The relationship between obesity and neurocognitive function in Chinese patients with schizophrenia

This study was part of the Early-stage Schizophrenia Outcome Study (ESOS), which involved 10 different sites across China [20]. Study participants were enrolled from psychiatric outpatient clinics. The inclusion criteria were: (1) age range between 18 to 50 years old; (2) diagnosis of schizophrenia or schizophreniform disorder as determined by the Structured Clinical Interview for DSM-IV Axis I Disorders–Clinician Version administered by study investigators or trained staff; (3) duration of illness ≤ 5 years; (4) in stable clinical condition (the Positive and Negative Syndrome Scale [PANSS][21]total scores ≤ 60); (5) on only one oral antipsychotic agent. The exclusion criteria were: (1) a history of neurologic disorders that might affect cognitive function (i.e. head injury, cerebrovascular disease, Parkinson’s disease, Alzheimer’s disease, seizure disorder), (2) serious or unstable medical condition (i.e. poorly controlled diabetes or hypertension, symptomatic coronary artery disease) or BMI ≥ 45 kg/m²; (3) current pregnancy or breastfeeding, or history of pregnancy in the past 12 months; (4) substance abuse or dependence in the past 3 months.

A cognitive battery was administered to all participants. The battery includes the following five cognitive tests:

The eight performance measures were grouped into 4 domains including Processing Speed (Digit Symbol; Trail Making Test, Part A), Attention/Working Memory (Digit Span total), Executive Functioning (Trail Making Test, Part B; WCST perseverative errors and categories completed), and Visual Memory (Visual Reproduction).

All the tests were administered by raters with a masters degree in clinical psychology; an inter-rater reliability was established. The raters were blind to participants’ medical status at the time of assessment.

Clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) [21] and the Montgomery and Asberg Depression Scale (MADRS) [26].

Height and weight were measured using the standardized procedures. BMI was calculated based on the formula: weight in kilograms divided by height in meters-squared (kg/m²). According to the World Health Organization Standard classification on body mass index for Asians [27], subjects were defined as underweight (< 18.5 kg/m²), normal weight (18.5-22.9 kg/m²), overweight (23–27.4 kg/m²) and obese (≥ 27.5 kg/m²).

All analyses were conducted using the Statistical Package for Social Sciences, version 15.0 (SPSS Inc, Chicago, Illinois). A cognitive composite score was derived based on the scores on the five cognitive tests. Raw scores were first converted to standardized z-scores with the mean of 0 and the standard deviation of 1. For domains with more than one test, a domain z-score was created by calculating the mean of the z-scores for the measures comprising the domain, then converting the mean to a z score with a mean of zero and a standard deviation of 1. A cognitive composite score was calculated by creating a z score of the average of the 4 standardized domain scores.

Demographic and clinical characteristics across different BMI groups were compared using analysis of variance (ANOVA) for continuous variables and Chi-square tests for categorical variables. Group comparisons of cognitive function were performed using analysis of covariance (ANCOVA) controlling for gender. If an overall group difference was found to be significant, post hoc comparisons were then evaluated using the Hochberg modification of the Bonferroni correction for multiple comparisons: the adjusted alpha for statistical significance was calculated as α* = α/[k (k-1)/2] =0.05/[4*(4–1)/2] = 0.008 [28]. The correlations between BMI and cognitive function measures were performed using partial correlation tests controlling for both age and gender.

A total of 896 patients completed the study and were included in the data analysis. The prevalence of obesity, overweight, and underweight were 18.1%, 35.6% and 6.4% respectively.

As shown in Table 1, there were no significant differences across different BMI groups (obesity, overweight, normal weight and underweight) on various demographic or clinical characteristics (p’s > 0.05) except for gender (p < 0.001).

Correlations between BMI and various cognitive measures are showed in Table 2. Partial correlation analysis adjusting for gender demonstrated that a higher BMI was significantly associated with lower scores on the WAIS-R Digit Symbol test, the WMS-R Visual Reproduction test, and the composite z score (p’s ≤ 0.024).

There were significant overall group differences in the Trail Making Test B, the WMS-R Visual Reproduction test, the WAIS Digit Symbol test, and the composite z score (p’s ≤ 0.040; Table 3). Post hoc analysis revealed that the obese group had significantly lower scores on the Trail Making Test B, the WMS-R Visual Reproduction test, the WAIS Digit Symbol test, and the composite z score compared with the normal weight group (p’s ≤ 0.004).

There was a significant difference in BMI between males and females (24.1 ± 3.7 and 22.8 ± 3.1, respectively, F = 29.872, p < 0.001). However, there were no significant differences in various cognitive measures between the two gender groups (p’s > 0.05; data not shown). Further analysis within each gender group showed that the obese group had significantly lower scores on the WMS-R Visual Reproduction test, the WAIS Digit Symbol test, and the composite z score compared with the normal weight group (p’s ≤ 0.004; data not shown).

There was a significant difference in BMI between patients on typical antipsychotics and those on atypical antipsychotics (22.9 ± 3.5 and 23.6 ± 3.5, respectively, F = 10.909, p = 0.001), but there were no significant differences in various cognitive measures between those taking typical vs. atypical antipsychotics (p’s >0.05; data not shown).

There are several limitations to this study. First, a more comprehensive cognitive battery might have revealed different results. Second, the participants were not randomly selected and the study excluded patients with serious or unstable medical conditions; as a result, the study sample may not be representative of individuals with schizophrenia in China. Third, given the cross-sectional study design, causal relationships cannot be drawn based on our findings. Fourth, BMI may not be the most appropriate measure to reflect obesity and its negative impact on cardiometabolic disorders and other health conditions [38]. Future studies using more sophisticated techniques, such as dual-energy X-ray absorptiometry, to measure body composition and percentage of fat are needed.