Association Between Cardiovascular Risk Factors and Cognitive Impairment in People With Schizophrenia: A Systematic Review and Meta-analysis

Key Points

Question  Is there a significant association between cardiovascular disease risk factors such as metabolic syndrome and its constituent disorders and cognitive impairment in schizophrenia assessed using validated measures?

Findings  In this systematic review and meta-analysis of 27 studies involving 10 174 individuals with schizophrenia, global cognitive deficits were larger in individuals with (vs without) metabolic syndrome (13 studies; n = 2800). Among constituent metabolic syndrome disorders, diabetes (8 studies; n = 2976) and hypertension (5 studies; n = 1899) were associated with significant cognitive impairment, while obesity and dyslipidemia were not.

Meaning  The presence of metabolic disorders is significantly associated with cognitive deficits observed during the course of schizophrenia.

Importance  Schizophrenia is associated with cognitive dysfunction and cardiovascular risk factors, including metabolic syndrome (MetS) and its constituent criteria. Cognitive dysfunction and cardiovascular risk factors can worsen cognition in the general population and may contribute to cognitive impairment in schizophrenia.

Objective  To study the association between cognitive dysfunction and cardiovascular risk factors and cognitive impairment in individuals with schizophrenia.

Data Sources  A search was conducted of Embase, Scopus, MEDLINE, PubMed, and Cochrane databases from inception to February 25, 2020, using terms that included synonyms of schizophrenia AND metabolic adversities AND cognitive function. Conference proceedings, clinical trial registries, and reference lists of relevant publications were also searched.

Study Selection  Studies were included that (1) examined cognitive functioning in patients with schizophrenia or schizoaffective disorder; (2) investigated the association of cardiovascular disease risk factors, including MetS, diabetes, obesity, overweight, obesity or overweight, hypertension, dyslipidemia, and insulin resistance with outcomes; and (3) compared cognitive performance of patients with schizophrenia/schizoaffective disorder between those with vs without cardiovascular disease risk factors.

Data Extraction and Synthesis  Extraction of data was conducted by 2 to 3 independent reviewers per article. Data were meta-analyzed using a random-effects model.

Main Outcomes and Measures  The primary outcome was global cognition, defined as a test score using clinically validated measures of overall cognitive functioning.

Results  Twenty-seven studies involving 10 174 individuals with schizophrenia were included. Significantly greater global cognitive deficits were present in patients with schizophrenia who had MetS (13 studies; n = 2800; effect size [ES] = 0.31; 95% CI, 0.13-0.50; P = .001), diabetes (8 studies; n = 2976; ES = 0.32; 95% CI, 0.23-0.42; P < .001), or hypertension (5 studies; n = 1899; ES = 0.21; 95% CI, 0.11-0.31; P < .001); nonsignificantly greater deficits were present in patients with obesity (8 studies; n = 2779; P = .20), overweight (8 studies; n = 2825; P = .41), and insulin resistance (1 study; n = 193; P = .18). Worse performance in specific cognitive domains was associated with cognitive dysfunction and cardiovascular risk factors regarding 5 domains in patients with diabetes (ES range, 0.23 [95% CI, 0.12-0.33] to 0.40 [95% CI, 0.20-0.61]) and 4 domains with MetS (ES range, 0.15 [95% CI, 0.03-0.28] to 0.40 [95% CI, 0.20-0.61]) and hypertension (ES range, 0.15 [95% CI, 0.04-0.26] to 0.27 [95% CI, 0.15-0.39]).

Conclusions and Relevance  In this systematic review and meta-analysis, MetS, diabetes, and hypertension were significantly associated with global cognitive impairment in people with schizophrenia.

Schizophrenia is a severe and disabling disorder characterized by a significantly reduced life expectancy that is largely attributable to increased rates of cardiovascular disease (CVD).^1,2 A major risk factor for CVD is metabolic syndrome (MetS) and its constituent medical criteria, both of which are notably higher in patients with schizophrenia.³ The diagnosis of schizophrenia appears to be associated with a MetS diathesis that is exacerbated by antipsychotic therapy, with marked differences reported across individual medications in their effects on weight and metabolic parameters.^3-5

Evidence suggests that MetS and its constituent medical criteria are risk factors for clinically significant cognitive impairment both in the general population and in patients with a diagnosis of schizophrenia.^6,7 Cognitive impairment is a hallmark of schizophrenia that is significantly correlated with functional outcomes and has been shown to be persistent over the course of illness and minimally improved by antipsychotic therapy.^8,9

In a prior systematic review and meta-analysis of 12 studies investigating the association of MetS and diabetes with cognitive impairment in schizophrenia,⁷ comorbid MetS (6 studies; n = 2343), and diabetes (6 studies; n = 2897) were significantly associated with cognitive deficits (effect size = 0.28 each). Because of the clinical importance of understanding the association between metabolic risk factors and cognitive impairment in schizophrenia, we conducted a systematic review and meta-analysis that included a much expanded data set consisting of all studies investigating the association of risk factors for CVD with cognitive function in schizophrenia. We hypothesized that MetS and its constituent metabolic risk factors would be significantly associated with cognitive deficits in schizophrenia.

An electronic literature search was conducted by 2 or more independent reviewers (K.H., T.N., and C.U.C.; investigators and research staff) in Embase, Scopus, MEDLINE, PubMed, and Cochrane library from database inception to February 25, 2020, using search terms (eAppendix in the Supplement) that included synonyms of schizophrenia AND metabolic adversities AND cognitive function. We also searched conference proceedings, clinical trial registries, and reference lists of relevant publications.

We included studies that (1) examined cognitive functioning in patients with schizophrenia or schizoaffective disorder; (2) investigated the association of CVD risk factors, including MetS, diabetes, obesity (body mass index [calculated as weight in kilograms divided by height in meters squared] ≥30), overweight (body mass index ≥25-29.9), obesity or overweight (body mass index, ≥25), hypertension, dyslipidemia, and insulin resistance with outcomes; and (3) compared cognitive performance of patients with schizophrenia/schizoaffective disorder between those with vs without CVD risk factors. A quality assessment of the included studies is provided in eTable 1 in the Supplement. All methods and results are reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.¹⁰

Data were extracted independently by 2 or more reviewers (K.H., T.N., and C.U.C.). Disagreements were resolved by consensus. Where necessary, authors of included articles were contacted to provide missing information and unpublished data. This included data from the corresponding author of Boyer et al¹¹ on patients with schizophrenia, diabetes, obesity, overweight, and hypertension (not reported in their study). We also obtained unpublished data on 613 patients with schizophrenia, obesity, and overweight from the corresponding author of Han et al¹² (not reported in their study); on 300 patients with schizophrenia, obesity, overweight, hypertension, and dyslipidemia from the corresponding author of Dickinson et al¹³ (not reported in their study); on 272 patients with schizophrenia, obesity, and overweight from the corresponding author of Friedman et al¹⁴ (not reported in their study); and on 596 patients with schizophrenia from the corresponding author of Rashid et al¹⁵ (not reported in their study).

The primary outcome was global cognition, defined as a test score using clinically validated measures of overall cognitive functioning. In studies that did not include a measure of global cognition, an effect size for global cognition was calculated by averaging effect sizes of individual cognitive domains. Secondary outcomes included individual cognitive domains (attention/vigilance, processing speed, reasoning/problem solving, verbal learning, visual learning, working memory). If more than 1 cognitive variable was available for a cognitive domain, an effect size for that cognitive domain was calculated by averaging effect sizes of individual tests. eTable 2 in the Supplement presents the specific tests included within each cognitive domain.

Data were double entered into and meta-analyzed with Comprehensive Meta-Analysis version 3 (BioStat) using a random-effects model. Effect sizes in global cognition and each individual cognitive domain were calculated within individual studies as Hedges g comparing the mean (SD) scores in patients with schizophrenia with vs without each specific CVD risk factor. We then calculated the mean Hedges g across studies along with 95% CIs. For Guo et al,¹⁶ the composite z scores of the normal weight, overweight, and obese groups were reported to be 0.20, 0.10, and 0.01, respectively, but when the effect sizes were calculated using these values, Hedges g for the obese group exceeded 5.0. As the authors described in their article¹⁶ that the statistical value for normal weight vs obese was P = .004, this clearly did not match the composite z score as described, which, with an effect size of more than 5.0, would have produced a P value by many orders of magnitude smaller. Therefore, because we were unable to get clarification from the authors, we recalculated the effect size, assuming that the correct composite z score for normal weight and overweight were 0.02 and 0.01, respectively.

As a measure of heterogeneity, I² is reported. If I² was 50% or more, we considered heterogeneity to be substantial. In case such heterogeneity was found in the analysis of the primary outcome, we performed a leave-1-out meta-analysis to check how each individual study affected the overall estimate of the rest of the studies. τ² Was used as an estimate of degree of between-study variance. To assess potential publication bias, funnel plots were examined visually. A funnel plot is a scatterplot that displays the treatment effect size against a measure of study precision. Funnel plots are used to visually detect bias or systematic heterogeneity, which is indicated by an asymmetric funnel (typically resulting from small study effects). Egger regression test and the trim-and-fill method were used for the primary outcome whenever 3 or more studies were analyzed. Significance for Egger test was set as P = .10 to account for the smaller number of studies for some outcomes.

Finally, we conducted exploratory subgroup and meta-regression analyses, investigating the potential effect of the following moderator variables with sufficient data: study region, mean population age, duration of education, and illness duration.

Altogether, 1678 references were identified (Figure 1). After removing 174 that were clearly not relevant or duplicates, we excluded 1466 of the remaining 1504 references based on title and abstract inspection. Of 38 full-text inspected articles, 15 were dropped (Figure 1). We added 4 hand-searched studies and unpublished data, resulting in 27 meta-analyzed studies (eTable 3 in the Supplement), 25 of which have been published.^11-35

The 27 studies included 10 174 participants (median [range] participants per study, 196 [46-1719]) with a mean (SD) age of 42.1 (8.7) years (range, 27.5-64.1 years). Overall, 6488 participants (63.8%) were male and 3000 (29.5%) were White. The mean (SD) illness duration was 14.4 (8.2) years (range, 2.1-37.0 years), and the mean (SD) years of education was 11.8 (1.7) (range, 8.4-14.7). Studies were mainly conducted in China (9 studies; n = 3489), followed by the US (7 studies; n = 3923), Europe (7 studies; n = 802), Singapore (1 study; n = 1719), India (1 study; n = 121), and Egypt (1 study; n = 120) (eTable 3 in the Supplement).

For global cognition, differences in Hedges g between patients with schizophrenia with vs without cognitive dysfunction and cardiovascular risk factors ranged from −0.20 to 0.70 for global cognition, with all point estimates, except for dyslipidemia, indicating poorer cognition for patients with specific cognitive dysfunction and cardiovascular risk factors, but with wide CIs for obesity, overweight, and obesity/overweight (Figure 2).

Global cognition was significantly associated with more impairment in patients with schizophrenia with MetS vs without MetS (13 studies; n = 2800; Hedges g = 0.31; 95% CI, 0.13-0.50; P = .001; I² = 77.7%) (Table; eFigure 1 in the Supplement). As a result of the leave-1-out meta-analysis, we found an obvious outlier study. Excluding the outlier study,²⁹ the primary finding remained similar, but heterogeneity decreased to less than 50% (12 studies; n = 2641; Hedges g = 0.23; 95% CI, 0.10-0.35; P < .001; I² = 45.5%) (eTable 4 in the Supplement). Similarly, patients with schizophrenia with vs without MetS also performed significantly worse in reasoning/problem solving (2 studies; n = 1282; Hedges g = 0.16; 95% CI, 0.05-0.27; P = .006; I² = 0%), speed of processing (11 studies; n = 2605; Hedges g = 0.18; 95% CI, 0.08-0.29; P < .001; I² = 23.4%), verbal learning (11 studies; n = 2564; Hedges g = 0.40; 95% CI, 0.20-0.61; P < .001; I² = 79.6%), and attention/vigilance (11 studies; n = 2405; Hedges g = 0.15; 95% CI, 0.03-0.28; P = .02; I^2 = 36.8%). There were no significant differences in visual learning and working memory (Table).

Global cognition was significantly associated with more impairment in patients with schizophrenia with vs without diabetes (8 studies; n = 2976; Hedges g = 0.32; 95% CI, 0.23-0.42; P < .001; I² = 0%) (Table; eFigure 2 in the Supplement). The same was true for attention/vigilance (7 studies; n = 2552; Hedges g = 0.28; 95% CI, 0.06-0.50; P = .01; I² = 72.9%), reasoning/problem solving (2 studies; n = 1484; Hedges g = 0.40; 95% CI, 0.26-0.54; P < .001; I² = 0%), speed of processing (7 studies; n = 2701; Hedges g = 0.28; 95% CI, 0.15-0.41; P < .001; I² = 29.6%), verbal learning (6 studies; n = 2605; Hedges g = 0.23; 95% CI, 0.12-0.33; P < .001; I² = 0%), and visual learning (1 study; n = 196; Hedges g = 0.30; 95% CI, 0.02-0.59; P = .04). There was no significant difference in working memory (Table).

Global cognition did not differ significantly between patients with schizophrenia with vs without comorbid obesity, overweight, or obesity/overweight (Table; eFigures 3-5 in the Supplement). As a result of the leave-1-out meta-analysis, we found an obvious outlier study. Exclusion of the outlier study¹⁶ from the overweight analysis did not substantially change the primary finding but decreased heterogeneity to less than 50% (7 studies; n = 2148; P = .79; I² = 0%) (eTable 4 in the Supplement).

Despite no association for obesity/overweight with global cognition, visual learning was significantly worse in patients with schizophrenia with obesity, overweight, and obesity/overweight, but results were based on the same single study (Table). There were no significant differences in the other cognitive domains (Table).

Global cognition was significantly associated with impairment in patients with schizophrenia with vs without hypertension (5 studies; n = 1899; Hedges g = 0.21; 95% CI, 0.11-0.31; P < .001; I² = 0%) (Table; eFigure 6 in the Supplement). The same was true for reasoning/problem solving (1 study; n = 1330; Hedges g = 0.27; 95% CI, 0.15-0.39; P < .001), speed of processing (4 studies; n = 1853; Hedges g = 0.18; 95% CI, 0.01-0.36; P = .04; I² = 36.7%), verbal learning (5 studies; n = 1899; Hedges g = 0.20; 95% CI, 0.04-0.36; P = .01; I² = 28.1%), and working memory (3 studies; n = 1713; Hedges g = 0.15; 95% CI, 0.04-0.26; P = .006; I² = 0%). There was no significant difference in attention/vigilance (Table). No data for visual learning were available.

Global cognition did not differ significantly between patients with schizophrenia with vs without dyslipidemia (Table; eFigure 7 in the Supplement). The same was true for all individual cognitive domains (Table), except for visual learning where no analyzable data were available.

In the 1 study that focused on insulin resistance, global cognition did not differ significantly between patients with schizophrenia with vs without this risk factor (Table; eFigure 8 in the Supplement). A significant difference favoring patients with schizophrenia with insulin resistance was evident for attention/vigilance, but no significant differences were found for the other individual cognitive domains (Table).

In 6 of 7 comparisons of the primary outcome global cognition with 3 or more studies, the funnel plot was asymmetrical. Conducting the trim-and-fill method to adjust for potential publication bias yielded similar effect sizes and statistical significance levels (eFigure 9 in the Supplement).

The associations between the 7 examined CVD risk factors and global cognition as well as the 6 cognitive subdomains were consistent across the examined moderator variables of study region, mean population age, percentage of male individuals, duration of education, and illness duration (eTable 5 in the Supplement). Regarding obesity and overweight, there was a negative correlation between mean age or illness duration and global cognition (eTable 6 in the Supplement).

To our knowledge, this is the largest and most comprehensive systematic review and meta-analysis of the association between CVD risk factors and cognition in people with schizophrenia. One prior meta-analysis⁷ only investigated associations between cognition and MetS and diabetes, while we added 5 additional CVD risk factors (+150%), including obesity, overweight, obesity/overweight, hypertension, dyslipidemia, and insulin resistance. Furthermore, whereas the prior systematic review and meta-analysis had investigated only 4 cognitive subdomains when assessing the association between MetS and cognition and 3 subdomains when doing so for diabetes,⁷ we investigated the associations of 6 cognitive subdomains for all investigated CVD risk factors.

Results of this meta-analysis of the association between MetS and its components, including diabetes, indicate that patients with schizophrenia with vs without comorbid MetS, diabetes, or hypertension have more severe global cognitive deficits. The effect sizes for the association of MetS (Hedges g = 0.31) and diabetes (Hedges g = 0.32) with global cognitive impairment were in the small to moderate range and slightly higher than the effect size reported in the prior meta-analysis (Cohen d = 0.28 for both MetS and diabetes).⁷ The effect size for the association between hypertension and global cognition was in the small range (Hedges g = 0.21). However, impairment in global cognition was not significantly associated with obesity, overweight, and obesity/overweight, although point estimates were in the same direction as for MetS, diabetes, and hypertension. Conversely, global cognitive function was almost the same for patients with schizophrenia with vs without dyslipidemia. These results are mostly consistent with data from the general population. For example, while 1 study reported that diabetes and hypertension were significantly associated with cognitive impairment, the association between obesity or hyperlipidemia and cognitive impairment was less consistent.³⁶ Another study reported that obesity was associated with cognitive impairment in children, adolescents, and adults; however, the association between obesity and cognition was uncertain in elderly individuals.³⁷ The proportion of individuals diagnosed with MetS or diabetes increases rapidly at age 50 to 60 years, but overweight or obesity begins to develop at a younger age. The difference in timing when such CVD risk factors likely occur may influence whether or not cognitive impairment and an association with CVD risk factors is observed.

There are several possible mechanisms linking CVD risk factors and cognitive impairment. Cognitive deficits associated with CVD risk factors are most likely associated with metabolic changes that are associated with microvascular and macrovascular processes thought to lead to structural abnormalities in the brain and subsequently to cognitive impairment. Indeed, a systematic review of imaging studies in patients with vascular risk factors suggested that CVD risk factors, including hypertension, diabetes, increased adiposity, and hyperlipidemia, are associated with abnormalities in white-matter microstructure and functional connectivity, gray matter reductions, and white-matter hyperintensities.³⁸ Furthermore, insulin resistance has been associated with decreased hippocampal volume and impaired cognition.³⁹ In addition, hypertension is a major risk factor for major and minor stroke events,⁴⁰ which in its most severe form can lead to serious cognitive impairment and vascular dementia.

Additionally, an increased level of systemic inflammation in patients with schizophrenia has been linked to cognitive difficulties.⁴¹ Moreover, in a 2019 meta-analysis of adjunctive anti-inflammatory drugs for schizophrenia, minocycline and pregnenolone were associated with significant improvement in cognition.⁴² Moreover, aerobic exercise, which reduces chronic inflammation, has been associated with increased hippocampal volume in patients with schizophrenia.⁴³ Taken together, these results underscore the potential link between systemic inflammation and cognitive dysfunction and suggest that lifestyle interventions and anti-inflammatory agents may help improve cognition in people with schizophrenia.

Our overall results of the association between CVD risk factors and cognitive dysfunction are strengthened by the fact that the findings were highly consistent when analyzing cognitive subdomains, in that presence of the respective CVD risk factor was significantly associated with poorer cognition in 5 of 6 cognitive domains for diabetes, 4 of 6 domains for MetS, and 4 of 5 domains with data for hypertension, whereas neither global cognition nor subdomains were significantly associated with obesity, overweight, obesity/overweight, or dyslipidemia when requiring 2 or more studies. Additionally, results were robust, as they were not altered when removing 1 large outlier study or adjusting analyses for potential publication bias. Moreover, in the exploratory subgroup analyses, results were consistent across the examined moderator variables.

Among individuals later diagnosed with schizophrenia, studies have found cognitive impairment to be detectable early in development almost a decade before the onset of psychosis.⁴⁴ In fact, several studies have provided preliminary evidence of connections between cognitive impairment in people with schizophrenia and genes in the neurotrophic, cell adhesion, serotonergic, dopaminergic, and sodium channel systems.⁴⁵ Considering these preexisting cognitive deficits, the additional cognitive impairment contributed by CVD risk factors is likely clinically important. The documented association between the degree of cognitive performance and level of functioning in schizophrenia suggests that further decrements in cognitive performance would likely contribute to additional functional decline.⁴⁶ Therefore, addressing CVD risk factors could potentially help prevent further deterioration in cognition and improve functioning.

Cross-sectional and intervention studies have found that physical activity and fitness are associated with better cognitive performance,⁴⁷ greater gray and white matter volumes,⁴⁸ and higher levels of brain plasticity promoting neurotrophic factors.⁴⁹ Consistent with these cross-sectional results, a meta-analysis of 10 intervention studies found that exercise was associated with improved cognitive functioning among people with schizophrenia.⁵⁰ Exercise had greatest associations with the neurocognitive domains of working memory, attention and vigilance, and social cognition.⁵¹ It is also possible that poor cognitive functioning results in a less active/poorer lifestyle choices.

Cognitive remediation therapy has been proposed as being a promising intervention to improve cognitive and functional impairment in schizophrenia; however, a 2018 study found that its positive effects were only apparent for individuals with schizophrenia without MetS.⁵¹ Thus, the brain alterations associated with MetS may provide an environment that interferes with benefits of cognitive remediation therapy.

Clearly, the full range of pharmacological interventions and other lifestyle changes (eg, diet) for CVD risk factors are treatment options for individuals with schizophrenia who have hypertension, diabetes, obesity, hypertension, and/or dyslipidemia. However, the problem is that those with schizophrenia have low rates of access to such care.⁵² Houselessness, separation of medical from psychiatric care, and illness symptoms, including negative symptoms, contribute to the low rates of medical care for CVD in schizophrenia. Routine physical health monitoring and interventions to improve physical health in people with schizophrenia are urgently needed to maintain or restore physical and mental health and improve functional outcomes.^53,54 Collaborative care models that integrate behavioral and medical care are likely to be particularly useful for addressing CVD risk in those with schizophrenia.⁵⁵

Several limitations of the current meta-analysis need to be considered. First, analyzed data were not longitudinal and were limited to nonrandomized studies; therefore, the current findings may have been influenced by uncontrolled confounds within each study (eg, illness severity, physical exercise, diet, smoking). Additionally, patients may have been receiving medications (eg, anticholinergics, antipsychotics, benzodiazepines) that can affect cognitive functioning and/or CVD risk factors, thereby possibly influencing the observed associations between CVD risk factors and cognition. Furthermore, although meta-regression analyses found that the results were generally consistent across tested moderators, it is possible that between-study differences in factors, such as treatment setting, treatments, cultural backgrounds, and selection criteria, may have influenced the findings. Second, cognitive test batteries varied across studies, which might have affected the results. Third, CVD risk factors were only assessed categorically. Particularly, dyslipidemia is a heterogeneous category, and potential effects of total or low density-lipoprotein cholesterol or triglyceride levels could not be examined. Therefore, further studies should also investigate hypercholesterolemia, hypertriglyceridemia, insulin resistance, prediabetes, and prehypertension but especially also the effect of continuously measured CVD risk factors. Fourth, CVD risk factors were considered in isolation. Given known correlations among CVD risk factors, separating unique contributions of each as well as possible additive effects of multiple risk factors is another agenda for future research. Fifth, results were significantly heterogeneous for several of the examined CVD risk factors and cognitive domains, meaning that the effect sizes varied significantly across the meta-analyzed studies, which is likely due to differences regarding study setting, population characteristics, and using cognitive tests. Finally, the number of studies, particularly those focusing on certain specific CVD risk factors, was still relatively small, which limited the power especially for the analyses of cognitive subdomains and the exploratory moderator analyses.

Nevertheless, this is the largest and most comprehensive meta-analysis of the association between common CVD risk factors in people with schizophrenia and global cognition as well as specific cognitive domains to date and to our knowledge. Results of this meta-analysis highlight potential targets for intervention and research. Additional cross-sectional and longitudinal studies examining the effect of presence of and changes in continuous and categorical CVD risk factors in large samples of patients with schizophrenia should be conducted. Of particular interest are studies assessing the effect on cognitive performance via improving individual or combinations of CVD risk factors.

Findings of this meta-analysis confirm and extend previous results documenting a significant association between cognitive impairment in schizophrenia and CVD risk factors, particularly MetS, diabetes, and arterial hypertension. In individuals with CVD risk factors, cognitive impairment should be detected, and CVD risk factors and cognition should be managed. Additionally, accumulation or worsening of CVD risk factors should be prevented as much as possible, choosing psychotropic medications with low risk to worsen CVD risk factors and cognition, and promoting healthy lifestyle behaviors. Well-designed prospective studies are needed to further explore the magnitude, mechanisms, and reversibility of the association between CVD risk factors and cognitive impairment among individuals with schizophrenia.

Corresponding Author: Christoph U. Correll, MD, The Zucker Hillside Hospital, Psychiatry Research, Northwell Health, 75-59 263rd St, Glen Oaks, NY 11004 (ccorrell@northwell.edu).

Accepted for Publication: December 10, 2020.

Published Online: March 3, 2021. doi:10.1001/jamapsychiatry.2021.0015

Author Contributions: Drs Hagi and Correll had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hagi, Nosaka, Lee, Friedman, Han, Correll.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Hagi, Nosaka, Han.

Critical revision of the manuscript for important intellectual content: Hagi, Nosaka, Dickinson, Lindenmayer, Lee, Friedman, Boyer, Abdul-Rashid, Correll.

Statistical analysis: Hagi, Han.

Administrative, technical, or material support: Nosaka, Dickinson, Lindenmayer, Lee, Friedman, Boyer.

Supervision: Lee, Friedman, Correll.

Conflict of Interest Disclosures: Dr Lindenmayer reports grants from Roche, Alkermes, Astellas, Takeda, Lundbeck, Otsuka, Neurocrine, and, Intra-Cellular Therapies during the conduct of the study; consulting for Janssen, Lundbeck, and Newron; and has a patent to Multi Health Systems issued and with royalties paid. Dr Lee reports grants from Ministry of Health National Medical Research Council in Singapore during the conduct of the study. Dr Correll reports grants and personal fees from Janssen/Johnson & Johnson and Takeda outside the submitted work; personal fees from Acadia, Alkermes, Allergan, Angelini, Axsome, Boehringer Ingelheim, Gedeon Richter, Gerson Lehrman Group, Indivior, Intra-Cellular Therapies, LB Pharmaceuticals, Lundbeck, MedAvante-ProPhase, Medscape, Merck, Neurocrine, Noven, Otsuka, Pfizer, Recordati, Rovi, Sumitomo Dainippon, Sunovion, Supernus, Teva, and Servier outside the submitted work; provided expert testimony for Janssen, Otsuka, and Bristol Myers Squibb; served on a data safety monitoring board for Boehringer Ingelheim, Lundbeck, Rovi, Supernus, and Teva; is a stock option holder of LB Pharma; and received royalties from UpToDate. No other disclosures were reported.

Funding/Support: This meta-analysis was financed by Sumitomo Dainippon Pharma.

Role of the Funder/Sponsor: The funder had no influence on the content of the article or the decision to publish it, and the corresponding author had full access to all data in the meta-analysis and had final responsibility for the decision to submit it for publication.

Additional Contributions: Editorial and medical writing support was provided Edward Schweizer, MD, of Paladin Consulting Group and was funded by Sumitomo Dainippon Pharma.