Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

The Effectiveness of Pharmacological and Non-Pharmacological Interventions for Improving Glycaemic Control in Adults with Severe Mental Illness: A Systematic Review and Meta-Analysis

The Effectiveness of Pharmacological and Non-Pharmacological Interventions for Improving Glycaemic Control in Adults with Severe Mental Illness: A Systematic Review and Meta-Analysis

  • Johanna Taylor, 
  • Brendon Stubbs, 
  • Catherine Hewitt, 
  • Ramzi A. Ajjan, 
  • Sarah L. Alderson, 
  • Simon Gilbody, 
  • Richard I. G. Holt, 
  • Prakash Hosali, 
  • Tom Hughes, 
  • Tarron Kayalackakom


People with severe mental illness (SMI) have reduced life expectancy compared with the general population, which can be explained partly by their increased risk of diabetes. We conducted a meta-analysis to determine the clinical effectiveness of pharmacological and non-pharmacological interventions for improving glycaemic control in people with SMI (PROSPERO registration: CRD42015015558). A systematic literature search was performed on 30/10/2015 to identify randomised controlled trials (RCTs) in adults with SMI, with or without a diagnosis of diabetes that measured fasting blood glucose or glycated haemoglobin (HbA1c). Screening and data extraction were carried out independently by two reviewers. We used random effects meta-analysis to estimate effectiveness, and subgroup analysis and univariate meta-regression to explore heterogeneity. The Cochrane Collaboration’s tool was used to assess risk of bias. We found 54 eligible RCTs in 4,392 adults (40 pharmacological, 13 behavioural, one mixed intervention). Data for meta-analysis were available from 48 RCTs (n = 4052). Both pharmacological (mean difference (MD), -0.11mmol/L; 95% confidence interval (CI), [-0.19, -0.02], p = 0.02, n = 2536) and behavioural interventions (MD, -0.28mmol//L; 95% CI, [-0.43, -0.12], p<0.001, n = 956) were effective in lowering fasting glucose, but not HbA1c (pharmacological MD, -0.03%; 95% CI, [-0.12, 0.06], p = 0.52, n = 1515; behavioural MD, 0.18%; 95% CI, [-0.07, 0.42], p = 0.16, n = 140) compared with usual care or placebo. In subgroup analysis of pharmacological interventions, metformin and antipsychotic switching strategies improved HbA1c. Behavioural interventions of longer duration and those including repeated physical activity had greater effects on fasting glucose than those without these characteristics. Baseline levels of fasting glucose explained some of the heterogeneity in behavioural interventions but not in pharmacological interventions. Although the strength of the evidence is limited by inadequate trial design and reporting and significant heterogeneity, there is some evidence that behavioural interventions, antipsychotic switching, and metformin can lead to clinically important improvements in glycaemic measurements in adults with SMI.


People with severe mental illness (SMI) (schizophrenia and other illnesses characterised by psychosis) have a lower life expectancy compared with the general population by around 15 to 20 years [1]. A higher prevalence of comorbid conditions (e.g. diabetes and cardiovascular disease) and poorer management of physical health contribute to this health inequality [2]. Around 13% of people with SMI have diabetes compared with 6% of the general population, and the difference is increasing [3]. As diabetes interventions are scaled up for the general population, these inequalities may increase further. This is because generic interventions are unlikely to be suitable for people with SMI due to the complex combination of psychological, social and financial barriers they face in managing their health [4].

Although there are more than 40 published systematic reviews of studies targeting physical health in people with SMI, these have focused mainly on anthropological outcomes [58], with few investigating diabetes prevention and treatment [9, 10]. It is well-established that modest improvements in glycated haemoglobin (HbA1c) and blood glucose levels can avoid onset of diabetes and have a significant impact on preventing diabetic complications in the general population [11]. A few reviews have investigated the effect of pharmacological [6, 12] and behavioural [7, 8, 13] interventions on these glycaemic measurements in people with SMI. An older review investigated both pharmacological and behavioural interventions [14]. However in all of these, glycaemic effects were examined as a secondary outcome only. This makes it difficult to determine which interventions are effective for improving glycaemic control in people with SMI. The aim of this systematic review and meta-analysis is to identify pharmacological and behavioural interventions for improving diabetes outcomes that have been tested in the adult SMI population, and to determine their effectiveness in lowering HbA1c and fasting blood glucose [15].


Eligibility criteria

We included randomised controlled trials (RCTs) of interventions to improve diabetes outcomes for adults (aged 18 years and over) with SMI. We defined SMI as schizophrenia, bipolar disorder, psychosis or other non-organic psychotic disorders, including schizoaffective disorder and severe depression. To be included, studies had to measure at least one of the following outcomes: i) in people without diabetes at baseline: incidence of diabetes, HbA1c or fasting glucose; and ii) in people with diabetes at baseline: HbA1c, fasting glucose, weight, body mass index (BMI), or diabetic complications.

We restricted studies to those published in peer reviewed journals and the English language.

The protocol for the review has been published on the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42015015558 [15]. We carried out the review in accordance with the PRISMA guidelines (see S1 PRISMA Checklist).

Search strategy

The search strategy comprised three concepts: ‘diabetes’, ‘SMI’, and ‘RCTs or systematic reviews’. An example of the strategy is provided in the supporting information (see S1 Appendix).

Literature searches were performed in CINAHL (EBSCO); Embase Classic+Embase (Ovid); PsycINFO (Ovid); Ovid Medline; PubMed; Cochrane Database of Systematic Reviews (Wiley) and Central Register of Controlled Trials; Database of Abstracts of Reviews of Effect (Wiley); and Conference Proceedings Citation Index (Thomson Reuters). We also searched three trial registries (, International Clinical Trials Registry Platform (WHO), ISRCTN registry). Searches were performed on 12/12/2014, and updated on 30/10/2015 (except for trial registries).

Study selection

Search results were managed in EndNote version 7 software. Citations and abstracts were screened to exclude studies that did not meet the selection criteria. References of relevant reviews identified during the screening process were also searched. Relevant full-text articles were retrieved and assessed for eligibility; missing data to help assess eligibility were sought from corresponding authors.

Data extraction and synthesis

Study characteristics and data for meta-analysis were extracted into a tailored and piloted data collection form [15]. Multiple reports from the same study were linked and missing data were requested from study authors. The Cochrane Collaboration tool was used to assess risk of bias [16]. All stages of study selection and data extraction were conducted independently by two reviewers, with discrepancies resolved through discussion and where consensus could not be reached, arbitration by a third reviewer.

Due to the heterogeneity of diabetes interventions, we categorised interventions as pharmacological, non-pharmacological or mixed (interventions combining medication with a non-pharmacological approach) [15]. Pharmacological interventions were further sub-grouped into categories: i) diabetes medications (including metformin, sulphonylureas, insulin and thiazolidinediones); ii) weight loss treatments (including antiparkinsonian, anticonvulsant and antidepressant medications thought to promote weight loss, as well as anti-obesity drugs and appetite suppressants); iii) combinations of weight loss and diabetes medications; iv) switching antipsychotic medication; and v) an ‘other’ category.

Non-pharmacological interventions were categorised as behavioural (targeting a change in an individual’s behaviour) or organisational (targeting a change in the environment or organisation of care).

We planned to explore effectiveness of interventions in prevention of diabetes. However, many studies did not distinguish between people with and without diabetes at baseline. Of the studies that excluded people with diabetes at baseline, none measured incidence of diabetes or reported data that would enable us to estimate this. We therefore pooled the results across all studies for glycaemic control, using outcome data for HbA1c and fasting glucose.

We analysed pharmacological and non-pharmacological interventions separately, and because we expected significant heterogeneity between studies, we used random-effects meta-analysis and assessed for heterogeneity using the I-squared statistic. To allow combining of post-intervention and change scores for outcomes, and since outcomes were reported consistently, we calculated the unstandardised difference in means (MD) [16].

To assess effects across key intervention characteristics, we conducted subgroup analyses for pharmacological interventions by type of drug category; and for behavioural interventions by duration (short (≤6 months) or long (>6 months)), and whether or not interventions included repeated physical activity. We also conducted univariate random effects meta-regression using intervention duration as a continuous variable (number of weeks). Both duration and physical activity have been identified as key components of effective diabetes interventions in the general population [17].

To explore potential differential effects in people with and without diabetes, we conducted separate subgroup analyses, for i) studies excluding participants with diabetes, and ii) those that only included people with diabetes and SMI or did not specify diabetes status. We also conducted univariate random effects meta-regression using mean HbA1c or fasting glucose at baseline to explore whether or not this explained some of the heterogeneity among studies [18].

To investigate possible baseline imbalance observed during data extraction, we repeated the main meta-analyses using mean difference at baseline [19]. We explored the impact of study quality and heterogeneity by undertaking sensitivity analyses, using ‘leave-one-out’ analyses to test if single studies had a disproportionate effect on the results. We used the trim-and-fill method and inspection of funnel plots to investigate publication and small study bias [20]. The trim-and-fill analysis adjusts for any funnel plot asymmetry and provides an effect size estimate that takes account of observed publication bias.

Comprehensive Meta-Analysis (CMA) version 2 software was used for all statistical analyses.


A total of 3,721 citations were identified by database searches, and a further 27 articles from the reference lists of systematic reviews. After removing duplicates, 2,278 records were screened for relevance by title and abstract, and 197 full text articles retrieved. Of these, 104 did not meet the selection criteria and were excluded. The remaining 93 articles described 73 studies. Nineteen of these were ongoing studies (see S1 Table).

A total of 54 studies were included in the systematic review [2174]. Six of these studies did not provide usable data for the meta-analysis [26, 32, 42, 44, 46, 51]. Fig 1 presents a study flow diagram.

Study characteristics are summarised in Table 1.


There were a total of 4,392 participants; 2,315 were assigned to intervention and 2,077 to control arms.

Participants were mainly drawn from mental health outpatient and inpatient settings (n = 47 studies). One study recruited from supported housing schemes [24], and one from residential care facilities and day programmes [29]. Five studies did not report setting [47, 57, 68, 69, 71].

All but three studies [48, 57, 71] included both men and women, although overall women were under-represented (41%). Mean age ranged from 25 to 53 years, with a mean age across studies of 43 years. Ethnicity was poorly recorded, but varied significantly due to the range of countries included.

Eighteen studies recruited participants with schizophrenia; 20 with schizophrenia and schizoaffective disorder; two with bipolar disorder; and 14 with various SMIs. The majority of studies included clinically stable participants who had been diagnosed for several years.

Inclusion of participants with diabetes varied. Only one study specifically recruited people with type 2 diabetes [29]. Twenty-three studies excluded participants with diabetes (one excluded type 1 diabetes [59]). The remaining studies did not specify this in eligibility criteria. Mean HbA1c at baseline ranged from 4.1% to 7.4% (n = 26 studies); 13 studies reported a mean in the American Diabetes Association (ADA) pre-diabetes category (5.7–6.4%) and two in the diabetes range (≥6.5%). Mean fasting glucose ranged from 4.3 to 6.8mmol/L (n = 43 studies); 14 studies reported a baseline mean in the ADA high risk category (5.6–6.9mmol/L) [75].

Thirty studies targeted overweight participants or those who had experienced significant weight gain. Mean BMI at baseline ranged from 20.2 to 41.9Kg/m2 (n = 49 studies); 41 studies reported a mean BMI of over 25Kg/m2.


Of the 54 RCTs identified, 40 assessed a pharmacological, 13 a non-pharmacological and one a mixed intervention.

Among the pharmacological studies, 32 used a placebo in the control arm [3543, 45, 46, 4855, 5760, 62, 63, 6567, 69, 71, 72, 74]. Seven trials evaluated an intervention against usual care [47, 56, 61, 64, 68, 70, 73]. One study compared two interventions but did not include a control arm [44].

Among the non-pharmacological studies, six compared an intervention with usual care [21, 22, 25, 27, 30, 32], and three provided basic information or advice to controls at baseline [23, 26, 29]. In one study, the intervention was also given to the control group after week 12 of 24 weeks [28]. Two studies included an active control arm [24, 31]. One trial did not describe the control intervention [33].

The mixed intervention study included four arms: metformin, metformin plus a lifestyle intervention, lifestyle plus placebo, and a control arm receiving placebo alone [34].

Pharmacological interventions.

In total, 23 different medications from 15 categories of drug were evaluated (see Table 2).

In nine studies, all participants in intervention and control arms were also enrolled onto a lifestyle programme [38, 39, 44, 5153, 58, 69, 70]. In seven further studies all participants had lifestyle advice at baseline [43, 45, 46, 56], personal wellness counselling [65], limited dietary intake [74], or mandatory monthly dietary counselling [42]. Details of these interventions and levels of engagement were not reported.

Intervention duration varied from four weeks to 12 months, being 6 months or less in most studies.

Non-pharmacological interventions.

All 14 non-pharmacological interventions targeted change in individual behaviour rather than organisation of care. Interventions were variously described as lifestyle interventions, weight loss programmes and physical exercise programmes; however, there was considerable overlap between these categories. In total, eight interventions included regular exercise sessions [2325, 27, 30, 31, 33, 34] and three restricted calorie intake [28, 33, 34]. All but one intervention [31] included dietary recommendations, and all but two [31, 33] employed educational and behavioural strategies promoting a healthier lifestyle.

Staff delivering interventions varied, but the majority were mental health staff. No intervention specifically included carers of participants, although in one, carers were invited to join a session [21].

Intervention duration varied from 12 weeks to 18 months, with the majority being between 4 and 6 months. Group sessions were provided in 12 interventions, 4 of which also included individual sessions or follow-up calls. Sessions varied from 30 minutes to 2 hours in length, with frequency ranging from 3-times weekly to once a month.


Our primary outcomes of interest were HbA1c and fasting glucose.

Nineteen pharmacological studies measured both of these outcomes. A further five measured HbA1c (one did not provide data) [42], and 16 measured fasting glucose (one did not provide data [51] and one provided dichotomous data that were not useable in meta-analysis [46]).

Three behavioural studies measured both HbA1c and fasting glucose [24, 29, 30]. One of these did not provide data for HbA1c [30], and one reported log transformed data for fasting glucose which were not useable in meta-analysis [29]. One study measured HbA1c only [25], and the other nine studies measured fasting glucose only (one did not provide data [32] and one provided dichotomous data that were not useable [26]).

The mixed intervention study only measured fasting glucose.

All studies measured HbA1c and/ or fasting glucose at the end of the intervention period. Details of the primary outcomes and follow-up period for each study are shown in Table 1.

Risk of bias

The risk of bias assessment for each study is provided in S2 Table. Only one study was assessed as low risk across all domains [74]. Reporting of trial design was limited in many studies. Attrition was a particular problem for behavioural interventions and also for antipsychotic switching trials, many of which reported higher discontinuation rates in the intervention compared to control groups.

Effectiveness of interventions

For HbA1c, six of 28 (five pharmacological and one behavioural), and for fasting glucose, nine of 48 studies (five pharmacological, three behavioural and one mixed intervention) showed improvement in the intervention group compared to the control interventions. The remainder reported no difference between groups (see Table 1).


In the 48 trials included, there were a total of 4,052 participants; 2,150 were assigned to intervention and 1,902 to control arms.

Pharmacological interventions.

For pharmacological interventions, we pooled data from 22 studies for HbA1c (n = 1515) and 34 for fasting glucose (n = 2536) (see Fig 2).

For HbA1c there was no evidence of a difference between the intervention and control groups (MD = -0.03%; 95% Confidence Interval (CI) [-0.12, 0.06]; p = 0.52). Results, however, were heterogeneous (I2 = 69%).

For fasting glucose there was a small but statistically significant improvement of -0.11mmol/L (95% CI, [-0.19, -0.02]; p = 0.02) for the intervention group compared to controls. Again, there was heterogeneity (I2 = 57%). Investigation of baseline imbalance (see S1 Fig) showed that the control group had slightly lower levels of fasting glucose (MD = 0.07mmol/L; 95% CI, [0.01, 0.14]; p = 0.03), a difference that while statistically significant, was very small, and if anything would lead to underestimation of the overall effect size.

For subgroup analysis of pharmacological interventions, we used the drug type categories described earlier (see Table 2). For the ‘diabetes medication’ category, we further subdivided interventions into ‘metformin’ and ‘other diabetes’ treatment. Meta-analysis (see Table 3) showed that antipsychotic switching (MD = -0.11%; 95% CI, [-0.18, -0.05]; p = 0.001; I2 = 0%) and metformin (MD = -0.08; 95% CI, [-0.14, -0.03]; p = 0.004; I2 = 0%) were effective in lowering HbA1c compared to placebo or usual care, albeit with modest effect sizes. For fasting glucose, only metformin was effective (MD = -0.15mmol/L; 95% CI, [-0.29, -0.01]; p = 0.04; I2 = 51%).

Subgroup analyses of studies that excluded participants with diabetes at baseline, and those that did not, showed that pharmacological interventions were effective in lowering HbA1c only in the mixed population (MD = -0.11%; 95% CI, [-0.21, -0.01]; p = 0.04; I2 = 59%). For fasting glucose, neither group showed a statistically significant improvement compared to controls (Table 3). The meta-regression found no association between baseline HbA1c or fasting glucose levels and effect size (see S3 Fig).

To explore this further, we repeated the subgroup analysis for certain categories of pharmacological interventions: diabetes medication, weight loss medication and antipsychotic switching for fasting glucose; and diabetes medication for HbA1c. No group showed statistically significant improvements compared to controls (Table 3). We observed larger effect sizes in studies that did not exclude diabetes at baseline for the diabetes medication and weight loss medication categories, but similar effects for antipsychotic switching (Table 3). We did not have sufficient data to examine the remaining categories.

Behavioural interventions.

For behavioural interventions, we pooled data from three studies for HbA1c (n = 140) and 10 for fasting glucose (n = 956) (see Fig 3).

Behavioural interventions were not found to be effective in lowering HbA1c, (MD = 0.18%; 95% CI, [-0.07, 0.42]; p = 0.16).

For fasting glucose, there was evidence of a difference of -0.28mmol/L (95% CI, [-0.43, -0.12]; p<0.001) comparing behavioural interventions with controls. Although there was evidence of heterogeneity (I2 = 46%), 7 of the 10 studies favoured the intervention. Investigation of baseline imbalance (see S2 Fig) showed that controls had slightly lower levels of fasting glucose (MD = 0.10mmol/L; 95% CI, [-0.02, 0.23]; p = 0.10).

For subgroup analysis and meta-regression of behavioural interventions, we only examined fasting glucose due to the small number of studies measuring HbA1c. Participants receiving an intervention that included physical activity showed an improvement in fasting glucose of -0.33mmol/L (95% CI, [-0.52, -0.14]; p = 0.001; I2 = 55%) compared to usual care (see Table 3). Participants receiving an intervention for 6 months or less had lower fasting glucose compared to usual care (MD = 0.23mmol/L; 95% CI, [-0.34, -0.12]; p<0.001; I2 = 0%); interventions of more than 6 months duration showed an even greater effect, lowering fasting glucose by 0.50mmol/L (95% CI, [-0.74, -0.25]; p<0.001; I2 = 31%), (Table 3). The meta-regression (see S4 Fig) confirmed that interventions of a longer duration had a greater effect on fasting glucose compared to usual care (coefficient = -0.006; 95% CI, [-0.01, -0.002]; p = 0.007).

Subgroup analysis of studies that excluded (MD = -0.28mmol/L; 95% CI, [-0.40, -0.15]; p<0.001; I2 = 0%) or did not exclude people with diabetes at baseline (MD = -0.28mmol/L; 95% CI, [-0.53, -0.03]; p = 0.03; I2 = 61%) showed similar statistically significant effects of behavioural interventions in improving fasting glucose compared to controls (Table 3). However, the meta-regression (S4 Fig) showed that effect size increased with higher baseline fasting glucose, suggesting that interventions may be more effective in those with poorer glycaemic control (coefficient = -0.36; 95% CI, [-0.59, -0.13]; p = 0.002).

Sensitivity analyses

Leave-one-out analyses showed that no single study had a disproportionate effect on each of the main meta-analyses. However, funnel plots showed some asymmetry (see Fig 4), suggesting potential publication bias for both the behavioural and pharmacological literature.

The trim-and-fill analysis suggests there is some evidence of missing studies (shown as black on the funnel plots in Fig 4).The adjusted effect sizes, accounting for publication bias are presented in Table 4. Publication bias adjusted effect sizes suggest that pharmacological interventions reduce both HbA1c and fasting glucose, and behavioural interventions are effective in reducing fasting glucose but not HbA1c.


Summary of evidence

Overall, compared to usual care, both pharmacological and behavioural interventions improved fasting glucose levels, but not HbA1c in people with SMI, with behavioural interventions showing a larger difference compared with pharmacological interventions. However, after adjusting for publication bias, there was some evidence that pharmacological interventions may also improve HbA1c. Subgroup analyses showed improvements in HbA1c for antipsychotic switching and metformin; and in fasting glucose for metformin. For behavioural interventions, those that included regular physical activity were more effective in lowering fasting glucose than those that did not. Subgroup analysis and meta-regression showed that interventions of longer duration resulted in greater improvements in fasting glucose compared to usual care, and this may help to explain why the small number of studies measuring HbA1c did not show an improvement, as only one of these was greater than 6 months in duration.

Some categories of pharmacological interventions (diabetes and weight loss medications), appeared to have a smaller effect on lowering glycaemic measurements in studies that excluded people with diabetes at baseline compared to the effect observed in studies that did not. However, it was not possible to investigate this robustly because of limited data, and the meta-regression of all pharmacological interventions showed no association between baseline levels of HbA1c or fasting glucose and effect size. For behavioural interventions, studies that included participants with higher baseline glucose levels appeared to be more effective in a meta-regression, although the subgroup analysis showed no difference between studies that excluded those with diabetes compared to those that did not.

Our findings are consistent with previous meta-analyses. Bruins et al., [7] found a significant improvement in fasting glucose levels with lifestyle interventions (standardised MD = -0.24, 95% CI, [-0.32, -0.10]; p = 0.001; n = 8 studies; I2 = 0%) but did not include HbA1c in their analysis or explore intervention characteristics. Mizuno et al., [6] explored pharmacological strategies to counteract the metabolic side effects of antipsychotic medication, and reported statistically significant improvements in fasting glucose for metformin (MD = -0.18mmol/L; 95% CI, [-0.35, 0.00]; n = 9 studies; I2 = 73%); and in HbA1c for metformin (MD = -0.08%; 95% CI, [-0.13, -0.03]; n = 3 studies; I2 = 0%) and aripiprazole (MD = -0.65%; 95% CI, [-1.25, -0.06]; n = 2 studies; I2 = 89%). Our findings were similar but included drugs in addition to aripiprazole in the antipsychotic switching group.

In common with these previous reviews, we found the improvements reported in HbA1c and fasting glucose were modest. However, there was considerable heterogeneity in results. Differences in effect sizes and direction of effect between studies made it difficult to assess the overall effectiveness of interventions. Several studies showed a reduction in fasting glucose and at the same time an increase in HbA1c or vice versa [3739, 43, 54]. These results are difficult to explain because logically one would expect a corresponding change, particularly in longer duration studies which would take account of the time required to alter HbA1c. However, there are a number of trials that demonstrate that although HbA1c and fasting glucose are well correlated, they do not always respond in similar ways [76].

For people with SMI, this relationship may be complicated further by the metabolic side effects of anti-psychotic medication, which will work against interventions designed to improve glycaemic control [77]. For example, in several of the pharmacological and behavioural intervention studies, fasting glucose or HbA1c increased in both the intervention and control groups, but with a smaller increase in the intervention group [24, 31, 43, 49, 56]. Through subgroup analysis and meta-regression, we have been able to identify certain intervention and population characteristics that may explain some of the differences in effect between studies, and identify particular interventions that show the most promise. However, these findings should be viewed within the context of methodologically limited trials, and for the antipsychotic switching and behavioural interventions, substantial dropout in follow-up.


Although we included a larger number of studies compared with previous reviews, a limitation of our findings relates to the quantity and quality of evidence included, and the substantial risk of potential bias identified in included studies. We were also unable to fully explore differential effects between those with and without diabetes, or to compare our findings to evidence in the general population because of the lack of data to measure onset of diabetes in those without diabetes, and diabetic complications in those with diabetes. Previous reviews have also commented on the paucity and poor quality of evidence in this area [10, 14]. Strengths of our review include a published protocol, robust search, independent screening and data extraction by at least two reviewers, and the use of appropriate meta-analytic methods to explore the results.

Implications for clinical practice

These results suggest that antipsychotic switching strategies, metformin, sustained behavioural interventions, and behavioural interventions that include regular physical activity offer the greatest potential to improve glycaemic control in the SMI population. Whilst the effect sizes were modest, such improvements in glycaemic control can help to avoid onset of diabetes and attenuate diabetes complications [11], therefore, the small differences reported in key subgroups may still be clinically significant. Also, combining pharmacological and behavioural strategies may incrementally (or perhaps even synergistically) increase effectiveness [34]. However, the effect sizes observed were modest when compared to the general population [18, 78], suggesting that tailored interventions which address the specific challenges faced by people with SMI are needed.

In real world settings, the SMI population will face challenges in adhering to new medications or engaging in sustained behavioural interventions involving attendance at regular group sessions [23, 79]. These challenges will likely be compounded when implementing multifaceted interventions. Moreover, we need to reflect carefully before pursuing adjunctive pharmacological therapies in a population for whom polypharmacy is already problematic; and the potential acceptability of switching from an antipsychotic medication providing clinical stability to one which may help to improve physical health, but for which the efficacy in preventing relapse in mental illness is uncertain. These considerations, along with the sparse evidence base, mean that recommendations for clinical practice remain limited. Nonetheless, this review does provide some evidence to support current practice of providing lifestyle interventions and switching to antipsychotics with a better metabolic profile in people with SMI.


Improving diabetes outcomes in SMI is a global priority, but the evidence-base to guide clinical practice is limited. Despite the challenges described above, a number of pharmacological and behavioural approaches warrant further exploration. Metformin is already a well-established treatment in diabetes [17]. Its use alongside antipsychotic prescriptions to prevent diabetes merits further investigation. Switching of antipsychotic medication is also common in clinical practice. Research is needed to understand which antipsychotics offer the greatest potential benefit, and to optimise dosage and timing of such interventions in order to reduce glycaemic burden, whilst maintaining clinical stability for people with SMI.

Behavioural interventions show perhaps more promise than pharmacological strategies, but little is known about the behaviour change techniques that might be most effective for people with SMI and diabetes. This is a key area for research, if we are to avoid ever-increasing inequalities in health and access to healthcare, as diabetes management becomes increasingly predicated on self-management. Future research should focus on the design of appropriate interventions, and test the potential acceptability and feasibility of delivering them in a real world setting, before establishing effectiveness in a trial evaluation.

Supporting Information

S2 Table. Risk of bias assessment for included studies.


S1 Fig. Meta-analysis of baseline imbalance in pharmacological studies.


S2 Fig. Meta-analysis of baseline imbalance in behavioural studies.


S3 Fig.

Meta-regression of the difference in means for pharmacological interventions by A) baseline HbA1c and B) baseline fasting glucose.


S4 Fig.

Meta-regression of the difference in mean fasting glucose for behavioural interventions by (A) intervention duration (B) baseline fasting glucose. 7



We wish to acknowledge the contribution of the Diamonds Research Group and PPI panel who contributed to the design of the review (

Author Contributions

  1. Conceptualization: JT BS CH RA SA SG RIGH PH TH TK IK HL NM KM RS JW NS.
  2. Formal analysis: JT BS CH SA RIGH PH IK HL RS NS.
  3. Funding acquisition: NS SG HL.
  4. Investigation: JT BS CH SA RIGH PH TK IK HL NM KM RS JW NS.
  6. Project administration: JT.
  7. Resources: JT KM JW.
  8. Supervision: JT NS.
  9. Validation: JT BS CH RA RIGH PH TH IK NS.
  10. Visualization: JT BS NS CH.
  11. Writing – original draft: JT NS.
  12. Writing – review & editing: JT BS CH RA SA SG RIGH PH TH TK IK HL NM KM RS JW NS.


  1. 1. Brown S, Kim M, Mitchell C, Inskip H. Twenty-five year mortality of a community cohort with schizophrenia. Br J Psychiatry. 2010;196(2): 116–21. pmid:20118455
  2. 2. Ward M, Druss B. The epidemiology of diabetes in psychotic disorders. Lancet Psychiatry. 2015;2(5): 431–51. pmid:26360287
  3. 3. Reilly S, Olier I, Planner C, Doran T, Reeves D, Ashcroft DM, et al. Inequalities in physical comorbidity: a longitudinal comparative cohort study of people with severe mental illness in the UK. BMJ Open. 2015;5(12): e009010. pmid:26671955
  4. 4. Chwastiak LA, Freudenreich O, Tek C, McKibbin C, Han J, McCarron R, et al. Clinical management of comorbid diabetes and psychotic disorders. Lancet Psychiatry. 2015;2(5): 465–76. pmid:26360289
  5. 5. Gierisch JM, Nieuwsma JA, Bradford DW, Wilder CM, Mann-Wrobel MC, McBroom AJ, et al. Pharmacologic and behavioral interventions to improve cardiovascular risk factors in adults with serious mental illness: a systematic review and meta-analysis. J Clin Psychiatry. 2014;75(5): e424–40. pmid:24922495
  6. 6. Mizuno Y, Suzuki T, Nakagawa A, Yoshida K, Mimura M, Fleischhacker WW, et al. Pharmacological strategies to counteract antipsychotic-induced weight gain and metabolic adverse effects in schizophrenia: a systematic review and meta-analysis. Schizophr Bull. 2014;40(6): 1385–403. pmid:24636967
  7. 7. Bruins J, Jorg F, Bruggeman R, Slooff C, Corpeleijn E, Pijnenborg M. The effects of lifestyle interventions on (long-term) weight management, cardiometabolic risk and depressive symptoms in people with psychotic disorders: a meta-analysis. PLoS One. 2014;9(12): e112276. pmid:25474313
  8. 8. Caemmerer J, Correll CU, Maayan L. Acute and maintenance effects of non-pharmacologic interventions for antipsychotic associated weight gain and metabolic abnormalities: A meta-analytic comparison of randomized controlled trials. Schizophr Res. 2012;140(1–3): 159–68. pmid:22763424
  9. 9. Cimo A, Stergiopoulos E, Cheng C, Bonato S, Dewa CS. Effective lifestyle interventions to improve type II diabetes self-management for those with schizophrenia or schizoaffective disorder: a systematic review. BMC Psychiatry. 2012;12: 24. pmid:22443212
  10. 10. McGinty EE, Baller J, Azrin ST, Juliano-Bult D, Daumit GL. Interventions to Address Medical Conditions and Health-Risk Behaviors Among Persons With Serious Mental Illness: A Comprehensive Review. Schizophr Bull. 2016;42(1): 96–124. pmid:26221050
  11. 11. Kontopantelis E, Springate DA, Reeves D, Ashcroft DM, Rutter MK, Buchan I, et al. Glucose, blood pressure and cholesterol levels and their relationships to clinical outcomes in type 2 diabetes: a retrospective cohort study. Diabetologia. 2015;58(3): 505–18. pmid:25512005
  12. 12. Maayan L, Vakhrusheva J, Correll CU. Effectiveness of medications used to attenuate antipsychotic-related weight gain and metabolic abnormalities: a systematic review and meta-analysis. Neuropsychopharmacology. 2010;35(7): 1520–30. pmid:20336059
  13. 13. Fernandez-San-Martin MI, Martin-Lopez LM, Masa-Font R, Olona-Tabuena N, Roman Y, Martin-Royo J, et al. The effectiveness of lifestyle interventions to reduce cardiovascular risk in patients with severe mental disorders: meta-analysis of intervention studies. Community Ment Health J. 2014;50(1): 81–95. pmid:23739948
  14. 14. Faulkner G, Cohn T, Remington G. Interventions to Reduce Weight Gain in Schizophrenia. Schizophr Bull. 2007;33(3): 654–6. pmid:17449900
  15. 15. Siddiqi N, Lewis H, Taylor J, Mahmoodi N, Wright J, McDermid K, et al. A systematic review of pharmacological and non-pharmacological interventions for improving diabetes outcomes in people with serious mental illness. PROSPERO International prospective register of systematic reviews. 2015;CRD42015015558.
  16. 16. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration; 2011.
  17. 17. Stevens JW, Khunti K, Harvey R, Johnson M, Preston L, Woods HB, et al. Preventing the progression to type 2 diabetes mellitus in adults at high risk: a systematic review and network meta-analysis of lifestyle, pharmacological and surgical interventions. Diabetes Res Clin Pract. 2015;107(3): 320–31. pmid:25638454
  18. 18. Aguiar PM, Brito Gde C, Lima Tde M, Santos AP, Lyra DP Jr., Storpirtis S. Investigating Sources of Heterogeneity in Randomized Controlled Trials of the Effects of Pharmacist Interventions on Glycemic Control in Type 2 Diabetic Patients: A Systematic Review and Meta-Analysis. PLoS One. 2016;11(3): e0150999. pmid:26963251
  19. 19. Trowman R, Dumville JC, Torgerson DJ, Cranny G. The impact of trial baseline imbalances should be considered in systematic reviews: a methodological case study. J Clin Epidemiol. 2007;60(12): 1229–33. pmid:17998076
  20. 20. Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. Performance of the trim and fill method in the presence of publication bias and between-study heterogeneity. Stat Med. 2007;26(25): 4544–62. pmid:17476644
  21. 21. Attux C, Martini LC, Elkis H, Tamai S, Freirias A, Camargo M, et al. A 6-month randomized controlled trial to test the efficacy of a lifestyle intervention for weight gain management in schizophrenia. BMC Psychiatry. 2013;13: 60. pmid:23418863
  22. 22. Cordes J, Thunker J, Regenbrecht G, Zielasek J, Correll CU, Schmidt-Kraepelin C, et al. Can an early weight management program (WMP) prevent olanzapine (OLZ)-induced disturbances in body weight, blood glucose and lipid metabolism? Twenty-four- and 48-week results from a 6-month randomized trial. World J Biol Psychiatry. 2014;15(3): 229–41. pmid:21745127
  23. 23. Daumit GL, Dickerson FB, Wang NY, Dalcin A, Jerome GJ, Anderson CA, et al. A behavioral weight-loss intervention in persons with serious mental illness. N Engl J Med. 2013;368(17): 1594–602. pmid:23517118
  24. 24. Forsberg KA, Bjorkman T, Sandman PO, Sandlund M. Physical health-A cluster randomized controlled lifestyle intervention among persons with a psychiatric disability and their staff. Nord J Psychiatry. 2008;62(6): 486–95. pmid:18843564
  25. 25. Gillhoff K, Gaab J, Emini L, Maroni C, Tholuck J, Greil W. Effects of a multimodal lifestyle intervention on body mass index in patients with bipolar disorder: a randomized controlled trial. Prim Care Companion J Clin Psychiatry. 2010;12(5).
  26. 26. Goldberg RW, Reeves G, Tapscott S, Medoff D, Dickerson F, Goldberg AP, et al. "MOVE!" Outcomes of a weight loss program modified for veterans with serious mental illness. Psychiatr Serv. 2013;64(8): 737–44. pmid:23584716
  27. 27. Green CA, Yarborough BJ, Leo MC, Yarborough MT, Stumbo SP, Janoff SL, et al. The STRIDE weight loss and lifestyle intervention for individuals taking antipsychotic medications: a randomized trial. Am J Psychiatry. 2015;172(1): 71–81. pmid:25219423
  28. 28. Mauri M, Simoncini M, Castrogiovanni S, Iovieno N, Cecconi D, Dell'Agnello G, et al. A psychoeducational program for weight loss in patients who have experienced weight gain during antipsychotic treatment with olanzapine. Pharmacopsychiatry. 2008;41(1): 17–23. pmid:18203047
  29. 29. McKibbin CL, Patterson TL, Norman G, Patrick K, Jin H, Roesch S, et al. A lifestyle intervention for older schizophrenia patients with diabetes mellitus: a randomized controlled trial. Schizophr Res. 2006;86(1–3): 36–44. pmid:16842977
  30. 30. Poulin MJ, Chaput JP, Simard V, Vincent P, Bernier J, Gauthier Y, et al. Management of antipsychotic-induced weight gain: prospective naturalistic study of the effectiveness of a supervised exercise programme. Aust N Z J Psychiatry. 2007;41(12): 980–9. pmid:17999270
  31. 31. Scheewe TW, Backx FJ, Takken T, Jorg F, van Strater AC, Kroes AG, et al. Exercise therapy improves mental and physical health in schizophrenia: a randomised controlled trial. Acta Psychiatr Scand. 2013;127(6): 464–73. pmid:23106093
  32. 32. Weber M, Wyne K. A cognitive/behavioral group intervention for weight loss in patients treated with atypical antipsychotics. Schizophr Res. 2006;83(1): 95–101. pmid:16507343
  33. 33. Wu MK, Wang CK, Bai YM, Huang CY, Lee SD. Outcomes of obese, clozapine-treated inpatients with schizophrenia placed on a six-month diet and physical activity program. Psychiatr Serv. 2007;58(4): 544–50. pmid:17412858
  34. 34. Wu RR, Zhao JP, Jin H, Shao P, Fang MS, Guo XF, et al. Lifestyle intervention and metformin for treatment of antipsychotic-induced weight gain: a randomized controlled trial. Jama. 2008;299(2): 185–93. pmid:18182600
  35. 35. Amrami-Weizman A, Maayan R, Gil-Ad I, Pashinian A, Fuchs C, Kotler M, et al. The effect of reboxetine co-administration with olanzapine on metabolic and endocrine profile in schizophrenia patients. Psychopharmacology. 2013;230(1): 23–7. pmid:23828160
  36. 36. Baptista T, Martinez J, Lacruz A, Rangel N, Beaulieu S, Serrano A, et al. Metformin for prevention of weight gain and insulin resistance with olanzapine: a double-blind placebo-controlled trial. Can J Psychiatry. 2006;51(3): 192–6. pmid:16618011
  37. 37. Baptista T, Rangel N, Fernandez V, Carrizo E, El Fakih Y, Uzcategui E, et al. Metformin as an adjunctive treatment to control body weight and metabolic dysfunction during olanzapine administration: a multicentric, double-blind, placebo-controlled trial. Schizophr Res. 2007;93(1–3): 99–108. pmid:17490862
  38. 38. Baptista T, Uzcategui E, Rangel N, El Fakih Y, Galeazzi T, Beaulieu S, et al. Metformin plus sibutramine for olanzapine-associated weight gain and metabolic dysfunction in schizophrenia: a 12-week double-blind, placebo-controlled pilot study. Psychiatry Res. 2008;159(1–2): 250–3. pmid:18374423
  39. 39. Baptista T, Rangel N, El Fakih Y, Uzcategui E, Galeazzi T, Beaulieu S, et al. Rosiglitazone in the assistance of metabolic control during olanzapine administration in schizophrenia: a pilot double-blind, placebo-controlled, 12-week trial. Pharmacopsychiatry. 2009;42(1): 14–9. pmid:19153941
  40. 40. Biedermann F, Fleischhacker WW, Kemmler G, Ebenbichler CF, Lechleitner M, Hofer A. Sibutramine in the treatment of antipsychotic-induced weight gain: a pilot study in patients with schizophrenia. Int Clin Psychopharmacol. 2014;29(3): 181–4. pmid:24300751
  41. 41. Borba CP, Fan X, Copeland PM, Paiva A, Freudenreich O, Henderson DC. Placebo-controlled pilot study of ramelteon for adiposity and lipids in patients with schizophrenia. J Clin Psychopharmacol. 2011;31(5): 653–8. pmid:21869685
  42. 42. Borovicka MC, Fuller MA, Konicki PE, White JC, Steele VM, Jaskiw GE. Phenylpropanolamine appears not to promote weight loss in patients with schizophrenia who have gained weight during clozapine treatment. J Clin Psychiatry. 2002;63(4): 345–8. pmid:12000209
  43. 43. Carrizo E, Fernandez V, Connell L, Sandia I, Prieto D, Mogollon J, et al. Extended release metformin for metabolic control assistance during prolonged clozapine administration: a 14 week, double-blind, parallel group, placebo-controlled study.[Erratum appears in Schizophr Res. 2009 Nov;115(1):96]. Schizophr Res. 2009;113(1): 19–26. pmid:19515536
  44. 44. Chen Y, Bobo WV, Watts K, Jayathilake K, Tang T, Meltzer HY. Comparative effectiveness of switching antipsychotic drug treatment to aripiprazole or ziprasidone for improving metabolic profile and atherogenic dyslipidemia: a 12-month, prospective, open-label study. J Psychopharmacol. 2012;26(9): 1201–10. pmid:22234928
  45. 45. Chen CH, Huang MC, Kao CF, Lin SK, Kuo PH, Chiu CC, et al. Effects of adjunctive metformin on metabolic traits in nondiabetic clozapine-treated patients with schizophrenia and the effect of metformin discontinuation on body weight: a 24-week, randomized, double-blind, placebo-controlled study. J Clin Psychiatry. 2013;74(5): e424–30. pmid:23759461
  46. 46. Deberdt W, Winokur A, Cavazzoni PA, Trzaskoma QN, Carlson CD, Bymaster FP, et al. Amantadine for weight gain associated with olanzapine treatment. Eur Neuropsychopharmacol. 2005;15(1): 13–21. pmid:15572269
  47. 47. Deberdt W, Lipkovich I, Heinloth AN, Liu L, Kollack-Walker S, Edwards SE, et al. Double-blind, randomized trial comparing efficacy and safety of continuing olanzapine versus switching to quetiapine in overweight or obese patients with schizophrenia or schizoaffective disorder. Ther Clin Risk Manag. 2008;4(4): 713–20. pmid:19209252
  48. 48. Fadai F, Mousavi B, Ashtari Z, Ali beigi N, Farhang S, Hashempour S, et al. Saffron aqueous extract prevents metabolic syndrome in patients with schizophrenia on olanzapine treatment: A randomized triple blind placebo controlled study. Pharmacopsychiatry. 2014;47(4–5): 156–61. pmid:24955550
  49. 49. Fan X, Borba CP, Copeland P, Hayden D, Freudenreich O, Goff DC, et al. Metabolic effects of adjunctive aripiprazole in clozapine-treated patients with schizophrenia. Acta Psychiatr Scand. 2013;127(3): 217–26. pmid:22943577
  50. 50. Fleischhacker WW, Heikkinen ME, Olie JP, Landsberg W, Dewaele P, McQuade RD, et al. Effects of adjunctive treatment with aripiprazole on body weight and clinical efficacy in schizophrenia patients treated with clozapine: a randomized, double-blind, placebo-controlled trial. Int J Neuropsychopharmacol. 2010;13(8): 1115–25. pmid:20459883
  51. 51. Graham KA, Gu H, Lieberman JA, Harp JB, Perkins DO. Double-blind, placebo-controlled investigation of amantadine for weight loss in subjects who gained weight with olanzapine. Am J Psychiatry. 2005;162(9): 1744–6. pmid:16135638
  52. 52. Henderson DC, Copeland PM, Daley TB, Borba CP, Cather C, Nguyen DD, et al. A double-blind, placebo-controlled trial of sibutramine for olanzapine-associated weight gain. Am J Psychiatry. 2005;162(5): 954–62. pmid:15863798
  53. 53. Henderson DC, Fan X, Copeland PM, Borba CP, Daley TB, Nguyen DD, et al. A double-blind, placebo-controlled trial of sibutramine for clozapine-associated weight gain. Acta Psychiatr Scand. 2007;115(2): 101–5. pmid:17244173
  54. 54. Henderson DC, Fan X, Copeland PM, Sharma B, Borba CP, Boxill R, et al. Aripiprazole added to overweight and obese olanzapine-treated schizophrenia patients. J Clin Psychopharmacol. 2009;29(2): 165–9. pmid:19512978
  55. 55. Henderson DC, Fan X, Sharma B, Copeland PM, Borba CP, Boxill R, et al. A double-blind, placebo-controlled trial of rosiglitazone for clozapine-induced glucose metabolism impairment in patients with schizophrenia. Acta Psychiatr Scand. 2009;119(6): 457–65. pmid:19183127
  56. 56. Hoffmann VP, Case M, Jacobson JG. Assessment of treatment algorithms including amantadine, metformin, and zonisamide for the prevention of weight gain with olanzapine: a randomized controlled open-label study. J Clin Psychiatry. 2012;73(2): 216–23. pmid:21672497
  57. 57. Holka-Pokorska JA, Radzio R, Jarema M, Wichniak A. The stabilizing effect of dehydroepiandrosterone on clinical parameters of metabolic syndrome in patients with schizophrenia treated with olanzapine—a randomized, double-blind trial. Psychiatr Pol. 2015;49(2): 363–76. pmid:26093599
  58. 58. Jarskog LF, Hamer RM, Catellier DJ, Stewart DD, Lavange L, Ray N, et al. Metformin for weight loss and metabolic control in overweight outpatients with schizophrenia and schizoaffective disorder. Am J Psychiatry. 2013;170(9): 1032–40. pmid:23846733
  59. 59. Joffe G, Takala P, Tchoukhine E, Hakko H, Raidma M, Putkonen H, et al. Orlistat in clozapine- or olanzapine-treated patients with overweight or obesity: a 16-week randomized, double-blind, placebo-controlled trial. J Clin Psychiatry. 2008;69(5): 706–11. pmid:18426261
  60. 60. Karagianis J, Grossman L, Landry J, Reed VA, de Haan L, Maguire GA, et al. A randomized controlled trial of the effect of sublingual orally disintegrating olanzapine versus oral olanzapine on body mass index: the PLATYPUS Study. Schizophr Res. 2009;113(1): 41–8. pmid:19535229
  61. 61. Kusumi I, Honda M, Uemura K, Sugawara Y, Kohsaka M, Tochigi A, et al. Effect of olanzapine orally disintegrating tablet versus oral standard tablet on body weight in patients with schizophrenia: a randomized open-label trial. Prog Neuropsychopharmacol Biol Psychiatry. 2012;36(2): 313–7. pmid:22119746
  62. 62. Lee SY, Chen SL, Chang YH, Chen PS, Huang SY, Tzeng NS, et al. Add-on memantine to valproate treatment increased HDL-C in bipolar II disorder. J Psychiatr Res. 2013;47(10): 1343–8. pmid:23870798
  63. 63. Li J, Li X, Liu E, Copeland P, Freudenreich O, Goff DC, et al. No effect of adjunctive, repeated dose intranasal insulin treatment on body metabolism in patients with schizophrenia. Schizophr Res. 2013;146(1–3): 40–5. pmid:23434504
  64. 64. Lu ML, Lane HY, Lin SK, Chen KP, Chang WH. Adjunctive fluvoxamine inhibits clozapine-related weight gain and metabolic disturbances. J Clin Psychiatry. 2004;65(6): 766–71. pmid:15291653
  65. 65. McElroy SL, Winstanley E, Mori N, Martens B, McCoy J, Moeller D, et al. A randomized, placebo-controlled study of zonisamide to prevent olanzapine-associated weight gain. J Clin Psychopharmacol. 2012;32(2): 165–72. pmid:22367654
  66. 66. Modabbernia A, Heidari P, Soleimani R, Sobhani A, Roshan ZA, Taslimi S, et al. Melatonin for prevention of metabolic side-effects of olanzapine in patients with first-episode schizophrenia: randomized double-blind placebo-controlled study. J Psychiatr Res. 2014;53(1): 133–40.
  67. 67. Narula PK, Rehan HS, Unni KE, Gupta N. Topiramate for prevention of olanzapine associated weight gain and metabolic dysfunction in schizophrenia: a double-blind, placebo-controlled trial. Schizophr Res. 2010;118(1–3): 218–23. pmid:20207521
  68. 68. Newcomer JW, Campos JA, Marcus RN, Breder C, Berman RM, Kerselaers W, et al. A multicenter, randomized, double-blind study of the effects of aripiprazole in overweight subjects with schizophrenia or schizoaffective disorder switched from olanzapine. J Clin Psychiatry. 2008;69(7): 1046–56. pmid:18605811
  69. 69. Smith RC, Jin H, Li C, Bark N, Shekhar A, Dwivedi S, et al. Effects of pioglitazone on metabolic abnormalities, psychopathology, and cognitive function in schizophrenic patients treated with antipsychotic medication: a randomized double-blind study. Schizophr Res. 2013;143(1): 18–24. pmid:23200554
  70. 70. Stroup TS, McEvoy JP, Ring KD, Hamer RH, LaVange LM, Swartz MS, et al. A randomized trial examining the effectiveness of switching from olanzapine, quetiapine, or risperidone to aripiprazole to reduce metabolic risk: comparison of antipsychotics for metabolic problems (CAMP). Am J Psychiatry. 2011;168(9): 947–56. pmid:21768610
  71. 71. Tek C, Ratliff J, Reutenauer E, Ganguli R, O'Malley SS. A randomized, double-blind, placebo-controlled pilot study of naltrexone to counteract antipsychotic-associated weight gain: proof of concept. J Clin Psychopharmacol. 2014;34(5): 608–12. pmid:25102328
  72. 72. Wang M, Tong JH, Zhu G, Liang GM, Yan HF, Wang XZ. Metformin for treatment of antipsychotic-induced weight gain: a randomized, placebo-controlled study. Schizophr Res. 2012;138(1): 54–7. pmid:22398127
  73. 73. Wani RA, Dar MA, Chandel RK, Rather YH, Haq I, Hussain A, et al. Effects of switching from olanzapine to aripiprazole on the metabolic profiles of patients with schizophrenia and metabolic syndrome: a double-blind, randomized, open-label study. Neuropsychiatr Dis Treat. 2015;11: 685–93. pmid:25792838
  74. 74. Wu RR, Zhao JP, Guo XF, He YQ, Fang MS, Guo WB, et al. Metformin addition attenuates olanzapine-induced weight gain in drug-naive first-episode schizophrenia patients: a double-blind, placebo-controlled study. Am J Psychiatry. 2008;165(3): 352–8. pmid:18245179
  75. 75. American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2015;38(Supplement 1): S8–S16.
  76. 76. Rodbard HW, Cariou B, Zinman B, Handelsman Y, Philis-Tsimikas A, Skjoth TV, et al. Comparison of insulin degludec with insulin glargine in insulin-naive subjects with Type 2 diabetes: a 2-year randomized, treat-to-target trial. Diabet Med. 2013;30(11): 1298–304. pmid:23952326
  77. 77. Rummel-Kluge C, Komossa K, Schwarz S, Hunger H, Schmid F, Lobos CA, et al. Head-to-head comparisons of metabolic side effects of second generation antipsychotics in the treatment of schizophrenia: a systematic review and meta-analysis. Schizophr Res. 2010;123(2–3): 225–33. pmid:20692814
  78. 78. Chen L, Pei J-H, Kuang J, Chen H-M, Chen Z, Li Z-W, et al. Effect of lifestyle intervention in patients with type 2 diabetes: A meta-analysis. Metabolism. 2015;64(2): 338–47. pmid:25467842
  79. 79. Gorczynski P, Patel H, Ganguli R. Adherence to diabetes medication in individuals with schizophrenia. Clin Schizophr Relat Psychoses. 2014;