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Clinical patterns of metabolic syndrome in young, clinically stable, olanzapine-exposed patients with schizophrenia

Annals of General Psychiatry volume 23, Article number: 46 (2024) Cite this article

Abstract

Background

Schizophrenia (SCZ) is a chronic, disabling mental illness with a high disease burden and is often comorbid with metabolic syndrome (MetS). The aim of this study was to determine the prevalence of MetS in young, clinically stable, olanzapine-exposed patients with SCZ and to explore predictive factors affecting the development and severity of MetS.

Methods

A total of 274 patients with SCZ who met the inclusion criteria were enrolled in this study, and their demographic data and general clinical information were collected. Concurrently, patients were assessed for psychopathology, illness severity, and antipsychotic drug–related adverse effects.

Results

The prevalence of MetS in the target population was 35.77%, and the MetS subtype of abdominal obesity + high triglycerides + low level of high-density lipoprotein cholesterol accounted for the majority of patients in the MetS subgroup. Binary logistic regression showed that body mass index (BMI), uric acid (UA), thyroid-stimulating hormone, and QT-c interval could significantly and positively predict the development of MetS. Multiple linear regression showed that olanzapine concentration, BMI, and UA could significantly and positively predict higher MetS scores.

Conclusion

This study reports the clinical patterns of MetS in young, clinically stable, olanzapine-exposed patients with SCZ and identifies the correlations influencing the development and severity of MetS. These findings could potentially be applied toward the prevention of and intervention in MetS.

Introduction

Schizophrenia (SCZ) is a severe, chronic, psychiatric syndrome that affects approximately 1% of the global population. It ranks among the top 10 causes of disability worldwide, imposing a high disease burden [1,2,3]. Studies have shown that the average life expectancy of patients with SCZ is reduced by 15 years [4]. Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular disease (CVD) [5] that often results in a common comorbidity in patients with SCZ due to the metabolic side effects of antipsychotics [6, 7]. CVD, as a long-term outcome of MetS, can significantly contribute to reduced life expectancy and premature death in patients with SCZ [8,9,10]. Thus, preventing and managing MetS is crucial in mitigating the reduction in life expectancy among patients with SCZ.

Atypical antipsychotics have been used as an unprecedented treatment option for the management of patients with SCZ [11]. They have been recommended by several major treatment guidelines as the treatment of choice for the first episodes and acute exacerbations of SCZ owing to their low incidence of adverse events and treatment discontinuation [12,13,14]. However, one of the most criticized aspects of this drug class is the onset of metabolic and cardiovascular side effects in patients [15, 16]. Olanzapine, as a representative atypical antipsychotics with high metabolic risk [16, 17], is notorious for inducing weight gain and disturbances in glucose and lipid metabolism through various pathways, such as the gut–brain axis and the induction of insulin resistance, among others [18, 19]. Nevertheless, national surveys on the prescribing patterns of antipsychotics in several countries have reported olanzapine as one of the most prescribed drugs [20,21,22]. It is therefore reasonable to assume that olanzapineexposed patients with SCZ may be experiencing the adverse effects of secondary MetS.

MetS is widely recognized as a metabolic disorder that is closely related to age, with its prevalence and risk increasing with advancing age [23, 24]. This relationship has been confirmed in individuals diagnosed with SCZ [25, 26]. Dysfunction of adipose-muscle crosstalk as well as mitochondrial dysfunction are key factors contributing to this correlation [27, 28]. Psychopathological symptoms can also interfere with metabolic parameters. For example, patients who are obese tend to exhibit lower levels of negative symptoms; moreover, the severity of positive symptoms is positively correlated with body mass index (BMI) and negatively correlated with fasting blood glucose (FBG) levels [29,30,31]. Increased activity of the hypothalamic–pituitary–adrenal axis, commonly seen in patients with SCZ and leading to cortisol hypersensitivity, is a key mechanism mediating the link between psychopathological symptoms and metabolic disorders [32, 33]. Therefore, young, stable patients with SCZ were chosen for this study to reduce the influence of age and psychopathology as confounding factors. The aim of this study was to explore the clinical patterns of MetS in young, clinically stable patients with SCZ exposed to olanzapine, and to provide actionable insights into the development of targeted interventions.

Methods

Subjects

This study recruited 274 olanzapine-exposed stable patients with SCZ who were treated as both outpatients and inpatients at the psychiatric department of Wuhan Mental Health Center from February 2020 to June 2023. Data sources and the flow chart of the study are shown in Fig. 1.

Fig. 1
figure 1

Flow diagram of participants in study

PANSS: Positive and Negative Syndrome Scale; MetS: Metabolic syndrome

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The inclusion criteria of this study were as follows:

  1. 1.

    Patients who met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for SCZ.

  2. 2.

    Patients who had a Positive and Negative Symptom Scale (PANSS) score of ≤ 70, with all 7 positive symptom items scored as ≤ 3.

  3. 3.

    Patients of both genders aged between 18 and 45 years who were of Chinese Han nationality.

  4. 4.

    Patients who received single oral olanzapine therapy at a dose of at least 5 mg/day.

  5. 5.

    Patients who had no antipsychotic dose or type adjustment for at least 8 weeks.

The exclusion criteria of this study were as follows:

  1. 1.

    Patients < 18 years or > 45 years of age.

  2. 2.

    Patients with current or past comorbidities of other psychiatric disorders such as bipolar disorder, major depressive disorder, intellectual developmental disorder, substance abuse disorder, or dependence.

  3. 3.

    Patients with severe comorbid physical and autoimmune disorders and persistent chronic infections and those receiving immunosuppressants.

  4. 4.

    Patients who had undergone any form of surgery within the last 6 months, or women who were pregnant or breastfeeding.

  5. 5.

    Patients with comorbid diabetes mellitus treated with exogenous insulin.

  6. 6.

    Patients with a metabolic disease that pre-dated the onset of psychosis.

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Wuhan Mental Health Center. Written informed consent was obtained from all participants, and they were given the option to withdraw from the study at any time.

Study design

This cross-sectional study was conducted to report the prevalence and associated factors of MetS in olanzapine-exposed, young (18–45 year-old) patients with SCZ who had stable psychiatric symptoms.

Clinical interview and assessment

Sociodemographic data of patients including race, gender, age, height and weight, marital status, and education were collected based on measurements and questionnaires. Additionally, 12-lead electrocardiography was performed for all patients.

The severity of psychopathological symptoms was assessed using the Positive and Negative Symptom Scale (PANSS), whereas the overall illness severity was evaluated using the Clinical Global Impression Scale - Severity of Illness (CGI-SI). Social functioning was measured using the Global Assessment of Functioning (GAF) Scale. The adverse effects of antipsychotics were assessed using the Treatment Emergent Symptom Scale (TESS). The severity of extrapyramidal reactions was evaluated using the Rating Scale for Extrapyramidal Side Effects (RESES), and the severity of akathisia to sit still was measured using the Barnes Akathisia Rating Scale (BARS).

Three attending psychiatrists completed assessments of the patients including using the scales listed above. Prior to the study, all evaluators were trained on the use of these scales and achieved consistency in assessment before commencing the study.

Measurement of physical and biochemical parameters

All patients were asked to fast after 8 pm the previous night. Venous blood collection and blood pressure (BD) measurements were performed between 6 am and 8 am the following morning. All collected blood samples were immediately sent to the biochemistry laboratory of the correctional medical facility and analyzed by 11 am. Biochemical parameters included FBG, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4). The two-dimensional liquid phase method was used to measure serum olanzapine concentration of patients (ranging from 20 to 80 ng/mL).

Diagnosis of MetS

The diagnostic criteria for MetS, as published by the Chinese Diabetes Society [34], include the presence of 3 or more of the following: (1) central obesity and/or abdominal obesity: waist circumference ≥ 90 cm for men and ≥ 85 cm for women; (2) hyperglycemia: FBG ≥ 6.10 mmol/L (110 mg/dL), or 2-h post-glycemic blood glucose levels ≥ 7.80 mmol/L (140 mg/dL), and/or those who have been diagnosed and treated for diabetes mellitus; (3) hypertension: BD ≥ 130/85 mm Hg and/or those who have been diagnosed and treated for hypertension; (4) fasting TG levels ≥ 1.7 mmol/L (150 mg/dL); and (5) fasting HDL-C levels < 1.04 mmol/L (40 mg/dL). Furthermore, those taking oral hypoglycemic drugs were labeled as meeting criterion (2), and those taking lipid-lowering drugs were labeled as meeting both criteria (4) and (5).

Severity of MetS

MetS was transformed into a continuous variable to effectively assess the severity of MetS in the study population. First, the mean arterial pressure (MAP) was calculated using the following formula: MAP = 1⁄3 × systolic blood pressure (SBP) + 2⁄3 × diastolic blood pressure (DBP). Next, based on the recent research results published by Shujuan Yang et al. for Chinese adults, MetS scores for Han Chinese patients with SCZ were calculated using the following formulae based on their gender [35]:

  1. 1)
    $$\eqalign{&{\rm Males:\, MetS\, score }= \:-2.9092\hspace{0.17em}+\:0.0262\:\times\:\:\text{W}\text{C}\hspace{0.17em}+\hspace{0.17em}0.3098\:\times\:\:\text{T}\text{G} \cr & -0.944\:\times\:\:\text{H}\text{D}\text{L}-\text{C}\:+\hspace{0.17em}0.0097\:\times\:\:\text{M}\text{A}\text{P}\hspace{0.17em}+\hspace{0.17em}0.0745\:\times\:\:\text{F}\text{B}\text{G}}$$
  2. 2)
    $$\eqalign{ &{\rm Females:\, MetS\, score} =\:-2.4981\:+\:0.0199\:\times\:\:\text{W}\text{C}\:+\:0.5218\:\times\:\:\text{T}\text{G}\:\cr & -0.8616\:\times\:\:\text{H}\text{D}\text{L}-\text{C}\:+\:0.0110\:\times\:\:\text{M}\text{A}\text{P}\:+\:0.1074\:\times\:\:\text{F}\text{B}\text{G}}$$

Data analysis

Categorical variables are reported as counts, and continuous variables are presented as means and standard deviations. Differences between continuous and categorical variables in the 2 subgroups with and without MetS were compared using t-tests for independent samples with Chi-square tests. First, pie charts were plotted to demonstrate the distribution of different subtypes of MetS. Next, binary logistic regression models were constructed to identify factors that could be used to predict the development of MetS using MetS as the outcome variable, and the variables that differed in univariate analyses as independent variables. Lastly, a multivariate linear regression model was constructed using MetS scores as the outcome variable and variables affecting the development of MetS identified in the binary logistic regression analyses as independent variables to identify factors that could be used to predict the severity of MetS. Data were analyzed using SPSS 27.0. p < 0.05 was considered to indicate statistical significance (two-tailed).

Results

Prevalence and composition ratio of MetS in the study population

Among the surveyed population, 98 participants, representing 35.77% of the total population, qualified for a diagnosis of MetS. Of these, 8 were being treated with oral hypoglycemics. Specifically, 5 participants were prescribed metformin at a median daily dose of 1.0 g, with an interquartile (IQR) range of 0.5 to 1.5 g. Three patients were receiving a combination of metformin and acarbose, with median daily doses of 1.0 g (IQR: 0.5 to 1.5 g) and 75 mg (IQR: 50 to 200 mg), respectively. Furthermore, 5 patients were on oral statin therapy. Among them, 4 patients were taking atorvastatin at a daily dose of 10 mg, whereas 1 patient was prescribed a daily dose of 5 mg simvastatin.

Figure 2A shows the distribution of MetS based on the number of components present, with 74 individuals (75.51%) showing 3 such components, and 24 (24.49%) exhibiting 4 components. Figure 2B details the distribution of various MetS component combinations, highlighting that the combination of Hyper-WC + Hyper-TG + Hypo-HDL-C was the most prevalent, constituting 57.14% of all cases of MetS.

Fig. 2
figure 2

Composition ratio for MetS. Figure 2A: Composition ratio based on the number of MetS abnormal components. Figure 2B: Composition ratios based on different combinations of abnormal components of MetS

Full size image

Between-group differences in patients with SCZ with and without MetS

Multiple clinical indicators were compared between patients in the MetS and nonMetS groups in the study population (Table 1). Patients in the MetS group demonstrated higher LDL-C (t = -2.80, p = 0.006), BMI (t = -8.08, p < 0.001), UA (t = -3.34, p = 0.001), TSH (t = -5.27, p < 0.001), and QT-c (t = -5.96, p < 0.001) values. Conversely, patients in the MetS group had lower levels of olanzapine (t = 2.69, p = 0.008), BUN (t = 6.16, p < 0.001), CRE (t = 2.37, p = 0.019), and FT3 (t = 2.85, p = 0.005). Additionally, patients in the MetS group showed more severe MetS scores and components compared with those in the non-MetS group.

Table 1 Sociodemographic and clinical parameters of included patients with and without MetS
Full size table

Clinical factors influencing the development of MetS

Binary logistic regression models (Backward: Wald) were used to identify factors to predict the development of MetS, and the results are shown in Table 2. MetS was used as the outcome variable, and the variables that differed in the univariate analyses (excluding MetS scores and components) were used as independent variables for the models. The results indicated that BMI (B = 0.24, p < 0.001, odds ratio [OR] = 1.27, 95% confidence interval [CI] = 1.16–1.39), UA (B = 0.01, p = 0.028, OR = 1.01, 95% CI = 1.00-1.01), TSH (B = 0.36, p < 0.001, OR = 1.44, 95% CI = 1.18–1.75), and QT-c (B = 0.03, p < 0.001, OR = 1.03, 95% CI = 1.01–1.04) were positive predictors of the development of MetS. Conversely, olanzapine concentration (B = -0.04, p = 0.002, OR = 0.97, 95% CI = 0.94–0.99) and BUN levels (B = -1.15, p < 0.001, OR = 0.32, 95% CI = 0.18–0.57) were negative predictors of the development of MetS.

Table 2 Factors influencing co-mets in all included patients: a binary logistic regression model
Full size table

Clinical factors affecting the severity of MetS

Multivariate linear regression models (backward) were constructed for the MetS subgroups to identify the correlates predicting the severity of MetS. The results, shown in Table 3, used the MetS score as the outcome variable and the factors predicting MetS development from the binary logistic regression model as independent variables. Olanzapine concentration (B = 0.01, t = 2.46, p = 0.016, 95% CI = 0.00-0.02), BMI (B = 0.03, t = 2.06, p = 0.042, 95% CI = 0.00-0.05), and UA (B = 0.00, t = 2.55, p = 0.012, 95% CI = 0.00–0.00) were positive predictors of the severity of MetS, whereas BUN (B = -0.15, t = -2.77, p = 0.025, 95% CI = -0.27- -0.02) was a negative predictor of the severity of MetS.

Table 3 Correlates affecting MetS scores: a multiple linear regression model
Full size table

Discussion

The prevalence of MetS and the clinical factors affecting the development and severity of MetS in olanzapine-exposed, stable, young patients with SCZ have been reported in this study after minimizing and controlling for potential factors affecting MetS, such as age, psychotic symptoms, and antipsychotic drugs.

The prevalence of MetS in the included population was 35.77%. The prescription of atypical antipsychotics, mainly olanzapine, is an important trigger for the development of medically induced MetS in patients with SCZ [36, 37]. This finding was further confirmed when comparing the prevalence of MetS in first-treatment SCZ patients (approximately 11-20%) with the prevalence after anti-psychotic drug exposure (up to 37–63%) [38, 39]. Clinical studies with small sample sizes on olanzapine that have been conducted in Serbia and South Korea have reported a prevalence of MetS of 34.4-39.0% [40, 41]. This finding is similar to that from our study. The composition ratio of MetS subtypes in the MetS population was analyzed, and the MetS diagnostic subtype of Hyper-WC (i.e., abdominal obesity) + Hyper-TG + Hypo-HDL-C was found to account for the majority (57.14%). However, most studies focus on the prevalence of MetS parameters [39, 41] and the results are generally consistent (Hyper-WC, Hyper-TG, and Hypo-HDL-C in the top 3 prevalence categories) and corroborate our findings to a certain extent.

When subgroup comparisons were made, the MetS population was worse not only in terms of MetS components but also in terms of clinical indicators such as increased BMI, elevated UA, and QT-c, as well as reduced thyroid function. Several studies have reported that patients with SCZ with comorbid MetS have significantly worse cognitive functioning [42], higher risk for cardiovascular disease [43], higher healthcare expenditure [44], and poorer quality of life than those without MetS [45]. Combined with the findings of our study, it would be reasonable to infer that patients with SCZ with comorbid MetS could experience additional potential somatic health risks.

Predictors of the development of MetS in stabilized patients with SCZ were the crucial elements analyzed in this study. BMI, UA, TSH, and QT-c were identified as risk factors for MetS development in a young population of patients with SCZ. BMI is a primary metabolic indicator of obesity that is frequently reported as a risk factor for MetS in patients with SCZ [46, 47]. A large clinical study has reported a 37% increase in the prevalence of MetS for each unit increase in BMI [48]. Furthermore, thyroid function plays a substantial role in the development of MetS [49]. A study compared thyroid function levels in patients with SCZ with and without MetS and found that FT3 and FT4 levels were notably elevated in those with MetS, maintaining a positive correlation with the syndrome [50]. Higher TSH levels in patients in the SCZ subgroup in our study were also found to be responsible for the development of MetS. Therefore, thyroid function plays a significant role in the development of MetS in patients with SCZ, and the negative impact of elevated TSH levels on MetS is particularly pronounced in young, stabilized individuals with SCZ. A study that focused on the association of UA with MetS derived conclusions that were similar to those reported in our study [51]. Numerous factors were found to influence the development of MetS, as a complex of multiple metabolic disorders, in both previous studies as well as ours. Although these factors have not been completely validated and identified, they continue to provide important leads for potential interventions to manage MetS.

The current customary dichotomous system to evaluate MetS does not assess its severity and is somewhat limited in its use in a clinical setting. Therefore, predictors of the severity of MetS were explored in the subgroup of comorbid MetS in the target population, and olanzapine concentration was found to significantly and positively predict the severity of this condition. Most studies have reported the dose-response relationship for olanzapine but have not focused on olanzapine concentrations or metabolic parameters (e.g., weight gain). The study findings are generally consistent in concluding that there is indeed some positive association [52, 53]. However, these findings do not provide direct evidence of olanzapine concentrations predicting the severity of MetS but rather only serve as supporting evidence. A high concentration of olanzapine can inhibit several genes involved in the regulation of mitochondrial function and also decrease the anti-inflammatory effects of the drug, ultimately leading to adverse metabolic effects [54, 55]. Therefore, it would be reasonable to assume that there is value in maintaining the minimum antipsychotic dose and concentration in patients to mitigate adverse metabolic effects.

Our study has several strengths. The inclusion criteria used in our study were stringent so that confounding factors such as age, psychopathological symptoms, and polypharmacy, which influence the development of MetS, could be minimized. On the other hand, our study also has some limitations that should be acknowledged. First, conclusions on causality could not be obtained in this crosssectional study. Second, patients using exogenous insulin were excluded, which may have reduced the reporting rate of MetS. Third, despite our best attempt to exclude metabolic disturbances that preceded the onset of psychosis, in the actual course of this study, patients only actively reported whether they had gained significant weight. There was a lack of valid reporting of other components of MetS. Fourth, although higher olanzapine levels were found to be a risk factor governing the severity of MetS, the levels of this antipsychotic drug in patients in the MetS group were lower than those in patients in the non-MetS group for univariate analysis. Whether this is due to a random event caused by an insufficient sample size is an aspect that requires further clarification. These shortcomings could be addressed by designing rigorous prospective studies.

Conclusions

The prevalence of MetS and associated clinical parameters were identified and analyzed in this study. Identification of these clinical characteristics could be useful in the development of MetS-targeted prevention and intervention strategies for patients with SCZ in a clinical setting.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

BD:

Blood pressure

BUN:

Blood urea nitrogen

BARS:

Barnes Akathisia Rating Scale

CGI-SI:

Clinical Global Impression Scale—Severity of Illness

CRE:

Blood creatinine

CVD:

Cardiovascular disease

DBP:

Diastolic blood pressure

DSM-5:

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

FBG:

Fasting blood glucose

FT3 :

Free triiodothyronine

FT4 :

Free tetraiodothyronine

GAF:

Global Assessment of Functioning scale

HDL-C:

High-density lipoprotein cholesterol

IQR:

Interquartile

LDL-C:

Low-density lipoprotein cholesterol

MAP:

Mean average pressure

MetS:

Metabolic syndrome

PANSS:

Positive and Negative Symptom Scales

QT-c:

QT-c interval

RESES:

Rating Scale for Extrapyramidal Side Effects

SBP:

Systolic blood pressure

SCZ:

Schizophrenia

TC:

Total cholesterol

TESS:

Treatment Emergent Symptom Scale

TG:

Triglycerides

TSH:

Thyroid stimulating hormone

UA:

Blood uric acid

WC:

Waist circumference

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Acknowledgements

We are grateful to all the medical staffs and patients in our study and to those who contributed to the diagnosis and clinical evaluation of the subjects.

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Authors and Affiliations

  1. Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China

    Jun Ma, Zhengyuan Huang & Gaohua Wang

  2. Department of Psychiatry, Wuhan Mental Health Center, Wuhan, China

    Lin Zhang

  3. Taikang center for life and medical sciences, Wuhan University, Wuhan, Hubei, China

    Gaohua Wang

  4. Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China

    Gaohua Wang

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  1. Jun Ma
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Contributions

Gaohua Wang and Jun Ma made substantial contributions to conception and design of the study. Jun Ma drafted the manuscript. Lin Zhang had polished and re-edited the language and logic of the article. Zhengyuan Huang was responsible for setting up and complement and modify the contents of the manuscript. Gaohua Wang gave final approval of the version to be published.

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Correspondence to Gaohua Wang.

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The ethics committees of the Wuhan mental health center reviewed and approved this study. All subject guardians knew about this study and signed informed consent. All procedures carried out in studies conformed to the 1964 Helsinki Declaration and its subsequent amendments or similar ethical standards.

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Ma, J., Zhang, L., Huang, Z. et al. Clinical patterns of metabolic syndrome in young, clinically stable, olanzapine-exposed patients with schizophrenia. Ann Gen Psychiatry 23, 46 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12991-024-00532-y

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12991-024-00532-y

Keywords

Annals of General Psychiatry

ISSN: 1744-859X