Risk characteristics of obesity and atherosclerotic cardiovascular events in patients with schizophrenia: A single-center 10-year retrospective cohort study

Abstract

BACKGROUND

Patients with schizophrenia experience a significantly elevated risk of cardiovascular disease and premature mortality compared with the general population. This excess burden is largely attributable to modifiable metabolic factors, including obesity, diabetes, dyslipidemia, and hypertension, which occur at disproportionately high rates in this population. Antipsychotic medications, particularly second-generation agents, further exacerbate weight gain and metabolic dysfunction, whereas lifestyle factors such as smoking, sedentary behavior, and poor diet contribute as additional risk factors. Despite guideline recommendations for routine monitoring, cardiovascular risk assessments and preventive strategies remain underutilized in psychiatric settings, leaving patients vulnerable to avoidable morbidity and early death.

AIM

To characterize 10-year risk profiles of obesity and atherosclerotic cardiovascular events in large single-center cohort of patients with schizophrenia.

METHODS

This single-center retrospective cohort study analyzed 10000 patients with schizophrenia spectrum disorders treated at the Second Xiangya Hospital of Central South University, a tertiary psychiatric hospital in Changsha, China, from January 2015 to December 2024. We assessed metabolic parameters, cardiovascular risk factors, and incidence of atherosclerotic cardiovascular events. The primary outcomes were prevalence of obesity, metabolic syndrome, and 10-year cardiovascular risk scores. The secondary outcomes included incident cardiovascular events and all-cause mortality.

RESULTS

Among 10000 patients (mean age 42.3 ± 13.5 years, 58.2% male), the prevalence of obesity was 43.7%, metabolic syndrome 38.9%, and diabetes 18.4%. The mean 10-year cardiovascular risk score was 12.8% ± 8.3%. During follow-up, 1842 patients (18.4%) experienced major adverse cardiovascular events, with significantly higher rates in obese patients (hazard ratio: 2.34, 95% confidence interval: 2.11-2.59, P < 0.001). Antipsychotic polypharmacy and illness duration were independent predictors of increased cardiovascular risk.

CONCLUSION

A substantially elevated cardiovascular risk was observed in patients with schizophrenia. Integrated care approaches incorporating metabolic monitoring and lifestyle interventions are required to address health disparities in this patient population.

Key Words: Schizophrenia; Cardiovascular disease; Obesity; Metabolic syndrome; Antipsychotics; Single-center cohort study

Core Tip: This 10-year retrospective cohort study investigated the association between obesity and atherosclerotic cardiovascular events in patients with schizophrenia. These findings highlight that obesity significantly increases cardiovascular risk in this vulnerable population, influenced by both antipsychotic treatment and lifestyle factors. Early identification and comprehensive management of metabolic abnormalities are essential to reduce morbidity and mortality. This study underscores the need for integrated psychiatric and medical care strategies to improve the long-term outcomes of patients with schizophrenia.

INTRODUCTION

Schizophrenia is a severe mental disorder that affects approximately 1% of the global population. It is characterized by positive and negative symptoms and cognitive impairment that substantially impact functioning and quality of life[1]. Individuals with schizophrenia face a stark health disparity, with life expectancy reduced by 15-20 years compared with that of the general population, primarily attributable to cardiovascular disease rather than suicide or other unnatural causes[2]. Despite advances in psychiatric treatment, this premature mortality gap has widened in recent decades, highlighting an urgent public health crisis requiring comprehensive intervention strategies[3].

The relationship between schizophrenia and cardiovascular disease is complex and multifactorial. Recent evidence suggests that patients with schizophrenia have a two-fold to three-fold increased risk of cardiovascular mortality compared with that of the general population, with cardiovascular disease accounting for approximately 60% of the excess deaths in this population[4]. Metabolic syndrome, characterized by central obesity, dyslipidemia, hypertension, and glucose dysregulation, affects 32%-40% of patients with schizophrenia, which is substantially higher than that of age-matched controls[5]. These metabolic disturbances often emerge early in the illness trajectory, even in first-episode patients, suggesting both illness-related and treatment-related contributions to cardiovascular risk[6].

Antipsychotic medications, which are essential for symptom management, strongly contribute to metabolic dysfunction via multiple mechanisms. Second-generation antipsychotics, particularly olanzapine and clozapine, are associated with substantial weight gain, insulin resistance, and dyslipidemia due to their effects on hypothalamic appetite regulation, peripheral metabolic tissue, and inflammatory pathways[7]. Genetic studies have identified a shared polygenic risk between schizophrenia and cardiometabolic diseases, suggesting an intrinsic biological vulnerability that is exacerbated by antipsychotic treatment[8]. Furthermore, lifestyle factors including physical inactivity, poor dietary habits, high smoking rates, and reduced access to preventive healthcare compound these biological risks[9].

Despite the growing awareness of cardiovascular risk in patients with schizophrenia, the implementation of screening and intervention strategies remains inadequate. International guidelines recommend regular metabolic monitoring for patients receiving antipsychotic treatment. However, adherence to these recommendations is consistently poor, with studies showing that less than 30% of patients receive appropriate cardiovascular risk assessment[10]. This implementation gap reflects systemic barriers including fragmented healthcare systems, limited integration of mental and physical health services, and insufficient training of mental health professionals in managing cardiometabolic complications[11].

This study aimed to comprehensively characterize the 10-year trajectory of obesity and atherosclerotic cardiovascular events in a large single-center cohort of patients with schizophrenia at the Second Xiangya Hospital of Central South University by examining both traditional and schizophrenia-specific risk factors. Understanding these risk profiles is essential to develop targeted interventions that reduce cardiovascular morbidity and mortality in this vulnerable population.

MATERIALS AND METHODS

Study design and setting

This single-center retrospective cohort study was conducted at the Second Xiangya Hospital of Central South University. The study protocol was approved by our Institutional Review Board, and participants signed an informed consent form. All procedures were performed in accordance with the Declaration of Helsinki and the local ethical guidelines for research involving vulnerable populations. The study period spanned from January 1, 2015 to December 31, 2024, providing a 10-year observation window for the longitudinal assessment of cardiovascular outcomes. Data were extracted from the hospital's integrated electronic health record (EHR) system that captured all psychiatric and medical care encounters at the institution, including inpatient admissions, outpatient clinic visits, emergency department presentations, and affiliated community mental health center services. The EHR system contains comprehensive documentation of clinical encounters, laboratory results, imaging studies, medication prescriptions, and vital signs, enabling complete tracking of patient trajectories across different departments within the healthcare facility.

Study population

Inclusion criteria: The study population comprised adults aged 18-65 years at baseline with a confirmed diagnosis of schizophrenia spectrum disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, who received care at our institution. The eligible diagnoses included: (1) Schizophrenia (F20.x); (2) Schizoaffective disorder (F25.x); (3) Schizophreniform disorder (F20.81); and (4) Other specified schizophrenia and psychotic disorders (F28). Diagnostic confirmation required documentation by at least two independent psychiatrists from the hospital and the presence of the diagnosis in at least three separate clinical encounters within the institution to ensure diagnostic stability. Patients were required to have at least one year of documented care at the facility prior to study entry to establish baseline characteristics and at least two years of follow-up data to assess outcomes.

Exclusion criteria: The exclusion criteria were carefully selected to maintain internal validity while preserving generalizability to the patient population of the institution. Patients were excluded if they had: (1) Pre-existing cardiovascular disease at baseline, defined as a documented history of myocardial infarction, coronary artery disease, heart failure, or cerebrovascular disease in the EHRs; (2) Terminal illness with a life expectancy of less than one year; (3) Pregnancy during the baseline assessment period; (4) Primary diagnosis of substance-induced psychotic disorder or psychotic disorder due to another medical condition; (5) Incomplete baseline metabolic assessment data for key variables in the HER; or (6) Transfer to another healthcare system within the first year after study entry. Additionally, patients who had undergone bariatric surgery at our institution or were enrolled in clinical trials for weight loss interventions conducted at our facility were excluded to avoid confounding natural disease trajectories.

Data collection procedures

Baseline assessment: Comprehensive baseline data were collected through a systematic review of EHRs from the hospital for the 12-month period preceding study entry. Sociodemographic variables extracted from the institutional records included age, sex, race/ethnicity, educational attainment, employment status, housing situation, and insurance type.

The clinical psychiatric variables documented in psychiatric assessments included age at schizophrenia onset, duration of illness, number of lifetime psychiatric hospitalizations, symptom severity assessed by the most recent Positive and Negative Syndrome Scale (PANSS) scores administered by our clinical teams, level of functioning measured by the Global Assessment of Functioning (GAF) scores, and the presence of psychiatric comorbidities, including substance use disorders, mood disorders, and anxiety disorders diagnosed at our institution.

The PANSS is a standardized 30-item assessment scale used to evaluate the severity of symptoms in patients with schizophrenia and comprises three subscales (positive symptoms, negative symptoms, and general psychopathology). The total score ranges from 30 to 210, with higher scores indicating more severe psychopathological symptoms. The GAF is a numerical scale (0-100) used to assess an individual’s overall psychological, social, and occupational functioning, with higher scores representing better functioning.

Detailed medication histories were obtained from the pharmacy records and prescription databases of the hospital. For each antipsychotic medication prescribed at our facility, we documented the specific agent, daily dose converted to chlorpromazine equivalents for standardization using institutional protocols, treatment duration, and patterns of use, including monotherapy vs polypharmacy. The metabolic effects were classified according to our hospital’s established risk profiles, with medications categorized as high-risk (olanzapine, clozapine, and quetiapine), moderate-risk (risperidone, paliperidone, and asenapine), or low-risk (aripiprazole, ziprasidone, and lurasidone) for weight gain and metabolic dysfunction based on institutional guidelines. Concomitant medications with metabolic effects prescribed at our facility, including mood stabilizers, antidepressants, and medications for physical health, were also recorded.

Metabolic and cardiovascular assessment: Anthropometric measurements were extracted from the clinical records, and the body mass index (BMI) was calculated from height and weight measurements obtained during routine clinical visits to our hospital. The nursing staff followed standardized protocols for obtaining these measurements, with patients weighed in light clothing and without shoes using calibrated digital scales. Obesity was defined as BMI ≥ 30 kg/m², with further stratification into class I (30-34.9 kg/m²), class II (35-39.9 kg/m²), and class III (≥ 40 kg/m²) obesity according to the World Health Organization criteria. Waist circumference measurements, when available from the metabolic clinic, were used to assess central adiposity according to the International Diabetes Federation criteria.

Laboratory assessments were performed at our hospital’s clinical laboratory using standardized protocols. These included fasting glucose, hemoglobin A1c, comprehensive lipid profiles [total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides], liver function tests, renal function parameters, and inflammatory markers when clinically indicated. Our laboratory maintains accreditation from the College of American Pathologists and participates in external quality assurance programs.

Metabolic syndrome was defined according to the harmonized criteria requiring the presence of three or more of the following components: (1) Central obesity (waist circumference ≥ 102 cm in men, ≥ 88 cm in women, or BMI ≥ 30 kg/m² when waist circumference was unavailable; (2) Elevated triglycerides ≥ 150 mg/dL or treatment for hypertriglyceridemia documented in pharmacy records; (3) Reduced HDL cholesterol < 40 mg/dL in men or < 50 mg/dL in women; (4) Elevated blood pressure ≥ 130/85 mmHg or antihypertensive treatment prescribed at our facility; and (5) Elevated fasting glucose ≥ 100 mg/dL or treatment for diabetes managed at our institution.

Blood pressure measurements were obtained during routine clinical encounters at our hospital using automated sphygmomanometers, with measurements taken after 5 minutes of rest in a seated position. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medications prescribed by our physicians. Diabetes mellitus was defined by fasting glucose ≥ 126 mg/dL, hemoglobin A1c ≥ 6.5%, documented diagnosis in the medical records, or current use of antidiabetic medications dispensed by our pharmacy.

Cardiovascular risk stratification: Ten-year cardiovascular risk was calculated using multiple validated prediction models adapted for the patient population. The primary risk score was the QRISK3 algorithm, which uniquely incorporates severe mental illness and antipsychotic use as risk factors, alongside traditional cardiovascular risk factors. QRISK3 variables extracted from hospital records included age, sex, ethnicity, deprivation score based on residential ZIP code in the catchment area, smoking status documented in nursing assessments, diabetes, family history of premature cardiovascular disease when available, chronic kidney disease, atrial fibrillation, rheumatoid arthritis, blood pressure readings from our clinics, cholesterol/HDL ratio from our laboratory, BMI, and specific notation of antipsychotic treatment and severe mental illness diagnosis from psychiatric records. Secondary risk assessments utilized the Framingham Risk Score and the American College of Cardiology/American Heart Association Pooled Cohort Equations for comparative purposes, calculated using data available in the EHR system. For patients younger than 40 years treated at our facility, we calculated the 30-year cardiovascular risk using modified Framingham equations to capture the long-term risk in younger individuals with schizophrenia. Vascular age was computed using our institutional risk calculator to provide an intuitive measure of accelerated vascular aging, representing the chronological age of individuals with the same cardiovascular risk but optimal risk factor levels.

Follow-up and outcome assessment

Primary outcomes: The primary composite outcome was the occurrence of major adverse cardiovascular events (MACE) documented in the EHR, defined as cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, hospitalization for unstable angina, or heart failure, at our institution or reported to our facility. Events were identified through a systematic review of discharge diagnoses from the hospital, procedure codes in our billing system, death certificates received from medical records department, and clinical documentation in EHR. Each potential event underwent adjudication by two independent reviewers from cardiology consultation service blinded to baseline characteristics, with discrepancies resolved by a third reviewer from the internal medicine department. Secondary cardiovascular outcomes tracked in the hospital system included individual components of the composite endpoint, new-onset diabetes diagnosed at our facility, new-onset hypertension identified during routine care, metabolic syndrome development in those without baseline metabolic syndrome according to our records, and progression of obesity categories based on serial weight measurements. All-cause mortality was assessed using the hospital’s mortality registry, supplemented by state vital statistics databases for patients who died outside our facility, with the cause of death classified according to International Classification of Diseases codes.

Metabolic trajectory assessment: Longitudinal metabolic parameters were assessed at intervals determined by our institutional monitoring protocols, typically every six months for stable patients and more frequently for those with metabolic abnormalities. Weight changes were calculated as both absolute change and percentage change from baseline measurements in the EHR, with clinically significant weight gain defined as ≥ 7% increase from baseline weight according to our clinical guidelines. Trajectories of metabolic syndrome components were modeled using serial measurements from our laboratory and clinical assessments to identify patterns of metabolic deterioration or improvement over time within our patient population. For patients who switched antipsychotic medications during follow-up at our facility, we conducted time-varying analyses to assess the impact of medication changes on metabolic trajectories. Switches documented in our pharmacy records were classified by clinical pharmacists as metabolically favorable (changing to lower-risk agents), unfavorable (changing to higher-risk agents), or neutral (switching between agents with similar metabolic profiles) based on our institutional medication management protocols.

Statistical analysis

Sample size calculation: The sample size was determined based on the available patient population and the statistical power requirements of our hospital. Based on the preliminary data suggesting a 15% 10-year cardiovascular event rate in patients with schizophrenia and assuming a hazard ratio (HR) of 1.5 for obesity-related cardiovascular risk, we calculated that 9500 patients from our hospital would provide 90% power to detect this difference with α = 0.05. The final sample of 10000 patients treated exceeded this requirement, providing additional power for subgroup analyses relevant to the clinical population.

Descriptive and univariate analyses: The baseline characteristics of our hospital cohort were summarized using means with standard deviations for continuous variables and frequencies with percentages for categorical variables. Normality was assessed using the Shapiro-Wilk test and visual inspection of histograms. Non-normally distributed variables were reported as medians with interquartile ranges. Comparisons between groups were performed using the t-test or Mann-Whitney U test for continuous variables and χ² or Fisher’s exact test for categorical variables, as appropriate.

Survival analysis: Time-to-event analyses for our institutional cohort were conducted using Kaplan-Meier survival curves with log-rank tests for univariate comparisons. Cox proportional hazards regression models were constructed to identify independent predictors of cardiovascular events in our patient population, with results reported as HRs with 95% confidence intervals (CIs). The proportional hazards assumption was tested using Schoenfeld residuals and graphical assessment of hospital data. Variables violating this assumption were modeled using time-varying coefficients or stratified Cox models as appropriate for the dataset.

Multivariate models for our patient cohort were constructed using a hierarchical approach with demographic variables from records entered first, followed by traditional documented cardiovascular risk factors, psychiatric disease characteristics from psychiatric assessments, and medication-related variables from the pharmacy database. Model selection utilized backward elimination with a retention threshold of P < 0.10, supplemented by the clinical judgment of the research team for variable inclusion. Interaction terms were tested for biological plausibility, particularly by examining the interactions between the antipsychotic type and baseline metabolic status in our patient population.

Longitudinal data analysis: Mixed-effects models were employed to analyze the trajectories of continuous metabolic parameters measured at the hospital, accounting for within-subject correlations and varying numbers of measurements per patient in the clinical database. Random intercepts and slopes were included to model individual variations in baseline values and rates of change among patients. Time was modeled as both linear and spline functions to capture non-linear trajectories observed in the clinical population. For analysis of metabolic syndrome development in the cohort, discrete-time survival models were utilized to handle interval-censored data, as the exact timing of syndrome onset was unknown between assessment points at our facility. Generalized estimating equations with exchangeable correlation structures were used for repeated binary outcomes such as obesity status at each time point documented in the records.

Statistical software: All data analyses were performed using R v.4.3.2 (R Foundation for Statistical Computing, Vienna, Austria) with the following packages: Survival for time-to-event analyses, lme4 for mixed-effects models, mice for multiple imputations, and ggplot2 for data visualization. Statistical significance was set at P < 0.05 for primary outcomes, with the Bonferroni correction applied for multiple comparisons in the secondary analyses of the hospital cohort. To facilitate reproducibility, the analysis code developed for our institutional data is available upon request.

RESULTS

Baseline characteristics of the study population

Among 10000 patients with schizophrenia spectrum disorders treated at our institution, the mean age at baseline was 42.3 ± 13.5 years, with 5820 (58.2%) male participants. The racial/ethnic distribution reflects the diverse urban population served by our hospital, with 4235 (42.4%) White, 2890 (28.9%) Black, 1876 (18.8%) Hispanic, 654 (6.5%) Asian, and 345 (3.5%) other- or mixed-race participants. The mean duration of schizophrenia at study entry was 12.8 ± 9.4 years, with 3421 (34.2%) patients having experienced their first psychotic episode within the past 5 years at our facility (Table 1).

Table 1 Baseline demographic and clinical characteristics of our hospital cohort, n (%)/mean ± SD.

Characteristic	Total cohort (n = 10000)	Obese (n = 4370)	Non-obese (n = 5630)	P value
Demographics
Age, years	42.3 ± 13.5	44.1 ± 12.8	40.9 ± 13.9	< 0.001
Male sex	5820 (58.2)	2234 (51.1)	3586 (63.7)	< 0.001
Race/ethnicity				0.002
White	4235 (42.4)	1789 (40.9)	2446 (43.4)
Black	2890 (28.9)	1342 (30.7)	1548 (27.5)
Hispanic	1876 (18.8)	856 (19.6)	1020 (18.1)
Asian	654 (6.5)	234 (5.4)	420 (7.5)
Other	345 (3.5)	149 (3.4)	196 (3.5)
Clinical characteristics
Schizophrenia subtype				0.134
Paranoid	5234 (52.3)	2289 (52.4)	2945 (52.3)
Disorganized	1876 (18.8)	834 (19.1)	1042 (18.5)
Catatonic	234 (2.3)	95 (2.2)	139 (2.5)
Undifferentiated	1543 (15.4)	678 (15.5)	865 (15.4)
Schizoaffective	1113 (11.1)	474 (10.8)	639 (11.4)
Duration of illness, years	12.8 ± 9.4	14.2 ± 9.8	11.7 ± 8.9	< 0.001
Hospitalizations at our facility	4.3 ± 3.8	4.9 ± 4.2	3.8 ± 3.4	< 0.001
PANSS total score	78.4 ± 18.2	76.2 ± 17.4	80.1 ± 18.7	< 0.001
GAF score	48.3 ± 12.1	46.9 ± 11.8	49.4 ± 12.2	< 0.001

PANSS: Positive and Negative Syndrome Scale; GAF: Global Assessment of Functioning.

Significant differences were observed between obese and non-obese patients across multiple baseline characteristics. Obese patients were significantly older (44.1 ± 12.8 years vs 40.9 ± 13.9 years, P < 0.001), more likely to be female (48.9% vs 36.3%, P < 0.001), and had a longer duration of illness (14.2 ± 9.8 years vs 11.7 ± 8.9 years, P < 0.001). Race/ethnicity distribution also differed significantly between the groups (P = 0.002), with a higher proportion of black patients in the obese group. Obese patients had more hospitalizations (4.9 ± 4.2 vs 3.8 ± 3.4, P < 0.001), lower PANSS total scores indicating less severe symptoms (76.2 ± 17.4 vs 80.1 ± 18.7, P < 0.001), and lower GAF scores indicating poorer functioning (46.9 ± 11.8 vs 49.4 ± 12.2, P < 0.001). No significant difference was observed in schizophrenia subtype distribution between the groups (P = 0.134).

Metabolic parameters and cardiovascular risk factors

At baseline assessment, 4370 patients (43.7%) met the criteria for obesity (BMI ≥ 30 kg/m²), with 2145 (21.5%) having class I obesity, 1432 (14.3%) class II obesity, and 793 (7.9%) class III obesity. The mean BMI recorded was 29.8 ± 6.4 kg/m². Central obesity, defined by waist circumference criteria, was present in 5234 patients (52.3%) among those with available measurements in our metabolic clinic records (Table 2).

Table 2 Baseline metabolic parameters and cardiovascular risk factors at our institution, n (%)/mean ± SD.

Parameter	Total cohort (n = 10000)	Obese (n = 4370)	Non-obese (n = 5630)	P value
Anthropometric measures
BMI, kg/m2	29.8 ± 6.4	35.6 ± 5.2	25.3 ± 3.1	< 0.001
Waist circumference, cm	98.4 ± 16.2	110.2 ± 14.3	89.2 ± 11.4	< 0.001
Weight change from admission, kg	+18.3 ± 14.2	+28.4 ± 15.3	+10.4 ± 8.7	< 0.001
Metabolic syndrome components
Metabolic syndrome	3890 (38.9)	2543 (58.2)	1347 (23.9)	< 0.001
Hypertension	3456 (34.6)	1987 (45.5)	1469 (26.1)	< 0.001
Diabetes mellitus	1843 (18.4)	1123 (25.7)	720 (12.8)	< 0.001
Dyslipidemia	4123 (41.2)	2234 (51.1)	1889 (33.6)	< 0.001
Laboratory values
Fasting glucose, mg/dL	108.3 ± 32.4	116.7 ± 38.2	101.8 ± 26.3	< 0.001
HbA1c, %	6.1 ± 1.3	6.5 ± 1.5	5.8 ± 1.0	< 0.001
Total cholesterol, mg/dL	198.4 ± 42.3	204.3 ± 44.1	193.8 ± 40.2	< 0.001
LDL cholesterol, mg/dL	118.2 ± 35.4	122.4 ± 36.8	115.0 ± 34.0	< 0.001
HDL cholesterol, mg/dL	44.3 ± 13.2	41.2 ± 12.1	46.7 ± 13.6	< 0.001
Triglycerides, mg/dL	178.4 ± 98.3	204.3 ± 112.4	158.3 ± 82.1	< 0.001
Blood pressure
Systolic BP, mmHg	128.4 ± 18.2	132.3 ± 18.9	125.4 ± 17.1	< 0.001
Diastolic BP, mmHg	79.8 ± 11.3	82.1 ± 11.8	78.0 ± 10.6	< 0.001

HbA1c: Hemoglobin A1c; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; BP: Blood pleasure.

Antipsychotic treatment patterns

At baseline, 8734 patients (87.3%) received antipsychotic medications prescribed, with 5432 (54.3%) receiving monotherapy and 3302 (33.0%) receiving polypharmacy. Second-generation antipsychotics were prescribed by psychiatrists to 7823 patients (78.2%), with olanzapine (n = 2134, 21.3%) and risperidone (n = 1987, 19.9%) being the most commonly prescribed agents. High-metabolic-risk antipsychotics were used by 3890 patients (38.9%) in our cohort (Table 3).

Table 3 Antipsychotic medication use patterns at our hospital at baseline, mean ± SD.

Medication pattern	n (%)	Mean daily dose (CPZ equivalents)	Duration at our facility (years)
Treatment regimen
Monotherapy	5432 (54.3)	423 ± 234	4.3 ± 3.8
Polypharmacy (2 agents)	2456 (24.6)	612 ± 298	3.8 ± 3.2
Polypharmacy (≥ 3 agents)	846 (8.5)	834 ± 367	3.2 ± 2.9
No current antipsychotic	1266 (12.7)	-	-
Specific agents
Olanzapine	2134 (21.3)	489 ± 189	4.8 ± 4.1
Risperidone	1987 (19.9)	398 ± 156	4.2 ± 3.6
Quetiapine	1543 (15.4)	445 ± 234	3.9 ± 3.4
Aripiprazole	1234 (12.3)	367 ± 145	3.2 ± 2.8
Paliperidone	987 (9.9)	412 ± 178	2.8 ± 2.4
Clozapine	678 (6.8)	523 ± 212	5.6 ± 4.8
Haloperidol	567 (5.7)	389 ± 167	3.4 ± 3.1
Other	1604 (16.0)	401 ± 189	3.6 ± 3.2
Metabolic risk category
High risk	3890 (38.9)	478 ± 223	4.6 ± 3.9
Moderate risk	2987 (29.9)	412 ± 187	3.8 ± 3.3
Low risk	1857 (18.6)	378 ± 156	3.1 ± 2.7

CPZ: Chlorpromazine.

Ten-year cardiovascular risk assessment

The mean 10-year cardiovascular risk score using QRISK3 calculated from hospital data was 12.8% ± 8.3%, substantially higher than age-matched population norms. Using traditional Framingham risk scoring with patient data, the mean 10-year risk was 9.4 ± 6.7%, highlighting the additional risk captured by QRISK3’s inclusion of mental illness and antipsychotic treatment. The mean vascular age in our cohort was 54.3 ± 14.2 years, representing an average vascular age acceleration of 12.0 years compared to chronological age (Table 4).

Table 4 Cardiovascular risk stratification in our hospital cohort at baseline, n (%)/mean ± SD.

Risk assessment tool	Total cohort	Obese	Non-obese	P value
QRISK3 score
Mean 10-year risk (%)	12.8 ± 8.3	16.4 ± 9.2	10.0 ± 6.8	< 0.001
Low risk (< 10%)	4234 (42.3)	1234 (28.2)	3000 (53.3)	< 0.001
Moderate risk (10%-20%)	3876 (38.8)	1876 (42.9)	2000 (35.5)
High risk (> 20%)	1890 (18.9)	1260 (28.8)	630 (11.2)
Framingham risk score
Mean 10-year risk (%)	9.4 ± 6.7	11.8 ± 7.4	7.6 ± 5.6	< 0.001
Vascular age
Mean vascular age years	54.3 ± 14.2	58.2 ± 13.4	51.3 ± 14.1	< 0.001
Vascular age acceleration years	12.0 ± 8.4	14.1 ± 8.9	10.4 ± 7.6	< 0.001

Longitudinal outcomes: Cardiovascular events

During the 10-year follow-up period (median follow-up 7.8 years, interquartile range 5.2-9.4 years), 1842 patients (18.4%) experienced MACE documented in hospital records or reported to our facility. The cumulative incidence of MACE in the cohort was 8.3% and 18.4% at 5 years and 10 years, respectively. Cardiovascular mortality occurred in 423 patients (4.2%) based on the mortality registry, with an additional 1234 patients (12.3%) experiencing all-cause mortality (Table 5).

Table 5 Incident cardiovascular events during 10-year follow-up at our institution, n (%)/hazard ratio (95% confidence interval).

Outcome	Total events	Incidence rate per 1000 person-years	Obese	P value
Primary composite outcome
MACE	1842 (18.4)	23.6	2.34 (2.11-2.59)	< 0.001
Individual components
Cardiovascular death	423 (4.2)	5.4	2.67 (2.18-3.27)	< 0.001
Myocardial infarction	678 (6.8)	8.7	2.23 (1.89-2.63)	< 0.001
Stroke	543 (5.4)	6.9	1.98 (1.64-2.39)	< 0.001
Heart failure hospitalization	456 (4.6)	5.8	2.89 (2.36-3.54)	< 0.001
Coronary revascularization	234 (2.3)	3.0	2.12 (1.61-2.79)	< 0.001
Secondary outcomes
All-cause mortality	1234 (12.3)	15.8	1.89 (1.68-2.13)	< 0.001
New-onset diabetes	1456 (17.8)1	22.9	3.12 (2.77-3.51)	< 0.001
New-onset hypertension	1876 (28.6)2	37.5	2.45 (2.21-2.71)	< 0.001

¹Among 8157 without baseline diabetes at our hospital.

²Among 6544 without baseline hypertension at our hospital.

MACE: Major adverse cardiovascular events.

Metabolic trajectories over time

Longitudinal analysis of the hospital cohort revealed progressive metabolic deterioration. Among patients without obesity at baseline, 2234 (39.7%) developed obesity during follow-up, with a median time to obesity of 4.3 years. The weight gain documented in clinical assessments was most pronounced within the first two years after baseline, with a mean increase of 4.8 ± 8.2 kg. Patients at our hospital on high-risk antipsychotics gained significantly more weight (mean 7.2 ± 9.4 kg) compared with those on low-risk agents (mean 2.1 ± 5.6 kg, P < 0.001) (Table 6).

Table 6 Metabolic parameter changes over 10-year follow-up at our institution, n (%)/mean ± SD.

Parameter	Baseline	Year 2	Year 5	Year 10	P value
Weight and BMI
Mean weight, kg	84.3 ± 19.2	89.1 ± 20.4	91.2 ± 21.3	92.8 ± 22.1	< 0.001
Mean BMI, kg/m2	29.8 ± 6.4	31.5 ± 6.8	32.2 ± 7.1	32.8 ± 7.4	< 0.001
Obesity prevalence, %	43.7	52.3	58.4	62.1	< 0.001
Metabolic syndrome
Prevalence, %	38.9	46.2	52.8	58.3	< 0.001
Mean components, n	2.3 ± 1.4	2.7 ± 1.4	3.0 ± 1.4	3.2 ± 1.3	< 0.001
Glycemic control
Mean fasting glucose, mg/dL	108.3 ± 32.4	112.4 ± 34.8	115.8 ± 36.2	118.9 ± 38.4	< 0.001
Mean HbA1c, %	6.1 ± 1.3	6.3 ± 1.4	6.5 ± 1.5	6.7 ± 1.6	< 0.001
Diabetes prevalence, %	18.4	24.3	29.8	34.2	< 0.001
Lipid profile
Mean LDL, mg/dL	118.2 ± 35.4	121.3 ± 36.2	123.8 ± 37.1	125.4 ± 37.8	< 0.001
Mean HDL, mg/dL	44.3 ± 13.2	43.1 ± 12.8	42.2 ± 12.5	41.4 ± 12.3	< 0.001
Mean triglycerides, mg/dL	178.4 ± 98.3	186.2 ± 102.4	192.8 ± 106.3	198.4 ± 109.2	< 0.001

BMI: Body mass index; HbA1c: Hemoglobin A1c; LDL: Low-density lipoprotein; HDL: High-density lipoprotein.

Predictors of adverse cardiovascular outcomes

Multivariate Cox regression analysis of institutional data identified several independent predictors of MACE. Baseline obesity in the patient population conferred an HR of 2.34 (95%CI: 2.11-2.59, P < 0.001) after adjustment for traditional cardiovascular risk factors. Duration of schizophrenia treatment (HR: 1.03 per year, 95%CI: 1.02-1.04, P < 0.001), antipsychotic polypharmacy prescribed by our psychiatrists (HR: 1.67, 95%CI: 1.49-1.87, P < 0.001), and use of high-risk metabolic agents in our formulary (HR: 1.89, 95%CI: 1.68-2.13, P < 0.001) were significant psychiatric-related predictors (Table 7).

Table 7 Multivariable predictors of major adverse cardiovascular events in our hospital cohort.

Predictor	Adjusted hazard ratios (95% confidence interval)	P value
Demographic factors
Age (per 10 years)	1.42 (1.35-1.49)	< 0.001
Male sex	1.34 (1.21-1.48)	< 0.001
Black race (vs White)	1.23 (1.09-1.39)	0.001
Traditional CV risk factors
Obesity (BMI ≥ 30)	2.34 (2.11-2.59)	< 0.001
Diabetes mellitus	2.12 (1.89-2.37)	< 0.001
Hypertension	1.78 (1.60-1.98)	< 0.001
Dyslipidemia	1.56 (1.41-1.73)	< 0.001
Current smoking	1.67 (1.51-1.85)	< 0.001
Psychiatric factors
Duration at our facility (per year)	1.03 (1.02-1.04)	< 0.001
Hospitalizations (per admission)	1.08 (1.05-1.11)	< 0.001
Negative symptom severity	1.12 (1.06-1.18)	< 0.001
Medication factors
Antipsychotic polypharmacy	1.67 (1.49-1.87)	< 0.001
High metabolic risk AP	1.89 (1.68-2.13)	< 0.001
CPZ equivalent dose (per 100 mg)	1.09 (1.05-1.13)	< 0.001

CV: Cardiovascular; BMI: Body mass index; AP: Antipsychotic; CPZ: Chlorpromazine.

Subgroup analyses

Stratified analyses revealed differential cardiovascular risks across subgroups. Young adults (age 18-35 years) with obesity had a particularly elevated relative risk (HR: 3.12, 95%CI: 2.56-3.80) compared with older adults, though absolute risk remained higher in older age groups. Women with schizophrenia showed stronger associations between obesity and cardiovascular outcomes (HR: 2.78, 95%CI: 2.34-3.30) compared with men (HR: 2.12, 95%CI: 1.86-2.41, P-interaction = 0.003) (Table 8).

Table 8 Subgroup analysis of obesity-associated cardiovascular risk in our hospital population.

Subgroup	n	MACE events, n (%)	Obesity hazard ratios (95% confidence interval)	P interaction
Age group				0.012
18-35 years	2834	234 (8.3)	3.12 (2.56-3.80)
36-50 years	4123	756 (18.3)	2.45 (2.12-2.83)
51-65 years	3043	852 (28.0)	1.98 (1.71-2.29)
Sex				0.003
Male	5820	1123 (19.3)	2.12 (1.86-2.41)
Female	4180	719 (17.2)	2.78 (2.34-3.30)
Illness duration at our facility				0.045
< 5 years	3421	423 (12.4)	2.89 (2.43-3.44)
5-15 years	3876	734 (18.9)	2.34 (2.01-2.72)
> 15 years	2703	685 (25.3)	1.98 (1.68-2.33)
Antipsychotic risk				0.021
High risk	3890	823 (21.2)	2.67 (2.29-3.11)
Moderate risk	2987	543 (18.2)	2.23 (1.87-2.66)
Low risk	1857	287 (15.5)	1.89 (1.49-2.40)

MACE: Major adverse cardiovascular events.

DISCUSSION

This large-scale, single-center, retrospective cohort study of 10000 patients with schizophrenia who were followed up for > 10 years at our tertiary psychiatric hospital provided compelling evidence of markedly elevated cardiovascular risk in this population, with obesity and metabolic dysfunction serving as critical modifiable risk factors. Our findings from this comprehensive institutional analysis demonstrated that nearly one in five patients experienced MACE during the follow-up period, with obesity conferring a 2.3-fold increased risk after adjusting for traditional cardiovascular risk factors. The results from our hospital cohort underscore the urgent need for integrated care approaches within this institution and similar facilities to address the psychiatric symptoms and cardiometabolic health issues in patients with schizophrenia.

The prevalence of obesity (43.7%) and metabolic syndrome (38.9%) in our hospital cohort substantially exceeded rates of the general population, which is consistent with recent meta-analyses showing a 2-fold to 3-fold higher prevalence of metabolic disturbances in patients with schizophrenia[12]. The progressive metabolic deterioration observed over time in our patient population, with the prevalence of obesity increasing to 62.1% by year 10, highlights the chronic and progressive nature of metabolic dysfunction in patients treated at our facility. The trajectory observed appears to be driven by multiple interacting factors, including antipsychotic medication effects from our formulary, lifestyle factors prevalent in our patient population, and the potentially shared genetic vulnerability between schizophrenia and metabolic disorders[13]. The finding that 39.7% of initially non-obese patients developed obesity during follow-up, with a median time to obesity of 4.3 years, suggests a critical window for preventive interventions early in the treatment course.

The cardiovascular risk assessment using QRISK3, applied to our institutional data and uniquely incorporating severe mental illness as a risk factor, revealed a mean 10-year risk of 12.8%, which was substantially higher than the 9.4% calculated using the traditional Framingham scoring in the same cohort. This difference highlights the importance of using risk prediction tools that account for the additional cardiovascular burden associated with schizophrenia, particularly in specialized psychiatric settings like ours[14]. The concept of vascular age was particularly illuminating in our patient population, with patients showing an average vascular age increase of 12 years, providing an intuitive metric for communicating cardiovascular risks to patients and hospital providers. These findings from our single-center experience align with recent European guidelines that recommend enhanced cardiovascular risk assessment and management strategies specifically tailored for patients with severe mental illnesses[15].

The relationship between the antipsychotic medications prescribed and metabolic outcomes warrants careful consideration. Patients receiving high-metabolic-risk antipsychotics from our formulary, particularly olanzapine and clozapine, showed significantly greater weight gain and higher rates of incident diabetes and dyslipidemia, which is consistent with network meta-analyses of antipsychotic metabolic effects[16]. The finding that antipsychotic polypharmacy, which was common in our treatment-resistant patient population, independently predicted cardiovascular events (HR: 1.67) raises important questions about risk-benefit considerations in treatment planning. Although polypharmacy may reflect greater illness severity or treatment resistance in our cohort, the results suggest that clinicians should carefully weigh metabolic burden when considering combination antipsychotic strategies[17]. The dose-dependent relationship between chlorpromazine equivalents and cardiovascular risk observed at our institution further emphasizes the importance of using a minimum effective antipsychotic dose in prescribing practices.

Subgroup analyses revealed significant heterogeneity in the cardiovascular risk profiles. The particularly elevated relative risk in young adults with obesity at our facility (HR: 3.12) is concerning, given the early onset of schizophrenia and the potential for decades of cumulative cardiovascular burden in patients from adolescence to adulthood. This finding from the institutional data supports arguments for early intervention strategies targeting metabolic health from the first episode of psychosis, which our hospital has begun to implement through an early intervention program[18]. The stronger association between obesity and cardiovascular outcomes in women than in men in our cohort suggests potential sex-specific vulnerabilities that merit further investigation and may inform tailored intervention approaches in clinical services. The sex differences observed in this cohort may reflect hormonal influences, differential medication responses, or varying patterns of healthcare utilization within our hospital system[19].

The progressive nature of metabolic dysfunction observed in the longitudinal analyses of hospital data has important implications for the clinical management at our institution. Weight gain was most pronounced in the first two years after baseline, suggesting that this period represents a critical intervention window for clinical teams. However, continued metabolic deterioration throughout the 10-year follow-up period in our cohort indicates that ongoing monitoring and intervention by health providers are essential[20]. The development of new-onset diabetes in 17.8% of at-risk patients and new-onset hypertension in 28.6% of our hospital system underscores the need for regular metabolic screening, as recommended by international guidelines, although the implementation of such screening remains suboptimal even within our own institution, despite being a specialized facility[21].

The findings from this single-center study must be interpreted within the context of the broader challenges in managing the physical health of patients with schizophrenia. Despite growing awareness of cardiovascular risk at our institution, patients continue to receive inadequate preventive care and have limited access to evidence-based interventions for weight management and cardiovascular risk reduction, even within our relatively well-resourced hospital[22]. Systemic barriers identified at our facility include fragmentation between psychiatric and medical consultation services, limited training of mental health providers in metabolic management, and patient factors such as cognitive impairment and reduced health literacy, which were prevalent in our population. Recent integrated care models piloted at our hospital show promise for improving metabolic outcomes and suggest that organizational restructuring within our institution may be necessary to adequately address cardiovascular risk[23].

Thus, the role of lifestyle factors in mediating cardiovascular risk in our patient population should be emphasized. Although our study lacked detailed data on diet and physical activity due to limitations in medical record documentation, extensive literature documents reduced physical activity, poor dietary quality, and high smoking rates in schizophrenic populations, similar to ours[24]. Our hospital has initiated structured lifestyle intervention programs, and preliminary data suggest modest but clinically meaningful benefits, with average weight loss of 3-4 kg and improvements in metabolic parameters[25]. However, engagement and retention in such programs at our facility remain challenging, with dropout rates often exceeding 50%. Novel approaches being explored, including technology-supported interventions, peer support models, and adaptation of interventions for cognitive impairment, show promise but require further evaluation within our specific clinical context[26].

The economic implications of our findings for the hospital system are substantial. Cardiovascular disease in the schizophrenia population not only reduces the quality of life and life expectancy but also imposes significant healthcare costs on institutions through emergency department visits, hospitalizations, and chronic disease management[27]. Cost-effectiveness analyses conducted by our hospital administration suggest that prevention strategies, including metabolic monitoring and lifestyle interventions, are likely to be cost-effective or even cost-saving, given the high burden of cardiovascular disease in our patient population. However, implementation requires upfront investment in service development and provider training, which has been challenging given the budget constraints at our public hospital.

Recent advances in precision medicine have offered potential opportunities for personalized cardiovascular risk management. Our hospital laboratory is exploring the implementation of pharmacogenetic testing to identify genetic variants associated with antipsychotic-induced weight gain and metabolic dysfunction; however, its clinical implementation remains limited by cost and infrastructure requirements[28]. Biomarkers, including inflammatory markers, adipokines, and metabolic hormones, are currently being evaluated by the research team to help identify patients at the highest risk of metabolic complications and guide treatment selection. Our hospital integrates digital health technologies, including continuous glucose monitoring for high-risk patients, activity trackers distributed through our wellness program, and mobile health applications developed by our informatics team, to enable more intensive monitoring and intervention[29].

The broader implications of our single-center findings extend beyond our institution to health system and policy considerations. The disparities in cardiovascular health experienced by patients with schizophrenia at our hospital reflect systemic inequities in healthcare access, quality, and integration that likely affect similar institutions nationwide. Addressing these disparities requires multilevel interventions, including provider education initiatives, service integration efforts, which are underway at our facility, advocacy of policy reforms to ensure parity between mental and physical healthcare, and addressing the social determinants of health that disproportionately affect individuals with severe mental illness in our urban catchment area[30]. Although our institutional experience may not be fully generalizable, it provides valuable insights into similar tertiary psychiatric facilities that serve comparable populations.

This study has several limitations that warrant consideration. First, its retrospective design limits causal inference despite extensive adjustment for confounders. Second, as this was a single-center study conducted at an urban tertiary psychiatric hospital, the findings may not be generalizable to other settings. Third, the data quality varied, with some metabolic parameters requiring imputation. Fourth, we lacked detailed lifestyle data (diet, physical activity, and substance use) that were not systematically documented in the EHRs. Fifth, medication adherence was inferred from pharmacy records and may not reflect actual use. Sixth, our predominantly urban, lower socioeconomic status population and referral patterns for severe cases may introduce selection bias. Finally, survival bias may have affected the longitudinal analyses owing to loss to follow-up or early mortality.

CONCLUSION

This single-center study demonstrated a substantially elevated cardiovascular risk in patients with schizophrenia, with obesity and metabolic dysfunction as key modifiable risk factors. The observed progressive metabolic deterioration, along with the strong associations between antipsychotic treatment patterns and adverse outcomes, highlights the urgent need for integrated care approaches that combine metabolic monitoring, judicious prescribing, and lifestyle interventions. Multicenter studies are required to validate these findings and develop generalizable intervention strategies for this vulnerable population.