Exploring low grade inflammation by soluble urokinase plasminogen activator receptor levels in schizophrenia: a sex-dependent association with depressive symptoms

Participants were recruited in the period 2003–2017 into the collaborating projects Effects of Physical Activity in Psychosis study (EPHAPS) [31] and Thematically Organized Psychosis (TOP) Research project/NORMENT (Norwegian Centre for Mental Disorder Research) [7]. The recruitment was mainly from outpatient psychiatric clinics from the southeast region of Norway. The study was observational and the main inclusion criterion was fulfilling the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria for schizophrenia spectrum disorder (4th and 5th edition), confirmed by the Structured Clinical Interview for DSM-IV (SCID-I). The participants were 18–67 years of age, understood and spoke a Scandinavian language, and had no mental retardation. In order to exclude participants with severe ongoing and acute infections, we excluded participants with serum levels of CRP above 20 mg/L. Most of the current participants were included as cases in a previous case-control study of suPAR levels (Bigseth et al. 2021). However, in the current study we did not exclude participants with comorbid chronic infectious and autoimmune diseases to reflect a more naturalistic sample.

Information and assessments were obtained or carried out by trained clinicians. Diagnosis was confirmed using the Structured Clinical Interview for DSM-IV axis I Disorders, SCID-I, [32] and the trained clinicians in both research groups (TOP/NORMENT and EPHAPS) underwent a SCID-I training program lead by experts from the University of California Los Angeles (UCLA). Information on sociodemographics, medication, mental and physical health was obtained through patient charts, self-reports and interviews. For baseline assessment of psychotic symptom levels, we used positive and negative subscale of the Positive and Negative Syndrome Scale (PANSS) [33]. The Calgary Depression Scale for Schizophrenia (CDSS) [34], as well as the depression dimension (PANSS depressed factor) in the five factor model of PANSS [35] were used to assess severity of depressive symptoms. A cutoff score CDSS ≥6 was used for depression [34, 36]. Antipsychotic medication doses were quantified by defined daily doses (DDD) according to WHO standards (http://​www.​whocc.​no/​) and categorized in either “no medication, low, moderate or high metabolic risk” (see Supplementary Table C, Additional File 3) [37]. We applied the AUDIT to assess alcohol use, and participants were categorized into a group of “problematic use of alcohol” when scores were above defined cutoff values (≥ 5 for females and ≥ 8 for males). DUDIT was applied to assess substance use, where “problematic use of drugs” was defined by cutoff values (≥ 2 for females and ≥ 6 for males) [38]. We used standardized assessment of blood pressure, and body mass index (BMI) was calculated based on standardized measurement of weight and height.

Fasting blood samples were collected in the morning and subsequently analyzed according to pre-defined protocols. Soluble uPAR and hsCRP were measured in duplicate using a commercially available enzyme-immunoassay (RnDSystems, Stillwater, MN, USA) in a 384-well format using the combination of a SELMA (Jena, Germany) pipetting robot and a BioTek (Winooski, VT, USA) dispenser/washer. Absorption was read at 450 nm with wavelength correction set to 540 nm using an ELISA plate reader (Bio-Rad, Hercules, CA, USA). Intra- and inter-assay coefficients of variation were < 10% [25].

Blood triglycerides, HDL and glucose were analyzed according to standardized procedures in the hospital lab where blood was sampled. We used the Atherogenic Index of Plasma ((AIP) = log(triglycerides/HDL-cholesterol)) as a proxy for cardiovascular disease (CVD) risk [39‐41] and fasting glucose as a proxy for diabetes risk (Table 2).

Descriptive statistics of demographic and clinical variables were presented as frequencies and proportions for categorical data and mean and standard deviation (SD) or median and interquartile range (IQR) for continuous data.

To identify factors associated with suPAR levels, we performed linear regression analyses. Variables with established association with suPAR (i.e. sex, age, current tobacco smoking and BMI) were included in the model regardless of the association with suPAR in our sample [18, 42] and hsCRP was used to adjust for inflammatory activity linked to different inflammatory pathways [12]. Because of the known association between age and LGI and a wide age range of included participants, age was adjusted for as a continuous variable, as was BMI and hsCRP. Due to the restricted sample size variables with less evidence from the literature (i.e. PANSS positive, PANSS negative, CDSS, hsCRP, AIP, fasting glucose, blood pressure, problematic use of alcohol and drugs, level of education and antipsychotic medication (DDD and metabolic risk level)) were included into the model according to the purposeful selection approach [43]. In brief, variables were included in the multivariable model if univariable analyses showed p < 0.1. Then the variables were removed one at a time, the one with the largest p-value first, until all remaining variables were statistically associated with the suPAR level. No outliers were identified. All continuous variables were examined and linearity found satisfactory. We observed no multicollinearity between the independent variables. Results are presented as beta coefficients with 95% confidence intervals (CI) and p-values.

To explore the possible effect modification by sex we tested for interactions, on the multiplicative scale, between sex and all the included variables in the final model. In the presence of a significant interaction, we conducted stratified analysis by sex.

Several sensitivity analyses were performed to assess the robustness of the results. To explore the impact of the measurement tool, we substituted the CDSS sum score by the CDSS cutoff score of ≥6 and subsequently PANSS depressed factor. Studying the dimensions of CDSS, we replaced CDSS sum score with each separate item of CDSS in the final model stratifying by sex. To compare effects of inflammation associated with endothelial dysfunction (suPAR) and inflammation associated with central obesity and acute infection response (hsCRP), we explored the relationship between hsCRP and depressive symptoms for the whole sample and stratified by sex. Post-hoc we investigated how levels of suPAR could predict depression in schizophrenia in males and females by constructing a Receiver Operating Characteristic (ROC) curve, defining CDSS ≥6 as positive cases. Psychometric properties of the CDSS, such as Cronbach’s alpha of internal consistency as well as associations between measured symptoms are presented in Supplementary Text 1, Additional File 4 and Supplementary Table B1 and B2, Additional File 2.

The naturalistic schizophrenia sample consisted of both males (n = 108) and females (n = 79), mean 32.6 years of age (range 18–67), and included participants with comorbid alcohol and drug use. Females scored higher than males on depression symptom scales, both the CDSS sum score and the CDSS cutoff value (CDSS ≥6) (Table 1). There were higher PANSS general scores in females compared to males (mean difference = 2.70), in particular for PANSS depressed factor (mean difference = 1.84).

Clinical somatic characteristics, blood indices and antipsychotic medication are presented in Table 2. Females had lower AIP (mean difference = 0.15) and systolic (mean difference = 9.42) and diastolic blood pressure (mean difference = 4.23) compared to males. Levels of suPAR were higher in females compared to males (mean difference = 0.29).

In the multivariable analyses we found that suPAR levels were positively associated with female sex, age, current tobacco smoking, hsCRP and depressive symptoms (CDSS sum score). In addition, BMI was negatively associated with suPAR in the multivariable analyses (Table 3). We found a statistically significant interaction between sex and CDSS sum score (p = 0.03) and re-ran the final model, stratified by sex. In males we found positive association between suPAR levels and age, current tobacco smoking and hsCRP, while BMI was negatively associated with suPAR. Depressive symptoms however, were not associated with suPAR in males. For females on the other hand, depressive symptoms and current tobacco smoking were positively associated with suPAR, but age, hsCRP and BMI were not.

The sensitivity analyses supported the main finding of the study. We found similar results for the associations between clinical factors and suPAR levels when CDSS sum score was substituted by CDSS cutoff score of ≥6 and subsequently PANSS depressed factor (for further details see Supplementary Table A, Additional File 1). Studying the separate CDSS items (C1-C9), for females there were positive associations between suPAR and the CDSS items C3 (self depreciation), C4 (guilty ideas of reference), C5 (pathological guilt), C6 (morning depression), C7 (early wakening) and C8 (suicide). For C1 (self described depression) and C2 (hopelessness) the associations were borderline significant and there was no association between suPAR levels and C9 (observed depression), the only item based on the clinicians interpretation. For males neither of the items were significantly associated with suPAR levels (Table 4).

Levels of hsCRP and depression measures (CDSS sum score, CDSS ≥6 and PANSS depressed factor) were not significantly associated (see Supplementary Text 2, Additional File 5). However, when stratifying by sex, there was a borderline significant association between hsCRP and CDSS sum score in females (beta 0.07, 95% CI 0.01 to 0.13) but not in males. When adjusting for BMI the association between hsCRP and CDSS sum score in females became clearly non-significant for females as well.

We found that suPAR levels could predict depression (CDSS ≥6) in participants with schizophrenia to a limited extent only (Fig. 1).