Effectiveness and Heterogeneity of Multimodal Interventions for Mood and Anxiety Outcomes in Insulin-Resistant Populations: A Systematic Review of Randomized Controlled Trials

Effectiveness and Heterogeneity of Multimodal Interventions for Mood and Anxiety Outcomes in Insulin-Resistant Populations: A Systematic Review of Randomized Controlled Trials | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Effectiveness and Heterogeneity of Multimodal Interventions for Mood and Anxiety Outcomes in Insulin-Resistant Populations: A Systematic Review of Randomized Controlled Trials Nuzhat Azim, Zahra Wakif, Prabhleen Kaur, Sana Siddiqui, Alishba Mahmood, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9336521/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Insulin Resistance (IR) is a prevalent metabolic condition affecting approximately 26.5% of adults globally and is increasingly recognised for its association with mood and anxiety disorders. This systematic review synthesises evidence from randomised controlled trials (RCTs) to evaluate the effectiveness and heterogeneity of multimodal interventions targeting mental health outcomes in insulin-resistant populations. Method A systematic search on PubMed and MEDLINE identified 1,848 records, of which 42 RCTs (N = 14,982 participants) met inclusion criteria. Included studies examined diverse interventions, including pharmacological, psychological, lifestyle, and integrated care approaches across populations with conditions such as type 2 diabetes, metabolic syndrome, and polycystic ovary syndrome. Results Across studies, interventions consistently demonstrated significant within-group improvements in depressive symptoms, with moderate-to-large effect sizes reported in several trials. Anxiety outcomes also showed reductions, although findings were more variable. However, between-group differences relative to control conditions were less consistent across studies. Bipolar disorder was minimally represented, limiting conclusions for this subgroup. Conclusion Overall, while interventions appear effective in improving mood outcomes within insulin-resistant populations, substantial heterogeneity and inconsistent comparative efficacy limit definitive conclusions. Future research should prioritise standardised outcome measures, longitudinal designs, and broader psychiatric inclusion to improve clinical and public health relevance. Insulin resistance mental health mood disorders RCT systematic review Figures Figure 1 Introduction Insulin resistance (IR) is a condition in which target tissues, including skeletal muscle, liver, and adipose tissue, show reduced responsiveness to insulin, leading to elevated blood glucose levels and compensatory hyperinsulinemia (Freeman, Acevedo, & Pennings, 2023). Beyond its critical role in glucose regulation, IR plays a crucial role in the development and progression of metabolic-related diseases, including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome. It is also linked to polycystic ovary syndrome (PCOS), cardiovascular disease, and certain cancers (Szablewski, 2025 ; Semple et al., 2011 ; Parker & Semple, 2013 ). Severe forms of IR, although rare, are characterized by profound hyperinsulinemia and abnormal glucose homeostasis, sometimes presenting initially with hypoglycemia before hyperglycemia develops (Semple et al., 2011 ). One meta-analysis of 87 studies determined a pooled global IR prevalence of approximately 26.5% (Ballena-Caicedo et al., 2025). In the United States, 40% aged 18 to 44 were insulin resistant in 2021 (Freeman et al., 2023). Particularly high prevalence of IR has been found in South Asians, Indigenous populations, East Asians, Latin Americans, and African Americans (Li et al., 2022 ; Raygor et al., 2019 ). Men and women experience differences in insulin sensitivity, with variations in hormones playing a key role (Ciarambino et al., 2023 ). Men are generally more susceptible than premenopausal women, while postmenopausal women show higher prevalence (Li et al., 2022 ). Lifestyle factors, including diet, physical inactivity, smoking, inadequate sleep, and chronic stress, further contribute to the development of IR. Given its high prevalence and widespread impact, IR represents a significant public health concern. Beyond metabolic consequences, IR has been increasingly linked to mental health outcomes, particularly mood and anxiety disorders. A systematic review and meta-analysis including 25,847 participants found a consistent association between IR and depressive symptoms (effect size = 0.19, 95% CI 0.11–0.27), although heterogeneity across studies was noted due to differences in assessment methods (Kan et al., 2013 ). Biological mechanisms, such as chronic inflammation and dysregulation of the hypothalamic-pituitary-adrenal axis, may mediate this relationship, with elevated proinflammatory cytokines like IL-6 and CRP implicated in both IR and depression (Mehdi et al., 2023 ). At the population level, this connection demonstrates a dual burden: metabolic dysfunction increases risk for depression and anxiety, while mental health disorders further complicate management of metabolic conditions, resulting in increased healthcare utilization, economic challenges, and decreased quality of life. Given its high prevalence and widespread impact, IR represents a significant public health concern. At the individual level, it is associated with an increased risk of multiple chronic conditions, including metabolic, cardiovascular, and endocrine disorders, which can substantially reduce quality of life. Emerging evidence also suggests that IR may negatively affect mental health, with links to mood and anxiety disorders, highlighting the broader impact of metabolic dysfunction on both physical and psychological well-being (Kan et al., 2013 ; Mehdi et al., 2023 ). Interventions targeting IR may therefore have important population-level benefits for mental health. Structured lifestyle interventions, including dietary modification and exercise programs, have been shown to improve insulin sensitivity and reduce depressive and mental health disorder symptoms. (Luciano et al., 2020; Vreijling et al., 2024). However, the effectiveness of these interventions varies depending on intervention type, population characteristics, and outcome assessment methods, highlighting substantial heterogeneity in the literature. Despite growing evidence, several gaps remain in understanding interventions for insulin-resistant populations. Existing systematic reviews have typically focused on a single intervention type, considered only metabolic or mental health outcomes, or evaluated narrow populations, such as individuals with type 2 diabetes (Kan et al., 2013 ; Cezaretto et al., 2016 ; Jeremiah et al., 2020 ). Methodological heterogeneity, including differences in measures of insulin resistance, mood, and anxiety assessment tools, further limits the comparability and generalizability of findings. No comprehensive systematic review has yet synthesized the effectiveness and heterogeneity of multimodal interventions targeting both metabolic and mood/anxiety outcomes across diverse insulin-resistant populations. Addressing these gaps is critical for guiding clinical practice, informing public health strategies, and developing population-level interventions that reduce the dual burden of metabolic and mental health disorders. This systematic review aims to synthesize evidence from randomized controlled trials to determine the effectiveness and heterogeneity of multimodal interventions in improving comorbid mental health outcomes among insulin-resistant populations. Specifically, this review has two objectives: (1) to examine the efficacy of randomized controlled trial interventions in improving mood outcomes among insulin-resistant populations, and (2) to evaluate their effectiveness in improving anxiety symptoms. At the population level, this relationship reflects a dual burden, where metabolic dysfunction increases the risk of depression and anxiety, while mental health disorders further complicate the management of metabolic conditions. This bidirectional interaction is associated with poorer treatment adherence, increased healthcare utilization, higher economic costs, and reduced overall quality of life (Kan et al., 2013 ; Vancampfort et al., 2016; Walker et al., 2015 ). Individuals with co-occurring metabolic and mental health conditions are also at greater risk for adverse health outcomes, including increased morbidity and premature mortality, further underscoring the broader public health implications of this association (Walker et al., 2015 ). Method This systematic review was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration ID: CRD420251238068 ). The PROSPERO record was accessed on 18 February 2026. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251238068 Information Sources and Search Strategy Authors conducted a comprehensive literature search on PubMed and MEDLINE databases on September 7th, 2025, developed in consultation with a University of Toronto library specialist, and finalised by N.A and F.N. The review was restricted to studies published in English. In addition to database searching, backward citation tracking of reference lists was undertaken to identify further eligible studies. The search strategy combined terms related to insulin resistance and associated metabolic conditions (including diabetes, polycystic ovary syndrome, hypertension, metabolic syndrome, and non-alcoholic fatty liver disease) with terms related to anxiety symptoms and mood disorders (including depression, bipolar disorder), alongside relevant mental health outcome measures. Controlled vocabulary (e.g., MeSH terms) and free-text keywords were used where appropriate. Eligibility Criteria Studies were included if i) included adult participants (≥18 years) with a condition affecting insulin regulation or associated insulin resistance such as insulin dysregulation or insulin-resistant conditions, such as polycystic ovary syndrome, nonalcoholic fatty liver disease, type 2 diabetes mellitus, type 1 diabetes mellitus, metabolic syndrome, and cardiovascular conditions such as hypertension ii) reported at least one mood-or-anxiety related outcome, including depressive disorders, bipolar disorder without psychotic features, or anxiety disorders, iii) used a randomized clinical trial design, iv) were primary research articles and v) were published in English. Studies were excluded if i) included participants younger than 18 years, ii) involved animal populations, iii) focused on psychiatric outcomes outside of mood and anxiety disorders, such as schizophrenia, eating disorders, or iv) were systematic reviews, meta-analyses, case studies, follow-up reports, or study protocols. Screening Procedure Records identified through database searching were imported into Covidence (Covidence, 2025) for study screening and data extraction. Titles and abstracts were screened independently by two reviewers against predefined eligibility criteria, followed by independent full-text assessment of potentially relevant studies. Any conflicts during either screening stage were resolved through discussion, and where consensus could not be reached, the lead author (N.A.) made a final decision in consultation with the supervising author (F.N.). . All reviewers were trained and supervised by N.A, who provided standardized instructions to ensure consistency in study selection. Screening was conducted by Z.W, P.K, A.N., A.J.K, S.K, V.I, and F.M. This dual-review process was implemented to enhance inter-reliability, in accordance with the predefined PROSPERO protocol. Data Extraction Data extraction was conducted using a duplicate extraction approach to ensure inter-rater reliability. All records were managed in Covidence, and extracted data were recorded using a standardized data extraction form. Each included study was independently extracted by two reviewers, and discrepancies were resolved through discussion amongst the team. All reviewers were trained and supervised by N.A. and F.N, and N.A., Z.W., P.K., A.N., A.J.K., S.K., A.M., and V.I. completed study extractions. Extracted data included study characteristics (author, year, country, and setting), study aims, participant characteristics (insulin resistance condition, psychiatric diagnosis, age, sex, and sample size), intervention details (type, duration, frequency, and comparator), and study design. Clinical outcomes included mood and anxiety clinician-administered or self-reported assessment tools, and their statistical outcomes (including pre–post changes, within-group and between-group differences, and follow-up outcomes). Data on intervention effectiveness, associations with psychiatric outcomes, and proposed biological mechanisms or biomarkers were also extracted. The study selection process is shown in the PRISMA flow diagram (Figure 1). A total of 1848 records were identified through database searching, of which 71 duplicates were removed after the records were imported to Covidence for screening. Following title and abstract screening of 1777 records, 192 full-text articles were assessed for eligibility. After full-text review, 150 studies were excluded due to an ineligible study design, intervention, population, outcomes, comparator, or lack of accessible full text. A total of 42 studies met the inclusion criteria and were included in the final review. Results Demographics Across 42 studies included in this review, a total of N= 14,982 participants were represented (see Table 1). Of these, n=7471 were allocated to intervention groups and n=7511 to control or treatment-as-usual (TAU) groups. The proportion of female participants varied across studies, ranging higher in sex-specific cohorts such as those examining polycystic ovary syndrome (PCOS) or gestational diabetes (n=5), with the majority of studies (n=30) reporting a female representation between 50-75%. The mean participant age of studies included middle-aged to older adults (50-65 years; n=30) and older populations (> 65 years; n=7). with some studies reporting early adulthood of ages 20-35 (n=5). Geographically, research was predominantly conducted in the United States (n = 13) and China (n=8), with additional contributions from Canada (n=4) and the United Kingdom (n=4); fewer studies were conducted in India (n=3), Brazil (n=2), Australia (n=2), and Germany (n=2), with single study representation from Finland, Thailand, Nigeria, Iran, Spain and Taiwan. Most studies were undertaken in clinical settings, including primary care (n=17) and hospital or speciality departments (n=11), with fewer conducted in community centres (n=6) or delivered via digital or fully remote platforms (n=8). Type 2 diabetes mellitus was the most frequently examined insulin resistance condition (n=29), followed by hypertension (n=6), metabolic syndrome (n=5), PCOS (n=4), type 1 diabetes mellitus (n=3), mixed diabetes populations or gestational diabetes (n=4). Populations were predominantly clinical and comorbid, with most studies including individuals with both an insulin resistance condition and a co-occurring depressive or anxiety symptom (n=34). Some papers addressed multiple conditions. Interventions were heterogeneous and included pharmacological (n=11), psychological (n=15), lifestyle (n=11), integrated care (n=9), digital or remote (n=8), education or self-management (n=7), and physiological approaches (n=4). Pharmacological interventions comprised morphine, antidepressants (e.g., citalopram, escitalopram, fluoxetine), and adjunctive compounds (e.g., curcumin or crocin), typically administered over 8 weeks to 6 months, with some extending to 12 months. Psychological interventions were primarily based on cognitive-behavioural therapy (CBT), delivered in in-person or internet-based formats over 6-12 sessions (approximately 8-12 weeks), alongside mindfulness, reminiscence therapy, and counselling approaches. Lifestyle interventions combined dietary modification and structured physical activity delivered over 3-12 months; integrated care models involved multidisciplinary management with structured follow-up over 6-12 months; while digital interventions included smartphone or web-based programmes delivered over 6-12 months. Education and self-management interventions focused on diabetes education and behavioural support, as well as physiological approaches (e.g., relaxation, yoga, biofeedback), were typically short-term (4-12 weeks). Overall intervention duration ranged from 2 weeks to 12 months, most commonly 8 weeks to 12 months. In summary, the included studies represent a diverse global sample, characterised by a high prevalence of metabolic and psychological comorbidity, and interventions were highly heterogeneous in modality and duration. Anxiety Outcomes As summarised in Table 2 , anxiety outcomes were assessed using validated instruments, most commonly the GAD-7, HADS-A, and SAS, with additional use of the STAI/STAI-6, SCL-90-R Anxiety subscale, and DDS. This variability in measurement approaches reflects heterogeneity in outcome operationalisation across studies. Overall, most interventions were within-group reductions in anxiety symptoms, although between-group differences were less consistently observed. Significant within-group improvements in intervention areas were reported in several studies. For instance, Arunjr et al. (2025) demonstrated significant reductions in both GAD-7 and DDS scores (p=0.001), while Liu et al. (2025) reported marked decreases in SAS scores (p<0.001). Similarly, A.L. Wroe et al. (2018) observed significant reductions in GAD-7 scores (p<0.001). In contrast, McGrady and Horner (1999), Kahloon et al. (2024), and Markle-Reid et al. (2018) reported no significant within-group changes, indicating variability in intervention responses across studies. Several studies also demonstrated significant reductions in both intervention and control groups, including Sha et al. (2025) and Fisher et al. (2011). Between-group comparisons showed significant superiority of the applied interventions in studies such as Guo et al. (2020) (p<0.001), Arunjr et al. (2025) (p<0.0001), Liu et al. (2025) (p=0.02), and A.L.Wroe et al. (2018) (p=0.032). However, several studies, including Markle-Reid et al. (2018), Baron et al. (2017), Zhang et al. (2024), Wayne et al. (2015), Wolff et al. (2016), and Pols et al. (2017) reported no significant between-group differences, despite some demonstrating within-group improvements of anxiety. Effect sizes were infrequently reported. Where available, they suggested small to moderate effects, with Liu et al. (2025) reporting a moderate effect (Cohen’s d = 0.58), Stark et al. (2025) a small effect (Cohen’s f = 0.17), and Wolf et al. (2016) a negligible to small negative effect (-0.2). Follow-up outcomes were also inconsistently reported. Sustained reductions in anxiety were observed in some studies (e.g., Stark et al., 2025), whereas others demonstrated minimal change or attenuation over time (e.g., Pols et al., 2017; Wayne et al., 2015), based on reported follow-up mean scores across timepoints. Notably, several studies reported significant reductions, including Sha et al. (2025; p<0.05), Fisher et al (2011; p 0.05 between-group despite within-group reductions), and Pols et al. (2017; p > 0.05 between-group) for both intervention and control groups. Overall, these findings indicate that while interventions are frequently associated with within-group improvements in anxiety, consistent between-group efficacy is less robust, and reported effect sizes are generally modest. Substantial heterogeneity in study design, intervention type, and outcome measurement limits direct comparability across studies. Mood Outcomes: Depression and Bipolar Disorders Depression outcomes were assessed using validated instruments, most commonly the BDI/BDI-II, PHQ-9/PHQ-8, HADS-D, and CES-D, with additional use of MADRS, HAM-D, and SCL-based measures, reflecting heterogeneity in outcome assessment across studies. Across studies reporting within-group analyses, the majority demonstrated statistically significant reductions in depressive symptom severity following intervention. Significant within-group improvements observed across multiple instruments, including BDI (e.g., Selçuk Tosun et al., 2024; p<0.001), MADRS (Calkin et al., 2022; p=0.0009), SCL-90-R Depression (Guo et al., 2020; p=0.008), CES-D (Thomson et al., 2010; p<0.001), PHQ-9 (Bogner et al., 2012; p<0.001), PHQ-8 (Fisher et al., 2011; p<0.0001), and SDS (Liu et al., 2025; p<0.001). Additional significant improvements were reported using HAM-D (Lustman et al., 2000; p=0.01) and HADS-D (Wolff et al., 2016; p<0.001). However, some studies reported non-significant within-group changes, particularly in smaller or older trials (e.g., McGrady & Hommer, 1999; p=n.s.; Kinder et al., 2006; p=0.2), indicating variability in intervention responsiveness. Between-group comparisons yielded more heterogeneous findings. Several studies demonstrated statistically significant superiority of intervention over control, including Selçuk Tosun et al. (2024; p<0.001), Wang et al. (2017; p<0.001), Bogner et al. (2012; p=0.007), Liu et al. (2025; p=0.01), and A. L. Wroe et al. (2018; p=0.010–0.008 across analyses). Additional significant between-group differences were observed in trials using PHQ-9 (Chiang et al., 2019; p<0.001) and SDS (Sha et al., 2025; p0.86), Fisher et al. (2011; p=0.28), Baron et al. (2017; p=0.53), and Pols et al. (2017; p=0.71). Some trials reported inconsistent findings across timepoints, such as Katon et al. (2004), where significance emerged only at later follow-ups (6 months: p=0.04; 12 months: p=0.03), suggesting delayed effects. Effect sizes, when reported, ranged from small to large. Moderate-to-large effects were observed in several studies, including Cohen’s d=0.61 (Liu et al., 2025), d=0.82–0.89 (Nobis et al., 2015), and d=0.44 (Wang et al., 2025), while Wolff et al. (2016) and Moncrieft et al. (2016) reported large negative effects (d=−0.9 and d=−0.85, respectively), indicating substantial symptom reduction. By contrast, Baron et al. (2017) demonstrated a moderate negative effect (d=−0.53) that did not reach statistical significance (p=0.232), highlighting discordance between effect magnitude and statistical significance. Statistically significant between-group differences were not always clinically meaningful, as reflected by small effect sizes in some trials (e.g., Williamson et al., 2009; d=0.13). Follow-up data were inconsistently reported, but available longitudinal data suggested that benefits were sometimes sustained beyond post-treatment. In Petrak et al. (2015), HAM-D-17 reductions were broadly maintained at follow-up, while Stark et al. (2025) showed persistent improvement in PHQ-8 scores with significant between-group differences at follow-up (p=0.012). Delayed effects were also observed in Katon et al.(2004), with non-significant between-group differences at baseline and 3 months becoming significant at 6 months (p=0.04) and 12 months (p=0.03); Suvada et al. (2023) similarly reported a significant 12-month difference (p<0.05). However, not all studies showed durable separation over time, as Pols et al. (2017) found comparable HADS-D scores across repeated follow-up assessments (p=0.71). Only one study assessed bipolar outcomes, with Calkin et al. (2022) reporting a modest but statistically significant within-group improvement on the CGI-BP (p=0.046), with no corresponding between-group comparison available. Overall, although within-group improvements in depressive symptoms were consistently observed, comparative efficacy relative to control conditions remained inconsistent, reflecting substantial heterogeneity in effect magnitude, statistical significance, and sustainability of outcomes. Discussion This systematic review is a comprehensive evaluation of randomized clinical trials (RCTs) investigating the impact of diverse interventions, ranging from pharmacological to lifestyle and psychological approaches, on mood and anxiety outcomes in adults with insulin resistance and associated metabolic conditions. Interventions consistently produced significant within-group improvements in depressive symptoms, with moderate-to-large effect sizes reported in several studies (Liu et al., 2025 ; Nobis et al., 2015 ). Anxiety symptoms also showed frequent within-group reductions across trials (Arunjr et al., 2025; Liu et al., 2025 ; Wroe et al., 2018 ). Furthermore, integrated care models and digital platforms emerged as promising modalities for sustaining these mental health improvements over follow-up periods of 6 to 12 months. While internal improvements were common, between-group efficacy compared to control or treatment-as-usual groups was notably less robust, with several trials failing to reach statistical significance despite observed symptom reductions. Substantial heterogeneity in measurement tools, such as the variation between GAD-7, PHQ-9, and HADS, and intervention durations limit the ability to draw definitive conclusions across the entire spectrum. Bipolar disorder was minimally represented, with only one study identified (Calkin et al., 2022), highlighting a critical gap in the literature. As such, findings, particularly for anxiety and bipolar outcomes, should be interpreted as preliminary and not broadly generalizable to associations with insulin resistance. The findings reveal a complex landscape where within-group improvements are common, yet between-group superiority remains inconsistent. The interpretation of within-group efficacy reflects whether participants improved over time. While most studies were statistically significant (p < 0.05) here, it was not evident whether the interventions were statistically superior to the control group. Interpretation is further complicated by clinical diversity and temporal dynamics. Although some included studies encompassed insulin-resistant conditions such as PCOS or NAFLD, this population was not consistently represented across trials, limiting phenotype-specific interpretation, particularly given that they are associated with distinct hormonal profiles (Dewani et al., 2023 ; Brutocao et al., 2018). Intervention effects varied over time, with some studies demonstrating delayed benefits at 6–12 months (e.g., Katon et al., 2004; Suvada et al., 2023), whereas others showed attenuation or non-significant differences over time (e.g., Pols et al., 2017; Wayne et al., 2015 ). Although within-group improvements were common (e.g., Liu et al., 2025 ; Selçuk Tosun et al., 2024 ), consistent between-group superiority was limited, with several trials reporting comparable outcomes in intervention and control groups (e.g., Fisher et al., 2011 ; Sha et al., 2025 ; Baron et al., 2017 ). Collectively, this suggests that a definitive “gold standard” intervention for the metabolic–mood interface remains unclear. Although depression and anxiety are frequently comorbid in insulin-resistant populations, they were examined as distinct clinical outcomes to help assess independent associations with insulin-resistant conditions. Depressive symptoms were more consistently assessed using validated severity scales such as the PHQ-9 and BDI II and showed more stable intervention responses, whereas anxiety outcomes measured using instruments such as the GAD-7 and SAS and demonstrated greater variability with less consistent effects. The present findings align with RCT literature, showing that interventions in insulin-resistant populations more consistently improve depressive symptoms than anxiety outcomes. Evidence for associations with depression is strong, with meta-analytic data demonstrating elevated insulin levels during acute depressive episodes, supporting a metabolic subtype of depression (Fernandes et al., 2022 ). In contrast, evidence for associations with anxiety is more limited and variable; while insulin resistance is associated with increased anxiety burden (Abdelfattah et al., 2025 ), findings are less consistent and mechanistically defined (Krupa et al., 2024 ; Possidente et al., 2023 ). Bipolar disorder remains underrepresented in the literature, potentially due to exclusion from RCTs, diagnostic complexity, and pharmacological confounding, resulting in a limited evidence base despite emerging associations with insulin resistance (Aguglia et al., 2026). Overall, these findings suggest that while insulin resistance is broadly linked to mood disturbances, the evidence is most robust and clinically actionable for depression, with greater uncertainty remaining for anxiety and bipolar disorder. The discrepancy likely reflects active control conditions, non-specific treatment effects, and substantial heterogeneity across insulin-resistant populations, which together reduce detectable between-group differences despite real symptom measurement sensitivity and closer metabolic linkage, whereas anxiety shows more variability. Temporal delays in treatment effects and underpowered psychiatric endpoints further limit between-group significance. Limitations A key limitation of this systematic review is the overrepresentation of type 2 diabetes (n = 29), making it difficult to conclude the generalizability of other insulin-resistant conditions. This imbalance is not unique to this review but reflects a broader trend in the literature, where metabolic-psychiatric research is largely anchored in diabetes or cardiometabolic cohorts (Chourpiliadis et al., 2024 ). Importantly, outcomes in the present review looked at outcomes based on self-reported or clinician-administered symptom scales, with only one study examining a clinically defined bipolar population. Evidence from bipolar suggests that insulin resistance is associated with greater illness severity and poorer treatment response, and consistent effects are observed particularly for anxiety (Miola et al., 2023). The inclusion of heterogeneous conditions (e.g., PCOS, NAFLD) and the lack of a clear intervention framework may have introduced additional variance. Together, these may contribute to the observed heterogeneity and modest effect sizes, consistent with prior findings (Fernandes et al., 2022 ). As a result, current evidence may overrepresent associations specific to advanced metabolic dysfunctions, while underrepresenting insulin resistance in primary psychiatric populations. This may partly explain the observed heterogeneity and modest effect sizes, and is consistent with prior meta-analytic findings of elevated but variable insulin resistance in depression (Fernandes et al., 2022 ). The strategy was restricted to PubMed and MEDLINE, which may have resulted in an underrepresentation of studies indexed in discipline-specific databases such as PsycINFO and CINAHL. These databases often capture research focused on psychological, behavioural, and psychotherapy-based interventions, which may differ methodologically from medically oriented trials. As a result, behavioural and psychosocial interventions may be underrepresented in this review, and the absence of a standardized framework for intervention selection may have further contributed to variability in the types of interventions included. Consequently, psychologically oriented or behavioural interventions may be less comprehensively captured. However, given the substantial indexing overlap for randomized controlled trials across these databases, this limitation is unlikely to have materially influenced the overall pattern of findings, particularly the observed heterogeneity and modest between-group effects. Future Research Future research should move beyond predominantly diabetes-focused samples and instead prioritize primary psychiatric populations, including major depressive disorder, anxiety disorders, bipolar disorder, and other relevant mental health conditions such as post-traumatic stress disorder or psychotic disorders. Greater clinical granularity is needed within these groups, including differentiation of disorder subtypes (e.g., atypical vs. melancholic depression; bipolar I vs. II, manic vs. hypomanic vs. rapid cycling), to better characterize how insulin resistance manifests across heterogeneous psychiatric profiles. Methodologically, standardization through the adoption of a core outcome set with consistent use of validated mood symptom measures is essential to improve cross-study compatibility. The literature is further limited by short follow-up durations, underscoring the need for longitudinal designs extending 6–12 months or longer to capture sustained effects and temporal relationships between metabolic and psychiatric changes. Finally, future studies should adopt mechanism-driven and integrative approaches by categorizing interventions based on underlying biological pathways and evaluating multimodal strategies combining pharmacological, psychological, and lifestyle components to effectively address the complexity of mental health outcomes in insulin-resistant populations. Conclusion Overall, the current evidence supports a meaningful but complex relationship between insulin resistance and mental health outcomes, with the strongest and most consistent effects observed for depressive symptoms. Despite growing within-group improvements across interventions, inconsistent between-group findings and substantial methodological heterogeneity limit definitive clinical conclusions. The underrepresentation of diverse psychiatric populations and limited longitudinal data further constrain generalizability and mechanistic understanding of these associations. Advancing this field will require more targeted, standardized, and integrative research approaches to clarify causal pathways and optimize intervention strategies. Declarations Authors report no conflicts of interest. Correspondence may be addressed to Nuzhat Azim [email protected] Authors received no funding or other monetary compensation to conduct this research. The authors acknowledge Margaret Wall (Univerisity of Toronto librarian) for her expert guidance in the development and optimization of the database search strategy. Author Contribution N.A. conceptualized the study, developed the methodology, registered the protocol, supervised all stages of the review process, and wrote the Methods, Results, and Discussion sections. F.N. contributed to the study design and provided senior oversight.N.A., Z.W., P.K., A.N., A.J.K., S.K., V.I., and F.M. conducted screening. N.A., Z.W., P.K., A.N., A.J.K., S.K., A.M., J.J., and V.I. performed data extraction and prepared Tables 1-3.N.A, Z.W., P.K., S.S., and V.I wrote the main manuscript text N.A., finalized Figures 1 and Tables 1-3.All authors reviewed and approved the final manuscript. Acknowledgement The authors acknowledge Margaret Wall (Univerisity of Toronto librarian) for her expert guidance in the development and optimization of the database search strategy. References Abdelfattah, H. E., Bekhet, M. M. M., Tawfik, F. A., Elias, D. G., & Saleh, A. M. M. A. E. H. (2025). The association between insulin resistance and risk of developing depression and anxiety disorders in a sample of Egyptian population. The Egyptian Journal of Internal Medicine, 37, Article 34 . https://doi.org/10.1186/s43162-025-00434-9 Aguglia, A., Meinero, M., Aprile, V., Cerisola, T., Mazzarello, G., Oggianu, A., Costanza, A., Amore, M., Amerio, A., & Serafini, G. (2026). 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Mindfulness-based intervention for hypertension patients with depression and/or anxiety in the community: a randomized controlled trial. Current Controlled Trials in Cardiovascular Medicine , 25 (1), Article 299. https://doi.org/10.1186/s13063-024-08139-0 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables13EffectivenessandHeterogeneityofMultimodalInterventionsforMoodandAnxietyOutcomesinInsulinResistantPopulationsASystematicReviewofRandomizedControlledTrials1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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University","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Wakif","suffix":""},{"id":618410649,"identity":"88d584ba-3294-49c9-b81d-7b119d07f400","order_by":2,"name":"Prabhleen Kaur","email":"","orcid":"","institution":"York University","correspondingAuthor":false,"prefix":"","firstName":"Prabhleen","middleName":"","lastName":"Kaur","suffix":""},{"id":618410650,"identity":"f810dff1-8857-40b0-ad03-f09ac5aa1e8b","order_by":3,"name":"Sana Siddiqui","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Siddiqui","suffix":""},{"id":618410651,"identity":"05d25d76-d2a8-43e8-adfd-dc1db362534f","order_by":4,"name":"Alishba Mahmood","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Alishba","middleName":"","lastName":"Mahmood","suffix":""},{"id":618410652,"identity":"f14dfece-66a5-464e-bac0-d49578a6544c","order_by":5,"name":"Amna Nahid","email":"","orcid":"","institution":"McGill University","correspondingAuthor":false,"prefix":"","firstName":"Amna","middleName":"","lastName":"Nahid","suffix":""},{"id":618410653,"identity":"38fee064-8528-4c08-a56c-bed10a4b7043","order_by":6,"name":"Vanessa Ip","email":"","orcid":"","institution":"University of Oklahoma","correspondingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Ip","suffix":""},{"id":618410654,"identity":"3efab444-6215-4ff5-bc28-8d09277ec456","order_by":7,"name":"Shumaila Khan","email":"","orcid":"","institution":"Shalamar Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shumaila","middleName":"","lastName":"Khan","suffix":""},{"id":618410655,"identity":"4aedf8f9-a342-4366-b263-fec2e6fa8562","order_by":8,"name":"Fatema Mala","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Fatema","middleName":"","lastName":"Mala","suffix":""},{"id":618410656,"identity":"45aa7239-5332-4a7d-8e91-2232a02b80ca","order_by":9,"name":"Ahmad Jamal Khan","email":"","orcid":"","institution":"Black Country Healthcare NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Jamal","lastName":"Khan","suffix":""},{"id":618410657,"identity":"673f1490-53ee-4a36-8e3b-5838938ec846","order_by":10,"name":"Juiena Jannat","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Juiena","middleName":"","lastName":"Jannat","suffix":""},{"id":618410658,"identity":"fcd38d98-169c-4428-b2d4-ce41a9f8cfcb","order_by":11,"name":"Farooq Naeem","email":"","orcid":"","institution":"Centre for Addiction and Mental Health","correspondingAuthor":false,"prefix":"","firstName":"Farooq","middleName":"","lastName":"Naeem","suffix":""}],"badges":[],"createdAt":"2026-04-06 18:23:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9336521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9336521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106540997,"identity":"4353223c-1fe4-42dd-b64d-c0ab038c4d49","added_by":"auto","created_at":"2026-04-09 16:03:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":322681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRISMA Flowchart\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 1: PRISMA diagram, generated through COVIDENCE, \u003c/em\u003eproviding a breakdown of how the selection process took place for this systematic review\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9336521/v1/0e4a04bb5e7feb5a394cb822.png"},{"id":107144004,"identity":"b3a1c3be-6f17-47ff-a2d1-46cd5e28b171","added_by":"auto","created_at":"2026-04-17 09:28:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":587896,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9336521/v1/11314079-3ad4-43ac-98b8-f6abae2c6332.pdf"},{"id":106724874,"identity":"84c91d44-1a2e-4336-8002-b01dafe10340","added_by":"auto","created_at":"2026-04-12 18:30:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44723,"visible":true,"origin":"","legend":"","description":"","filename":"Tables13EffectivenessandHeterogeneityofMultimodalInterventionsforMoodandAnxietyOutcomesinInsulinResistantPopulationsASystematicReviewofRandomizedControlledTrials1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9336521/v1/eb34f5643a81b62e8437b1e7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness and Heterogeneity of Multimodal Interventions for Mood and Anxiety Outcomes in Insulin-Resistant Populations: A Systematic Review of Randomized Controlled Trials","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInsulin resistance (IR) is a condition in which target tissues, including skeletal muscle, liver, and adipose tissue, show reduced responsiveness to insulin, leading to elevated blood glucose levels and compensatory hyperinsulinemia (Freeman, Acevedo, \u0026amp; Pennings, 2023). Beyond its critical role in glucose regulation, IR plays a crucial role in the development and progression of metabolic-related diseases, including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome. It is also linked to polycystic ovary syndrome (PCOS), cardiovascular disease, and certain cancers (Szablewski, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Semple et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Parker \u0026amp; Semple, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Severe forms of IR, although rare, are characterized by profound hyperinsulinemia and abnormal glucose homeostasis, sometimes presenting initially with hypoglycemia before hyperglycemia develops (Semple et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne meta-analysis of 87 studies determined a pooled global IR prevalence of approximately 26.5% (Ballena-Caicedo et al., 2025). In the United States, 40% aged 18 to 44 were insulin resistant in 2021 (Freeman et al., 2023). Particularly high prevalence of IR has been found in South Asians, Indigenous populations, East Asians, Latin Americans, and African Americans (Li et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Raygor et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Men and women experience differences in insulin sensitivity, with variations in hormones playing a key role (Ciarambino et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Men are generally more susceptible than premenopausal women, while postmenopausal women show higher prevalence (Li et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lifestyle factors, including diet, physical inactivity, smoking, inadequate sleep, and chronic stress, further contribute to the development of IR. Given its high prevalence and widespread impact, IR represents a significant public health concern.\u003c/p\u003e \u003cp\u003eBeyond metabolic consequences, IR has been increasingly linked to mental health outcomes, particularly mood and anxiety disorders. A systematic review and meta-analysis including 25,847 participants found a consistent association between IR and depressive symptoms (effect size\u0026thinsp;=\u0026thinsp;0.19, 95% CI 0.11\u0026ndash;0.27), although heterogeneity across studies was noted due to differences in assessment methods (Kan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Biological mechanisms, such as chronic inflammation and dysregulation of the hypothalamic-pituitary-adrenal axis, may mediate this relationship, with elevated proinflammatory cytokines like IL-6 and CRP implicated in both IR and depression (Mehdi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the population level, this connection demonstrates a dual burden: metabolic dysfunction increases risk for depression and anxiety, while mental health disorders further complicate management of metabolic conditions, resulting in increased healthcare utilization, economic challenges, and decreased quality of life. Given its high prevalence and widespread impact, IR represents a significant public health concern. At the individual level, it is associated with an increased risk of multiple chronic conditions, including metabolic, cardiovascular, and endocrine disorders, which can substantially reduce quality of life. Emerging evidence also suggests that IR may negatively affect mental health, with links to mood and anxiety disorders, highlighting the broader impact of metabolic dysfunction on both physical and psychological well-being (Kan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mehdi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterventions targeting IR may therefore have important population-level benefits for mental health. Structured lifestyle interventions, including dietary modification and exercise programs, have been shown to improve insulin sensitivity and reduce depressive and mental health disorder symptoms. (Luciano et al., 2020; Vreijling et al., 2024). However, the effectiveness of these interventions varies depending on intervention type, population characteristics, and outcome assessment methods, highlighting substantial heterogeneity in the literature.\u003c/p\u003e \u003cp\u003eDespite growing evidence, several gaps remain in understanding interventions for insulin-resistant populations. Existing systematic reviews have typically focused on a single intervention type, considered only metabolic or mental health outcomes, or evaluated narrow populations, such as individuals with type 2 diabetes (Kan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cezaretto et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jeremiah et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Methodological heterogeneity, including differences in measures of insulin resistance, mood, and anxiety assessment tools, further limits the comparability and generalizability of findings. No comprehensive systematic review has yet synthesized the effectiveness and heterogeneity of multimodal interventions targeting both metabolic and mood/anxiety outcomes across diverse insulin-resistant populations. Addressing these gaps is critical for guiding clinical practice, informing public health strategies, and developing population-level interventions that reduce the dual burden of metabolic and mental health disorders.\u003c/p\u003e \u003cp\u003e This systematic review aims to synthesize evidence from randomized controlled trials to determine the effectiveness and heterogeneity of multimodal interventions in improving comorbid mental health outcomes among insulin-resistant populations. Specifically, this review has two objectives: (1) to examine the efficacy of randomized controlled trial interventions in improving mood outcomes among insulin-resistant populations, and (2) to evaluate their effectiveness in improving anxiety symptoms. At the population level, this relationship reflects a dual burden, where metabolic dysfunction increases the risk of depression and anxiety, while mental health disorders further complicate the management of metabolic conditions. This bidirectional interaction is associated with poorer treatment adherence, increased healthcare utilization, higher economic costs, and reduced overall quality of life (Kan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vancampfort et al., 2016; Walker et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Individuals with co-occurring metabolic and mental health conditions are also at greater risk for adverse health outcomes, including increased morbidity and premature mortality, further underscoring the broader public health implications of this association (Walker et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis systematic review was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration ID: \u003cstrong\u003eCRD420251238068\u003c/strong\u003e). The PROSPERO record was accessed on 18 February 2026. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251238068 \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eInformation Sources and Search Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors conducted a comprehensive literature search on PubMed and MEDLINE databases on September 7th, 2025, developed in consultation with a University of Toronto library specialist, and finalised by N.A and F.N. The review was restricted to studies published in English. In addition to database searching, backward citation tracking of reference lists was undertaken to identify further eligible studies.\u003c/p\u003e\n\u003cp\u003eThe search strategy combined terms related to insulin resistance and associated metabolic conditions (including diabetes, polycystic ovary syndrome, hypertension, metabolic syndrome, and non-alcoholic fatty liver disease) with terms related to anxiety symptoms and mood disorders (including depression, bipolar disorder), alongside relevant mental health outcome measures. Controlled vocabulary (e.g., MeSH terms) and free-text keywords were used where appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies were included if i) included adult participants (\u0026ge;18 years) with a condition affecting insulin regulation or associated insulin resistance such as insulin dysregulation or insulin-resistant conditions, such as polycystic ovary syndrome, nonalcoholic fatty liver disease, type 2 diabetes mellitus, type 1 diabetes mellitus, metabolic syndrome, and cardiovascular conditions such as hypertension ii) reported at least one mood-or-anxiety related outcome, including depressive disorders, bipolar disorder without psychotic features, or anxiety disorders, iii) used a randomized clinical trial design, iv) were primary research articles and v) were published in English. Studies were excluded if i) included participants younger than 18 years, ii) involved animal populations, iii) focused on psychiatric outcomes outside of mood and anxiety disorders, such as schizophrenia, eating disorders, or iv) were systematic reviews, meta-analyses, case studies, follow-up reports, or study protocols.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eScreening Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecords identified through database searching were imported into Covidence (Covidence, 2025) for study screening and data extraction. Titles and abstracts were screened independently by two reviewers against predefined eligibility criteria, followed by independent full-text assessment of potentially relevant studies. Any conflicts during either screening stage were resolved through discussion, and where consensus could not be reached, the lead author (N.A.) made a final decision in consultation with the supervising author (F.N.). . \u003c/p\u003e\n\u003cp\u003eAll reviewers were trained and supervised by N.A, who provided standardized instructions to ensure consistency in study selection. Screening was conducted by Z.W, P.K, A.N., A.J.K, S.K, V.I, and F.M. This dual-review process was implemented to enhance inter-reliability, in accordance with the predefined PROSPERO protocol.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData extraction was conducted using a duplicate extraction approach to ensure inter-rater reliability. All records were managed in Covidence, and extracted data were recorded using a standardized data extraction form. Each included study was independently extracted by two reviewers, and discrepancies were resolved through discussion amongst the team. All reviewers were trained and supervised by N.A. and F.N, and N.A., Z.W., P.K., A.N., A.J.K., S.K., A.M., and V.I. completed study extractions.\u003c/p\u003e\n\u003cp\u003eExtracted data included study characteristics (author, year, country, and setting), study aims, participant characteristics (insulin resistance condition, psychiatric diagnosis, age, sex, and sample size), intervention details (type, duration, frequency, and comparator), and study design. Clinical outcomes included mood and anxiety clinician-administered or self-reported assessment tools, and their statistical outcomes (including pre\u0026ndash;post changes, within-group and between-group differences, and follow-up outcomes). Data on intervention effectiveness, associations with psychiatric outcomes, and proposed biological mechanisms or biomarkers were also extracted.\u003c/p\u003e\n\n\n\u003cp\u003eThe study selection process is shown in the PRISMA flow diagram (Figure 1). A total of 1848 records were identified through database searching, of which 71 duplicates were removed after the records were imported to Covidence for screening. Following title and abstract screening of 1777 records, 192 full-text articles were assessed for eligibility. After full-text review, 150 studies were excluded due to an ineligible study design, intervention, population, outcomes, comparator, or lack of accessible full text. A total of 42 studies met the inclusion criteria and were included in the final review.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAcross 42 studies included in this review, a total of N= 14,982 participants were represented (see Table 1). Of these, n=7471 were allocated to intervention groups and n=7511 to control or treatment-as-usual (TAU) groups. The proportion of female participants varied across studies, ranging higher in sex-specific cohorts such as those examining polycystic ovary syndrome (PCOS) or gestational diabetes (n=5), with the majority of studies (n=30) reporting a female representation between 50-75%. The mean participant age of studies included middle-aged to older adults (50-65 years; n=30) and older populations (\u0026gt; 65 years; n=7). with some studies reporting early adulthood of ages 20-35 (n=5).\u003c/p\u003e\n\u003cp\u003eGeographically, research was predominantly conducted in the United States (n = 13) and China (n=8), with additional contributions from Canada (n=4) and the United Kingdom (n=4); fewer studies were conducted in India (n=3), Brazil (n=2), Australia (n=2), and Germany (n=2), with single study representation from Finland, Thailand, Nigeria, Iran, Spain and Taiwan. Most studies were undertaken in clinical settings, including primary care (n=17) and hospital or speciality departments (n=11), with fewer conducted in community centres (n=6) or delivered via digital or fully remote platforms (n=8).\u003c/p\u003e\n\u003cp\u003eType 2 diabetes mellitus was the most frequently examined insulin resistance condition (n=29), followed by hypertension (n=6), metabolic syndrome (n=5), PCOS (n=4), type 1 diabetes mellitus (n=3), mixed diabetes populations or gestational diabetes (n=4). Populations were predominantly clinical and comorbid, with most studies including individuals with both an insulin resistance condition and a co-occurring depressive or anxiety symptom (n=34). Some papers addressed multiple conditions.\u003c/p\u003e\n\u003cp\u003eInterventions were heterogeneous and included pharmacological (n=11), psychological (n=15), lifestyle (n=11), integrated care (n=9), digital or remote (n=8), education or self-management (n=7), and physiological approaches (n=4). Pharmacological interventions comprised morphine, antidepressants (e.g., citalopram, escitalopram, fluoxetine), and adjunctive compounds (e.g., curcumin or crocin), typically administered over 8 weeks to 6 months, with some extending to 12 months. Psychological interventions were primarily based on cognitive-behavioural therapy (CBT), delivered in in-person or internet-based formats over 6-12 sessions (approximately 8-12 weeks), alongside mindfulness, reminiscence therapy, and counselling approaches. Lifestyle interventions combined dietary modification and structured physical activity delivered over 3-12 months; integrated care models involved multidisciplinary management with structured follow-up over 6-12 months; while digital interventions included smartphone or web-based programmes delivered over 6-12 months. Education and self-management interventions focused on diabetes education and behavioural support, as well as physiological approaches (e.g., relaxation, yoga, biofeedback), were typically short-term (4-12 weeks). Overall intervention duration ranged from 2 weeks to 12 months, most commonly 8 weeks to 12 months.\u003c/p\u003e\n\u003cp\u003eIn summary, the included studies represent a diverse global sample, characterised by a high prevalence of metabolic and psychological comorbidity, and interventions were highly heterogeneous in modality and duration.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnxiety Outcomes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs summarised in \u003cstrong\u003eTable 2\u003c/strong\u003e, anxiety outcomes were assessed using validated instruments, most commonly the GAD-7, HADS-A, and SAS, with additional use of the STAI/STAI-6, SCL-90-R Anxiety subscale, and DDS. This variability in measurement approaches reflects heterogeneity in outcome operationalisation across studies.\u003c/p\u003e\n\u003cp\u003eOverall, most interventions were within-group reductions in anxiety symptoms, although between-group differences were less consistently observed. Significant within-group improvements in intervention areas were reported in several studies. For instance, Arunjr et al. (2025) demonstrated significant reductions in both GAD-7 and DDS scores (p=0.001), while Liu et al. (2025) reported marked decreases in SAS scores (p\u0026lt;0.001). Similarly, A.L. Wroe et al. (2018) observed significant reductions in GAD-7 scores (p\u0026lt;0.001). In contrast, McGrady and Horner (1999), Kahloon et al. (2024), and Markle-Reid et al. (2018) reported no significant within-group changes, indicating variability in intervention responses across studies. Several studies also demonstrated significant reductions in both intervention and control groups, including Sha et al. (2025) and Fisher et al. (2011).\u003c/p\u003e\n\u003cp\u003eBetween-group comparisons showed significant superiority of the applied interventions in studies such as Guo et al. (2020) (p\u0026lt;0.001), Arunjr et al. (2025) (p\u0026lt;0.0001), Liu et al. (2025) (p=0.02), and A.L.Wroe et al. (2018) (p=0.032). However, several studies, including Markle-Reid et al. (2018), Baron et al. (2017), Zhang et al. (2024), Wayne et al. (2015), Wolff et al. (2016), and Pols et al. (2017) reported no significant between-group differences, despite some demonstrating within-group improvements of anxiety.\u003c/p\u003e\n\u003cp\u003eEffect sizes were infrequently reported. Where available, they suggested small to moderate effects, with Liu et al. (2025) reporting a moderate effect (Cohen\u0026rsquo;s d = 0.58), Stark et al. (2025) a small effect (Cohen\u0026rsquo;s f = 0.17), and Wolf et al. (2016) a negligible to small negative effect (-0.2). Follow-up outcomes were also inconsistently reported. Sustained reductions in anxiety were observed in some studies (e.g., Stark et al., 2025), whereas others demonstrated minimal change or attenuation over time (e.g., Pols et al., 2017; Wayne et al., 2015), based on reported follow-up mean scores across timepoints.\u003c/p\u003e\n\u003cp\u003eNotably, several studies reported significant reductions, including Sha et al. (2025; p\u0026lt;0.05), Fisher et al (2011; p\u0026lt;0.05), Wayne et al. (2015; p \u0026gt; 0.05 between-group despite within-group reductions), and Pols et al. (2017; p \u0026gt; 0.05 between-group) for both intervention and control groups.\u003c/p\u003e\n\u003cp\u003eOverall, these findings indicate that while interventions are frequently associated with within-group improvements in anxiety, consistent between-group efficacy is less robust, and reported effect sizes are generally modest. Substantial heterogeneity in study design, intervention type, and outcome measurement limits direct comparability across studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMood Outcomes: Depression and Bipolar Disorders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepression outcomes were assessed using validated instruments, most commonly the BDI/BDI-II, PHQ-9/PHQ-8, HADS-D, and CES-D, with additional use of MADRS, HAM-D, and SCL-based measures, reflecting heterogeneity in outcome assessment across studies.\u003c/p\u003e\n\u003cp\u003eAcross studies reporting within-group analyses, the majority demonstrated statistically significant reductions in depressive symptom severity following intervention. Significant within-group improvements observed across multiple instruments, including BDI (e.g., Sel\u0026ccedil;uk Tosun et al., 2024; p\u0026lt;0.001), MADRS (Calkin et al., 2022; p=0.0009), SCL-90-R Depression (Guo et al., 2020; p=0.008), CES-D (Thomson et al., 2010; p\u0026lt;0.001), PHQ-9 (Bogner et al., 2012; p\u0026lt;0.001), PHQ-8 (Fisher et al., 2011; p\u0026lt;0.0001), and SDS (Liu et al., 2025; p\u0026lt;0.001). Additional significant improvements were reported using HAM-D (Lustman et al., 2000; p=0.01) and HADS-D (Wolff et al., 2016; p\u0026lt;0.001). However, some studies reported non-significant within-group changes, particularly in smaller or older trials (e.g., McGrady \u0026amp; Hommer, 1999; p=n.s.; Kinder et al., 2006; p=0.2), indicating variability in intervention responsiveness.\u003c/p\u003e\n\u003cp\u003eBetween-group comparisons yielded more heterogeneous findings. Several studies demonstrated statistically significant superiority of intervention over control, including Sel\u0026ccedil;uk Tosun et al. (2024; p\u0026lt;0.001), Wang et al. (2017; p\u0026lt;0.001), Bogner et al. (2012; p=0.007), Liu et al. (2025; p=0.01), and A. L. Wroe et al. (2018; p=0.010\u0026ndash;0.008 across analyses). Additional significant between-group differences were observed in trials using PHQ-9 (Chiang et al., 2019; p\u0026lt;0.001) and SDS (Sha et al., 2025; p\u0026lt;0.05). In contrast, several studies reported no statistically significant differences between intervention and control groups despite within-group improvement, including Thomson et al. (2010; p\u0026gt;0.86), Fisher et al. (2011; p=0.28), Baron et al. (2017; p=0.53), and Pols et al. (2017; p=0.71). Some trials reported inconsistent findings across timepoints, such as Katon et al. (2004), where significance emerged only at later follow-ups (6 months: p=0.04; 12 months: p=0.03), suggesting delayed effects.\u003c/p\u003e\n\u003cp\u003eEffect sizes, when reported, ranged from small to large. Moderate-to-large effects were observed in several studies, including Cohen\u0026rsquo;s d=0.61 (Liu et al., 2025), d=0.82\u0026ndash;0.89 (Nobis et al., 2015), and d=0.44 (Wang et al., 2025), while Wolff et al. (2016) and Moncrieft et al. (2016) reported large negative effects (d=\u0026minus;0.9 and d=\u0026minus;0.85, respectively), indicating substantial symptom reduction. By contrast, Baron et al. (2017) demonstrated a moderate negative effect (d=\u0026minus;0.53) that did not reach statistical significance (p=0.232), highlighting discordance between effect magnitude and statistical significance. Statistically significant between-group differences were not always clinically meaningful, as reflected by small effect sizes in some trials (e.g., Williamson et al., 2009; d=0.13).\u003c/p\u003e\n\u003cp\u003eFollow-up data were inconsistently reported, but available longitudinal data suggested that benefits were sometimes sustained beyond post-treatment. In Petrak et al. (2015), HAM-D-17 reductions were broadly maintained at follow-up, while Stark et al. (2025) showed persistent improvement in PHQ-8 scores with significant between-group differences at follow-up (p=0.012). Delayed effects were also observed in Katon et al.(2004), with non-significant between-group differences at baseline and 3 months becoming significant at 6 months (p=0.04) and 12 months (p=0.03); Suvada et al. (2023) similarly reported a significant 12-month difference (p\u0026lt;0.05). However, not all studies showed durable separation over time, as Pols et al. (2017) found comparable HADS-D scores across repeated follow-up assessments (p=0.71).\u003c/p\u003e\n\u003cp\u003eOnly one study assessed bipolar outcomes, with Calkin et al. (2022) reporting a modest but statistically significant within-group improvement on the CGI-BP (p=0.046), with no corresponding between-group comparison available.\u003c/p\u003e\n\u003cp\u003eOverall, although within-group improvements in depressive symptoms were consistently observed, comparative efficacy relative to control conditions remained inconsistent, reflecting substantial heterogeneity in effect magnitude, statistical significance, and sustainability of outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review is a comprehensive evaluation of randomized clinical trials (RCTs) investigating the impact of diverse interventions, ranging from pharmacological to lifestyle and psychological approaches, on mood and anxiety outcomes in adults with insulin resistance and associated metabolic conditions. Interventions consistently produced significant within-group improvements in depressive symptoms, with moderate-to-large effect sizes reported in several studies (Liu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nobis et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Anxiety symptoms also showed frequent within-group reductions across trials (Arunjr et al., 2025; Liu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wroe et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, integrated care models and digital platforms emerged as promising modalities for sustaining these mental health improvements over follow-up periods of 6 to 12 months. While internal improvements were common, between-group efficacy compared to control or treatment-as-usual groups was notably less robust, with several trials failing to reach statistical significance despite observed symptom reductions. Substantial heterogeneity in measurement tools, such as the variation between GAD-7, PHQ-9, and HADS, and intervention durations limit the ability to draw definitive conclusions across the entire spectrum. Bipolar disorder was minimally represented, with only one study identified (Calkin et al., 2022), highlighting a critical gap in the literature. As such, findings, particularly for anxiety and bipolar outcomes, should be interpreted as preliminary and not broadly generalizable to associations with insulin resistance.\u003c/p\u003e \u003cp\u003eThe findings reveal a complex landscape where within-group improvements are common, yet between-group superiority remains inconsistent. The interpretation of within-group efficacy reflects whether participants improved over time. While most studies were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) here, it was not evident whether the interventions were statistically superior to the control group. Interpretation is further complicated by clinical diversity and temporal dynamics. Although some included studies encompassed insulin-resistant conditions such as PCOS or NAFLD, this population was not consistently represented across trials, limiting phenotype-specific interpretation, particularly given that they are associated with distinct hormonal profiles (Dewani et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Brutocao et al., 2018). Intervention effects varied over time, with some studies demonstrating delayed benefits at 6\u0026ndash;12 months (e.g., Katon et al., 2004; Suvada et al., 2023), whereas others showed attenuation or non-significant differences over time (e.g., Pols et al., 2017; Wayne et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although within-group improvements were common (e.g., Liu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sel\u0026ccedil;uk Tosun et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), consistent between-group superiority was limited, with several trials reporting comparable outcomes in intervention and control groups (e.g., Fisher et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sha et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Baron et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Collectively, this suggests that a definitive \u0026ldquo;gold standard\u0026rdquo; intervention for the metabolic\u0026ndash;mood interface remains unclear.\u003c/p\u003e \u003cp\u003eAlthough depression and anxiety are frequently comorbid in insulin-resistant populations, they were examined as distinct clinical outcomes to help assess independent associations with insulin-resistant conditions. Depressive symptoms were more consistently assessed using validated severity scales such as the PHQ-9 and BDI II and showed more stable intervention responses, whereas anxiety outcomes measured using instruments such as the GAD-7 and SAS and demonstrated greater variability with less consistent effects.\u003c/p\u003e \u003cp\u003eThe present findings align with RCT literature, showing that interventions in insulin-resistant populations more consistently improve depressive symptoms than anxiety outcomes. Evidence for associations with depression is strong, with meta-analytic data demonstrating elevated insulin levels during acute depressive episodes, supporting a metabolic subtype of depression (Fernandes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, evidence for associations with anxiety is more limited and variable; while insulin resistance is associated with increased anxiety burden (Abdelfattah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), findings are less consistent and mechanistically defined (Krupa et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Possidente et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bipolar disorder remains underrepresented in the literature, potentially due to exclusion from RCTs, diagnostic complexity, and pharmacological confounding, resulting in a limited evidence base despite emerging associations with insulin resistance (Aguglia et al., 2026). Overall, these findings suggest that while insulin resistance is broadly linked to mood disturbances, the evidence is most robust and clinically actionable for depression, with greater uncertainty remaining for anxiety and bipolar disorder.\u003c/p\u003e \u003cp\u003eThe discrepancy likely reflects active control conditions, non-specific treatment effects, and substantial heterogeneity across insulin-resistant populations, which together reduce detectable between-group differences despite real symptom measurement sensitivity and closer metabolic linkage, whereas anxiety shows more variability. Temporal delays in treatment effects and underpowered psychiatric endpoints further limit between-group significance.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eA key limitation of this systematic review is the overrepresentation of type 2 diabetes (n\u0026thinsp;=\u0026thinsp;29), making it difficult to conclude the generalizability of other insulin-resistant conditions. This imbalance is not unique to this review but reflects a broader trend in the literature, where metabolic-psychiatric research is largely anchored in diabetes or cardiometabolic cohorts (Chourpiliadis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, outcomes in the present review looked at outcomes based on self-reported or clinician-administered symptom scales, with only one study examining a clinically defined bipolar population. Evidence from bipolar suggests that insulin resistance is associated with greater illness severity and poorer treatment response, and consistent effects are observed particularly for anxiety (Miola et al., 2023). The inclusion of heterogeneous conditions (e.g., PCOS, NAFLD) and the lack of a clear intervention framework may have introduced additional variance. Together, these may contribute to the observed heterogeneity and modest effect sizes, consistent with prior findings (Fernandes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a result, current evidence may overrepresent associations specific to advanced metabolic dysfunctions, while underrepresenting insulin resistance in primary psychiatric populations. This may partly explain the observed heterogeneity and modest effect sizes, and is consistent with prior meta-analytic findings of elevated but variable insulin resistance in depression (Fernandes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strategy was restricted to PubMed and MEDLINE, which may have resulted in an underrepresentation of studies indexed in discipline-specific databases such as PsycINFO and CINAHL. These databases often capture research focused on psychological, behavioural, and psychotherapy-based interventions, which may differ methodologically from medically oriented trials. As a result, behavioural and psychosocial interventions may be underrepresented in this review, and the absence of a standardized framework for intervention selection may have further contributed to variability in the types of interventions included. Consequently, psychologically oriented or behavioural interventions may be less comprehensively captured. However, given the substantial indexing overlap for randomized controlled trials across these databases, this limitation is unlikely to have materially influenced the overall pattern of findings, particularly the observed heterogeneity and modest between-group effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eFuture research should move beyond predominantly diabetes-focused samples and instead prioritize primary psychiatric populations, including major depressive disorder, anxiety disorders, bipolar disorder, and other relevant mental health conditions such as post-traumatic stress disorder or psychotic disorders. Greater clinical granularity is needed within these groups, including differentiation of disorder subtypes (e.g., atypical vs. melancholic depression; bipolar I vs. II, manic vs. hypomanic vs. rapid cycling), to better characterize how insulin resistance manifests across heterogeneous psychiatric profiles. Methodologically, standardization through the adoption of a core outcome set with consistent use of validated mood symptom measures is essential to improve cross-study compatibility. The literature is further limited by short follow-up durations, underscoring the need for longitudinal designs extending 6\u0026ndash;12 months or longer to capture sustained effects and temporal relationships between metabolic and psychiatric changes. Finally, future studies should adopt mechanism-driven and integrative approaches by categorizing interventions based on underlying biological pathways and evaluating multimodal strategies combining pharmacological, psychological, and lifestyle components to effectively address the complexity of mental health outcomes in insulin-resistant populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, the current evidence supports a meaningful but complex relationship between insulin resistance and mental health outcomes, with the strongest and most consistent effects observed for depressive symptoms. Despite growing within-group improvements across interventions, inconsistent between-group findings and substantial methodological heterogeneity limit definitive clinical conclusions. The underrepresentation of diverse psychiatric populations and limited longitudinal data further constrain generalizability and mechanistic understanding of these associations. Advancing this field will require more targeted, standardized, and integrative research approaches to clarify causal pathways and optimize intervention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors report no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrespondence may be addressed to Nuzhat Azim [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors received no funding or other monetary compensation to conduct this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge Margaret Wall (Univerisity of Toronto librarian) for her expert guidance in the development and optimization of the database search strategy.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.A. conceptualized the study, developed the methodology, registered the protocol, supervised all stages of the review process, and wrote the Methods, Results, and Discussion sections. F.N. contributed to the study design and provided senior oversight.N.A., Z.W., P.K., A.N., A.J.K., S.K., V.I., and F.M. conducted screening. N.A., Z.W., P.K., A.N., A.J.K., S.K., A.M., J.J., and V.I. performed data extraction and prepared Tables 1-3.N.A, Z.W., P.K., S.S., and V.I wrote the main manuscript text N.A., finalized Figures 1 and Tables 1-3.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge Margaret Wall (Univerisity of Toronto librarian) for her expert guidance in the development and optimization of the database search strategy.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAbdelfattah, H. E., Bekhet, M. M. M., Tawfik, F. A., Elias, D. G., \u0026amp; Saleh, A. M. M. A. E. H. 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Effect of Community-Based Integrated Care for Patients With Diabetes and Depression (CIC-PDD) in China: A Pragmatic Cluster-Randomized Trial. \u003cem\u003eDiabetes care\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(2), 226\u0026ndash;234. https://doi.org/10.2337/dc24-1593 \u003c/p\u003e\n\u003cp\u003eWayne, N., Perez, D. F., Kaplan, D. M., \u0026amp; Ritvo, P. (2015). Health Coaching Reduces HbA1c in Type \u003c/p\u003e\n\u003cp\u003e2 Diabetic Patients From a Lower-Socioeconomic Status Community: A Randomized Controlled Trial. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(10), e224\u0026ndash;e224. https://doi.org/10.2196/jmir.4871 \u003c/p\u003e\n\u003cp\u003eWilliamson, D. A., Rejeski, J., Lang, W., Van Dorsten, B., Fabricatore, A. N., \u0026amp; Toledo, K. 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Is Cognitive Behavioural \u003c/p\u003e\n\u003cp\u003eTherapy focusing on Depression and Anxiety Effective for People with Long-Term Physical Health Conditions? A Controlled Trial in the Context of Type 2 Diabetes Mellitus. \u003cem\u003eBehavioural and Cognitive Psychotherapy\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 129\u0026ndash;147. https://doi.org/10.1017/S1352465817000492 \u003c/p\u003e\n\u003cp\u003eYaikwawong, M., Jansarikit, L., Jirawatnotai, S., \u0026amp; Chuengsamarn, S. (2024). Curcumin Reduces \u003c/p\u003e\n\u003cp\u003eDepression in Obese Patients with Type 2 Diabetes: A Randomized Controlled Trial. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(15), 2414. https://doi.org/10.3390/nu16152414 \u003c/p\u003e\n\u003cp\u003eZhang, H., Zhang, X., Jiang, X., Dai, R., Zhao, N., Pan, W., Guo, J., Fan, J., \u0026amp; Bao, S. (2024). \u003c/p\u003e\n\u003cp\u003eMindfulness-based intervention for hypertension patients with depression and/or anxiety in the community: a randomized controlled trial. \u003cem\u003eCurrent Controlled Trials in Cardiovascular Medicine\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), Article 299. https://doi.org/10.1186/s13063-024-08139-0 \u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Insulin resistance, mental health, mood disorders, RCT, systematic review","lastPublishedDoi":"10.21203/rs.3.rs-9336521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9336521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eInsulin Resistance (IR) is a prevalent metabolic condition affecting approximately 26.5% of adults globally and is increasingly recognised for its association with mood and anxiety disorders. This systematic review synthesises evidence from randomised controlled trials (RCTs) to evaluate the effectiveness and heterogeneity of multimodal interventions targeting mental health outcomes in insulin-resistant populations.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA systematic search on PubMed and MEDLINE identified 1,848 records, of which 42 RCTs (N\u0026thinsp;=\u0026thinsp;14,982 participants) met inclusion criteria. Included studies examined diverse interventions, including pharmacological, psychological, lifestyle, and integrated care approaches across populations with conditions such as type 2 diabetes, metabolic syndrome, and polycystic ovary syndrome.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAcross studies, interventions consistently demonstrated significant within-group improvements in depressive symptoms, with moderate-to-large effect sizes reported in several trials. Anxiety outcomes also showed reductions, although findings were more variable. However, between-group differences relative to control conditions were less consistent across studies. Bipolar disorder was minimally represented, limiting conclusions for this subgroup.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOverall, while interventions appear effective in improving mood outcomes within insulin-resistant populations, substantial heterogeneity and inconsistent comparative efficacy limit definitive conclusions. Future research should prioritise standardised outcome measures, longitudinal designs, and broader psychiatric inclusion to improve clinical and public health relevance.\u003c/p\u003e","manuscriptTitle":"Effectiveness and Heterogeneity of Multimodal Interventions for Mood and Anxiety Outcomes in Insulin-Resistant Populations: A Systematic Review of Randomized Controlled Trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:03:26","doi":"10.21203/rs.3.rs-9336521/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35126a9d-b0f3-41fe-ae41-e8244c4b3ce1","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T09:27:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:03:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9336521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9336521","identity":"rs-9336521","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}