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Prevalence of and factors associated with overweight and obesity in patients with severe mental disorders in Shenzhen: results from the urban Chinese population

Published online by Cambridge University Press:  07 November 2024

Wei Shan
Affiliation:
Affiliated Mental Health Center, Southern University of Science and Technology, Shenzhen, Guangdong, China Department of Public Health, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
Zhijian Zhou
Affiliation:
Affiliated Mental Health Center, Southern University of Science and Technology, Shenzhen, Guangdong, China Department of Public Health, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
Guojun Wang
Affiliation:
Affiliated Mental Health Center, Southern University of Science and Technology, Shenzhen, Guangdong, China Department of Public Health, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
Xiaodong Peng*
Affiliation:
Department of Public Health, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
*
*Corresponding author: Email [email protected]
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Abstract

Objective:

To determine the prevalence of overweight and obesity in patients with severe mental disorders (SMD) and the factors associated with their socio-demographic and disease characteristics in a cross-sectional population-based study.

Design:

This analysis examined the prevalence of overweight and obesity in 14 868 managed SMD patients in an urban area of Shenzhen city based on data from the health information monitoring system in 2021. Multivariate logistic regression were used to identify the factors associated with the prevalence of overweight and obesity in patients with SMD.

Setting:

China.

Participants:

14 868 patients with SMD.

Results:

The prevalence of overweight and obesity in patients with SMD in this study was 32·6 % and 16·1 %, respectively. In multivariate analysis, married status, Shenzhen household registration, management durations of 5–10 years and >10 years, participation in family physician services, taking clozapine or aripiprazole, FPG > 6·1 mmol/l, hypertension, TC ≥ 5·2 mmol/l, TG ≥ 1·7 mmol/l, and more frequent follow-ups in the past year were associated with higher odds of overweight and obesity. Compared to their respective reference categories, living with parents, spouse and children, taking risperidone, aripiprazole, amisulpride and perphenazine, FPG > 6·1 mmol/l, hypertension, TC ≥ 5·2 mmol/l, TG ≥ 1·7 mmol/l, and more frequent follow-ups in the past year were associated with higher odds of obesity.

Conclusion:

We observed a high prevalence of overweight and obesity in patients with SMD in this study. The findings highlight the need for integrated management of overweight and obesity risk factors among patients with SMD.

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Nutrition Society

In China, severe mental disorders (SMD) are considered to be mental disorders with severe symptoms of mental illness that result in serious impairment of the patient’s social adaptation and other functions, incomplete recognition of his or her own health condition or objective reality, or inability to handle his or her own affairs, including schizophrenia, schizoaffective disorder, paranoid psychosis, bipolar disorder, epilepsy-induced mental disorder, and intellectual disability concomitant with psychotic disorder(1). The burden of disease from SMD continues to rise and is associated with poorer health outcomes and increased mortality, in addition to contributing to most of the prevalence of mental health disability(2). The average death rate of patients with SMD is 2–3 times higher than that of the general population, and their life expectancy is shortened by 10–20 years(Reference Liu, Daumit and Dua3). Although people with SMD are at higher risk of death from unnatural causes than the general population, the majority of deaths among people with SMD are due to physical health conditions, both noncommunicable and communicable(4). In addition, patients with SMD are more likely to have lifestyles that include risk factors for noncommunicable diseases, such as lack of physical activity and unhealthy diet(Reference Correll, Solmi and Veronese5).

Obesity and overweight are major risk factors for chronic noncommunicable diseases. In 2019, the number of deaths due to chronic noncommunicable diseases in China accounted for 88·5 % of the total deaths, and the base number of patients with chronic diseases is still expanding(6,Reference Dhawan and Sharma7) . Obesity is considered an independent risk factor for CVDs. Not only does it greatly increase the risk of CVDs such as hypertension, CHD, myocardial infarction, angina pectoris and stroke, but it is also associated with increased mortality from CVDs(Reference Powell-Wiley, Poirier and Burke8). Patients with SMD are more severely affected by overweight and obesity, and the increased rate of obesity is higher than that of the general population(Reference Holt and Peveler9). A survey in European and American countries reported that 26–55 % of SMD patients were obese, and the obesity rate of schizophrenia patients was approximately 4·3 times that of the general population(Reference Janssen, McGinty and Azrin10,Reference Gurpegui, Martínez-Ortega and Gutiérrez-Rojas11) . A recent cross-sectional study in China reported that the obesity rate of schizophrenia was 16·4–20·9 %(Reference Wang, Zhang and Yang12,Reference Li, Du and Zhang13) . which was higher than that of the general population(14). There is growing evidence that obesity and overweight lead to an increased incidence of CVD, which in turn is the most common cause of SMD premature natural death(Reference Brown, Kim and Mitchell15). Therefore, obesity and overweight pose a greater potential threat to the physical health of patients with SMD.

However, SMD for a variety of reasons has led to high obesity and overweight rates. Growing evidence has reported an association between the use of second-generation antipsychotics (SGAs) and an increased risk of obesity, weight gain, type 2 diabetes, and increased risk of dyslipidemia(Reference Leucht, Cipriani and Spineli16). Lack of exercise(Reference Vancampfort, Probst and Sweers17), a sedentary lifestyle(Reference Janney, Ganguli and Tang18), poor diet(Reference Sugawara, Yasui-Furukori and Sato19), and adverse life events in childhood are also important factors affecting obesity and overweight in patients with SMD(Reference Guenzel, Houfek and Watanabe-Galloway20). Other important factors include the genetic susceptibility of patients and several psychosocial and socio-economic risk factors, such as being female(Reference Hui, Ye and Tang21), older and having a lower educational level(Reference Subramaniam, Lam and Guo22). In recent decades, despite a series of governmental policies to improve the health status of patients with SMD living in the community, the overall quality of life remains relatively low(Reference Peng, Wang and Bi23).

Most studies have focused on schizophrenia, but there are relatively few reports on bipolar disorder, schizoaffective disorder, paranoid psychosis, epileptic-induced mental disorder, and intellectual disabilities that are associated with mental disorders(Reference Nielsen, Banner and Jensen24). In China, few large-sample studies have been reported on the physical health of community-dwelling patients with SMD who were managed by a health monitoring information system. Considering that patients with SMD return to the community after hospital discharge or outpatient visits, studying the physical health of such populations will be of great public health significance for targeted interventions and adjustment of health management strategies. The purpose of the present study was to examine the prevalence of overweight and obesity in patients with SMD and to identify the association between overweight and obesity and socio-demographic and clinical factors in these patients.

Methods

Data sources

All data for this study were obtained from the Shenzhen Mental Health Prevention and Control Management System. Shenzhen is a coastal city located in the southern part of China’s Guangdong Province, with one of the country’s leading economies, a resident population of 17,681,600 (13·9 % of Guangdong’s population), and a highly mobile population. Based on China’s household registration system, the ‘Hukou’ policy, non-Shenzhen household registration or the ‘floating population’ are defined as people who leave their registered residence areas (e.g. cities, towns and villages) to engage in various jobs in non-residence areas. Coastal urban cities, such as Shenzhen in the Pearl River Delta area, are the major destinations of internal migration. By the end of December 2021, a total of 32 349 patients with severe mental disorders were registered in the Shenzhen Mental Health Prevention and Control Management System. There were 31 580 patients (97·62 %) who accepted the community health management follow-up service, 448 patients (1·38 %) refused services, and 321 patients (0·99 %) missed visits. This information monitoring system was established and started to operate in 2006. The data system is managed by the professional staff of Shenzhen Mental Health Center.

First, patients with SMD were screened and reported in two ways. One involved the screening and assessment of suspected patients by community workers based on the Abnormal Behavior Inventory and inviting psychiatrists to confirm the diagnosis(1). The other was an assessment and diagnosis by psychiatrists in medical institutions (psychiatric and nonpsychiatric hospitals) based on the International Statistical Classification of Diseases and Related Health Problems (10th edition) (ICD-10)(25). When a diagnosis of SMD (i.e. schizophrenia, bipolar disorder, schizoaffective disorder, paranoid psychosis, epilepsy-induced mental disorder, and intellectual disability associated with mental disorder) was confirmed, the community health management program for SMD was explained(Reference Zhang and Ma26). After the patient and guardian fully understood the program, they voluntarily participated and signed an informed consent form, and then the psychiatrist registered the report in the Shenzhen Mental Health Prevention and Control Management System, and the relevant information was transferred to the primary mental health institution in the area where the patient lived.

Second, health management cases for patients diagnosed with SMD were established. Patients were contacted by general practitioners from primary mental health institutions after uniform training and assessment to inform them about community health management and mental health policies. Standardised tools were used to collect socio-demographic information and assess the patient’s mental status(1). After the individual health management case was established, the patient was formally integrated into the community health management project.

Third, follow-up was conducted. General practitioners in primary mental health institutions start the first follow-up assessment of patients when they establish community health records. The follow-up assessment includes the patient’s psychiatric symptoms, violence risk, insight, medication compliance, adverse drug reactions, social functioning and physical health status. After each follow-up assessment of the patients by the general practitioners in primary mental health institutions, targeted health interventions are carried out according to the conditions and needs of the patients, such as medication adjustment, management of medication side effects, guidance on psychiatric rehabilitation training, psychological counselling, health education and care skills for family members, guidance on healthy lifestyles, publicising the government’s mental health support policy, etc. The frequency of follow-up depends on the patient’s disease condition. The criteria for disease stable are the absence of obvious psychiatric symptoms, no risk of violence, no adverse drug reactions or serious physical illness, good insight and social functioning. The criterion for a basically stable condition is the presence of risky behaviour or psychiatric symptoms, with at least one aspect of insight or social functioning being poor. The criteria for instability are the presence of overtly risky behaviour or psychiatric symptoms, lack of insight, serious medication side effects, or serious physical illness. During the follow-up period, patients could apply to enter or leave the cohort at any time. The study was approved by the Medical Ethics Review Committee of Shenzhen Kangning Hospital.

Study measures

We used BMI as the main outcome measure in the study of physical health management of SMD. Patients with SMD who were registered in the Shenzhen Mental Health Prevention and Control Management System not only received regular mental health assessments and health advice but also received an annual health check-up if desired, all of which were free of charge(27). When a patient makes an appointment for a physical exam at a primary mental health facility, the general practitioner measures height and weight, and the patient wears light clothing and no shoes. BMI was defined as the weight (kg) divided by the square of height (m2). Overweight and obesity are defined according to the cutoff values of BMI within the Chinese population(Reference Wu, Huxley and Li28). A BMI of <23·9 kg/m2 is considered normal weight or underweight while BMI ≥ 24–27·9 kg/m2 is considered overweight and BMI ≥ 28 kg/m2 is considered obese.

Statistical analysis

Statistical analyses were performed using SPSS version 21·0 and R version 3·5·1. We used the mean or median, as appropriate, to summarise the measured variables, while the count variables are reported as frequencies and percentages. The normality of continuous variables was tested with the Shapiro‒Wilk test. The measurement data were analysed using an independent t test and Wilcoxon rank test according to the normal distribution test, and the chi-square test was used for count data. One-way binary logistic regression was used to assess OR and 95 % confidence intervals for factors associated with overweight (BMI ≥ 24 kg/m2 v. BMI < 24 kg/m2) and obesity (BMI ≥ 28 kg/m2 v. BMI < 28 kg/m2). Variables that were statistically significant in the univariate analysis were again entered into a multivariate logistic regression model. Forest plots were generated using the R package ‘forest plot’. A two-sided α = 0·05 was used as the test level, and P < 0·05 was considered to indicate statistical significance.

Results

General characteristics of the study population

The study included 14 868 patients with SMD: 6932 males and 7936 females (Table 1). The mean values of age, duration of untreated psychosis (DUP), and number of years in the register were 41·79 years, 0·39 years, and 5·44 years, respectively; 8126 patients (54·7 %) were married, 7356 (49·5 %) were unemployed, 3340 (22·5 %) were in primary school and below, 1193 (8·0 %) were living alone, 8881 (8·0 %) were nonlocal household members (59·7 %), 9388 (63·1 %) were without medical insurance, and 12 006 (80·8 %) had a nonpoor family economy; the disease diagnosis was most often schizophrenia (57·2 %), followed by bipolar disorder (24·7 %); and 12 037 (81·0 %) participated in family physician services. There were 4841 (32·6 %) patients with overweight and 2396 (16·1 %) patients with obesity; the mean values of fasting blood glucose, TC and TG were 5·30 mmol/l, 4·74 mmol/l and 1·44 mmol/l, respectively. The mean values of fasting glucose, TC, and TG were 5·30 mmol/l, 4·74 mmol/l, and 1·44 mmol/l, respectively. SBP and DBP were 118·59 mmHg and 75·27 mmHg, respectively (Table 1). There were significant differences between men and women in age, years of enrollment management, marriage, employment status, level of education, living status, disease diagnosis, participation in family physician services, fasting glucose, TC, TG, BMI, SBP, and DBP.

Table 1 Sociodemographic and physiological characteristics of the study population from 2006 to 2021

* Mean (sd).

Median (interquartile range).

DUP, duration of untreated psychosis; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Logistic regression analysis of overweight and obesity

Table 2 shows the univariate logistic regression analysis for obesity or overweight. We found that age, marriage, education level, living status, type of household registration, schizophrenia, intellectual disability concomitant with psychotic disorder, DUP, years of enrollment management, participation in family physician services, olanzapine, clozapine, risperidone, aripiprazole, amisulpride, FPG, hypertension, TC, TGs, and number of follow-ups in the past year were associated with overweight and obesity. Therefore, we included these factors in a multivariate logistic regression model for analysis.

Table 2 Results of univariate logistic regression analysis for BMI

DUP, duration of untreated psychosis; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride.

Multivariate logistic regression analysis showed (Fig. 1) that individuals aged ≥ 60 years, with education levels of high school and college and above, and intellectual disability concomitant with mental disorders had lower odds of being overweight and obese. Being married, Shenzhen household registration, number of years of management 5–10 years, number of years of management >10 years, participation in family physician services, clozapine, aripiprazole, FPG, hypertension, TC, TGs, and number of follow-ups in the past year were associated with higher odds of overweight and obesity.

Fig. 1 Results of multiple logistic regression analysis for overweight and obesity. FPG, fasting plasma glucose; TC, serum total cholesterol; TG, triglyceride

Logistic regression analysis of obesity

Table 2 shows the univariate logistic regression analysis for obesity. We found that marriage, employment status, level of education, living status, medical insurance, family financial status, schizophrenia, bipolar disorder, number of years in the register, participation in family physician services, clozapine, risperidone, aripiprazole, amisulpride, perphenazine, FPG, hypertension, TC, TGs, and number of follow-ups in the past year were associated with obesity. Therefore, we included these factors in a multivariate logistic regression model for analysis.

Multivariate logistic regression analysis showed that education levels of high school, college, and above and schizophrenia were associated with lower odds of obesity. Living status with parents, spouse and children, risperidone, aripiprazole, amisulpride, perphenazine, FPG, hypertension, TC, TGs, and number of follow-ups in the past year were associated with higher odds of obesity (Fig. 2).

Fig. 2 Results of multiple logistic regression analysis for obesity. FPG, fasting plasma glucose; TC, serum total cholesterol; TG, triglyceride

Discussion

This is the first community-based study to explore the physical health of patients with SMD in China. The results of the study provide important scientific guidance for improving community management strategies and measures for SMD and improving the physical health of patients. In this study, we found that approximately half of the participants were overweight and obese. Married status, Shenzhen household registration, management of 5–10 years, management of > 10 years, participation in family physician services, clozapine, aripiprazole, FPG, hypertension, TC, TG, and number of follow-ups in the past year were associated with a higher risk of overweight and obesity. Age ≥ 60, high school educational status, college and above educational status, and intellectual disability concomitant with mental disorders were associated with a lower likelihood of being overweight or obese. All these data suggest that obesity in patients with SMD has become a serious public health burden for the Chinese population and needs more attention and intervention.

We found that the prevalence of obesity in patients with SMD in Shenzhen was close to the prevalence of obesity in the general population but higher than the average of the general population in large cities(Reference Pan, Wang and Pan29). Consistent with our findings, a meta-analysis concluded that people with severe mental illness had higher odds of obesity than the general population in all regions(Reference Afzal, Siddiqi and Ahmad30). These findings are also similar to those of an obesity survey of schizophrenia patients in China (16 %–20 %)(Reference Wang, Zhang and Yang12,Reference Tian, Liu and Wang31) . Meanwhile, we found that the prevalence of overweight in patients with SMD in Shenzhen was lower than that of the general population and that of the general population in large cities but was close to the global findings on the overweight rate of patients with SMD(Reference Pan, Wang and Pan29,Reference Afzal, Siddiqi and Ahmad30) . A study from 39 034 rural adults showed that the overweight rate in the rural population was 34·5 % and the obesity rate was 16·8 %, both of which were higher than those observed in our study(Reference Liu, Wu and Mao32). The possible reasons for this phenomenon are as follows. First, our research population is concentrated in Shenzhen, a city with a 100 % urbanisation rate in China, and its economy is relatively developed. Residents have more labor opportunities and exercise space, so they can exercise more. Second, urban residents have access to more medical resources and more health knowledge. Since 2010, the Chinese government has carried out a number of national prevention and control programs for obesity(Reference Wang, Zhou and Zhao33). The results showed that interventions tend to be effectively implemented in urban areas but that they have little effect in rural areas(Reference Pfledderer, Burns and Byun34). Third, population migration leads to a change in diet structure, and Shenzhen is dominated by an internal migrant population. Related studies have shown(Reference Shu, Zheng and Zhang35) that changes in diet structure are more likely to lead to obesity, especially changes toward animal-based diets, and urban residents are more likely to take in more protein. Fourth, patients with SMD have a sense of stigma(Reference Hampson, Watt and Hicks36), and the stress caused by low income, unemployment and discrimination can not only affect their recovery and life but can also result in obesity(Reference Tomiyama37).

Although antipsychotic drugs can better treat psychiatric disorders, they can also cause a number of problems, such as metabolic disorders(Reference Schneider-Thoma, Chalkou and Dörries38). In our study, patients with SMD treated with clozapine and aripiprazole were more likely to be overweight and obese, while patients with SMD treated with perphenazine, risperidone, aripiprazole and amisulpride were more likely to have obesity. This finding is consistent with most research reports that antipsychotics might reduce caloric utilisation in the body, leading to an increased appetite in patients who, in turn, lack sufficient exercise, leading to weight gain(Reference Schneider-Thoma, Chalkou and Dörries38,Reference Zhang, Lian and He39) . Previous studies(Reference Powell-Wiley, Poirier and Burke8,Reference Gronholm, Chowdhary and Barbui40) have reported that obesity and overweight can lead to vascular aging, dyslipidemia and carotid atherosclerosis, impaired glucose tolerance, and abnormal blood glucose, increasing the risk of hypertension(Reference Harvey, Montezano and Touyz41), coronary atherosclerotic heart disease(Reference Roos, Quax and Jukema42), type 2 diabetes(Reference Vancampfort, Stubbs and Mitchell43), stroke and many different cancers(Reference Vucenik and Stains44). In this study, we found an association between overweight and obesity and hypertension, TC, TG(Reference Tian, Liu and Wang31), and FPG(Reference Powell-Wiley, Poirier and Burke8) in patients with SMD, suggesting an increased risk of CVD.

Interestingly, not all patients with SMD are prone to obesity and overweight. Our study found that intellectual disability concomitant with mental disorders was associated with a lower risk of obesity and overweight, while schizophrenia was associated with a lower risk of obesity. Most patients with intellectual disabilities have difficulty in self-care and require care from others, and those with severe disabilities can become completely disabled, with refusal to eat and occasional self-vomiting likely to be the main causes of this phenomenon(Reference Hove45), which is more likely to occur in patients with intellectual disabilities concomitant with mental disorders. More than half of the patients with SMD participating in this study were community-dwelling patients with schizophrenia, and most of them were nonlocal residents who needed to participate in work to pay for various living expenses and medical expenses(Reference Younger46).

However, physical health management in community mental health services needs more attention in the future. Our study found that patients with SMD who participated in family physician services, had longer years of management, and had more follow-up visits were more likely to be overweight and obese. The possible reason is that community mental health services in China have always been focused on providing better mental health services and rehabilitation services for patients with SMD while paying less attention to their physical health and lack of relevant physical health management(Reference Liang, Mays and Hwang47). At present, family physician services are in the initial stage in China, and there are still many deficiencies. Meanwhile, there is a large gap in the quality assurance of primary health care in China, and the education and training of primary health care practitioners are poor, which requires a more complete employee training system and higher training quality(Reference Li, Krumholz and Yip48).

In addition, our study identified several risk factors for obesity in Chinese patients with SMD. Married patients with SMD were more likely to have obesity and overweight. Studies have shown that having a partner is more likely to lead to overweight and obesity(Reference Cobb, McAdams-DeMarco and Gudzune49). Men and women are more than twice as likely to become obese after marriage compared with single people, and obesity in one spouse will increase the risk of obesity in the other. At the same time, we also found that patients with Shenzhen household registration who lived with parents, spouses, or children were more prone to overweight and obesity. The possible reason for this phenomenon is that patients with registered residence in Shenzhen have better treatment in social welfare, medical insurance, housing and other aspects than those without registered residence(Reference Wu, Huxley and Li28). Patients living with parents, spouses or children may receive better care and easily form a sedentary and eating lifestyle, which leads to obesity and overweight. Furthermore, in our study, patients with SMD with higher education were less likely to be overweight and obese than those with lower education. Previous studies have confirmed that people with low education are more likely to be obese and overweight(Reference Ogden, Fakhouri and Carroll50). People’s education will affect their way of life. People with higher levels of education may pay more attention to maintaining physical fitness and a healthy diet and are more willing to leave the home.

The advantage of our study is that we provided population-based data on the physical health of patients with SMD in Chinese communities. However, there are still several limitations in this study. First, this study has a cross-sectional design, and the patients with severe mental disorders involved in this study were all receiving community mental health services, so more epidemiological investigations with large samples are needed to obtain more representative research results. Second, there are no variable data in the study, such as exercise habits, eating habits, lifestyle habits, genomics data, use of other antipsychotics and other diseases and their drug treatments (such as CVD, diabetes, glucose-lowering drugs, and antihypertensive drugs), which are considered to be potential risk factors for an increased BMI.

Conclusions

In summary, we reported a high prevalence of overweight and obesity in patients with SMD in the community, and their physical health was shown to be often neglected. Our study provides insights into interventions for controllable risk factors for overweight and obesity in Chinese patients with SMD. Research on the physical health of this population will have great public health significance for targeted interventions and adjustment of health management strategies, and more efforts should be made to improve the overall management of people with SMD in urban communities.

Acknowledgements

The authors would like to thank the general practitioners from primary mental health institutions and social worker who assisted in data collection. The authors would also like to thank the patients for participating in the study.

Financial support

This study was supported by the Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No. SZGSP013) and the Shenzhen Key Medical Discipline Construction Fund (No. SZXK072).

Conflict of interest

There are no conflicts of interest.

Authorship

W.S., and X.P. contributed to formulating the research question. W.S., Z.Z., and X.P. contributed to designing the study. W.S., Z.Z., and G.W. contributedto data collection.

W.S. and G.W. contributed to data analysis. W.S. and X.P. are responsible for writing the draft manuscript. G.W. and X.P. revised the manuscript. All authors contributed to interpreting the results, drafting the manuscript, and approving the final manuscript.

Ethics of human subject participation

All procedures performed in this study were in accordance with the ethical standards of the institutional, national and international research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study protocol was approved by the Human Ethics Committee of Shenzhen Kangning Hospital (NO. KN-2020-04). All the respondents who participated in the study signed informed consent forms before the investigation.

References

National Health Commission of the People’s Republic of China (2018) The National Health Commission Issued a Notice on the Management and Treatment of Severe Mental Disorders Notice on Work Specifications. Beijing: National Health Commission of the People’s Republic of China.Google Scholar
GBD 2016 DALYs and HALE Collaborators (2017) Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global burden of disease study 2016. Lancet 390, 12601344.CrossRefGoogle Scholar
Liu, NH, Daumit, GL, Dua, T et al. (2017) Excess mortality in persons with severe mental disorders: a multilevel intervention framework and priorities for clinical practice, policy and research agendas. World Psychiatry 16, 3040.CrossRefGoogle ScholarPubMed
World Health Organization (2018) Management of Physical Health Conditions in Adults with Severe Mental Disorders. Geneva: World Health Organization Google Scholar
Correll, CU, Solmi, M, Veronese, N et al. (2017) Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls. World Psychiatry 1, 163180.CrossRefGoogle Scholar
The Central People’s Government of the People’s Republic of China (2020) The State Council Information Office Held a Press Conference on the “Report on the Nutrition and Chronic Disease Status of Chinese Residents (2020)”. Beijing: The Central People’s Government of the People’s Republic of China.Google Scholar
Dhawan, D & Sharma, S (2020) Abdominal obesity, adipokines and non-communicable diseases. J Steroid Biochem Mol Biol 203, 105737.CrossRefGoogle ScholarPubMed
Powell-Wiley, TM, Poirier, P, Burke, LE et al. (2021) Obesity and cardiovascular disease: a scientific statement from the American heart association. Circulation 14, e984e1010.Google Scholar
Holt, RI & Peveler, RC (2009) Obesity, serious mental illness and antipsychotic drugs. Diabetes Obes Metab 11, 665679.CrossRefGoogle ScholarPubMed
Janssen, EM, McGinty, EE, Azrin, ST et al. (2015) Review of the evidence: prevalence of medical conditions in the United States population with serious mental illness. Gen Hosp Psychiatry 37, 199222.CrossRefGoogle ScholarPubMed
Gurpegui, M, Martínez-Ortega, JM, Gutiérrez-Rojas, L et al. (2012) Overweight and obesity in patients with bipolar disorder or schizophrenia compared with a non-psychiatric sample. Prog Neuropsychopharmacol Biol Psychiatry 3, 169175.CrossRefGoogle Scholar
Wang, J, Zhang, Y, Yang, Y et al. (2021) The prevalence and independent influencing factors of obesity and underweight in patients with schizophrenia: a multicentre cross-sectional study. Eat Weight Disord 26, 13651374.CrossRefGoogle ScholarPubMed
Li, Q, Du, X, Zhang, Y et al. (2017) The prevalence, risk factors and clinical correlates of obesity in Chinese patients with schizophrenia. Psychiatry Res 251, 131136.CrossRefGoogle ScholarPubMed
World Health Organization (2021) Obesity and Overweight. Geneva: WHO.Google Scholar
Brown, S, Kim, M, Mitchell, C et al. (2010) Twenty-five year mortality of a community cohort with schizophrenia. Br J Psychiatry 196, 116121.CrossRefGoogle ScholarPubMed
Leucht, S, Cipriani, A, Spineli, L et al. (2013) Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 38, 951962.CrossRefGoogle Scholar
Vancampfort, D, Probst, M, Sweers, K et al. (2011) Relationships between obesity, functional exercise capacity, physical activity participation and physical self-perception in people with schizophrenia. Acta Psychiatr Scand 12, 423430.CrossRefGoogle Scholar
Janney, CA, Ganguli, R, Tang, G et al. (2015) Physical activity and sedentary behavior measured objectively and subjectively in overweight and obese adults with schizophrenia or schizoaffective disorders. J Clin Psychiatry 76, e127784.CrossRefGoogle ScholarPubMed
Sugawara, N, Yasui-Furukori, N, Sato, Y et al. (2014) Dietary patterns are associated with obesity in Japanese patients with schizophrenia. BMC Psychiatry 14, 184.CrossRefGoogle ScholarPubMed
Guenzel, N, Houfek, J & Watanabe-Galloway, S (2016) Adverse events in childhood as a risk factor for elevated BMI among people with schizophrenia and bipolar disorder. Issues Ment Health Nurs 37, 829838.CrossRefGoogle ScholarPubMed
Hui, L, Ye, M, Tang, W et al. (2015) Obesity correlates with fewer symptoms in schizophrenia treated with long-term clozapine: gender difference. Psychiatry Res 225, 741742.CrossRefGoogle ScholarPubMed
Subramaniam, M, Lam, M, Guo, ME et al. (2014) Body mass index, obesity, and psychopathology in patients with schizophrenia. J Clin Psychopharmacol 3, 4046.CrossRefGoogle Scholar
Peng, X, Wang, S, Bi, J et al. (2021) Gender differences in socio-demographics, clinical characteristic and quality of life in patients with schizophrenia: a community-based study in Shenzhen. Asia Pac Psychiatry 1, e12446.CrossRefGoogle Scholar
Nielsen, RE, Banner, J & Jensen, SE (2021) Cardiovascular disease in patients with severe mental illness. Nat Rev Cardiol 1, 136145.CrossRefGoogle Scholar
Pan American Health Organization (1997) Implementation of the international statistical classification of diseases and related health problems, tenth revision (ICD-10). Epidemiological Bull 18, 14.Google Scholar
Zhang, W & Ma, N (2017) China’s national comprehensive management pilot project for mental health. BJPsych Int 14, 4446.CrossRefGoogle ScholarPubMed
National Health Commission of the People’s Republic of China (2022) Notice of the National Health and Family Planning Commission on the issuance of the “National Basic Public Health Service Standards (Third Edition)”. Beijing: National Health Commission of the People’s Republic of China.Google Scholar
Wu, Y, Huxley, R, Li, L et al. (2008) Prevalence, awareness, treatment, and control of hypertension in China: data from the China national nutrition and health survey 2002. Circulation 118, 26792686.CrossRefGoogle Scholar
Pan, XF, Wang, L & Pan, A (2021) Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol 9, 373392.CrossRefGoogle ScholarPubMed
Afzal, M, Siddiqi, N, Ahmad, B et al. (2021) Prevalence of overweight and obesity in people with severe mental illness: systematic review and meta-analysis. Front Endocrinol (Lausanne) 12, 769309.CrossRefGoogle ScholarPubMed
Tian, Y, Liu, D, Wang, D et al. (2020) Obesity in Chinese patients with chronic schizophrenia: prevalence, clinical correlates and relationship with cognitive deficits. Schizophr Res 215, 270276.CrossRefGoogle ScholarPubMed
Liu, X, Wu, W, Mao, Z et al. (2018) Prevalence and influencing factors of overweight and obesity in a Chinese rural population: the Henan rural cohort study. Sci Rep 8, 13101.CrossRefGoogle Scholar
Wang, L, Zhou, B, Zhao, Z et al. (2021) Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004–18. Lancet 398, 5363.CrossRefGoogle Scholar
Pfledderer, CD, Burns, RD, Byun, W et al. (2021) School-based physical activity interventions in rural and urban/suburban communities: a systematic review and meta-analysis. Obes Rev 2, e13265.CrossRefGoogle Scholar
Shu, L, Zheng, PF, Zhang, XY et al. (2015) Association between dietary patterns and the indicators of obesity among chinese: a cross-sectional study. Nutrients 7, 79958009.CrossRefGoogle ScholarPubMed
Hampson, ME, Watt, BD & Hicks, RE (2020) Impacts of stigma and discrimination in the workplace on people living with psychosis. BMC Psychiatry 20, 288.CrossRefGoogle ScholarPubMed
Tomiyama, AJ (2019) Stress and obesity. Annu Rev Psychol 70, 703718.CrossRefGoogle ScholarPubMed
Schneider-Thoma, J, Chalkou, K, Dörries, C et al. (2022) Comparative efficacy and tolerability of 32 oral and long-acting injectable antipsychotics for the maintenance treatment of adults with schizophrenia: a systematic review and network meta-analysis. Lancet 399, 824836.CrossRefGoogle ScholarPubMed
Zhang, Q, Lian, J, He, M et al. (2014) Olanzapine reduced brown adipose tissue thermogenesis and locomotor activity in female rats. Prog Neuropsychopharmacol Biol Psychiatry 51, 172180.CrossRefGoogle ScholarPubMed
Gronholm, PC, Chowdhary, N, Barbui, C et al. (2021) Prevention and management of physical health conditions in adults with severe mental disorders: WHO recommendations. Int J Ment Health Syst 15, 22.CrossRefGoogle ScholarPubMed
Harvey, A, Montezano, AC & Touyz, RM (2015) Vascular biology of ageing-Implications in hypertension. J Mol Cell Cardiol 83, 112121.CrossRefGoogle ScholarPubMed
Roos, CJ, Quax, PH & Jukema, JW (2012) Cardiovascular metabolic syndrome: mediators involved in the pathophysiology from obesity to coronary heart disease. Biomark Med 6, 3552.CrossRefGoogle ScholarPubMed
Vancampfort, D, Stubbs, B, Mitchell, AJ et al. (2015) Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis. World Psychiatry 14, 339347.CrossRefGoogle ScholarPubMed
Vucenik, I & Stains, JP (2012) Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann N Y Acad Sci 1271, 3743.CrossRefGoogle ScholarPubMed
Hove, O (2004) Weight survey on adult persons with mental retardation living in the community. Res Dev Disabil 25, 917.CrossRefGoogle ScholarPubMed
Younger, DS (2016) Health care in China. Neurol Clin 34, 11151125.CrossRefGoogle ScholarPubMed
Liang, D, Mays, VM & Hwang, WC Integrated mental health services in China: challenges and planning for the future. Health Policy Plan 33, 107122.CrossRefGoogle Scholar
Li, X, Krumholz, HM, Yip, W et al. (2020) Quality of primary health care in China: challenges and recommendations. Lancet 395, 18021812.CrossRefGoogle Scholar
Cobb, LK, McAdams-DeMarco, MA, Gudzune, KA et al. (2016) Changes in body mass index and obesity risk in married couples over 25 years: the ARIC cohort study. Am J Epidemiol 183, 435443.CrossRefGoogle ScholarPubMed
Ogden, CL, Fakhouri, TH, Carroll, MD et al. (2017) Prevalence of obesity among adults, by household income and education - United States, 2011–2014. MMWR Morb Mortal Wkly Rep 66, 13691373.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Sociodemographic and physiological characteristics of the study population from 2006 to 2021

Figure 1

Table 2 Results of univariate logistic regression analysis for BMI

Figure 2

Fig. 1 Results of multiple logistic regression analysis for overweight and obesity. FPG, fasting plasma glucose; TC, serum total cholesterol; TG, triglyceride

Figure 3

Fig. 2 Results of multiple logistic regression analysis for obesity. FPG, fasting plasma glucose; TC, serum total cholesterol; TG, triglyceride