie V, Ciobanu AM, Toma VA, Manea MC, Baldea I, Olteanu D, et al. Escitalopram targets oxidative stress, caspase-3, BDNF and meCP2 in the hippocampus and frontal cortex of a rat model of depression induced by chronic unpredictable mild stress. Int J Mol Sci. (2021) 22:7483. doi: 10.3390/ijms22147483

PubMed Abstract | Crossref Full Text | Google Scholar

20. Fuertig R, Azzinnari D, Bergamini G, Cathomas F, Sigrist H, Seifritz E, et al. Mouse chronic social stress increases blood and brain kynurenine pathway activity and fear behaviour: Both effects are reversed by inhibition of indoleamine 2,3-dioxygenase. Brain Behav Immun. (2016) 54:59–72. doi: 10.1016/j.bbi.2015.12.020

PubMed Abstract | Crossref Full Text | Google Scholar

21. Unis A, Abdelbary A, and Hamza M. Comparison of the effects of escitalopram and atorvastatin on diet-induced atherosclerosis in rats. Can J Physiol Pharmacol. (2014) 92:226–33. doi: 10.1139/cjpp-2013-0168

PubMed Abstract | Crossref Full Text | Google Scholar

22. Brożek JL, Akl EA, Compalati E, Kreis J, Terracciano L, Fiocchi A, et al. Grading quality of evidence and strength of recommendations in clinical practice guidelines part 3 of 3. The GRADE approach to developing recommendations. Allergy. (2011) 66:588–95. doi: 10.1111/j.1398-9995.2010.02530.x

PubMed Abstract | Crossref Full Text | Google Scholar

23. Higgins JP, Thompson SG, Deeks JJ, and Altman DG. Measuring inconsistency in meta-analyses. BMJ (Clin Res Ed). (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557

PubMed Abstract | Crossref Full Text | Google Scholar

24. Khazaie H, Rahimi M, Tatari F, Rezaei M, Najafi F, and Tahmasian M. Treatment of depression in type 2 diabetes with Fluoxetine or Citalopram? Neurosci (Riyadh Saudi Arabia). (2011) 16:42–5.

Google Scholar

25. Gehlawat P, Gupta R, Rajput R, Gahlan D, and Gehlawat VK. Diabetes with comorbid depression: role of SSRI in better glycemic control. Asian J Psychiatr. (2013) 6:364–8. doi: 10.1016/j.ajp.2013.03.007

PubMed Abstract | Crossref Full Text | Google Scholar

26. Nicolau J, Rivera R, Francés C, Chacártegui B, and Masmiquel L. Treatment of depression in type 2 diabetic patients: effects on depressive symptoms, quality of life and metabolic control. Diabetes Res Clin Pract. (2013) 101:148–52. doi: 10.1016/j.diabres.2013.05.009

PubMed Abstract | Crossref Full Text | Google Scholar

27. Kumar K, Salman M, Shukla V, Ahmad A, Verma VK, Rizvi D, et al. Comparative effect of agomelatine versus escitalopram on glycemic control and symptoms of depression in patients with type 2 diabetes mellitus and depression. Int J Pharm Sci Res. (2015) 6:4304–9. doi: 10.13040/IJPSR.0975-8232.6(10).4304-09

Crossref Full Text | Google Scholar

28. Subedi S, Shrestha L, Shrivastava AK, Joshi B, Chhetri P, Pokhrel BR, et al. Effect of escitalopram on glycemic control in type 2 diabetes mellitus patients with depression. J Chitwan Med Coll. (2020) 10:90–5.

Google Scholar

29. Khassawneh AH, Alzoubi A, Khasawneh AG, Abdo N, Abu-Naser D, Al-Mistarehi AH, et al. The relationship between depression and metabolic control parameters in type 2 diabetic patients: A cross-sectional and feasibility interventional study. Int J Clin Pract. (2021) 75(4):e13777. doi: 10.1111/ijcp.13777

PubMed Abstract | Crossref Full Text | Google Scholar

30. Israt U, Iqbal M, Rowshan M, Sultana S, Ahmed S, Sultana N, et al. Efficacy of escitalopram (SSRI) for better glycemic control in the diabetic depressive patients. Sir Salimullah Med Coll J. (2022) 29:100–4. doi: 10.3329/ssmcj.v29i2.58854

Crossref Full Text | Google Scholar

31. Wei F, Zhou L, Wang Q, Zheng G, and Su S. Effect of compound lactic acid bacteria capsules on the small intestinal bacterial overgrowth in patients with depression and diabetes: A blinded randomized controlled clinical trial. Dis Markers. (2022) 2022:6721695. doi: 10.1155/2022/6721695

PubMed Abstract | Crossref Full Text | Google Scholar

32. Santi NS, Biswal SB, Naik BN, Sahoo JP, and Rath B. Metabolic effects of antidepressants: results of a randomized study’s interim analysis. Cureus. (2023) 15:e42585. doi: 10.7759/cureus.42585

PubMed Abstract | Crossref Full Text | Google Scholar

33. Shubha D, Maganalli A, Nayak V, Kasturi E, and Bali A. Effect of intervention on glycemic control among adult population with type 2 diabetes mellitus and comorbid depression in Central Karnataka, India. RGUHS Natl J Public Health. (2023) 8:12–9.

Google Scholar

34. Tiwary S, Vishnuvardhan G, Tripathi R, and Manojprithviraj M. Effect of escitalopram on glycemic control and C-reactive protein in patients with depression and co morbid type 2 diabetes mellitus–a study on Indian population. BJPsych Open. (2024) 10:S291. doi: 10.1192/bjo.2024.693

Crossref Full Text | Google Scholar

35. Papakostas GI, Fava M, Baer L, Swee MB, Jaeger A, Bobo WV, et al. Ziprasidone augmentation of escitalopram for major depressive disorder: efficacy results from a randomized, double-blind, placebo-controlled study. Am J Psychiatry. (2015) 172:1251–8. doi: 10.1176/appi.ajp.2015.14101251

PubMed Abstract | Crossref Full Text | Google Scholar

36. Kudyar P, Gupta BM, Khajuria V, and Banal R. Comparison of efficacy and safety of escitalopram and vilazodone in major depressive disorder. Natl J Physiol Pharm Pharmacol. (2018) 8:1147–52. doi: 10.5455/njppp.2018.8.0412120042018

Crossref Full Text | Google Scholar

37. Ji HF, Zhuang QS, and Shen L. Genetic overlap between type 2 diabetes and major depressive disorder identified by bioinformatics analysis. Oncotarget. (2016) 7:17410–4. doi: 10.18632/oncotarget.8202

PubMed Abstract | Crossref Full Text | Google Scholar

38. Parashar A, Mehta V, and Malairaman U. Type 2 diabetes mellitus is associated with social recognition memory deficit and altered dopaminergic neurotransmission in the amygdala. Ann Neurosci. (2018) 24:212–20. doi: 10.1159/000479637

PubMed Abstract | Crossref Full Text | Google Scholar

39. Rebolledo-Solleiro D, Araiza LFO, Broccoli L, Hansson AC, Rocha-Arrieta LL, Aguilar-Roblero R, et al. Dopamine D1 receptor activity is involved in the increased anxiety levels observed in STZ-induced diabetes in rats. Behav Brain Res. (2016) 313:293–301. doi: 10.1016/j.bbr.2016.06.060

PubMed Abstract | Crossref Full Text | Google Scholar

40. Khanh DV, Choi YH, Moh SH, Kinyua AW, and Kim KW. Leptin and insulin signaling in dopaminergic neurons: relationship between energy balance and reward system. Front Psychol. (2014) 5:846. doi: 10.3389/fpsyg.2014.00846

PubMed Abstract | Crossref Full Text | Google Scholar

41. Milstein JL and Ferris HA. The brain as an insulin-sensitive metabolic organ. Mol Metab. (2021) 52:101234. doi: 10.1016/j.molmet.2021.101234

PubMed Abstract | Crossref Full Text | Google Scholar

42. Anthony K, Reed LJ, Dunn JT, Bingham E, Hopkins D, Marsden PK, et al. Attenuation of insulin-evoked responses in brain networks controlling appetite and reward in insulin resistance: the cerebral basis for impaired control of food intake in metabolic syndrome? Diabetes. (2006) 55:2986–92. doi: 10.2337/db06-0376

PubMed Abstract | Crossref Full Text | Google Scholar

43. Jukić MM, Haslemo T, Molden E, and Ingelman-Sundberg M. Impact of CYP2C19 genotype on escitalopram exposure and therapeutic failure: A retrospective study based on 2,087 patients. Am J Psychiatry. (2018) 175:463–70. doi: 10.1176/appi.ajp.2017.17050550

PubMed Abstract | Crossref Full Text | Google Scholar

44. Johansen A, Armand S, Plavén-Sigray P, Nasser A, Ozenne B, Petersen IN, et al. Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial. Mol Psychiatry. (2023) 28:4272–9. doi: 10.1038/s41380-023-02285-8

PubMed Abstract | Crossref Full Text | Google Scholar

45. Roy K, Iqbal S, Gadag V, and Bavington B. Relationship between psychosocial factors and glucose control in adults with type 2 diabetes. Can J Diabetes. (2020) 44:636–42. doi: 10.1016/j.jcjd.2020.01.005

PubMed Abstract | Crossref Full Text | Google Scholar

46. Wu C, Gong WG, Wang YJ, Sun JJ, Zhou H, Zhang ZJ, et al. Escitalopram alleviates stress-induced Alzheimer’s disease-like tau pathologies and cognitive deficits by reducing hypothalamic-pituitary-adrenal axis reactivity and insulin/GSK-3β signal pathway activity. Neurobiol Aging. (2018) 67:137–47. doi: 10.1016/j.neurobiolaging.2018.03.011

PubMed Abstract | Crossref Full Text | Google Scholar

47. Nguyen L, Kakeda S, Watanabe K, Katsuki A, Sugimoto K, Igata N, et al. Brain structural network alterations related to serum cortisol levels in drug-naïve, first-episode major depressive disorder patients: a source-based morphometric study. Sci Rep. (2020) 10:22096. doi: 10.1038/s41598-020-79220-2

PubMed Abstract | Crossref Full Text | Google Scholar

48. Tyagi K, Agarwal NB, Kapur P, Kohli S, and Jalali RK. Evaluation of stress and associated biochemical changes in patients with type 2 diabetes mellitus and obesity. Diabetes Metab Syndr Obes. (2021) 14:705–17. doi: 10.2147/DMSO.S294555

PubMed Abstract | Crossref Full Text | Google Scholar

49. Pinti MV, Fink GK, Hathaway QA, Durr AJ, Kunovac A, and Hollander JM. Mitochondrial dysfunction in type 2 diabetes mellitus: an organ-based analysis. American journal of physiology. Mitochondrial dysfunction in type 2 diabetes mellitus: an organ-based analysis. Am J Physiol Endocrinol Metab. (2019) 316:E268–e285. doi: 10.1152/ajpendo.00314.2018

PubMed Abstract | Crossref Full Text | Google Scholar

50. Chukaew P, Leow A, Saengsawang W, and Rasenick MM. Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids. Mol Psychiatry. (2021) 26:2402–14. doi: 10.1038/s41380-020-0724-6

PubMed Abstract | Crossref Full Text | Google Scholar

51. Richards-Belle A, Austin-Zimmerman I, Wang B, Zartaloudi E, Cotic M, Gracie C, et al. Associations of antidepressants and antipsychotics with lipid parameters: Do CYP2C19/CYP2D6 genes play a role? A UK population-based study. J Psychopharmacol. (2023) 37:396–407. doi: 10.1177/02698811231152748

PubMed Abstract | Crossref Full Text | Google Scholar

52. Petimar J, Young JG, Yu H, Rifas-Shiman SL, Daley MF, Heerman WJ, et al. Medication-induced weight change across common antidepressant treatments: A target trial emulation study. Ann Intern Med. (2024) 177:993–1003. doi: 10.7326/M23-2742

PubMed Abstract | Crossref Full Text | Google Scholar

53. Fernandes BS, Salagre E, Enduru N, Grande I, Vieta E, and Zhao Z. Insulin resistance in depression: A large meta-analysis of metabolic parameters and variation. Neurosci Biobehav Rev. (2022) 139:104758–8. doi: 10.1016/j.neubiorev.2022.104758

PubMed Abstract | Crossref Full Text | Google Scholar

54. Hersey M, Woodruff JL, Maxwell N, Sadek AT, Bykalo MK, Bain I, et al. High-fat diet induces neuroinflammation and reduces the serotonergic response to escitalopram in the hippocampus of obese rats. Brain Behav Immun. (2021) 96:63–72. doi: 10.1016/j.bbi.2021.05.010

PubMed Abstract | Crossref Full Text | Google Scholar

55. Kurhe Y and Mahesh R. Pioglitazone, a PPARγ agonist rescues depression associated with obesity using chronic unpredictable mild stress model in experimental mice. Neurobiol Stress. (2016) 3:114–21. doi: 10.1016/j.ynstr.2016.05.001

PubMed Abstract | Crossref Full Text | Google Scholar

56. Qin X, Wang W, Wu H, Liu D, Wang R, Xu J, et al. PPARγ-mediated microglial activation phenotype is involved in depressive-like behaviors and neuroinflammation in stressed C57BL/6J and ob/ob mice. Psychoneuroendocrinology. (2020) 117:104674. doi: 10.1016/j.psyneuen.2020.104674

PubMed Abstract | Crossref Full Text | Google Scholar

57. Sepanjnia K, Modabbernia A, Ashrafi M, Modabbernia MJ, and Akhondzadeh S. Pioglitazone adjunctive therapy for moderate-to-severe major depressive disorder: randomized double-blind placebo-controlled trial. Neuropsychopharmacology. (2012) 37:2093–100. doi: 10.1038/npp.2012.58

PubMed Abstract | Crossref Full Text | Google Scholar

58. Kurhe Y, Mahesh R, Gupta D, and Devadoss T. QCM-4, a serotonergic type 3 receptor modulator attenuates depression co-morbid with obesity in mice: an approach based on behavioral and biochemical investigations. Eur J Pharmacol. (2014) 740:611–8. doi: 10.1016/j.ejphar.2014.06.020

PubMed Abstract | Crossref Full Text | Google Scholar

59. Kurhe Y, Mahesh R, and Devadoss T. Novel 5-HT(3) receptor antagonist QCM-4 attenuates depressive-like phenotype associated with obesity in high-fat-diet-fed mice. Psychopharmacology. (2017) 234:1165–79. doi: 10.1007/s00213-017-4558-0

PubMed Abstract | Crossref Full Text | Google Scholar