Skip to main content
NCBI home page
European Psychiatry logoLink to European Psychiatry
. 2023 Dec 13;67(1):e7. doi: 10.1192/j.eurpsy.2023.2477

Association of clinical symptoms and cardiometabolic dysregulations in patients with schizophrenia spectrum disorders

Chenxu Zhao 1,✉, Tesfa Dejenie Habtewold 1, Elnaz Naderi 1, Edith J Liemburg 2; GROUP Investigators#, Richard Bruggeman 2, Behrooz Z Alizadeh 1,✉
PMCID: PMC10964276  PMID: 38088065

Abstract

Background

Patients with schizophrenia spectrum disorders (SSD) have a shortened life expectancy related to cardiovascular diseases. We investigated the association of cognitive, positive, and negative symptoms with cardiometabolic dysregulations in SSD patients.

Methods

Overall, 1,119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) study were included. Cognitive function, positive and negative symptoms were assessed at baseline, 3-year, and 6-year. Cardiometabolic biomarkers were measured at 3-year follow-up. We used linear and multinomial logistic regression models to test the association between cardiometabolic biomarkers and clinical trajectories and performed mediation analyzes, while adjusting for clinical and demographic confounders.

Results

Cognitive performance was inversely associated with increased body mass index (mean difference [β], βhigh = −1.24, 95% CI = –2.28 to 0.20, P = 0.02) and systolic blood pressure (βmild = 2.74, 95% CI = 0.11 to 5.37, P = 0.04). The severity of positive symptoms was associated with increased glycated hemoglobin (HbA1c) levels (βlow = −2.01, 95% CI = −3.21 to −0.82, P = 0.001). Increased diastolic blood pressure (ORhigh-decreased = 1.04, 95% CI = 1.01 to 1.08, P = 0.02; ORhigh-increased = 1.04, 95% CI = 1.00 to 1.08, P = 0.048) and decreased high-density lipoprotein (OR high-increased = 6.25, 95% CI = 1.81 to 21.59, P = 0.004) were associated with more severe negative symptoms. Increased HbA1c (ORmoderate = 1.05, 95% CI = 1.01 to 1.10, P = 0.024; ORhigh = 1.08, 95% CI = 1.02 to 1.14, P = 0.006) was associated with more severe positive symptoms. These associations were not mediated by antipsychotics.

Conclusions

We showed an association between cardiometabolic dysregulations and clinical and cognitive symptoms in SSD patients. The observed associations underscore the need for early identification of patients at risk of cardiometabolic outcomes.

Keywords: cardiometabolic biomarkers, cognition, metabolic syndrome, psychotic symptoms, schizophrenia

Introduction

Schizophrenia spectrum disorder (SSD) is a severe and disabling psychotic disorder manifested by positive symptoms (e.g., delusions, hallucinations, disorganized thinking, and speech), negative symptoms (e.g., social withdrawal, loss of motivation, and reduced communication), and cognitive symptoms (e.g., deficits in attention, concentration, and memory) [1]. Patients with SSD have a 15–20 years shortened life expectancy compared with the general population [2, 3], mostly attributable to the increased risk of cardiovascular diseases [4, 5].

Cardiometabolic disorders such as obesity, type 2 diabetes, and metabolic syndrome (MetS) are the primary risk factors of cardiovascular diseases and compelling causes of shorter life [6]. The specific measurable cardiometabolic biomarkers including body mass index, blood pressure, and cholesterol levels, are closely associated with the presence and development of cardiometabolic disorders. Despite the denouncing effect of cardiometabolic biomarkers on SSD clinical symptoms, the nature of association between them remains to be elucidated. So far, cardiometabolic biomarkers have been related to cognitive impairment, and positive and negative symptoms [7–9] in patients with SSD. High blood glucose and blood pressure are associated with delayed memory, vigilance, processing speed [10–14], and reasoning abilities [15]. Low-density lipoprotein and triglycerides are related to psychotic symptoms, impaired executive function [16], and verbal memory [17]. Impaired cognitive capacities, on the other hand, may increase the risk of developing cardiometabolic disorders subsequently through unhealthy lifestyles including poor diet, less physical activity, and substance abuse [18, 19]. Negative symptoms also impact the cardiometabolic biomarkers due to the lack of autonomous motivation to maintain healthy lifestyles [20].

Genetic liability, side effects of atypical antipsychotics, and inadequate health-care services, may provoke the increasing cardiometabolic disorders in SSD [3]. However, previous results have also suggested that patients with SSD may be genetically predisposed to cardiometabolic disorders independent of antipsychotic side effects [21] as abnormal glucose and lipid metabolism have been observed in drug-naïve SSD patients. Meanwhile, several studies have shown a wide range of results. For instance, systolic blood pressure and glucose level were not correlated with cognitive impairment in Chinese patients with SSD found by Peng et al. [22]. Similarly, Depp et al. [23] reported no association between hypertension and obesity with cognitive ability.

The inconsistent findings are partly caused by methodological differences among studies, inclusion of patients at different stages of the illness, applied statistical modeling, selection of confounders, and misclassifications [10]. Additionally, neglecting the effect of disease heterogeneity could also be a reason causing the discrepancy [24]. In patients experiencing various levels of cognitive deficits, it is unclear whether all patients or only subgroups of patients are at a higher risk of developing cardiometabolic dysregulations. The associations, and absence of associations, found in previous studies need to be validated in a large sample. The mediated effect of the use of antipsychotics has not been tested in previous studies.

The relationship between clinical symptoms and cardiometabolic dysregulation is complex and multifaceted. Rates of non-treatment of cardiometabolic disorders ranged from 30.2% to 88.0% in patients with SSD [25]. Therefore, understanding these associations is crucial for comprehensive management of mental and physical health in patients with SSD. We investigated the relationships between longitudinal cognitive, positive, and negative symptoms trajectories with cardiometabolic biomarkers in patients with SSD from the Dutch national Genetic Risk and Outcome of Psychoses (GROUP) cohort [26]. We also tested whether and how much these possible associations are mediated by antipsychotics.

Methods

Study design and participants

This study included 1,119 patients with SSD at baseline from the GROUP cohort study, a multicenter longitudinal study in the Dutch population. Patients were included based on the following criteria: i) age range of 16 to 50 years at baseline (extremes included); ii) a diagnosis of non‐affective psychotic disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) criteria [27]; iii) good command of the Dutch language; and iv) able and willing to give written informed consent. In general, data was collected at enrolment and follow-up measurements approximately at 3-year and 6-year. The blood samples for metabolic biomarkers assay were collected at the 3-year follow-up. All patients from the GROUP study who had measurements for predictors and outcomes were included in the current study. The study protocol was centrally approved by the Ethical Review Board of the University Medical Center Utrecht and by local review boards of each participating institute. Details regarding sample characteristics, recruitment, and assessment procedures have been published elsewhere [26]. GROUP release number 8 was used for the current analyzes.

Measurements

Demographic and clinical characteristics

Patients were asked about demographic information, such as age, gender, ethnicity, education, number of cigarettes use per day, and number of alcohol units use per week. Clinical data were also collected through medical record review.

Cardiometabolic biomarkers

All eligible patients at the 3-year follow-up had physical examination and blood assay for biomarkers. Weight (kg), height (m), waist circumference (cm), and systolic (SBP) (mmHg) and diastolic blood pressure (DBP) (mmHg) data were collected by physical examination. Glycated hemoglobin (HbA1c) (mmol/mol), low-density lipoprotein (LDL) (mmol/l) cholesterol, high-density lipoprotein (HDL) (mmol/l) cholesterol, and triglycerides (TG) (mmol/l) levels were measured in the whole blood sample.

MetS was defined using the U.S. National Cholesterol Education Programme Adult Treatment Panel III (NCEP-ATP-III) [28], when any three of the following five features are present: i) waist circumference ≥ 88 cm in women or 102 cm in men; ii) BP ≥130/85 mmHg or being prescribed antihypertensives; iii) HDL cholesterol <50 mg/dl (=1.30 mmol/l) in women or <40 mg/dl (=1.03 mmol/l) in men; or being prescribed HDL increasing drugs; iv) triglycerides ≥150 mg/dl (=1.7 mmol/l) or being prescribed triglyceride-lowering drugs; and v) fasting plasma glucose ≥100 mg/dl [29] (=5.6 mmol/l) or being prescribed antidiabetics. As plasma glucose levels were not available, a HbA1c ≥5.1% (=32 mmol/mol) was used as a criterion [30].

Metabolic composite scores were calculated based on the definition of MetS by summing up the standardized value of each of the components [31]. Each individual mean blood pressure was standardized using mean arterial pressure (MAP). Means and standard deviations of the patients ranging within healthy reference values were used to standardize HDL (≥1.30 mmol/l in female and ≥ 1.03 mmol/l in male patients), TG (<1.7 mmol/l), and HbA1c (<32 mmol/mol). The HDL score was reversed because higher scores represent a better outcome. Finally, the average metabolic composite score was calculated by dividing the sum of all standardized components by five [31] and treated as a continuous value.

Cognitive function, positive and negative symptoms

Cognitive function was assessed using the consensus cognitive battery test called “Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS)” [32]. The eight assessment protocols included CPT performance and CPT variance of continuous performance test (CPT-HQ) [33], immediate and delayed recall of word learning task (WLT), and the digit symbol coding, block design, arithmetic, and information subtests of the Wechsler Adult Intelligence Scale (WAIS) III [34, 35]. A shortened version of WAIS III [36] that consists of digit symbol coding subset, and every second (or third) item of block design, information, and the arithmetic was administered at wave 3. At each wave of assessment, the tests used were administered in a fixed order approximately for 2 h with break in case of subject fatigue. Positive and negative symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) which consists of 30 items with a seven-point Likert scale that characterizes positive and negative symptoms and general psychopathology in patients [37].

Clinical trajectories

The clinical trajectory refers to the discernible pattern, such as stability, fluctuations, decline, or improvement, in the overall cognitive functioning and the severity of positive and negative symptoms. To ascertain groups of patients exhibiting similar patterns of cognitive function, as well as levels of positive and negative symptoms, we employed group-based trajectory modeling (GBTM) as described in detail elsewhere [38–40]. This analysis utilized composite scores from cognitive assessments and mean scores of PANSS positive and negative subscales, based on measurements collected at three time points of baseline, 3 years, and 6 years during the follow-up period spanning 6 years. Our previous studies have delineated five distinct cognitive trajectory groups, three distinct positive symptom trajectory groups, and three distinct negative symptom trajectory groups [38–40].

Antipsychotics use

We used the type of first prescribed antipsychotics at three waves and sorted as a categorical variable based on the risk of cardiometabolic side effects [41] as the following: i) low risk/no risk: typical antipsychotics, including haloperidol, flupentixol, penfluridol, pimozide, zuclopentixol, broomleridol, perfenazine, and pipamperon; ii) medium risk: atypical antipsychotics without metabolic side effect including aripiprazol, amisulpride, sulpiride, paliperidon, and risperidone; and iii) high risk: atypical antipsychotics with metabolic side effect including olanzapine, quetiapine, and clozapine. The missing data at 3-year and 6-year were imputed using best guest assumption by adjacent wave given the observations that the use of the antipsychotic remained stable in patients over the follow-up period.

Data analysis

Association analysis

One-way ANOVA and Kruskal-Wallis tests were conducted to compare the differences of cardiometabolic biomarkers among clinical trajectories when the cardiometabolic outcomes were normally distributed and not normally distributed, respectively. Post hoc analyzes were followed for pairwise comparisons by t-test and Dunn test and adjusted by Bonferroni correction method.

Linear regression models were fitted to regress each of the numerical cardiometabolic components as an outcome over clinical trajectories adjusted for age, gender, education level, ethnicity, IQ, illness duration, cigarette use, and alcohol. Multinomial logistic regression models were fitted using MetS and its components as exposure and clinical trajectories as an outcome adjusted for the above-mentioned confounders as well. A stepwise method was conducted to select the most important candidate predictor variables, with an entry-threshold set as Inline graphic and remove-threshold set as Inline graphic . Metabolic composite score was not modeled in multivariable multinomial logistic regressions to avoid overfitting given that composite score was calculated using the individual metabolic components.

Mediation analysis

The cross-sectional mediation analysis [42] of antipsychotics use (mediator) was performed using PROCESS macro [43] between the association of cognitive trajectory (main predictor), and each of nine metabolic components, as well as the metabolic composite score (outcome). The significant predictors of age, sex, ethnicity, education, illness duration, IQ, number of cigarettes use per day, and number of alcohol units use per week in regression results were included in the mediation models. In total, four regression models were fitted: model I on the association of predictors and mediators, model II on the association of predictors and outcome (direct effect), model III on the association of mediator and outcome (indirect effect), and model IV on the association of predictors and outcome without considering mediator. The significance level of direct and total effect was set to α = 0.05, whereas the significance of indirect effect was evaluated by non-parametric bootstrapping with 5,000 bootstrap samples. The indirect effect was determined whether the 95% confidence interval of coefficient estimated by bootstrapping contained zero. The reported effects and direct effect were unstandardized according to the recommendation from Hayes guidelines [44].

Sensitivity analysis

We performed sensitivity analyzes to assess the impact of outliers (deviated from mean ± 3 standard deviation) of each cardiometabolic biomarker.

Power calculation

For the association of clinical trajectories and cardiometabolic components, the required sample size was 44 observations calculated using G*Power software (version 3.1). We met the requirements (N = 1,119), with medium effect size f2 = 0.25 at α = 0.05, and power of 0.8 with 11 predictors. For the cross-sectional mediation analysis, we used simulation analysis method recommended by Fritz and Mackinnon [45], the required sample size was 78 observations with medium effect size (0.39) in both path A and path B, using percentile bootstrapping method.

Results

Demographic and clinical profiles at 3-year follow up

More than three-fourths (76.14%) of patients were male and the mean (±SD) age and age onset of SSD was 30.60 (±7.22) years and 23.07 (±7.81) years, respectively. Patients had an average duration of illness of 8.45 ± 4.44 years at wave 2. In the past 3 years, 41.18% of the patients had more than one psychotic episode. The majority (78.87%) of the patients were currently using antipsychotics. The most used antipsychotics were risperidone (17.82%), olanzapine (23.70), and clozapine (21.80%).

Of the 1,119 patients, 41.64%, 14.39%, and 2.32% had mild, moderate, and severe cognitive impairment, respectively. Similarly, 8.40% and 11.71% of patients had severe positive and negative symptoms, respectively. Detailed characteristics of clinical trajectories could be found in previous papers [46].

Mean (±SD) body mass index (BMI) was 26.11 (±4.87) kg/m2. Mean (±SD) TG, HDL cholesterol, and LDL cholesterol were 1.81 (±1.44), 1.24 (±0.63), 3.11 (±0.93) mmol/mol, respectively. Mean estimated DBP and SBP were 79.38 ± 11.04, 127.27 ± 15.28 mmHg, respectively (Table 1).

Table 1.

Descriptive characteristics of the sample of patients with SSD

Demographic characteristics
Age, mean years (SD) 30.60 (7.22)
Sex, male n (%) 852 (76.14)
Ethnicity, Caucasian n (%) 859 (79.24)
Years of education, mean (SD) 12.41 (3.81)
Marital status, n (%)
   Not married 930 (85.40)
   Married/living together 128 (11.75)
   Other (divorced and widowed) 18 (2.85)
Estimated IQ, mean (SD) 98.82 (16.61)
Age onset illness, mean (SD) 23.07 (7.81)
Duration of illness, mean (SD) 8.45 (4.44)
Number of psychotic episodes in the past 3 years n (%)
   More than 1 episode 334 (41.18)
   No episode 477 (58.82)
Use of antipsychotics n (%)
   Not currently using 37 (5.11)
   Currently using 571 (78.87)
   Unknown if currently using 116 (16.02)
Type of 1st prescribed antipsychotics at wave 2
   Olanzapine (Zyprexa) 137 (23.70)
   Clozapine (Leponex) 126 (21.80)
   Risperidone (Risperdal) 103 (17.82)
   Aripiprazol (Abilify) 71 (12.28)
   Quetiapine (Seroquel) 61 (10.55)
   Haloperidol (Haldol) 23 (3.98)
   Others 57 (9.86)
Cardiometabolic biomarkers, mean (SD)
   Body mass index (kg/m2) 26.11 (4.87)
   Waist circumference (cm) 95.00 (14.39)
   Glycated hemoglobin (mmol/mol) 35.06 (5.87)
   Triglycerides (mmol/l) 1.81 (1.44)
   High-density lipoprotein (mmol/l) 1.24 (0.63)
   Low-density lipoprotein (mmol/l) 3.11 (0.93)
   Diastolic blood pressure (mmHg) 79.38 (11.04)
   Systolic blood pressure (mmHg) 127.27 (15.28)
   Pulse rate (beat/min) 75.62 (15.40)
Cognitive trajectories, n (%)
   High 113 (10.10)
   Normal 353 (31.55)
   Mild 466 (41.64)
   Moderate 161 (14.39)
   Severe 26 (2.32)
Positive symptoms trajectories, n (%)
   Low 788 (70.42)
   Moderate 237 (21.18)
   High 94 (8.40)
Negative symptoms trajectories, n (%)
   Low 828 (73.99)
   High-decreased 160 (14.30)
   High-increased 131 (11.71)
Open in a new tab

Abbreviation: SSD, schizophrenia spectrum disorders.

Pairwise comparisons of cardiometabolic biomarkers

Patients with mild to severe cognitive impairment had higher BMI (meanmild = 26.78 kg/m2, meanmoderate = 27.69 kg/m2, P = 7.6e-08), waist circumference (meanmild = 97.00 cm, meanmoderate = 89.50 cm, P = 4.7e-08), TG (meanmoderate = 2.25 mmol/mol, P = 0.031), DBP (meanmild = 80.82 mmHg, P = 0.02), pulse rate (meanmild = 76.22 beats/min, meanmoderate = 82.30 beats/min, P = 1.1e-05) and metabolic composite score (meanmoderate = 7.40, P = 0.00012) and lower HDL cholesterol (mean = 1.07 mmol/mol, P = 0.00016) compared to patients with “High” and “Normal” cognitive function (Figure 1).

Figure 1.

Figure 1.

Open in a new tab

The cardiometabolic profiles of cognitive trajectories (coding represents 1, high; 2, normal; 3, mild; 4, moderate; 5, severe cognitive trajectory).

Patients with severe positive symptoms had a significantly higher LDL level (meanhigh = 3.46 mmol/mol, P = 0.02), pulse rate (meanhigh = 80.68 beats/min, P = 0.0037) and metabolic composite score (meanhigh = 7.37, P = 0.027), and lower HDL (meanhigh = 1.09 mmol/mol, P = 0.018) compared with those with “low” severity positive symptoms (Figure 2). The mean HDL levels (meanhigh-increased = 1.10 mmol/mol, P = 0.008) of patients with more severe negative symptoms were significantly lower than that in “low” severity subgroup (Figure 3).

Figure 2.

Figure 2.

Open in a new tab

The cardiometabolic profiles of positive symptoms trajectories (coding represents 1, low; 2, moderate; 3, high positive symptoms trajectory).

Figure 3.

Figure 3.

Open in a new tab

The cardiometabolic profiles of negative symptoms trajectories (coding represents 1, low; 2, high-decreased; 3, high-increased negative symptoms trajectory).

Cognitive trajectories and cardiometabolic biomarkers

Cognitive impairment was significantly associated with increased BMI (mean difference [β], βhigh = −1.24, 95% CI = –2.28 to 0.20, P = 0.02), TG (βmoderate = 0.54, 95% CI = 0.17 to 0.92, P < 0.001), and SBP (βmild = 2.74, 95% CI = 0.11 to 5.37, P = 0.04, Table 2A). No significant associations were observed in multinomial regression of cognitive trajectories (Table 3A).

Table 4.

The mediated effect of antipsychotics in the relationship of cognitive trajectories and cardiometabolic biomarkers

Predictors β (95% CI) Total effect Direct effect Indirect effect A-path effect
BMI 1.19 (0.78, 1.61)*** 1.19 (0.78, 1.61)*** −0.00(−0.02, 0.02) 0.00 (−0.06, 0.07)
WC 1.79 (−0.22, 3.80)* 1.79 (−0.23, 3.81)* −0.01 (−0.21, 0.17) −0.09 (−0.20, 0.02)
TG 0.10 (−0.14, 0.35) 0.10 (−0.15, 0.34 0.01 (−0.02, 0.04) −0.09(−0.20, 0.02)*
Reversed HDL −0.03 (−0.15, 0.09) −0.03 (−0.16, 0.09) 0.00 (−0.01, 0.02) −0.10 (−0.22, 0.01)
LDL −0.01 (−0.10, 0.08) −0.01 (−0.10, 0.08) −0.00 (−0.01, 0.01) 0.01 (−0.06, 0.08)
HbA1c 0.30 (−0.22, 0.891) 0.29 (−0.22, 0.80) 0.01 (−0.02, 0.05) −0.02 (−0.10, 0.05)
DBP −0.03 (−1.64, 1.59) −0.04 (−1.66, 1.58) 0.01 (−0.11, 0.15) −0.06 (−0.17, 0.04)
SBP 1.21 (−0.11, 2.52)* 1.22 (−0.09, 2.64) −0.01 (−0.11, 0.05) 0.02 (−0.04, 0.09)
PR 1.64 (−0.59, 3.86) 1.47 (−0.76, 3.69) 0.17 (−0.06, 0.53) −0.08 (−0.19, 0.02)
MCS 0.05 (−0.13, 0.23) 0.04 (−0.14, 0.22) 0.01 (−0.01, 0.04) −0.10 (−0.23, 0.02)*
Open in a new tab

Abbreviations: β, effect size; CI, confidence interval; BMI, body mass index; WC, waist circumference; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycated hemoglobin; DBP, diastolic blood pressure; SBP, systolic blood pressure; PR, pulse rate; MCS, metabolic composite score.

Significance level: ***P-value <0.001; **P-value <0.05; *P-value <0.1.

Table 2.

Linear association of cardiometabolic biomarkers over clinical trajectories

Predictors
β (95%CI) 		BMI
(N=559) 		WC
(N= 532) 		TG
(N=474) 		Reversed HDL
(N=474) 		LDL
(N=467) 		HbA1c
(N=456) 		DBP
(N=546) 		SBP
(N=546) 		PR
(N=544) 		MCS
(N=404)
A. Cognitive trajectory
High -1.24 (-2.28-0.20)** -3.25 (-8.03,1.79) Removed Removed Removed Removed -2.71 (-5.59,0.17)⁎ Removed Removed -0.07(-0.52,0.38)
Normal -1.54 (-3.28,0.85)* -2.55 (-5.57,0.46) Removed -0.11 (-0.24,0.02) Removed Removed Removed Removed Removed -0.21(-0.48,0.06)
Mild Removed Removed Removed Removed Removed Removed 1.84 (-0.11,3.78) ⁎ 2.74 (0.11,5.37)** 1.36(-1.93,4.65) Removed
Moderate Removed Removed 0.54 (0.17,0.92)*** Removed Removed Removed Removed Removed 4.31(-0.54,9.15)⁎ Removed
Severe Removed Removed Removed Removed Removed Removed Removed Removed Removed Removed
B. Positive symptoms trajectory
Low Removed Removed Removed Removed Removed -2.01(-3.21, -0.82)** Removed Removed -2.25(-5.17, -0.67) Removed
Moderate Removed Removed Removed Removed Removed Removed Removed Removed Removed Removed
High Removed Removed Removed Removed Removed Removed Removed Removed Removed Removed
C. Negative symptoms trajectory
Low Removed Removed Removed -0.11 (-0.24,0.02) Removed Removed Removed Removed Removed -0.20(-0.43,0.03)⁎
High-Decreased Removed Removed Removed Removed Removed Removed Removed Removed Removed Removed
High-Increased Removed Removed Removed Removed Removed Removed Removed -3.92(-7.66,-0.17)** Removed Removed
D. Covariates
Age 0.02 (-0.04, 0.08) 0.21 (0.03, 0.38)** 0.01 (-0.01, 0.03) 0.01 (0.00, 0.02)* 0.03 (0.02, 0.04)*** 0.22 (0.14, 0.30)*** 0.17 (0.05, 0.29)** 0.14 (-0.03, 0.31) -0.25(-0.44,-0.05)** 0.03(0.01,0.04)***
Gender (Female) 0.30 (-0.32, 1.23) -4.56 (-7.19, -1.92)*** -0.55 (-0.87, 0.22)*** -0.26 (-0.40, -0.11)*** -0.08 (-0.28, 0.12) -1.69 (-3.02, -0.36)** -0.99 (-3.14, 1.17) -9.41 (-12.39, -6.42)*** 1.42(-1.63,4.47) -0.62(-0.86,-0.39)***
Ethnicity (Caucasian) Removed Removed Removed Removed Removed Removed Removed Removed Removed Removed
IQ -0.03 (-0.06,0.00)* -0.12(-0.21,-0.02)** Removed Removed Removed Removed Removed Removed -0.09 (-0.19, 0.01)⁎ -0.01 (-0.02, 0.00)⁎
Illness duration 0.19 (0.10, 0.29)*** 0.37(0.10, 0.65)** Removed Removed Removed Removed Removed Removed 0.50(0.18,0.81)** Removed
Education Removed Removed Removed Removed Removed Removed -0.27 (-0.50, -0.04)** Removed Removed Removed
Cigarettes use Removed Removed Removed Removed Removed Removed Removed Removed 0.10(0.02,0.19)** Removed
Alcohol use Removed Removed Removed Removed 0.01 (0.00, 0.01)* Removed 0.08(-0.00,0.16) ⁎ Removed Removed Removed
Open in a new tab

Abbreviations: β: effect size; CI: Confidence Interval; BMI: body mass index; WC: waist circumference; TG: Triglycerides; HDL: Reversed High density lipoprotein; LDL: Low density lipoprotein; HbA1c: Glycated haemoglobin; DBP: Diastolic blood pressure; SBP: Systolic blood pressure; PR: Pulse rate; MCS: Metabolic composite score

Removed: The variable was excluded from the final model

N: sample size of the model fitting

Significance level: ***: P-value < 0.001; **: P-value < 0.05; ⁎: P-value < 0.1

Table 3.

The association of clinical trajectories over cardiometabolic biomarkers

Predictors OR (95% CI) A. Cognitive trajectories
Normal (N = 150) Mild (N = 136) Moderate (N = 55) Severe (N = 6)
Age 1.07 (0.99, 1.15) 1.14 (1.04, 1.25)** 1.17 (1.06,1.30)** 1.20 (1.02, 1.42)**
Gender (Female) 0.92 (0.34, 2.50) 1.02 (0.32, 3.25) 1.86 (0.44, 7.91) 2.14 (0.15, 31.52)
IQ 0.90 (0.87, 0.93)*** 0.79 (0.76, 0.83)*** 0.69 (0.64, 0.73)*** 0.58 (0.47,0.70)***
Illness duration 1.02 (0.93, 1.12) 0.98 (0.87, 1.10) 1.09 (0.94, 1.26) 0.89 (0.62, 1.28)
Cigarettes use 1.02 (0.99, 1.06) 1.01 (0.97, 1.05) 1.03 (0.98, 1.08) 0.97 (0.87, 1.09)
HbA1c 1.07 (0.96,1.18) 1.11 (0.99, 1.24)*** 1.13 (0.99, 1.28)*** 0.97 (0.76, 1.23)
Open in a new tab

Abbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; HDL, high-density lipoprotein; HbA1c, glycated hemoglobin; N, sample size of the model fitting; OR, odds ratio; SBP, systolic blood pressure; WC, waist circumference.

Note: Reference category of cognitive trajectories: high-performance (n = 46); reference category of positive symptoms trajectories: low (n = 280); reference category of negative symptoms trajectories: low (n = 288).

Significance level: ***P-value <0.001; **P-value <0.05; *P-value <0.1.

Positive symptoms trajectories and cardiometabolic biomarkers

Increased HbA1c levels were associated with severity of positive symptoms in both linear (βlow = −2.01, 95% CI = −3.21 to −0.82, P = 0.001, Table 2B) and multinomial (ORmoderate = 1.05, 95% CI = 1.01 to 1.10, P = 0.024; ORhigh = 1.08, 95% CI = 1.02 to 1.14, P = 0.006, Table 3B) regression.

Negative symptoms trajectories and cardiometabolic biomarkers

Cardiometabolic outcomes were not associated with negative symptoms in the linear model (Table 2C). In the multinomial model, more severe negative symptoms were associated with increased DBP (ORhigh-decreaced = 1.04, 95% CI = 1.01 to 1.08, P = 0.02; ORhigh-increaced = 1.04, 95% CI = 1.00 to 1.08, P = 0.048) and decreased HDL (ORhigh-increased = 6.25, 95% CI = 1.81 to 21.59, P = 0.004, Table 3C).

Mediation analysis for antipsychotics

The direct effect (βtotal = 1.19, 95% CI = 0.78 to 1.61, P < 0.001) and total effect (βdirect = 1.19, 95% CI = 0.78 to 1.61, P < 0.001) of cognitive performance on BMI were significant. Antipsychotic use was neither related to cognitive performance (A-path effect, P > 0.05) nor cardiometabolic parameters (indirect effect, bootstrapped CI contained zero). Nondirect or indirect significant effects were observed on other cardiometabolic components, and metabolic composite score (Figure 4 and Table 4).

Figure 4.

Figure 4.

Open in a new tab

The path model of mediation analysis, e.g., BMI. See also Table 4.

Sensitivity analysis

Outliers had low effect on cardiometabolic outcomes. Specifically, there were still significant associations between cognitive impairment and TG (βmoderate = 0.34, 95% CI = 0.05 to 0.64, P = 0.02), DBP (βhigh = −4.26, 95% CI = −7.02 to −1.51, P = 0.002), and SBP (βhigh = −5.00, 95% CI = −8.74 to −1.26, P = 0.009; βhigh = −3.85, 95% CI = 6.33 to −0.57, P = 0.002). However, the association with BMI disappeared. Decreased HbA1c was associated with more severe negative symptoms (βhigh-decreased = −1.23, 95% CI = −2.44 to −0.01, P = 0.049, Supplementary Table S1).

Discussion

We investigated the association between cardiometabolic biomarkers, metabolic composite score, and cognitive, positive, negative, and symptoms trajectories in patients with SSD. We found that increased cognitive impairment was significantly associated with increased BMI, TG, and SBP. The higher severity of positive symptoms was also associated with increased HbA1c. We also found that increased DBP and decreased HDL cholesterol were associated with increased severity of negative symptoms. We found no mediation effect for antipsychotics.

Cognitive impairment and BMI

We found a significant association between cognitive inefficiencies and increased BMI, in line with previous findings in the Chinese [47] and Japanese [48] populations. On the other hand, several factors could affect the BMI level in patients with SSD. First, people with reduced cognitive function are more likely to become overweight or obese because of a decline in executive function [49], which will lead to less frequent energy maintain behaviors like self-monitoring [50]. Besides, a “selfish brain” theory [51, 52] has been put forward and discussed by researchers, stating that cognitive impairment would contribute to an inefficient regulation of brain energy which increases the risk of metabolic dysfunctions.

Cognitive impairment and blood pressure

We found that cognitive impairment was associated with increased SBP, while both increased diastolic and systolic blood pressure were not associated with cognitive impairment. Our results were partly in line with previous evidence which suggested high blood pressure [53, 54]may cause disruption of the blood–brain barrier and lead to structural abnormalities in blood vessels. The micro- and macro-cerebrovascular alteration and diminished blood flow may eventually lead to memory impairment and other cognitive dysfunction [55].

Cognitive impairment and dyslipidemia

The relationship between cognitive impairment and dyslipidemia remains controversial. We observed an association between cognitive impairment and TG, but no significant relation was found between HDL and LDL. Liu et al. [56] found the association between lipid parameters and cognitive impairment was heterogeneous in age and gender subgroups. On the contrary, the nonsignificant result was reported by a recent systematic review that cognitive function in all domains did not differ in SSD with or without dyslipidemia [57].

Cognitive impairment and HbA1c

There was no association between HbA1c and cognitive impairment. Previous studies have suggested a correlation between HbA1c level and poor cognition in recent onset psychosis patients [58, 59]. Most of the previous results were found in patients with diabetes or in elderly population [60, 61], and with differences in race among the included subjects. Chronic hyperglycemia decreases glucose transfer through the blood–brain barriers, resulting in the loss of acetylcholine [62, 63] and dysregulation of cortical neurons [64]. Most patients in our samples who had a recent psychosis onset did not exceed the HbA1c threshold of diabetes, which seems to suggest that the association between glucose level and cognitive dysfunction may only be present in patients with a longer illness duration and more severe hyperglycemia.

Positive and negative symptoms and cardiometabolic biomarkers

Our study indicated that increased positive symptom severity was associated with increased HbA1c levels, while negative symptom severity was related to DBP and HDL cholesterol. The relationship between cardiometabolic parameters and positive and negative symptoms remains inconclusive. Chen et al.’s study [65] suggested negative association between insulin resistance and positive symptoms in Chinese SSD patients, though no correlation was found with negative symptoms. Wedervang-Resell et al. [66] found a correlation between higher PANSS negative score and elevated TG levels. Conversely, the severity of negative symptoms exhibited an inverse association with BMI and TG levels, and a positive association with HDL levels, while no correlation was observed between positive symptoms and cardiometabolic parameters [67, 68]. Consequently, further investigation is needed to determine whether the severity of positive and negative symptoms influences the risk of developing cardiometabolic outcomes.

Antipsychotics and cardiometabolic biomarkers

We found the association between cognitive symptoms and cardiometabolic parameters is independent of the use of antipsychotics. The side effect of second-generation antipsychotics is often seen as an important factor to develop cardiometabolic outcomes [69]. For example, Gupta et al. found atypical antipsychotics were related to glucose dysregulation or diabetes mellitus [70]; Melkersson et al. observed elevated levels of insulin and blood lipids in patients treated with olanzapine [71]. However, we did not observe an indirect effect for the use of antipsychotics on cardiometabolic biomarkers in our samples. This may be ascribed to the antipsychotic medication switch in clinical practice based on the appearance of adverse effects [72] like weight gain, which has not been captured at follow-up point. An alternative explanation could be that patients who exhibit poor medication compliance might experience less antipsychotic-induced cardiometabolic disorders.

Strengths and limitations

This comprehensive study had a large sample of patients with SSD. The assessments of these symptoms were also comprehensive, which contributes to the accurate estimate of long-term cognitive trajectories and psychotic symptoms trajectories. Along with these advantages, this is a cross-sectional analysis, which hampers evaluation of the causal effect of cognitive impairment on metabolic outcomes or vice versa. Secondly, the mean age of our samples is young, which can lead to underestimate the effect of cardiometabolic multimorbidity in subjects. Thirdly, while our study focused on the primary factors of interest, it’s worth noting several factors like diet, exercise, and concomitant medications that might impact the cardiometabolic outcomes, were not taken into our analysis. Although their effect could be less likely to bias the estimates of associations, it may potentially neglect the assessment of studying interactions. Finally, our study followed up to 6 years. However, considering the long-term nature of clinical symptoms and cardiometabolic dysregulations in patients with SSD, for example, cardiometabolic dysregulations could be associated with the decline of cognitive function over 6 years in middle-aged [73] and old [74] populations, it would be an important avenue for future research.

Clinical and public health implementations

The current findings emphasize the need for regular monitoring and screening of cardiometabolic risk biomarkers in patients with SSD. Earlier interventions such as dosage adjustment or switching to different antipsychotics with a lower metabolic risk if necessary, would help to decrease the risk of developing cardiovascular diseases in their later life.

Conclusion

We demonstrated the association between BMI, TG and SBP and cognitive impairment, and between elevated levels of HbA1c, HDL cholesterol, and DBP with positive and negative symptoms in patients with SSD. The results suggested poorer cardiometabolic parameters are associated with both worse cognitive function and more severe schizophrenia symptoms. The observed associations underscore the need for early identification of patients with SSD at risk of cardiometabolic outcomes. Future studies would investigate patients with a wider age range and severity of metabolic complications to elucidate the underlying causality of the observed associations. For instance, studies have highlighted that inflammation is a shared characteristic of both cardiometabolic disorders and psychosis [75]. Therefore, it is advisable to include inflammatory biomarkers, such as C-reactive protein, and pro-inflammatory cytokines like interleukin-6, for a more comprehensive exploration.

Supporting information

Zhao et al. supplementary material

Zhao et al. supplementary material

Acknowledgements

We are grateful for the generosity of time and effort by the patients, their families, and healthy subjects. Furthermore, we would like to thank all research personnel involved in the GROUP project, in particular, Joyce van Baaren, Erwin Veermans, Ger Driessen, Truda Driesen, Erna van’t Hag.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1192/j.eurpsy.2023.2477.

Author contribution

Conceptualization, methodology, and project administration: R.B. and B.Z.A.; Supervision: E.J.L., R.B., and B.Z.A. Data curation, formal analysis, visualization, and writing ─ original draft: C.Z., T.D.H.; Writing ─ review & editing: C.Z., T.D.H., E.N., E.J.L., R.B., B.Z.A., and GROUP investigators; Investigation: GROUP investigators; Funding acquisition and resources: GROUP investigators.

Financial support

The infrastructure for the GROUP study is funded through the Geestkracht programme of the Dutch Health Research Council (Zon-Mw, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical center, The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal and Delta.) C.Z. is funded by China Scholarship Council and Graduate School of Medical Science, University Medical Center Groningen.

Competing interest

The authors declare none.

References

  • [1].Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388(10039):86–97. doi: 10.1016/S0140-6736(15)01121-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Hennekens CH, Hennekens AR, Hollar D, Casey DE. Schizophrenia and increased risks of cardiovascular disease. Am Heart J. 2005;150(6):1115–21. doi: 10.1016/j.ahj.2005.02.007. [DOI] [PubMed] [Google Scholar]
  • [3].De Hert M, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry. 2011;10(1):52–77. doi: 10.1002/j.2051-5545.2011.tb00014.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Correll CU, Solmi M, Veronese N, Bortolato B, Rosson S, Santonastaso P, et al. 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 2017;16(2):163–80. doi: 10.1002/wps.20420. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Correll CU, Bitter I, Hoti F, Mehtala J, Wooller A, Pungor K, et al. Factors and their weight in reducing life expectancy in schizophrenia. Schizophr Res. 2022;250:67–75. doi: 10.1016/j.schres.2022.10.019. [DOI] [PubMed] [Google Scholar]
  • [6].Mazereel V, Detraux J, Vancampfort D, van Winkel R, De Hert M. Impact of psychotropic medication effects on obesity and the metabolic syndrome in people with serious mental illness. Front Endocrinol (Lausanne). 2020;11:573479. doi: 10.3389/fendo.2020.573479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Atmaca M, Kuloglu M, Tezcan E, Ustundag B. Serum leptin and triglyceride levels in patients on treatment with atypical antipsychotics. J Clin Psychiatry. 2003;64(5):598–604. doi: 10.4088/jcp.v64n0516. [DOI] [PubMed] [Google Scholar]
  • [8].Suvisaari JM, Saarni SI, Perala J, Suvisaari JV, Harkanen T, Lonnqvist J, et al. Metabolic syndrome among persons with schizophrenia and other psychotic disorders in a general population survey. J Clin Psychiatry. 2007;68(7):1045–55. doi: 10.4088/jcp.v68n0711. [DOI] [PubMed] [Google Scholar]
  • [9].Haupt DW, Newcomer JW. Hyperglycemia and antipsychotic medications. J Clin Psychiatry. 2001;62(Suppl 27):15–26. [PubMed] [Google Scholar]
  • [10].Bora E, Akdede BB, Alptekin K. The relationship between cognitive impairment in schizophrenia and metabolic syndrome: a systematic review and meta-analysis. Psychol Med. 2017;47(6):1030–40. doi: 10.1017/S0033291716003366. [DOI] [PubMed] [Google Scholar]
  • [11].Goughari AS, Mazhari S, Pourrahimi AM, Sadeghi MM, Nakhaee N. Associations between components of metabolic syndrome and cognition in patients with schizophrenia. J Psychiatr Pract. 2015;21(3):190–7. doi: 10.1097/PRA.0000000000000065. [DOI] [PubMed] [Google Scholar]
  • [12].Dickinson D, Gold JM, Dickerson FB, Medoff D, Dixon LB. Evidence of exacerbated cognitive deficits in schizophrenia patients with comorbid diabetes. Psychosomatics. 2008;49(2):123–31. doi: 10.1176/appi.psy.49.2.123. [DOI] [PubMed] [Google Scholar]
  • [13].Friedman JI, Wallenstein S, Moshier E, Parrella M, White L, Bowler S, et al. The effects of hypertension and body mass index on cognition in schizophrenia. Am J Psychiatry. 2010;167(10):1232–9. doi: 10.1176/appi.ajp.2010.09091328. [DOI] [PubMed] [Google Scholar]
  • [14].Han M, Huang XF, Chen DC, Xiu M, Kosten TR, Zhang XY. Diabetes and cognitive deficits in chronic schizophrenia: a case-control study. PLoS One. 2013;8(6):e66299. doi: 10.1371/journal.pone.0066299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Takayanagi Y, Cascella NG, Sawa A, Eaton WW. Diabetes is associated with lower global cognitive function in schizophrenia. Schizophr Res. 2012;142(1–3):183–7. doi: 10.1016/j.schres.2012.08.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Wysokiński A, Dzienniak M, Kłoszewska I. Effect of metabolic abnormalities on cognitive performance and clinical symptoms in schizophrenia. Arch Psychiatry Psychother. 2013;15(4):13–25. doi: 10.12740/app/19967. [DOI] [Google Scholar]
  • [17].Botis AC, Miclutia I, Vlasin N. Cognitive function in female patients with schizophrenia and metabolic syndrome. Eur Psychiatry. 2016;33(S1):S97. doi: 10.1016/j.eurpsy.2016.01.070. [DOI] [Google Scholar]
  • [18].Scott D, Happell B. The high prevalence of poor physical health and unhealthy lifestyle behaviours in individuals with severe mental illness. Issues Ment Health Nurs. 2011;32(9):589–97. doi: 10.3109/01612840.2011.569846. [DOI] [PubMed] [Google Scholar]
  • [19].Green MF, Kern RS, Braff DL, Mintz J. Neurocognitive deficits and functional outcome in schizophrenia: are we measuring the “right stuff”? Schizophr Bull. 2000;26(1):119–36. doi: 10.1093/oxfordjournals.schbul.a033430. [DOI] [PubMed] [Google Scholar]
  • [20].Vancampfort D, De Hert M, Stubbs B, Ward PB, Rosenbaum S, Soundy A, et al. Negative symptoms are associated with lower autonomous motivation towards physical activity in people with schizophrenia. Compr Psychiatry. 2015;56:128–32. doi: 10.1016/j.comppsych.2014.10.007. [DOI] [PubMed] [Google Scholar]
  • [21].So HC, Chau KL, Ao FK, Mo CH, Sham PC. Exploring shared genetic bases and causal relationships of schizophrenia and bipolar disorder with 28 cardiovascular and metabolic traits. Psychol Med. 2019;49(8):1286–98. doi: 10.1017/S0033291718001812. [DOI] [PubMed] [Google Scholar]
  • [22].Peng XJ, Hei GR, Li RR, Yang Y, Liu CC, Xiao JM, et al. The association between metabolic disturbance and cognitive impairments in early-stage schizophrenia. Front Hum Neurosci. 2021;14:599720. doi: 10.3389/fnhum.2022.1094810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Depp CA, Strassnig M, Mausbach BT, Bowie CR, Wolyniec P, Thornquist MH, et al. Association of obesity and treated hypertension and diabetes with cognitive ability in bipolar disorder and schizophrenia. Bipolar Disord. 2014;16(4):422–31. doi: 10.1111/bdi.12200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Shmukler AB, Gurovich IY, Agius M, Zaytseva Y. Long-term trajectories of cognitive deficits in schizophrenia: a critical overview. Eur Psychiatry. 2015;30(8):1002–10. doi: 10.1016/j.eurpsy.2015.08.005. [DOI] [PubMed] [Google Scholar]
  • [25].Nasrallah HA, Meyer JM, Goff DC, McEvoy JP, Davis SM, Stroup TS, et al. Low rates of treatment for hypertension, dyslipidemia and diabetes in schizophrenia: data from the CATIE schizophrenia trial sample at baseline. Schizophr Res. 2006;86(1–3):15–22. doi: 10.1016/j.schres.2006.06.026. [DOI] [PubMed] [Google Scholar]
  • [26].Korver N, Quee PJ, Boos HB, Simons CJ, de Haan L, Investigators Group. Genetic risk and outcome of psychosis (GROUP), a multi-site longitudinal cohort study focused on gene-environment interaction: objectives, sample characteristics, recruitment and assessment methods. Int J Methods Psychiatr Res. 2012;21(3):205–21. doi: 10.1002/mpr.1352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].APA. Diagnostic and statistical manual of mental disorders (DSM‐IV‐TR). 4th ed. Washington, DC: American Psychiatric Association; 2000. [Google Scholar]
  • [28].Expert Panel on Detection Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III).JAMA. 2001;285(19):2486–97. doi: 10.1001/jama.285.19.2486. [DOI] [PubMed] [Google Scholar]
  • [29].Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care. 2003;26(11):3160–7. doi: 10.2337/diacare.26.11.3160. [DOI] [PubMed] [Google Scholar]
  • [30].International Expert Committee. International Expert committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32(7):1327–34. doi: 10.2337/dc09-9033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Eisenmann JC. On the use of a continuous metabolic syndrome score in pediatric research. Cardiovasc Diabetol. 2008;7(1):17. doi: 10.1186/1475-2840-7-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Nuechterlein KH, Green MF, Kern RS, Baade LE, Barch DM, Cohen JD, et al. The MATRICS consensus cognitive battery, part 1: test selection, reliability, and validity. Am J Psychiatry. 2008;165(2):203–13. doi: 10.1176/appi.ajp.2007.07010042. [DOI] [PubMed] [Google Scholar]
  • [33].Smid HG, de Witte MR, Homminga I, van den Bosch RJ. Sustained and transient attention in the continuous performance task. J Clin Exp Neuropsychol. 2006;28(6):859–83. doi: 10.1080/13803390591001025. [DOI] [PubMed] [Google Scholar]
  • [34].Blyler CR, Gold JM, Iannone VN, Buchanan RW. Short form of the WAIS-III for use with patients with schizophrenia. Schizophr Res. 2000;46(2–3):209–15. doi: 10.1016/s0920-9964(00)00017-7. [DOI] [PubMed] [Google Scholar]
  • [35].Wechsler D. WAIS-III: administration and scoring manual: Wechsler adult intelligence scale. 3rd ed. San Antonio, TX: Psychological Corporation; 1997. [Google Scholar]
  • [36].Velthorst E, Levine SZ, Henquet C, de Haan L, van Os J, Myin-Germeys I, et al. To cut a short test even shorter: reliability and validity of a brief assessment of intellectual ability in schizophrenia—a control-case family study. Cogn Neuropsychiatry. 2013;18(6):574–93. doi: 10.1080/13546805.2012.731390. [DOI] [PubMed] [Google Scholar]
  • [37].Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261–76. doi: 10.1093/schbul/13.2.261. [DOI] [PubMed] [Google Scholar]
  • [38].Islam MA, Habtewold TD, van Es FD, Quee PJ, van den Heuvel ER, Alizadeh BZ, et al. Long-term cognitive trajectories and heterogeneity in patients with schizophrenia and their unaffected siblings. Acta Psychiatr Scand. 2018;138(6):591–604. doi: 10.1111/acps.12961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Habtewold TD, Rodijk LH, Liemburg EJ, Sidorenkov G, Boezen HM, Bruggeman R, et al. A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits. Transl Psychiatry. 2020;10(1):244. doi: 10.1038/s41398-020-00919-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Habtewold TD, Tiles-Sar N, Liemburg EJ, Sandhu AK, Islam MA, Boezen HM, et al. Six-year trajectories and associated factors of positive and negative symptoms in schizophrenia patients, siblings, and controls: genetic risk and outcome of psychosis (GROUP) study. Sci Rep. 2023;13(1):9391. doi: 10.1038/s41598-023-36235-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Pillinger T, McCutcheon RA, Vano L, Mizuno Y, Arumuham A, Hindley G, et al. Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis. Lancet Psychiatry. 2020;7(1):64–77. doi: 10.1016/S2215-0366(19)30416-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82. doi: 10.1037//0022-3514.51.6.1173. [DOI] [PubMed] [Google Scholar]
  • [43].Hayes AF, Rockwood NJ. Regression-based statistical mediation and moderation analysis in clinical research: observations, recommendations, and implementation. Behav Res Ther. 2017;98:39–57. doi: 10.1016/j.brat.2016.11.001. [DOI] [PubMed] [Google Scholar]
  • [44].Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. 3rd ed. New York: The Guilford Press; 2022. [Google Scholar]
  • [45].Fritz MS, Mackinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233–9. doi: 10.1111/j.1467-9280.2007.01882.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Islam MA, Habtewold TD, van Es FD, Quee PJ, van den Heuvel ER, Alizadeh BZ, et al. Long-term cognitive trajectories and heterogeneity in patients with schizophrenia and their unaffected siblings. Acta Psychiatr Scand. 2018;138(6):591–604. doi: 10.1111/acps.12961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Guo X, Zhang Z, Wei Q, Lv H, Wu R, Zhao J. The relationship between obesity and neurocognitive function in Chinese patients with schizophrenia. BMC Psychiatry. 2013;13:109. doi: 10.1186/1471-244X-13-109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [48].Hidese S, Matsuo J, Ishida I, Hiraishi M, Teraishi T, Ota M, et al. Relationship of handgrip strength and body mass index with cognitive function in patients with schizophrenia. Front Psych. 2018;9:156. doi: 10.3389/fpsyt.2018.00156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [49].Salthouse TA, Atkinson TM, Berish DE. Executive functioning as a potential mediator of age-related cognitive decline in normal adults. J Exp Psychol Gen. 2003;132(4):566–94. doi: 10.1037/0096-3445.132.4.566. [DOI] [PubMed] [Google Scholar]
  • [50].Gunstad J, Paul RH, Cohen RA, Tate DF, Spitznagel MB, Gordon E. Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr Psychiatry. 2007;48(1):57–61. doi: 10.1016/j.comppsych.2006.05.001. [DOI] [PubMed] [Google Scholar]
  • [51].Mansur RB, Brietzke E. The “selfish brain” hypothesis for metabolic abnormalities in bipolar disorder and schizophrenia. Trends Psychiatry Psychother. 2012;34(3):121–8. doi: 10.1590/s2237-60892012000300003. [DOI] [PubMed] [Google Scholar]
  • [52].Peters A. The selfish brain: competition for energy resources. Am J Hum Biol. 2011;23(1):29–34. doi: 10.1002/ajhb.21106. [DOI] [PubMed] [Google Scholar]
  • [53].Elias MF, Wolf PA, D’Agostino RB, Cobb J, White LR. Untreated blood pressure level is inversely related to cognitive functioning: the Framingham study. Am J Epidemiol. 1993;138(6):353–64. doi: 10.1093/oxfordjournals.aje.a116868. [DOI] [PubMed] [Google Scholar]
  • [54].Novak V, Hajjar I. The relationship between blood pressure and cognitive function. Nat Rev Cardiol. 2010;7(12):686–98. doi: 10.1038/nrcardio.2010.161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [55].Jennings JR, Muldoon MF, Ryan CM, Mintun MA, Meltzer CC, Townsend DW, et al. Cerebral blood flow in hypertensive patients: an initial report of reduced and compensatory blood flow responses during performance of two cognitive tasks. Hypertension. 1998;31(6):1216–22. doi: 10.1161/01.hyp.31.6.1216. [DOI] [PubMed] [Google Scholar]
  • [56].Liu H, Huang Z, Zhang X, He Y, Gu S, Mo D, et al. Association between lipid metabolism and cognitive function in patients with schizophrenia. Front Psych. 2022;13:1013698. doi: 10.3389/fpsyt.2022.1013698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [57].Hagi K, Nosaka T, Dickinson D, Lindenmayer JP, Lee J, Friedman J, et al. Association between cardiovascular risk factors and cognitive impairment in people with schizophrenia: a systematic review and meta-analysis. JAMA Psychiatry. 2021;78(5):510–8. doi: 10.1001/jamapsychiatry.2021.0015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [58].Tang SX, Oliver LD, Hansel K, DeRosse P, John M, Khairullah A, et al. Metabolic disturbances, hemoglobin A1c, and social cognition impairment in schizophrenia spectrum disorders. Transl Psychiatry. 2022;12(1):233 doi: 10.1038/s41398-022-02002-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [59].Montalvo I, Gonzalez-Rodriguez A, Cabezas A, Gutierrez-Zotes A, Sole M, Algora MJ, et al. Glycated haemoglobin is associated with poorer cognitive performance in patients with recent-onset psychosis. Front Psych. 2020;11:455. doi: 10.3389/fpsyt.2020.00455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [60].Di Bonito P, Di Fraia L, Di Gennaro L, Vitale A, Lapenta M, Scala A, et al. Impact of impaired fasting glucose and other metabolic factors on cognitive function in elderly people. Nutr Metab Cardiovasc Dis. 2007;17(3):203–8. doi: 10.1016/j.numecd.2005.07.011. [DOI] [PubMed] [Google Scholar]
  • [61].Moheet A, Mangia S, Seaquist ER. Impact of diabetes on cognitive function and brain structure. Ann N Y Acad Sci. 2015;1353:60–71. doi: 10.1111/nyas.12807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [62].Ragozzino ME, Unick KE, Gold PE. Hippocampal acetylcholine release during memory testing in rats: augmentation by glucose. Proc Natl Acad Sci USA. 1996;93(10):4693–8. doi: 10.1073/pnas.93.10.4693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [63].Sherman KA, Gibson GE, Perrino P, Garrett K. Acetylcholine formation from glucose following acute choline supplementation. Neurochem Res. 1991;16(9):1009–15. doi: 10.1007/BF00965844. [DOI] [PubMed] [Google Scholar]
  • [64].Kumari M, Brunner E, Fuhrer R. Minireview: Mechanisms by which the metabolic syndrome and diabetes impair memory. J Gerontol A Biol Sci Med Sci. 2000;55(5):B228–32. doi: 10.1093/gerona/55.5.b228. [DOI] [PubMed] [Google Scholar]
  • [65].Chen S, Broqueres-You D, Yang G, Wang Z, Li Y, Wang N, et al. Relationship between insulin resistance, dyslipidaemia and positive symptom in Chinese antipsychotic-naive first-episode patients with schizophrenia. Psychiatry Res. 2013;210(3):825–9. 10.1016/j.psychres.2013.08.056. [DOI] [PubMed] [Google Scholar]
  • [66].Wedervang-Resell K, Friis S, Lonning V, Smelror RE, Johannessen C, Agartz I, et al. Lipid alterations in adolescents with early-onset psychosis may be independent of antipsychotic medication. Schizophr Res. 2020;216:295–301. doi: 10.1016/j.schres.2019.11.039. [DOI] [PubMed] [Google Scholar]
  • [67].Mezquida G, Savulich G, Garcia-Rizo C, Garcia-Portilla MP, Toll A, Garcia-Alvarez L, et al. Inverse association between negative symptoms and body mass index in chronic schizophrenia. Schizophr Res. 2018;192:69–74. doi: 10.1016/j.schres.2017.04.002. [DOI] [PubMed] [Google Scholar]
  • [68].Chen SF, Hu TM, Lan TH, Chiu HJ, Sheen LY, Loh EW. Severity of psychosis syndrome and change of metabolic abnormality in chronic schizophrenia patients: severe negative syndrome may be related to a distinct lipid pathophysiology. Eur Psychiatry. 2014;29(3):167–71. 10.1016/j.eurpsy.2013.04.003. [DOI] [PubMed] [Google Scholar]
  • [69].Riordan HJ, Antonini P, Murphy MF. Atypical antipsychotics and metabolic syndrome in patients with schizophrenia: risk factors, monitoring, and healthcare implications. Am Health Drug Benefits. 2011;4(5):292–302. [PMC free article] [PubMed] [Google Scholar]
  • [70].Gupta S, Lentz B, Lockwood K, Frank B. Atypical antipsychotics and glucose dysregulation: a series of 4 cases. Prim Care Companion J Clin Psychiatry. 2001;3(2):61–5. doi: 10.4088/pcc.v03n0203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [71].Melkersson KI, Hulting AL, Brismar KE. Elevated levels of insulin, leptin, and blood lipids in olanzapine-treated patients with schizophrenia or related psychoses. J Clin Psychiatry. 2000;61(10):742–9. doi: 10.4088/jcp.v61n1006. [DOI] [PubMed] [Google Scholar]
  • [72].Newcomer JW, Weiden PJ, Buchanan RW. Switching antipsychotic medications to reduce adverse event burden in schizophrenia: establishing evidence-based practice. J Clin Psychiatry. 2013;74(11):1108–20. doi: 10.4088/JCP.12028ah1. [DOI] [PubMed] [Google Scholar]
  • [73].Knopman D, Boland LL, Mosley T, Howard G, Liao D, Szklo M, et al. Cardiovascular risk factors and cognitive decline in middle-aged adults. Neurology. 2001;56(1):42–8. doi: 10.1212/wnl.56.1.42. [DOI] [PubMed] [Google Scholar]
  • [74].Wu X, Wang H, Chen C, Xiong Y, Zhu L, Jia J, et al. The association between cardiovascular risk burden and cognitive function amongst the old: a 9-year longitudinal cohort study. Eur J Neurol. 2021;28(9):2907–12. doi: 10.1111/ene.14947. [DOI] [PubMed] [Google Scholar]
  • [75].Leonard BE, Schwarz M, Myint AM. The metabolic syndrome in schizophrenia: is inflammation a contributing cause? J Psychopharmacol. 2012;26(5 Suppl):33–41. doi: 10.1177/0269881111431622. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Zhao et al. supplementary material

Zhao et al. supplementary material

S092493382302477Xsup001.docx (32.9KB, docx)

Articles from European Psychiatry are provided here courtesy of Cambridge University Press

RESOURCES